The Economics of
Financing and In tegrating
Renew able Energies
v orgelegt v on
Nils Gün ter Ma y , M.Sc.
geb oren in Gütersloh
v on der F akultät VI I - Wirtsc haft und Managemen t
der T ec hnisc hen Univ ersität Berlin
zur Erlangung des ak ademisc hen Grades
Doktor der Wirtsc haftswissensc haften
do ctor r erum o e c onomic arum
(Dr. rer. o ec.)
- genehmigte Dissertation -
Promotionsaussc h uss:
V orsitzender: Prof. Dr. Christian v on Hirsc hhausen
Gutac h ter: Prof. Karsten Neuhoff, PhD
Gutac h ter: Prof. Dr. Rolf Wüstenhagen
T ag der wissensc haftlic hen Aussprac he: 30. April 2018
Berlin, 2018

A c kno wledgemen ts
I am grateful to all the p eople who made the completion of this dissertation p ossible.
Ab o v e all, I express my sincere gratitude to Karsten Neuhoff, m y first sup ervisor. I
v ery m uc h appreciate that y ou pro vided me with feedbac k when needed and ask ed
me critical questions when necessary . It mean t a lot to me to kno w I could alw a ys
approac h y ou when I required help. W orking on this dissertation w as fun not least
b ecause y ou created a friendly and pleasurable w orking en vironmen t in the Cl imate
P olicy departmen t at DIW Berlin. Y our en th usiasm for energy and climate economics
is con tagious. F urther, I am grateful to m y second sup ervisor Rolf Wüstenhagen for
his supp ort and pro viding me with the opp ortunit y to presen t m y researc h man y
times o v er the y ears.
P art of this thesis w as written while I sta y ed at UCL London. I thank Mic hael
Grubb for hosting me and ensuring that I got in to con tact with the lo cal researc h
comm unit y .
I thank the DIW’s Graduate Cen ter for its help o v er the y ears, in particular
Juliane Metzner and Y un Cao. The summer w orkshops, the coursew ork and all the
extra ev en ts pro vided man y v aluable exp eriences.
Next, I w ould lik e to thank Øivind An ti Nilsen for our join t w ork on a pap er
outside this dissertation. I learned plen t y from y our exp ertise on p ersev ering in ligh t
of the scien tific publishing pro cess and enjo y ed w orking together.
I wrote this thesis while w orking in the Climate P olicy departmen t at DIW
Berlin. I w an t to thank all m y colleagues who made the office an enjo y able, com-
fortable w ork en vironmen t. Our team drinks, excursions, and coun tless coffee breaks
pro vided man y relaxing and insigh tful momen ts and I could not ha v e hop ed for a
friendlier group. This is esp ecially true for William A cw orth, Olga Chiappinelli, Ker-
iii

stin F erguson, Thilo Grau, Ingmar Jürgens, Heiner v on Lüpk e, Jörn Richstein, Nolan
Ritter, Sebastian P etric k, Carlotta Pian tieri, Anne Sc hopp, Puja Singhal, Jan Stede,
Sebastian Sc h w enen, and Olga Zh ylenk o. P articularly , I thank m y office-mate V era
Zipp erer, for turning ours in to the institute’s greenest office and all the con v ersations
ab out the PhD-life.
My friends from m y 2013 Graduate Cen ter cohort deserv e a sp ecial thanks, b egin-
ning with the great help in the initial coursew ork. Sasc ha Drahs, Mathias Hüb ener,
Katharina Lehmann-Usc hner, Roman Mendelevitc h, Alexandra Peev a, Clara W el-
tek e, you w ere alw a ys up for an y fun activit y and for what usually started out as “but
only one quic k round of fo osball”. I am v ery happ y that while lo oking for a PhD, I
found friends.
Last but b y no means least, I thank m y family and friends for their lo v e and
their con tin uous encouragemen t and b eliev e in me. My sisters Merle and Nina alw a ys
sho w ed the greatest trust in their brother’s abilities and I kno w I can alw a ys coun t
on y our unconditional supp ort. My paren ts Christiane and Klaus-Gün ter b eliev ed in
me, bac k ed an y decisions I to ok, and made me the p erson I am. Thank y ou. Bey ond
all, I thank Suus for her lo ving supp ort and sharing all the ups and do wns along the
w a y . Kno wing that I w ould b e in Berlin for the PhD for a while to come, y ou came
to Berlin. Y ou are m y deligh t.
iv

Con ten ts
List of Figures ix
List of T ables xi
Prior Publications xiii
Abstract xv
Zusammenfassung xix
General In tro duction 1
1 The Impact of Wind P o w er Supp ort Sc hemes on T ec hnology Choices 9
1 . 1 I n t r o d u c t i o n ................................ 1 0
1 . 2 M e t h o d o l o g y ............................... 1 4
1.2.1 Wind p ow er in v estmen t . . . . . . . . . . . . . . . . . . . . . 15
1.2.2 Fixed feed-in tariff . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2.3 Sliding feed-in premium . . . . . . . . . . . . . . . . . . . . . 16
1.2.4 Pro duction v alue-based b enc hmark approac h . . . . . . . . . . 19
1 . 3 D a t a .................................... 2 3
1.3.1 Wind turbine tec hnology . . . . . . . . . . . . . . . . . . . . . 23
1 . 3 . 2 P r i c e s ............................... 2 8
1 . 3 . 3 W i n d s p e e d d a t a ......................... 2 9
1.3.4 Numerical application . . . . . . . . . . . . . . . . . . . . . . 30
1 . 4 R e s u l t s ................................... 3 2
v

vi CONTENTS
1.4.1 Fixed feed-in tariff . . . . . . . . . . . . . . . . . . . . . . . . 33
1.4.2 Sliding feed-in premium . . . . . . . . . . . . . . . . . . . . . 34
1.4.3 Pro duction v alue-based b enc hmark approac h . . . . . . . . . . 37
1 . 5 C o n c l u s i o n ................................. 3 8
1 . 6 A p p e n d i x ................................. 4 1
1.6.1 Pro duction volume-based b enc hmark approac h . . . . . . . . . 41
1 . 6 . 2 S e n s i t i v i t i e s ............................ 4 3
1 . 6 . 3 R E M i x m o d e l ........................... 4 9
2 Financing P o w er: Impacts of Energy P olicies in Changing Regula-
tory En vironmen ts 53
2 . 1 I n t r o d u c t i o n ................................ 5 4
2.2 In v estmen ts in to renew able energy . . . . . . . . . . . . . . . . . . . . 56
2.3 Estimating in v estors’ financing costs . . . . . . . . . . . . . . . . . . 58
2 . 3 . 1 D a t a ................................ 5 8
2.3.2 Estimation strategy . . . . . . . . . . . . . . . . . . . . . . . . 60
2 . 3 . 3 R e s u l t s ............................... 6 2
2.3.4 Robustness c hec ks . . . . . . . . . . . . . . . . . . . . . . . . 65
2 . 4 L o n g - t e r m c o n t r a c t s ........................... 6 7
2.4.1 Implications of long-term con tracts for priv ate off-tak ers . . . 68
2.4.2 Estimation of off-tak ers’ costs . . . . . . . . . . . . . . . . . . 71
2.4.3 Financial p osition of priv ate off-tak ers . . . . . . . . . . . . . 74
2.5 A dditional costs under green certificate sc hemes . . . . . . . . . . . . 76
2 . 6 C o n c l u s i o n ................................. 7 8
2 . 7 A p p e n d i x ................................. 8 0
2.7.1 Normalit y of w eigh ted a verage cost of capital estimates . . . . 80
2.7.2 Sensitivit y analyses regarding the co ding of resp onses . . . . . 82
2.7.3 F unctional form of the in terest rate function . . . . . . . . . . 84

CONTENTS vii
3 T o o Go o d to Be T rue? Ho w Time-Inconsisten t Renew able Energy
P olicies Can Deter In v estmen ts 87
3 . 1 I n t r o d u c t i o n ................................ 8 8
3.2 Setup of the regulation game . . . . . . . . . . . . . . . . . . . . . . . 90
3 . 3 R e g u l a t o r y o p t i m a ............................ 9 4
3.3.1 Commitmen t b enc hmark . . . . . . . . . . . . . . . . . . . . . 94
3.3.2 Dynamic optimization: no commitmen t case . . . . . . . . . . 96
3.4 The role of p olicies and targets . . . . . . . . . . . . . . . . . . . . . 98
3.4.1 Time-inconsistency under differen t p olicy regimes . . . . . . . 98
3.4.2 T argets as commitmen t devices . . . . . . . . . . . . . . . . . 102
3.5 Wh y did Spain deviate when German y did not? . . . . . . . . . . . . 107
3.5.1 P arameters in 2012 . . . . . . . . . . . . . . . . . . . . . . . . 107
3 . 5 . 2 R e s u l t s ............................... 1 1 1
3 . 6 C o n c l u s i o n ................................. 1 1 4
3 . 7 A p p e n d i x ................................. 1 1 6
3 . 7 . 1 L e v y c o n d i t i o n .......................... 1 1 6
3.7.2 Limited deviations . . . . . . . . . . . . . . . . . . . . . . . . 116
3.7.3 Levy calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 117
3.7.4 Demand calculation . . . . . . . . . . . . . . . . . . . . . . . . 118
General Conclusion 121
Bibliograph y 127

List of Figures
1-1 Optimal turbines with lo w shares of renew ables . . . . . . . . . . . . 17
1-2 Optimal turbines with high shares of renew ables . . . . . . . . . . . . 20
1-3 P o w er curv es of t w o exemplary wind p o w er tec hnologies . . . . . . . . 26
1-4 Comparison of tec hnologies’ pro duction . . . . . . . . . . . . . . . . . 27
1-5 The turbines’ tec hnology configurations . . . . . . . . . . . . . . . . . 28
1-6 Results for Boltenhagen . . . . . . . . . . . . . . . . . . . . . . . . . 33
1-7 Results for Heligoland . . . . . . . . . . . . . . . . . . . . . . . . . . 34
1-8 Comparison of mon thly pro duction and v alues . . . . . . . . . . . . . 35
1-9 A v erage pro duction v alues . . . . . . . . . . . . . . . . . . . . . . . . 36
1-10 A v erage rem uneration at differen t lo cations . . . . . . . . . . . . . . . 42
1 - 1 1 R e s u l t s f o r H a n o v e r ............................ 4 3
1-12 Results for F eldb erg . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
1-13 Results for Kahl er Asten . . . . . . . . . . . . . . . . . . . . . . . . . 45
1-14 Sensitivities of resu lts for Boltenhagen: In v estor . . . . . . . . . . . . 46
1-15 Sensitivities of resu lts for Boltenhagen: Regulator . . . . . . . . . . . 47
1-16 Sensitivities of resu lts for Boltenhagen: Regulator I I . . . . . . . . . . 49
2-1 Onshore wind p o w er p olicies in the EU . . . . . . . . . . . . . . . . . 61
2-2 Default spread as function of corp orate credit rating . . . . . . . . . . 70
2-3 Extra re-financing costs for priv ate off-tak ers . . . . . . . . . . . . . . 73
2-4 A v erage debt-equit y ratio of large EU utilities . . . . . . . . . . . . . 75
ix

x LIST OF FIGURES
2-5 Credit ratings of large EU utilities . . . . . . . . . . . . . . . . . . . . 76
2-6 A dditional costs under green certificates . . . . . . . . . . . . . . . . 77
2-7 Normalit y assumption in lev els . . . . . . . . . . . . . . . . . . . . . . 80
2-8 Normalit y assumption in logs . . . . . . . . . . . . . . . . . . . . . . 81
2-9 Extra re-financing costs for off-tak ers with linear interest rate . . . . 85
3-1 Timing of the p erio d game . . . . . . . . . . . . . . . . . . . . . . . . 92
3-2 Bulgarian renew able energy target ac hiev emen t . . . . . . . . . . . . 105
3-3 German renew able energy target ac hiev emen t . . . . . . . . . . . . . 106
3-4 Spanish renew able energy target ac hiev emen t . . . . . . . . . . . . . . 106
3-5 Differences Spain and German y . . . . . . . . . . . . . . . . . . . . . 111
3-6 Spanish renew able energy levy . . . . . . . . . . . . . . . . . . . . . . 112
3-7 German renew able energy levy . . . . . . . . . . . . . . . . . . . . . . 113

List of T ables
1 Ov erview o v er the dissertation’s three c hapters . . . . . . . . . . . . . 8
1.1 Capacit y mix in 2030 in the REMix mo del . . . . . . . . . . . . . . . 51
2.1 Descriptiv e statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
2.2 OLS estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.3 In terv al regression estimation results . . . . . . . . . . . . . . . . . . 66
2.4 In terest rate as quadratic function of credit ratings . . . . . . . . . . 72
2.5 Credit grade as function of debt-equit y ratio . . . . . . . . . . . . . . 73
2.6 OLS estimation results with alternativ e co ding . . . . . . . . . . . . . 82
2.7 OLS estimation results with alternativ e co ding I I . . . . . . . . . . . 83
2.8 In terest rate as linear function of credit ratings . . . . . . . . . . . . . 84
xi

Prior Publications
Chapter 1: The Impact of Wind P o w er Supp ort Sc hemes on T ec hnology Choices
• No co-author
• Published in Ener gy Ec onomics (2017), V olume 65: 343-354
• Previously published as: DIW Discussion P ap er 1485
• P arts of this c hapter ha ve been pub lished in
– Neuhoff, Karsten, Ma y , Nils and Jörn Ric hstein. 2017. "Anreize für die
langfristige In tegration v on erneuerbaren Energien: Plädo y er für ein Mark-
t w ertmo dell." DIW W o chenb ericht 42.
– Neuhoff, Karsten, Ma y , Nils and Jörn Ric hstein. 2017. "Incen tiv es for
the long-term in tegration of renew able energies: a plea for a mark et v alue
mo del." DIW Ec onomic Bul letin 46-47.
– Ma y , Nils, Neuhoff, Karsten and F rieder Borggrefe. 2015. "Marktanreize
für systemdienlic he Auslegungen v on Windkraftanlagen." DIW W o chen-
b ericht 24.
– Ma y , Nils, Neuhoff, Karsten and F rieder Borggrefe. 2015. "Mark et incen-
tiv es for system-friendly designs of wind turbines." DIW Ec onomic Bul letin
24.
Chapter 2: Financing P o w er: Impacts of Energy P olicies in Changing Regulatory
En vironmen ts
• Co-author: Karsten Neu hoff (DIW Berlin, TU Berlin)
• Published as: DIW Discussion P ap er 1684
• P arts of this c hapter ha ve been pub lished in
– Ma y , Nils, Jürgens, Ingmar and Karsten Neuhoff. 2017. "Erneuerbare
Energien: Risik oabsic herung wird zu zen traler Aufgab e der Förderinstru-
men te." DIW W o chenb ericht 39.
– Ma y , Nils, Jürgens, Ingmar and Karsten Neuhoff. 2017. "Renew able en-
ergy p olicy: risk hedging is taking cen ter stage." DIW Ec onomic Bul letin
39–40.
xiii

xiv PRIOR PUBLICA TIONS
Chapter 3: T o o go o d to b e true? Ho w time-inconsisten t renew able energy p olicies
can deter in v estmen ts
• Co-author: Olga Chiappin elli (DIW Berlin)
• Published as: DIW Discussion P ap er 1726

Abstract
This dissertation comprises three c hapters on the economics of financing and in tegrat-
ing renew able energies and on designing asso ciated supp ort p olicies. The transition
to w ard carb on-neutral economies requires large-scale in v estmen ts into renew able en-
ergy . The in tegration of these in termitten t renew able energies p oses new c hallenges to
p o w er systems designed around dispatc hable thermal p o w er plan ts. As, for example,
more and more wind p o w er is generated in high wind, it is increasingly imp ortan t
to pro vide incen tiv es that encourage pro ject dev elop ers to c ho ose alternativ e wind
p o w er tec hnologies that supply p o w er also under medio cre wind conditions. Another
c hallenge are the in v estments’ financing costs, as they , to a large exten t, define ov er-
all in v estmen t costs and are affected b y the design of supp ort p olicies. The question
arises whic h p olicies lead to higher financing costs. F urther, in order to facilitate large
in v estmen t v olumes, it is crucial to design p olicies suc h that they constitute credible
commitmen ts on the basis of whic h in vestmen ts can b e made.
Chapter 1 analyzes the in tegration of wind p o w er in to energy systems with in-
creasing shares of renew able energies. In energy systems with large shares of v ariable
renew able energies, electricity generation is lo w er during unfa v orable w eather con-
ditions. System-friendly wind turbines rectify this b y pro ducing a l arger share of
their electricit y at lo w wind sp eeds. The c hapter analyzes to what exten t the b en-
efits of system-friendly wind turbines out-w eigh their additional costs and ho w to
incen tivize in v estmen ts in to them. A wind p o w er in v estmen t mo del for German y
sho ws that system-friendly wind turbines indeed deliv er b enefits for the energy sys-
tem that o v ercomp ensate for their cost premium. Sliding feed-in premia incen tivize
xv

xvi ABSTRA CT
their deplo ymen t only where in v estors b ear significan t price risks and p ossess sufficien t
foresigh t. Alternativ ely , a new pro duction v alue-based b enc hmark triggers in v estors
to install turbines that meet the requiremen ts of p o w er systems with increasing shares
of v ariable renew able energies, without inducing additional in v estmen t risks.
Chapter 2 pro vides no v el evidence that some supp ort p olicies lead to higher fi-
nancing and o v erall deplo ymen t costs than others. P o w er systems with increasing
shares of wind and solar p o w er generation ha v e higher capital and lo w er op erational
costs than p o w er systems based on fossil fuels. This increases the imp ortance of the
cost of financing for total system cost. W e quan tify ho w renew able p olicy design can
influence the cost of financing b y addressing regulatory risk and facilitating hedging.
First, w e use in terview data on wind p o w er financing costs from the EU and deriv e
effects on pro ject dev elop ers’ financing costs. Second, w e mo del how long-term con-
tracts signed b etw een pro ject dev elop ers and energy suppliers impact financing costs
in the con text of green certificate sc hemes. The costs of renew able energies increase b y
ab out 30 p ercen t in comparison to p olicies that pro vide implicit long-term con tracts
b et w een pro ject dev elop ers and electricit y consumers.
Chapter 3 sho ws that time-inconsistency issues can arise for renew able energy
in v estmen ts, deterring in v estments, and ho w these issues can b e addressed b y p olicy-
mak ers. In v estmen ts in to renew able energies are commonly enabled b y supp ort p oli-
cies. Y et, go v ernments can ha v e incen tives – and the abilit y – to deviate from
previously-announced supp ort once in v estmen ts are made, whic h can deter in v est-
men ts. In the first step, w e analyze a renew able energy regulation game and apply
a mo del of time-inconsistency to renew able energy p olicies. Based on the mo del,
w e deriv e under what conditions go v ernmen ts ha v e incen tiv es to deviate from their
commitmen ts, analyzing the p oten tially mitigating effects of v arious p olicy designs
and targets. In the second step, w e pro vide a n umerical example of our theoretical
mo del and explain wh y Sp ain conducted retrosp ective c hanges in 2010-2013 whereas
German y stuc k to its commitmen ts. The mo del suggests that, on the one hand, the
extra costs of renew able energies w ere considerably lo w er in Spain due to the higher
wholesale electricit y price, rendering compliance more attractiv e in Spain. Ho w ev er,

xvii
on the other hand, this is out w eighed b y the dirtier German con v en tional p o w er plan t
fleet and esp ecially by the larger m y opia of the Spanish regulator, caused b y high dis-
coun ting during the financial crisis of future b enefits of sustained renew able energy
deplo ymen t.

Zusammenfassung
Diese Dissertation umfasst drei Kapitel üb er die v olkswirtsc haftlic hen Effekte der
Finanzierung und In tegration erneuerbarer Energien und das Design en tsprec hen-
der Förderinstrumen te. Die Umstellung hin zu einer CO 2 -neutralen Wirtsc haft b e-
dingt groß angelegte In v estitionen in erneuerbare Energien. Die In tegration w etter-
abhängiger erneuerbarer Energien stellt neue Herausforderungen dar für Energiesys-
teme, die für regelbare thermisc he Kraft w erk e ausgelegt wurden. Beispielsw eise wird
b ei stark em Wind mehr und mehr Windenergie erzeugt. Deshalb ist es zunehmend
wic h tig, Anreize zu sc haffen, die Pro jekten t wic kler dazu erm utigen, alternativ e Wind-
krafttec hnologien zu w ählen, w elc he Strom auc h un ter mittelmäßigen Windb eding-
ungen liefern. Eine w eitere Herausforderung sind die Finanzierungsk osten der In-
v estitionen, da sie die Gesam tin v estitionsk osten w eitgehend b estimmen und v on der
Gestaltung der Förderp olitik b eeinflusst w erden. Es stellt sic h die F rage, w elc he
P olitikinstrumen te mit höheren Finanzierungsk osten einhergehen. Außerdem ist es,
um große In v estitionsv olumina zu ermöglic hen, essen tiell, P olitikinstrumen te so zu
gestalten, dass sie glaub würdige Zusagen darstellen, auf deren Grundlage In v estitio-
nen getätigt w erden k önnen.
Kapitel 1 analysiert die In tegration v on Windenergie in Energiesysteme mit
steigenden An teilen erneuerbarer Energien. In solc hen Energiesystemen ist die Strom-
erzeugung b ei ungünstigen W etterb edingungen geringer. Systemfreundlic he Winden-
ergieanlagen k orrigieren dies, indem sie einen größeren An teil ihres Stroms b ereits b ei
niedrigen Windgesc h windigk eiten pro duzieren. In diesem Beitrag wird analysiert, in-
wiew eit die V orteile systemfreundlic her Windenergieanlagen ihre zusätzlic hen K osten
xix

xx ZUSAMMENF ASSUNG
üb ersteigen und wie man Anreize für Inv estitionen in solc he Anlagen geb en k ann. Ein
Windkraft-In v estitionsmo dell für Deutsc hland zeigt, dass systemfreundlic he Winden-
ergieanlagen tatsäc hlic h V orteile für das Energiesystem bieten, die ihre zusätzlic hen
K osten üb erk omp ensieren. Gleitende Marktprämien bieten n ur dann einen Anreiz
für solc he Anlagen, w enn In v estoren ein erheblic hes Preisrisik o tragen und ausre-
ic hend v oraussc hauend sind. Alternativ v eranlasst ein neues Referenzw ertmo dell In-
v estoren dazu, Anlagen zu installieren, die die Anforderungen v on Energiesystemen
mit steigenden An teilen erneuerbarer Energien erfüllen, ohne dass In v estoren dadurc h
zusätzlic he Risik en en tstehen.
Kapitel 2 liefert neue Belege dafür, dass einige Förderinstrumen te zu höheren
Finanzierungs- und Gesam tk osten der erneuerbaren Energien führen als andere. En-
ergiesysteme mit steigenden An teilen an Wind- und Solarenergie hab en höhere Kapi-
talk osten und geringere Betriebsk osten als Energiesysteme auf Basis fossiler Brennstoffe.
Dies erhöh t die Bedeutung der Finanzierungsk osten für die Gesam tsystemk osten. Wir
quan tifizieren, wie Förderinstrumen te die Finanzierungsk osten b eeinflussen k önnen,
indem sie regulatorisc he Risik en adressieren und Absic herungen ermöglic hen. Zum
einen v erw enden wir Befragungsdaten zu den Finanzierungsk osten für Windenergie
aus der EU und ermitteln daraus die Auswirkungen v on Förderinstrumen ten auf
die Finanzierungsk osten der Pro jekten t wic kler. Zum anderen mo dellieren wir, wie
langfristige V erträge zwisc hen Pro jekten t wicklern und Energiev ersorgungsun terneh-
men die Finanzierungsk osten b ei grünen Zertifik atehandeln b eeinflussen. Die K osten
für erneuerbare Energien steigen um et w a 30 Prozen t im V ergleich zu Förderinstru-
men ten, w elc he implizite Langzeitv erträge zwischen Pro jekten t wic klern und Strom v er-
brauc hern darstellen.
Kapitel 3 zeigt einerseits, dass Probleme zeitlic her Ink onsistenzen b ei In v esti-
tionen in erneuerbare Energien auftreten k önnen, w o durc h In v estitionen v erhindert
w erden, und andererseits, wie diese Probleme v on p olitisc hen En tsc heidungsträgern
angegangen w erden k önnen. In v estitionen in erneuerbare Energien w erden üblic her-
w eise durc h Förderinstrumen te un terstützt. Regierungen k önnen jedo c h Anreize und
die Möglic hk eit hab en, v on zuv or angekündigten V ergütungszahlungen abzuw eic hen,

xxi
sobald In v estitionen getätigt wurden, w as In v estoren absc hrec k en k ann. Im ersten
Sc hritt analysieren wir ein Regulierungsspiel v on In v estitionen in erneuerbare En-
ergien und w enden ein Mo dell der zeitlic hen Ink onsistenz auf die Förderung erneuer-
barer Energien an. Auf der Grundlage des Mo dells leiten wir ab, un ter w elc hen Be-
dingungen Regierungen Anreize hab en, v on ihren V erpflic h tungen abzu w eic hen, und
analysieren die p otenziell mildernd en Auswirkungen v ersc hiedener Förderinstrumen te
und v on erneuerbare-Energien-Zielen. Im zw eiten Sc hritt geb en wir ein n umerisc hes
Beispiel für unser theoretisc hes Mo dell und erklären, w arum Spanien 2010-2013 ret-
rosp ektiv e Änderungen seiner Förderung durc hführte, während Deutsc hland seinen
V erpflic h tungen nac hk am. Die Mo dellergebnisse deuten darauf hin, dass die zusät-
zlic hen K osten für erneuerbare Energien in Spanien aufgrund des höheren Großhan-
delsstrompreises deutlic h niedriger w aren als in Deutsc hland, w as eigen tlic h die Er-
füllung der V erpflic h tungen in Spanien attraktiv er mac h te. Jedo c h wird dies durc h
die sc hm utzigere deutsc he k on v en tionelle Kraft werksflotte und insb esondere durc h
die Kurzsic h tigk eit der spanisc hen Regulierer mehr als aufgew ogen, w elc he in der Fi-
nanzkrise dem zukünftigen Nutzen erneuerbarer Energien durc h hohe Disk on tierung
w enig W ert b eimaßen.

General In tro duction
An throp ogenic climate c hange increases the Earth’s a v erage global temp erature and
leads to the acidification of o ceans (IPCC, 2014). Bet w een 1880 and 2012, global
a v erage temp erature has already increased b y ab out 0.85 degrees Celsius. When the
temp erature increase surpasses tipping p oin ts, this results in self-reinforcing, irre-
v ersible climate-c hanging pro cesses, p ossibly causing catastrophic damages (IPCC,
2014). As greenhouse gases accum ulating in the atmosphere and the o ceans are
iden tified as the main culprit for climate c hange, the 2015 P aris Agreemen t sets the
bac kdrop for large-scale mitigation actions, aiming to k eep total w arming w ell b elo w
t w o degrees (UNF CCC, 2015). By early 2018, 195 coun tries had signed the agree-
men t and submitted nationally-determined con tributions, la ying out climate c hange
mitigation and adaptation measures (UNF CCC, 2018).
Decreasing the emissions of the electricit y sector through renew able energy de-
plo ymen t is a k ey means to implemen ting the national plans under the P aris Agree-
men t. The energy supply sector represen ts around 35 p ercen t of all man-made emis-
sions, mostly stemming from electricit y supply (IPCC, 2014). Therefore, renew able
energy deplo ymen t is included in the national plans of 145 coun tries and 109 coun-
tries set themselv es renew able energy targets (IRENA, 2017b). Global solar and wind
p o w er capacit y has already gro wn from 94 Giga w atts (GW) in 2007 to around 467
GW in 2017 (IRENA, 2017a) and strong future gro wth is exp ected (IEA, 2017). F or
example, China is gro wing its renew able capacit y b y ab out 54 GW a y ear (IRENA,
2017a). The Europ ean Union set itself a target of 20 p ercen t of energy to stem from
renew able energies b y 2020 and at least 27 p ercen t b y 2030 (Europ ean Union, 2009).
1

2 GENERAL INTR ODUCTION
India plans to increase its renew able energy capacit y to 175 GW b y 2022, based on
gro wth in solar p o w er from 2016’s 10 GW to 100 GW and in wind p o w er from 2016’s
29 GW to 60 GW (NREL et al., 2017, IRENA, 2017a).
Moreo v er, electricit y from renew able energy is frequen tly en visioned to decar-
b onize sectors b ey ond the electricit y sector, indicating strong future demand gro wth
for electricit y from, in particular, wind and solar p o w er. Electric v ehicles tak e on im-
p ortan t roles in long-term decarb onization plans (IEA, 2017). Through heat pumps,
wind and solar p o w er can fuel part of the heating and co oling sector (see e.g. German
Ministry for Economic Affairs and Energy (2017)). In ligh t of these decarb onization
plans, for example the up coming German coalition go v ernmen t in tends to significan tly
raise the coun try’s 2030 renew able energies target explicitly in order to account for
electricit y demand gro wth from other sectors (CDU et al., 2018).
In order to facilitate the required large in v estmen t v olumes, renew able energy in-
v estmen ts are bac k ed b y supp ort p olicies in almost all coun tries. As capital-in tensiv e
in v estmen ts, they commonly require supp ort p olicies to underpin future rev en ue
streams. Suc h p olicies aim to decrease rev en ue risks and financing costs. Three
t yp es of p olicies dominate globally: feed-in tariffs, sliding feed-in premia and green
certificate sc hemes. In 2015, 82 coun tries had feed-in tariffs or sliding feed-in premia.
Green certificate sc hemes supp orted renew able energies in 34 coun tries. In addition,
35 coun tries auctioned the eligibilit y to supp ort through tenders, whic h in principle
can b e combined with an y of the supp ort systems (REN21, 2017).
The supp ort p olicy defines to a large exten t how in v estors can finance their assets.
F eed-in tariffs used to b e, and in some regions still are, the most common supp ort p ol-
icy for renew able energy . Regulators (or regulated grid companies) tak e the electricit y
from in v estors, 1 who are paid a certain supp ort lev el p er electricit y output. Th us, p er
unit of output, they generally do not face an y rev enue risks. With increasing shares
of renew able energies, sliding feed-in premia hav e gained p opularit y to facilitate mar-
k et in tegration. In v estors sell their electricity themselv es (or transfer the mark eting
1 This dissertation uses the terms in vestor, pro ject developer and op erator in terchangeably , unless
stated otherwise. The differences matter particularly when discussing the financing structure of
pro jects, where pro ject dev elop ers seek capital from inv estors.

3
righ ts to another firm), receiving the p o w er price and in tro ducing balancing risks,
while they additionally receiv e a premium pa ymen t. Therefore, they face some un-
certain t y ab out their exact total rem uneration lev el. The lev el of the premium v aries
with the tec hnology-sp ecific w eigh ted p o w er price, in most designs effectiv ely shield-
ing in v estors from a large degree of the rev en ue uncertain t y . Among others, German y ,
the Netherlands and the UK ha v e shifted to feed-in premia (Eclareon, 2017). 2 Under
quan tit y-based supp ort p olicies, lik e green certificate sc hemes, implemen ted for ex-
ample in Sw eden and man y US states, in v estors sell their electricit y and receiv e green
certificates prop ortional to their output. Retail electricit y companies are obliged to
obtain suc h certificates, th us creating demand for them and represen ting a rev en ue
stream for renew able energy in v estors in addition to the sale of electricit y .
With increasing shares of renew able energies, supp ort p olicies increasingly need
to accoun t for the time and lo cation of p o wer generation. In p o w er systems originally
designed around inflexible thermal plan ts, electricit y supply and electricit y prices
c hange with wind and solar conditions when in tro ducing renew ables. Whereas in
windy p erio ds, wind p o w er supply is large and the pro duction v alue is lo w, generation
is scarce when there is no wind. Th us, with more renew able energy in the system,
the a v erage pro duction v alue of renew able energy falls (Wiser et al., 2017, F raunhofer
IWES, 2013a, F raunhofer ISE, 2014, Hirth, 2013). Rin tamäki et al. (2017) sho w
that in German y , this effect is particularly pronounced for wind p o w er, as the bulk
of pro duction tak es place in hours of rather lo w demand. Regarding the lo cational
dimension, P ec han (2017), Sc hmidt et al. (2013) and Grothe and Müsgens (2013)
analyze ho w differen t p olicies affect the optimal lo cations for wind p o w er in v estmen ts.
System-friendly tec hnologies can help to mitigate the decreasing v alue of wind
p o w er b y ha ving a larger share of their pro duction in times of mo derate wind (F raun-
hofer IWES, 2013a). Hirth and Müller (2016) compare one system-friendly and one
con v en tional wind turbine and demonstrate that, when installing system-friendly tur-
2 F requently , fixed feed-in premia are discussed where the premium is fixed, whic h exp oses in-
v estors to all p o w er price fluctuations, see for example (Kitzing, 2014) and (Pec han, 2017). Ho w ev er,
due to the asso ciated risks and, th us, higher financing costs, fixed feed-in premia are rarely used.
This dissertation fo cuses on sliding feed-in premia and explicitly uses the term fixed premia when
suc h premia are mean t.

4 GENERAL INTR ODUCTION
bines, the v alue of wind p o w er remains significan tly higher for high wind p o w er p en-
etration lev els. Ho w ev er, they do not consider in ho w far individual in v estors can
capture this additional v alue of system-friendly tec hnologies and whether it is, hence,
optimal for in v estors to c ho ose suc h turbines. The o v erall output of system-friendly
turbines tends to b e lo w er, increasing their lev elized cost of electricit y (Molly, 2014),
a measure of discoun ted lifetime costs p er unit of output.
Chapter 1 asks ho w the requiremen ts for renew able energy in v estmen ts sh ift
with increasing shares of renew able energies and ho w system-friendly wind p o w er can
con tribute to the long-term in tegration of renew able energies. It analyzes in v estors’
incen tiv es to c ho ose system-friendly tec hnologies under differen t supp ort p olicies. F ur-
ther, it deriv es a so cially-optimal lev el of system-friendliness and explores the design
of a p olicy to align priv ately-optimal with so cially-optimal tec hnologies. Ho w can in-
cen tiv es based on electricit y prices b e giv en to in v estors without inducing additional
rev en ue risks and, th us, financing costs?
The c hapter emplo ys a simple analytical analysis, mo deling the net presen t v alue
of wind p o w er tec hnologies under differen t supp ort p olicies, extending the analysis of
Sc hmidt et al. (2013) to tec hnology c hoices. Based on these theoretical argumen ts,
the wind p o w er tec hnology maximizing v alue from a so cial p ersp ectiv e is deriv ed. A
n umerical mo del of the net presen t v alue of pro jects for sp ecific lo cations with a large
n um b er of wind p o w er tec hnologies allows the iden tification of marginal c hanges b e-
t w een tec hnologies and go es b ey ond the literature’s usual limited technology options.
The mo del incorp orates the wind p o w er tec hnologies and their resp ectiv e electricit y
yield as functions of rotor blade length, h ub heigh t and capacit y . Exp osing pro ject
dev elop ers to differen t supp ort p olicies allows for the iden tification of the impact of
differen t supp ort p olicies on in v estors’ tec hnology c hoices.
The role of supp ort p olicies is c hanging due to the lo w er costs of renew ab les. The
costs of solar p ow er deplo ymen t in Germany , for example, hav e dropp ed b y roughly
89 p ercen t b et w een 2007 and 2018 from e 379 to e 43 p er MWh (mega w att hour)
(IWR, 2018, Bundesnetzagen tur, 2018a). Costs for onshore wind p ow er ha v e fallen
b y 40 p ercen t from around e 78 to e 47 p er MWh (IWR, 2018, Bundesnetzagen tur,

5
2018b). 3 Diminishing additional funding is required b ey ond the electricit y wholesale
price. Rather than pro viding additional funding, supp ort p olicies act as facilitators
of lo w-cost financing, enabling in v estments at lo w capital costs.
As the share of renew able energies increases, the costs of renew able energies, de-
fined largely b y their financing costs, represen t growing shares of the energy system’s
total costs. Based on a c hoice exp erimen t, Lüthi and Wüstenhagen (2012) find that
differences in the designs of supp ort p olicies can directly translate in to additional
deplo ymen t costs. Analyzing the differences in financing costs, Haas et al. (2011)
scrutinize descriptiv e statistics on installation n um b ers and supp ort costs for a small
n um b er of Europ ean coun tries, finding that feed-in tariffs are more successful in b oth
resp ects. Applying a mean-v ariance approac h, Kitzing (2014) sho ws that the risks
and financing costs under feed-in tariffs are lo w er than fixed feed-in premia. Cou-
ture and Gagnon (2010) argue that feed-in tariffs lead to lo w er financing costs than
sliding feed-in premia as w ell. Ho w ev er, Klobasa et al. (2013) find no suc h effect
after a c hange from tariff to premium in German y . Butler and Neuhoff (2008) find
that the German feed-in tariff has led to more in v estmen ts at lo w er costs than the
British green certificate sc heme. Y et, Bo omsma and Linnerud (2015) argue, based on
a sim ulation of the green certificate sc heme in Norw a y and Sw eden, that the addi-
tional risks under green certificates are not economically significan t. Ho w ev er, these
p olicy comparisons are either based on observ ations from only a few coun tries or on
theoretical or sim ulated argumen ts.
Chapter 2, join tly written with Karsten Neuhoff, analyzes the role of renew able
energy supp ort p olicies in facilitating lo w-cost financing. The c hapter studies the
differences in capital costs b et w een supp ort p olicies empirically , based on financing
cost data from 23 EU coun tries. It further lo oks at ho w the absence of implicit
long-term con tracts b et w een pro ject dev elop ers and consumers under some p olicies
influences con tractual arrangemen ts. It scrutinizes ho w priv ate long-term con tracts
3 2007’s v alues are for large-scale installations to mak e them more comparable to 2018’s auction
results. Successful bids in the auctions hav e tw o years of time to implemen t their pro jects, whereas
2007’s v alues refer to the actual date of implemen tation. 2007’s v alue for wind p ow er is based on
discoun ting of 4 p ercen t since the supp ort v aried o ver the installations’ lifetimes.

6 GENERAL INTR ODUCTION
b et w een pro ject dev elop ers and off-tak ers, curren tly debated as long-term alternativ es
to explicit supp ort p olicies (BDEW, 2018), affect the financial situation of off-tak ers.
Ho w do priv ate long-term con tracts for renewable energies affect off-tak ers’ o wn re-
financing costs and, ev en tually , o verall deplo ymen t costs of renew able energies?
The analysis in c hapter 2 ev aluates a surv ey conducted by Diacore (2015) for
whic h in v estors, bank ers, academics, and utilit y emplo y ees from 23 EU coun tries
pro vided estimates of the financing costs of onshore wind p o w er in their coun tries.
The econometric estimation tak es the nature of their replies in to accoun t, where some
resp onden ts do not pro vide p oin t estimates, but rather answ er that the financing
costs are higher or lo w er than indicated thresholds. W e accoun t for this through
v arious in terpretations and an in terv al estimator that assumes the replies follo w a
normal distribution. As priv ate long-term con tracts are seen as a w a y to mitigate
the additional risks under some p olicies, w e then pro ceed to analyze the effects of
suc h long-term con tracts on off-tak ers financial p osition, applying a simple mo del of
firms’ re-financing costs and estimating the effects of c hanges in debt-equit y ratios
and credit ratings.
Sev eral Europ ean coun tries, including Bulgaria, the Czech Republic, Italy , and,
prominen tly , Spain – previously a fron t-runner in renew able energy deplo ymen t –
implemen ted some kind of retrosp ectiv e c hanges to their renew able energy supp ort
p olicies (F ouquet and Nysten, 2015), meaning they reduced their supp ort pa yments
for renew able energy in v estments earlier than initially promised. 4 Other coun tries
stuc k to their initial commitmen ts. The incen tiv es for regulators to conduct retro-
sp ectiv e c hanges can b e understo o d through mo dels of time-inconsistency . These
explain ho w regulators can ha v e incen tiv es to promise supp ort pa ymen ts to renew-
able energy in v estors, y et to deviate from these commitmen ts once the in v estmen ts
are completed.
Mo dels of time-inconsistency ha v e previously b een applied b oth in the wider
4 This dissertation uses the term “retrosp ective c hanges” in line with the definition by F ouquet
and Nysten (2015), i.e. changes to previously-made regulations that are v alid as of the in tro duction
of the c hanges. This stands in con trast to “retroactiv e c hanges” that are v alid as of a past date and
in v estors ha v e to pa y bac k previously-receiv ed supp ort.

7
macro economics literature and to more sp ecific climate p olicies. Kydland and Prescott
(1977) in tro duced the concept, initially in the con text of the in teractions b et w een in-
flation and unemplo ymen t. It has since b een applied widely to general questions in
climate p olicy , e.g. b y Laffon t and Tirole (1996) and Helm et al. (2004). Salan t and
W oro c h (1992) explore ho w commitmen t equilibria can b e sustained through trigger
strategies in dynamic regulation games. Applying time-inconsistency to a general
question of abatemen t, Jak ob and Brunner (2014) demonstrate the nature and im-
p ortance of target-setting b y the regulator. Hab ermac her and Lehmann (2017) apply
a mo del of time-inconsistency to renew able energy in v estmen ts, y et fo cus on adjust-
men ts to the supp ort for new installations, rather than the classic time-inconsistency
issue where optimal regulation c hanges o v er time, ev en in the absence of new in-
formation. Hence, ev en though sev eral coun tries ha v e conducted p olicy c hanges to
their renew able energy supp ort p oten tially caused b y time-inconsistency , the concept
– and p oten tial remedies – ha v e not y et b een applied to renew able energy p olicies
and in v estmen ts.
Chapter 3, join tly written with Olga Chiappinelli, extends a mo del of time-
inconsistency b y Chiappinelli and Neuhoff (2017) to renew able energy in v estmen ts
and regulation. The pap er analyzes ho w time-inconsistency issues can arise for re-
new able energy p olicies and what regulators can do to address them. When do es
time-inconsistency o ccur and ho w can regulators use p olicy design to o v ercome it?
What role can deplo ymen t targets pla y? Wh y did some coun tries, e.g. Spain in the
2010 to 2013 p erio d, conduct retrosp ectiv e cuts, indicating time-inconsistency issues,
while other coun tries, lik e German y , di d not?
Analyzing a dynamic game setup, c hapter 3 captures that supp ort is commonly
paid for output rather than capacit y , whic h implies that past commitmen ts last w ell
in to future p erio ds, rendering in v estmen ts p oten tially prone to time-inconsistency .
W e deriv e the incen tiv es that regulators ha ve to deviate from announced supp ort.
Through a n umerical example of the resp ectiv e situations in Spain and German y
around 2012, w e test our mo del’s applicabilit y and deriv e the reasons wh y Spain
conducted retrosp ectiv e c hanges and wh y German y lack ed the incen tiv es to do so.

8 GENERAL INTR ODUCTION
T able 1: Overview o v er the dissertation’s three c hapters
Chapter 1 2 3
Title
The Impact of
Wind P o w er
Supp ort Sc hemes on
T ec hnology Choices
Financing P o w er:
Impacts of Energy
P olicies in Changing
Regulatory En vironmen ts
T o o Go o d to Be T rue?
Ho w Time-Inconsisten t
Renew able Energy P olicies
Can Deter In v estmen ts
Authors Nils Ma y Nils Ma y & Karsten Neuhoff Nils Ma y & Olga Chiappinelli
Metho-
dology
a) Small analytical mo del of
wind p o w er in v estmen ts;
b) Numerical wind p ow er
in v estmen t mo del.
a) Econometric analysis of a
surv ey of wind p o w er
financing costs in the EU;
b) Analytical mo del of how
priv ate long-term con tracts
translate in to higher re-
financing costs of off-tak ers.
a) Regulatory game
b et w een regulator and
renew able energy in v estors;
b) Numerical example of
the renew able energy p olicy
settings in Spain and
German y in 2012.
Con tri-
butions
to the
litera-
ture
1.) Identifies the incen tiv es
to in v est in to system-
friendly tec hnologies
under differen t p olicies;
2.) Defines a w a y to
measure system-optimal
wind p o w er tec hnologies;
3.) Derives an optimal p olicy
that aligns priv ate and
public optima.
1.) Finds empirical evidence
that green certificates are
asso ciated with higher financing
costs than feed-in tariffs and
sliding feed-in premia;
2.) Explores implications
of priv ate long-term con tracts;
3.) Shows that suc h con tracts
raise off-tak ers’ re-financing
costs, increasing o v erall costs.
1.) Derives when time-
inconsistency issues can arise;
2.) Shows ho w supp ort p o-
licies and deplo ymen t targets
can alleviate these issues;
3.) Identifies the reasons
wh y Spain around 2012
had incen tiv es to
retrosp ectiv ely cut its sup-
p ort and Germany did not.
Own
con tri-
bution
Single-authored pap er
Nils Ma y con tributed to all
parts; Karsten Neuhoff
particularly to
3.1, 3.3, 3.4, and 3.6.
Nils Ma y con tributed to all
parts; Olga Chiappinelli
particularly to 4.1,
4.2, 4.3, and 4.5.

Chapter 1
The Impact of Wind P o w er Supp ort
Sc hemes on T ec hnology Choices ∗
Abstract
In energy systems with large shares of v ariable renew able energies, electricity gener-
ation is lo w er during unfa vorable w eather conditions. System-friendly wind turbines
(SFT s) rectify this b y pro ducing a larger share of their electricit y at lo w wind sp eeds.
This c hapter analyzes to what exten t the b enefits of SFT s out-w eigh their additional
costs and ho w to incen tivize in v estmen ts in to them. A wind p o w er in v estmen t mo del
for German y sho ws that SFT s indeed deliv er b enefits for the energy system that o v er-
comp ensate for their cost premium. S liding feed-in premia incen tivize their deplo y-
men t only where in v estors b ear significan t price risks and p ossess sufficien t foresigh t.
Alternativ ely , a new pro duction v alue-based b enc hmark approac h triggers in v estors
to install SFT s that meet the requiremen ts of p o w er systems with increasing shares
of v ariable renew able energies.
∗ I am grateful to Karsten Neuhoff for guidance in approac hing this pap er. I thank F rieder
Borggrefe, Thilo Grau, Kira Lanc k er, Philipp M. Ric h ter, Nolan Ritter, W olf-P eter Sc hill, Sebas-
tian Sc h w enen, Alexander Zerrahn, and tw o anonymous referees for their helpful commen ts and
suggestions. I also b enefited from commen ts b y participan ts at the 21st EAERE conference, the
10th A UR Ö w orkshop, the 5th INREC conference, the 2015 conference of the German Economic
Asso ciation, seminars at the Universit y of St. Gallen, t w o Berlin Strommarkttreffen sessions, and
in ternal seminars at DIW Berlin. Data pro vided b y the German Aerospace Cen ter (DLR) is greatly
appreciated.
9

10 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
1.1 In tro duction
Since 2000, global deplo ymen t of renew able energies, suc h as wind and solar p o w er,
has gro wn strongly . German y has b een at the forefron t of this dev elopmen t, undergo-
ing the Ener giewende , whic h facilitates the coun try’s transition to renew able energy .
Renew ables pro vided ab out 32 . 5 % of German y’s gross electricit y consumption in 2015
(A G Energiebilanzen, 2015). The official national goal is a renew able share of at least
80 % b y 2050. T o ac hiev e this, the German go v ernmen t targets an annual capacit y
increase on the order of 2.8- 2 . 9 GW in onshore wind (Bundestag, 2016).
Ho w ev er, the volatile pow er generation of solar and wind p o w er p oses new c hal-
lenges and costs to an energy system originally designed around thermal p o w er plan ts.
In times of little sunshine and lo w wind sp eeds, bac k-up capacit y , storage and demand
side resp onse measures can b e required in order to meet the – rather inelastic – de-
mand for electricit y .
Y et, there is also the option to directly address the v olatile generation from
renew ables. F or solar, alternativ e orien tations facing east and w est are discussed in
this con text, so that the p o w er is supplied more smo othly throughout the da y , see for
example F raunhofer ISE (2014). F or wind p o w er, the v ast ma jorit y of electricit y is
curren tly pro duced in high wind, recen tly debated system-friend ly turbines can serv e
this purp ose. These ha v e a larger share of their pro duction in lo w and medium wind,
i.e. when less wind p o w er is in the system. Ceteris p aribus , a lo w er supply of wind
p o w er means a lo w er supply of electricit y , suc h that the price-setting p o w er plan t
has higher marginal costs. A dditionally , system-friendly turbines mak e b etter use of
existing infrastructure, since their maxim um output tends to b e lo w er, meaning that
there is less need to expand the grid and in tegration costs are lo w er. F or the purp ose
of this study , only the increase in mark et prices is analyzed, as the a v oided costs for
grid expansion, in tegration, storage, bac k-up capacit y , and demand side resp onses are
hardly quan tifiable. These b enefits w ould p ersist ev en if all new installations shifted
to system-friendly turbines. Could these b enefits also b e captured in this analysis, the
𝑜𝑝𝑡𝑖𝑚𝑎𝑙 𝑙 𝑦 -deplo y ed turbines w ould b e more system-friendly than what is iden tified

1.1. INTR ODUCTION 11
here.
Whether in v estors c ho ose system-friendly turbines dep ends on the p olicy sc heme.
Originally , fixed fe e d-in tariffs (FIT) w ere the metho d of c hoice for increasing capac-
ities of solar and wind p o w er. Through the Renew able Energy Sources A ct, a feed-in
tariff w as in tro duced in German y in 2000. Under feed-in tariffs, in v estors receiv e a
sp ecific rem uneration p er pro duced MWh. Th us, the more electricit y they pro duce,
the higher the absolute amoun t of rem uneration receiv ed. 1 As this rem uneration is
the only source of rev en ues, in v estors are indifferen t to the actual wholesale electric-
it y prices. Y et, the wholesale price reflects, to a certain degree, if supply is lo w and
demand is high. In times of a relativ ely lo w p o w er supply , prices will, c eteris p aribus ,
b e higher and vice versa prices b e lo w er in relativ ely high supply . Summarized, fixed
feed-in tariffs pro vide in v estors with a high degree of certaint y , but little incen tiv es
to install system-friendly wind turbines.
The sliding fe e d-in pr emium (FIP) aims to bring the wind p o w er supply closer to
demand. German y first in tro duced the feed-in premium on a v olun tary basis in 2012,
making it obligatory in August 2014, th us ab olishing the fixed feed-in tariff except for
v ery small installations. The FIP exp oses op erators to the wholesale electricit y price
and additionally pro vides them with a v ariable premium (Ga wel and Purkus, 2013).
The o v erall pa ymen t is based on ho w strongly a turbine’s generation correlates with
o v erall wind p o w er pro duction and whether deviations from it o ccur in hours of lo w er
or higher electricit y prices. Therefore, the co v ariance b et w een a turbine’s electricit y
generation with the o v erall German wind p o w er feed-in pla ys an imp ortan t role in
determining in v estors’ rev en ues (Sc hmidt et al., 2013).
This co v ariance with the o verall German wind pow er feed-in is p oten tially influ-
enced b y the lo cation. Grothe and Müsgens (2013) find that, under the FIP , lo cations
in German y gain or lose to differen t degrees, dep ending on their correlation with the
o v erall German feed-in. Sc hmidt et al. (2013) analyze the co v ariance b et w een the
generation at Austrian sites with o v erall generation and find that under a feed-in pre-
1 Due to the adjustmen ts of the pro duction v olume-based b enc hmark approac h, this do es not
exactly hold true in German y . Higher generation can lead to a shorter extension of the higher
feed-in tariff and can, hence, also partially lo wer rem uneration; see app endix 3.7.

12 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
mium, the optimal allo cation of turbines differs compared to the optimal allo cation
under a feed-in tariff.
Tisdale et al. (2014) analyze ho w feed-in premia influence the reliance on pro ject
finance for in v estors and find that the feed-in premium incurs additional risks to in-
v estors. The rem uneration is p oten tially lo w er compared to feed-in tariffs. Therefore,
in v estors’ return on in v estmen t requiremen t is higher under feed-in premia than un-
der feed-in tariffs. In order to ha v e access to suc h c heap debt, in v estors are b ound to
conserv ativ e estimates of their future cash flo ws as these are usually the only source
from whic h creditors are paid (Tisdale et al., 2014). Bürer and Wüstenhagen (2009)
find that Europ ean in v estors esp ecially prefer the secure rev en ue streams from feed-in
tariffs o v er feed-in premia.
The prev ailing p olicy regime also p oten tially affects the turbine tec hnology de-
plo y ed, y et the consequences of the shift to w ard the feed-in premium are not clear.
Ök o-Institut (2014) assume p erfect foresigh t on the in v estors’ side, y et find a minimal
impact. Ho w ev er, they only enable in v estors to c ho ose b et w een t w o turbine mo dels
and no gradual c hanges are observ able.
In 2015, installed turbines in German y w ere more system-friendly compared to
previous y ears (Deutsc he WindGuard, 2015). This dev elopmen t can b e driv en by sev-
eral reasons: A generally differen t in v estmen t en vironmen t, the (initially v olun tary)
in tro duction of a sliding feed-in premium in 2012, and the supply-side a v ailabilit y
of more system-friendly turbines. F raunhofer IWES (2013b) states that there is no
clear evidence that turbine tec hnologies in wind-ric h regions ha v e c hanged, but pri-
marily b ecame more sp ecialized at lo w wind sp eeds at lo w-wind sites. In con trast,
F raunhofer IWES (2015) find that at sites with in termediate wind conditions, more
system-friendly turbines ha v e also gained in p opularit y . Among others, Deutsc he
WindGuard (2014), Molly (2011, 2012, 2014), F raunhofer IWES (2013a,b), and Hirth
and Müller (2016) argue that more system-friendly turbines w ould b enefit the energy
system as a whole.
One so-far neglected asp ect is the question of ho w the system-optimal turbine
should b e defined. The aforemen tioned authors only generally state that more system-

1.1. INTR ODUCTION 13
friendly turbines b enefit the system. Only Molly (2012) defines an optimalit y crite-
rion: The costs for a turbine that is com bined with a storage, in order to p erfectly
smo othen the p o w er generation o v er the y ear. Ho w ev er, this in tro duces excessiv e
costs since the p o w er pro duction of an y individual turbine do es not necessarily need
to b e smo oth. Alternativ ely , I define the system-optimal turbine to minimize the
discoun ted difference b et w een costs p er MWh and the exp ected electricit y v alue, i.e.
price, p er MWh. 2 This difference sets the subsidy lev el, so the required subsidy is
minimized. This minimizes o v erall costs as the wind p o w er output at times of higher
v alue replaces costlier alternativ e electricit y supply . The turbine that optimizes this
criterion is considered system-optimal .
This study assesses the impact of differen t p olicy measures, suc h as the FIP , on
in v estors’ tec hnology c hoices. By applying the optimalit y criterion, I scrutinize ho w
close these tec hnologies get to this system-optim um. Kno wing ab out the effects on
risk and lo cational c hoices, I analyze the effect of the FIP on in v estors’ tec hnology
c hoices and the c hannels through whic h suc h effects can b e induced. This is conducted
b y mo deling in v estors’ in v estmen t optimization problem. As in Sc hmidt et al. (2013)
and Grothe and Müsgens (2013), in v estors are assumed to maximize the net presen t
v alue of their in v estmen t, treating the prev ailing p olicy as exogenously giv en. Y et,
since in v estors dep end on risk-a v erse pro ject-finance, I assume they cannot in tegrate
long-term exp ected p o w er mark et c hanges in to their in v estmen t decision, th us basing
it on the curren t p o w er price profile. F urthermore, unlik e Grothe and Müsgens (2013),
Sc hmidt et al. (2013), and Ök o-Institut (2014), who tak e only one or t w o turbine t yp es
in to accoun t, I analyze in v estors who are free to c ho ose from more than 140 turbine
configurations. Imp ortan tly , I extend the analysis of Sc hmidt et al. (2013), who find
that the co v ariance b et w een turbines’ pro duction and the o verall wind pow er supply
affects the net presen t v alue. I allo w this difference in co v ariances not only to o ccur
b et w een turbine lo cations, but also b et w een turbine tec hnologies.
F urthermore, I suggest and mo del a new alternativ e p olicy , the pr o duction value-
2 As Josk o w (2011) p oin ts out, it is not sufficient to merely compare the lev elized cost of electricit y
and opt for the v olatile tec hnology that comes at the least costs p er MWh b ecause the pro duction
v alues can v ary b etw een technologies.

14 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
b ase d b enchmark appr o ach . Based on a mo del of the f uture energy system, it a
priori adjusts a turbine’s rem uneration lev el dep ending on its pro duction’s future
mark et v alue. Th us, it replicates the cost-co v ering nature of the existing pro duction
volume -based b enc hmark approac h (where rem uneration is adjusted to the lo cation,
see app endix 3.7) and applies it to the turbine configuration and system-friendliness
of turbines. In v estors fully receiv e the a v erage pro duction v alue their turbines are
forecast to obtain in the future. Hence, turbines that will pro vide a greater mark et
v alue in the future are eligible for a higher rem uneration lev el. This w a y , the system-
optimal turbine is also most attractiv e to in v estors. 3
The remainder of this c hapter is structured as follo ws: In section 1.2, I presen t
the in v estmen t mo del. Then, I giv e an o v erview of the calculations for the fee-in tariff,
the feed-in premium, and the pro duction v alue-based b enc hmark approac h. I describ e
the data and wind turbine tec hnologies in section 1.3. The results are discussed in
section 1.4. Section 1.5 dra ws conclusions and iden tifies p olicy implications.
1.2 Metho dology
In v estors optimize their discoun ted future rev en ues and costs, taking the prev ailing
renew able supp ort p olicy as exogenously giv en . I analyze one scenario p er p olicy and
in v estigate the differences b et w een these. I outline the fixed feed-in tariff and the slid-
ing feed-in premium in sections 1.2.2 and 1.2.3, indicating ho w they are implemen ted
in the in v estmen t decision mo del. Finally , the pro duction v alue-based b enc hmark ap-
proac h is a p olicy explicitly gran ting rem uneration dep ending on the turbine’s future
system-friendliness, as laid out in section 1.2.4.
3 Ök o-Institut (2014) suggest a differen t rem uneration sc heme where the rem uneration dep ends
on a turbine’s pro duction c haracteristics. This approach can support the developmen t of system-
friendly turbines. Ho wev er, it do es so explicitly , generally assuming their deplo yment is adv an tageous
for the system.

1.2. METHODOLOGY 15
1.2.1 Wind p o w er in v estmen t
The in v estor maximizes their net presen t v alue (NPV) with resp ect to turbine tec h-
nology 𝑖 . Three technology c haracteristics are imp ortan t determinan ts of output: h ub
heigh t, generator nominal p o w er and rotor blade length (cp. section 1.3.1). In its
general form, turbine 𝑖 ’s NPV 𝑁 𝑖 is defined as:
𝑁 𝑖 = − 𝛼 𝑖 + ∑︁
𝑡
𝛿 𝑡 𝜔 𝑖,𝑡 ( 𝜋 𝑝𝑜𝑙 𝑖𝑐𝑦 𝑖,𝑡 − 𝛽 𝑡 ) (1.1)
𝛼 𝑖 represen ts the turbine’s fixed costs. Its generated electricit y at time 𝑡 is 𝜔 𝑖,𝑡 . It
is discoun ted with the discoun t factor 𝛿 𝑡 . The p olicy-sp ecific rem uneration p er MWh
is captured in 𝜋 𝑝𝑜𝑙 𝑖𝑐𝑦 𝑖,𝑡 . The v ariable op erations and main tenance costs are 𝛽 𝑡 . 4
1.2.2 Fixed feed-in tariff
Fixed feed-in tariffs comp ensate in v estors with a fixed pa ymen t p er generated MWh.
Ev en though the rate is fixed, in the German implemen tation, a turbine receiv es
either the initial high pa ymen t 𝐹 ℎ𝑖𝑔 ℎ or a lo w er, subsequen t pa ymen t 𝐹 𝑙 𝑜𝑤 . The initial
p erio d lasts at least fiv e y ears and can b e extended to co v er up to the en tire lifetime.
The exact time span dep ends on the lo cation and tec hnology c hosen, cp. app endix
3.7. The paymen t 𝜋 𝐹 𝑖,𝑡 is the pa ymen t p er MWh for turbine 𝑖 and is:
𝜋 𝐹 𝑖,𝑡 = 𝐹 𝑖,𝑡 (1.2)
= 𝜒 𝑖,𝑡 𝐹 ℎ𝑖𝑔 ℎ + (1 − 𝜒 𝑖,𝑡 ) 𝐹 𝑙𝑜𝑤 (1.3)
The pa ymen t is simply equal to the FIT 𝐹 𝑖,𝑡 b ecause there is no other rev enue
source. The v ariable 𝜒 𝑖,𝑡 lies in the in terv al b et w een 0 and 1. It reflects that at an y
time 𝑡 , a turbine receiv es either the high initial FIT 𝐹 ℎ𝑖𝑔 ℎ or the lo w er subsequen t
FIT 𝐹 𝑙𝑜𝑤 .
4 The op erations and main tenance costs do not c hange the qualitativ e analysis in the follo wing
and are th us omitted from the theoretical analysis.

16 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
F ollo wing Sc hmidt et al. (2013) and treating 𝜔 𝑖,𝑡 as a random v ariable, it is
p ossible to tak e the exp ectations of a com bination of equations (1.1) and (1.2) to
express the in v estor’s exp ected NPV under the FIT 𝑁 𝐹 as in equation (1.4). The
binary v ariable 𝜇 𝑖 indicates whic h turbine an in v estor c ho oses.
𝐸 ( 𝑁 𝐹 ) = ∑︁
𝑖
𝜇 𝑖 (︃ − 𝛼 𝑖 + ∑︁
𝑡
𝛿 𝑡 (︂ 𝐸 ( 𝜔 𝑖,𝑡 ) 𝐸 ( 𝜋 𝐹 𝑖,𝑡 ) + 𝐶 𝑜𝑣 ( 𝜔 𝑖,𝑡 , 𝜋 𝐹 𝑖,𝑡 ) )︂ )︃ (1.4)
This equation simply states that the NPV is influenced b y the exp ected pro duc-
tion amoun t, the exp ected rem uneration and the co v ariance b et w een the t w o. With
uniform rem uneration lev els across turbines, the co v ariance b et w een pro duction and
rem uneration is equal across turbines. Consequen tly , in v estors will c ho ose – from
among turbines with equal costs – the turbine with the largest exp ected energy yield.
Figure 1-1 depicts that with lo w shares of wind p o w er, the turbine with the lo w est
lev elized cost of electricit y minimizes supp ort and o v erall costs and is, th us, optimal.
P arallel to the increase in nominal p o w er sho wn on the x-axis, the rotor blade length
and the h ub heigh t decrease, so that more system-friendly turbines app ear on the left
of the figure. The v alue curv e runs horizon tally . This is the case b ecause without
significan t shares of wind p o w er, the electricit y price is indep enden t of wind strengths
and is, therefore, equal across turbines.
Equal rem uneration across turbines means a FIT. Consequen tly , the cost-minimizing
turbine (i.e. the turbine that has the lo w est lev elized cost of electricit y) is optimal
for in v estors, in the example a turbine with a nominal p o w er of 3 MW (Mega w att).
1.2.3 Sliding feed-in premium
The rem uneration for a generated MWh b y wind p o w er is (Bundestag, 2014):
𝜋 𝑀 𝑖,𝑡 = 𝑝 ( 𝛾 − 𝑖,𝑡 ) + 𝑃 𝑖,𝑡 (1.5)
𝜋 𝑀 𝑖,𝑡 represen ts the pa ymen t for one MWh of generated electricit y b y turbine 𝑖

1.2. METHODOLOGY 17
Figure 1-1: Illustrativ e visualization of turbine c hosen under cost-minimizing FIT.
The v alue/costs refer to lev elized lifetime v alue/costs p er MWh.
at time 𝑡 . 𝑝 ( 𝛾 − 𝑖,𝑡 ) is the electricit y price as a function of the wind p o w er pro duction
𝛾 − 𝑖,𝑡 of all other turbines − 𝑖 , assuming that an individual in v estor’s decision to install
turbine 𝑖 has no effect on the electricit y price. Ho w ev er, the cum ulativ e output 𝛾 − 𝑖,𝑡
of all German wind turbines depresses electricit y prices via the merit-order effect.
The feed-in premium 𝑃 𝑖,𝑡 is defined as
𝑃 𝑖,𝑡 = 𝐹 𝑖,𝑡 − 𝜓 𝑡 ¯ 𝑝 𝑡 (1.6)
The feed-in premium is comparable to the supp ort lev el under fixed feed-in tariffs
min us the a v erage German da y-ahead mark et price ¯ 𝑝 𝑡 times 𝜓 𝑡 , a mon th-sp ecific
wind-v alue adjustmen t factor. In an y mon th, if German wind p o w er w as sold at
90 % of the a v erage mark et price, 𝜓 𝑡 w ould b e equal to 0.9. Sliding feed-in premia
ha v e b een implemen ted with differen t arrangemen ts: While the UK’s Con tracts for
Differences ha v e hourly adjustmen t factors and th us v ery little price risk for in v estors,
ann ual adjustmen ts as in the Netherlands are also conceiv able, where in v estors’ price
exp osure is considerably higher. App endix 1.6.2 indicates the implications of suc h
ann ual adjustmen ts.

18 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Under this supp ort scheme, it is theoretically possi ble for turbines to earn more
compared to the FIT. T urbin es that pro duce at times of scarcit y are rew arded. Pro-
duction at times of surplus is p enalized. An intended consequence is that turbines
stop op erating in times in whic h their o v erall pa ymen t p er MWh is negativ e. This is
the case when the electricit y price is negativ e and its absolute v alue is larger than the
feed-in premium. This can similarly b e ac hiev ed under the FIT through the pro vision
of resp ectiv e wind spill regulations. In all scenarios, turbines are assumed to op erate
only when the pa ymen t p er MWh is p ositiv e.
F or the FIP , the NPV of ev ery turbine tec hnology 𝑖 is
𝑁 𝑃 𝑉 𝑀 𝑖 = − 𝛼 𝑖 + ∑︁
𝑡
𝛿 𝑡 𝜔 𝑖,𝑡 𝜋 𝑀 𝑖,𝑡 (1.7)
T aking exp ectations of and transforming equation (1.7) and com bining the result
with equations (1.5) and equation (1.6) yields
𝐸 ( 𝑁 𝑀 𝑖 ) = − 𝛼 𝑖 + ∑︁
𝑡
𝛿 𝑡 (︂ 𝐸 ( 𝜔 𝑖,𝑡 ) 𝐸 ( 𝑝 ( 𝛾 − 𝑖,𝑡 ) + 𝑃 𝑖,𝑡 ) + 𝐶 𝑜𝑣 ( 𝜔 𝑖,𝑡 , 𝑝 ( 𝛾 − 𝑖,𝑡 ) + 𝑃 𝑖,𝑡 ) )︂ (1.8)
Regardless of the absolute merit-order effect, the resulting price as function of
the merit order effect can b e expressed as 𝑝 ( 𝛾 − 𝑖,𝑡 ) = 𝑝 *
𝑡 − 𝑓 ( 𝑝 *
𝑡 , 𝛾 − 𝑖,𝑡 ) , where 𝑝 *
𝑡 is
the reference price without an y wind p o w er supply and 𝑓 is the merit order effect as
function of the initial price lev el 𝑝 *
𝑡 and the o v erall wind p o w er supply 𝛾 − 𝑖,𝑡 . Next,
the equation can b e rewritten as 5
𝐸 ( 𝑁 𝑀 𝑖 ) = − 𝛼 𝑖 + ∑︁
𝑡
𝛿 𝑡 (︂ 𝐸 ( 𝜔 𝑖,𝑡 ) 𝐸 ( 𝑝 *
𝑡 − 𝑓 ( 𝑝 *
𝑡 , 𝛾 − 𝑖,𝑡 ) + 𝑃 𝑖,𝑡 )
− 𝐶 𝑜𝑣 ( 𝜔 𝑖,𝑡 , 𝑓 ( 𝑝 *
𝑡 , 𝛾 − 𝑖,𝑡 ) − 𝑃 𝑖,𝑡 ) )︂ (1.9)
5 F or simplicity , assuming that 𝐶 𝑜𝑣 ( 𝜔 𝑖,𝑡 , 𝑝 *
𝑡 )=0 . In the numerical mo del, the price is an exogenous
v ariable, suc h that this assumption is dropp ed.

1.2. METHODOLOGY 19
Again, the NPV dep ends on the exp ected pro duction amoun t and the exp ected
rem uneration lev el. Moreo v er, the exp ected price 𝑝 *
𝑡 − 𝑓 ( 𝑝 *
𝑡 , 𝛾 − 𝑖,𝑡 ) is in tro duced. Most
imp ortan tly , the equation sho ws that the exp ected NPV of a turbine tec hnology 𝑖
decreases the larger the co v ariance b et w een on the one hand the turbine’s generation
𝜔 𝑖,𝑡 and on the other hand the o v erall German wind p o w er supply 𝛾 − 𝑖,𝑡 and the
negativ e feed-in premium − 𝑃 𝑖,𝑡 . Notably , a turbine’s NPV decreases, the larger the
co v ariance b et w een its o wn pro duction and the o v erall German wind p o w er feed-
in. Stated p ositiv ely , the subtraction of 𝐶 𝑜𝑣 ( 𝜔 𝑖,𝑡 , 𝑓 ( 𝑝 *
𝑡 , 𝛾 − 𝑖,𝑡 )) implies that a turbine
tec hnology is more attractiv e to in vestors, the lo w er the co v ariance of its electricit y
generation with German wind p ow er supply . An in v estor can p oten tially lo w er this
“p enalt y for a high p ositive co v ariance” b y opting for a system-friendly wind turbine,
i.e. a turbine tec hnology under whic h a larger share of pro duction o ccurs in times of
generally lo w wind p o w er feed-in.
1.2.4 Pro duction v alue-based b enc hmark approac h
The pro duction v alue-based b enc hmark approac h incen tivizes the deplo ymen t of tur-
bines that in the future pro vide the greatest v alue to the system, measured as the
lev elized difference b et w een costs and mark et v alue of the pro duction. This differ-
ence sets the required subsidy lev el in energy system where renew ables are unable to
en tirely re-finance themselv es through the electricit y mark et. 6 Minimization of this
difference th us defines the system-optimal turbine.
In systems with increasing shares of wind p o w er, more system-friendly turbines
b ecome optimal from th e system’s p ersp ectiv e. Wind turbines ha v e a stronger im-
pact on electricit y prices, suc h that more system-friendly turbines pro duce at higher
a v erage mark et v alues in systems with high shares of wind p o w er. The difference
b et w een costs and mark et v alue is minimized with a more-system friendly turbine, as
depicted in figure 1-2. This turbine comes at a higher cost than the cost-minimizing 7
6 Ideally , the p olicy-maker kno ws the costs of companies and sets the rem uneration lev el accord-
ingly . The imp ortant question ho w to find this remuneration lev el is b ey ond the scop e of this c hapter.
In the German con text, tenders hav e b een in tro duced in 2017 (Bundestag, 2016).
7 The turbine with the lo west lev elized cost of electricity .

20 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-2: Illustrativ e visualization of turbine costs and v alues. The v alue/costs
refer to lev elized lifetime v alue/costs p er MWh.
one, but o vercompensates for these additional costs through the increased v alue of its
pro duction. The higher the share of wind p o w er in the system, the steep er the drop
in mark et v alue for less system-friendly turbines. While the nominal p o w er increases,
the rotor blade length and the h ub heigh t decrease, rendering turbines on the righ t
of the figure again less system-friendly . If this difference in pro duction v alues is not
accoun ted for, the turbines will not b e system-friendly enough as compared to the
system-optim um, leading to an increased required subsidy lev el.
Since the considered lev el of wind p o w er p enetration matters, the system-optim um
dep ends on the c hosen time horizon and the discoun t factor. Lo w er so cial discoun t
factors lead to stronger v aluations of the future. Dep ending on these parameters, the
system-optimal turbine will b e more or less system-friendly , i.e. lies further righ t or
left in figure 1-2. In the follo wing, I abstract from the discussion of whic h p erio d to
consider and what discoun t factor to apply . I simplify b y assuming that the energy
system of 2030 is what solely defines the system optimal turbine. Sensitivities in the
app endix 1.6.3 indicate that also with alternativ e definitions, the results hold. 8
8 Arguably , the question as to which time span to consider at what discoun t factor lea v es ample
space for researc h. The presen ted mechanism w orks as a proxy for an in-depth treatmen t of this

1.2. METHODOLOGY 21
In order to o v ercome the o v erv aluation of curren t p o w er price profiles c harac-
terized b y lo w shares of wind p o w er, ren umeration can b e directly based on future
mark et v alues, taking in to accoun t renew able energy deplo ymen t targets. This can b e
implemen ted analogous to the existing pro duction volume -based b enc hmark approac h
where rem uneration is adjusted comparing pro duction volumes for ev ery lo cation with
a b enchmark lo c ation . Analogously , rem uneration can b e adjusted comparing pro duc-
tion values for ev ery turbine t yp e with a b enchmark turbine . In consequence, inde-
p enden t of in v estors’ foresigh t of p o w er price profile c hanges, they are incen tivized
to build wind turbines that are system-optimal for energy systems with increasing
shares of wind p ow er.
This installation-sp ecific rem uneration lev el is implemen ted based on a fixed
feed-in tariff and could alternativ ely b e based on an hourly-sliding feed-in premium.
Rem uneration is adjusted based on an installation’s pro duction c haracteristics: Those
that pro vide a higher v alue to the system, measured as the a v erage electricit y price
they obtain in the future, receiv e a higher rem uneration lev el than other installations.
This means that the rem uneration lev el under the FIT, 𝐹 ℎ𝑖𝑔 ℎ / 𝐹 𝑙𝑜𝑤 is individualized
to 𝐹 ℎ𝑖𝑔 ℎ 𝑖 / 𝐹 𝑙 𝑜𝑤 𝑖 . This comparison is conducted with resp ect to a b enc hmark turbine 𝜆 .
F or this turbine, its future a v erage electricit y v alue ¯ 𝑝 2030 𝜆 is calculated. In tro ducing
this b enc hmark turbine erases the relev ance of the general price lev el of the considered
future p erio d and only deviations from the a v erage lev el influence the rem uneration. 9
The p o w er price forecasts are pro vided b y a p o w er mark et mo del for 2030 b y the
German A erospace Cen ter, see section 1.3.2.
T o calculate the turbine-sp ecific rem uneration lev els, the difference b et w een the
turbines’ future pro duction v alue and the b enc hmark turbine’s future pro duction
issue. New tec hnologies could challenge some, but not all, of the adv antages of system-friendly
turbines. Under the production v alue-based b enc hmark approach, the regulator carries this risk, in
order to prev en t in v estors from ha ving to pa y excessiv e risk premia or underinv est in system-friendly
turbines.
9 In practice, the b enc hmark turbine 𝜆 could b e an y turbine. In the analysis, for ev ery lo cation
it is the turbine that in vestors c ho ose there under the normal FIT. This allo ws for comparing the
rem uneration directly , as only the differences in rem uneration lev els b et w een turbine t yp es matter
to the analysis, not the absolute lev el. Using a differen t b enc hmark turbine w ould only c hange the
absolute rem uneration lev el, but not the differences in rem uneration levels.

22 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
v alue is added to the baseline rem uneration lev el. Com bining future electricit y prices,
wind sp eed data at the analyzed sites, and an y turbine’s p o w er curv e, it is p ossible to
calculate the exp ected av erage price a turbine will obtain in 2030, ¯ 𝑝 2030 𝑖 . The a v erage
pro duction v alue in 2030 of the b enc hmark turbine 𝜆 is ¯ 𝑝 2030 𝜆 . The difference b et w een
the t w o is added to the default rem uneration lev el, as sho wn in equation (1.10).
𝐹 ℎ𝑖𝑔 ℎ/𝑙 𝑜𝑤 𝑖 = 𝐹 ℎ𝑖𝑔 ℎ/𝑙𝑜𝑤 + ( ¯ 𝑝 2030 𝑖 − ¯ 𝑝 2030 𝜆 ) (1.10)
The adv an tage of this approac h o v er explicit extra-rem uneration for certain more
system-friendly turbines is that ¯ 𝑝 2030 𝑖 only dep ends on v alue to the system, but is
turbine-tec hnology indep enden t. It is based on pr o duction c haracteristics rather than
te chnolo gic al c haracteristics. Remuneration can be computed in adv ance, suc h that
no additional in v estmen t risks are incurred for in v estors and in v estors are not required
to p ossess p erfect foresigh t. A limitation is that, in adv ance, the in v estor requires
a wind sp eed time-series from the baseline y ear at the analyzed lo cation(s) whic h is
reliable enough to calculate ¯ 𝑝 2030 𝑖 . If no reliable enough wind sp eed information w as
a v ailable, an alternativ e option could b e the use of a v erage wind sp eeds at nearb y
measuring p oin ts. Moreo v er, the regulator needs to pro vide a forecast for the future
p o w er price profile. While suc h pro jections are inheren tly uncertain, e.g. link ed
to fuel and carb on price assumptions, not the absolute lev el, but only deviations
from a v erage prices are utilized. 10 The most relev an t uncertain ties link to shares of
renew ables, grid and storage dev elopmen t and, th us, are largely determined b y public
p olicy c hoices. As suc h, the commitmen t of a regulator to a sp ecific future p ersp ectiv e,
for example with the p o w er price profile forecast, is the basis for co ordinating priv ate
actor c hoices. Assuming that future electricit y prices differ b et w een hours with high
and lo w amoun ts of wind p o w er seems reasonable to exp ect and is in line with the
findings of Hirth and Müller (2016) and Uec k erdt et al. (2013).
10 Leaving the regulator with the risk that future price v ariabilit y is m uc h lo w er than exp ected,
i.e. the case for system-friendly turbines is lo w er than an ticipated.

1.3. D A T A 23
1.3 Data
T urbine tec hnologies, their pro duction c haracteristics and ho w these ha v e b een im-
plemen ted are sho wn in section 1.3.1. P ast da y-ahead electricit y prices and mo deled
mark et prices in future energy systems are explained in section 1.3.2. Measured wind
sp eeds and their extrap olation to actual h ub heigh ts are describ ed in section 1.3.3.
The n umerical application is detailed in section 1.3.4.
1.3.1 Wind turbine tec hnology
In v estors c ho ose certain turbine tec hnologies whic h decide the turbines’ system-friendliness.
Based on t w o of the three tec hnology parameters, the p o w er curv e describ es ho w m uc h
electricit y a turbine pro duces under differen t wind conditions. Based on this mo del-
ing, I allo w in v estors to c ho ose from a wide range of turbines, unlik e most mo dels in
the literature where in v estors can only c ho ose from one or a few turbine t yp es.
T ec hnology parameters
Three main tec hnology parameters define a turbine’s pro duction pattern and its
system-friendliness: the h ub heigh t, the rotor blade length and the nominal p o w er.
First, a higher turbine induces higher costs for materials, but as wind strengths in-
crease in greater heigh ts, a turbin e encoun ters higher wind sp eeds. Th us, it is able to
generate electricit y at more times and more regularly . The more obstacles there are
on the ground, the more the wind strength increases with additional heigh t. This is
one reason wh y w e observ e that wind turbines in southern German y tend to b e higher
than turbines at the northern coasts, whic h are exp osed to the op en sea. Second, the
rotor blade length defines ho w m uc h energy a turbine can harv est from the wind at
an y giv en wind strength. With a longer rotor blade and a larger rotor sw ept area,
the turbine is exp osed to more wind energy . Third, a low er nominal p o w er of the
generator implies that the maxim um con v ersion lev el is already obtained at a lo w er
wind sp eed.

24 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
The ratio of nominal p o w er to rotor sw ept area is the sp e cific p ower , measured
in W m − 2 . In the case of a v ery lo w sp ecific p o w er (large rotor blades and a generator
with a lo w nominal p o w er), a turbine is almost alw a ys able to capture enough wind
energy to op erate its generator at full nominal p o w er. Suc h a turbine w ould ha v e a
v ery high capacit y factor and high n um b er of full load hours. A ccordingly , turbines
with a lo w er sp ecific p o w er are considered more system-friend ly (Molly, 2011).
In con trast, a turbine with a high sp ecific p o w er (short rotor blades and generator
with a large nominal p o w er) can rarely reap enough energy from the wind to run its
generator at full capacit y . With ev ery small change in wind sp eeds, the amoun t
of generated electricit y c hanges, as the generator is op erating b elo w its maxim um
(Molly, 2011).
P o w er curv e scaling
P o w er curv es describ e ho w efficien tly turbines con v ert kinetic energy from the wind
in to electricit y . They are defined b y turbines’ rotor blade lengths and their nominal
p o w ers. The third tec hnology parameter h ub heigh t is not reflected in the p o w er
curv e as it merely influences whic h wind sp eed a turbine is exp osed to, but not ho w
w ell this energy is con v erted. Based on Narb el et al. (2014), the general form ula for
the p oten tially generated electricit y 𝑃 𝑝𝑜𝑡 is:
𝑃 𝑝𝑜𝑡 = 1
2 𝜙 𝑎𝑖𝑟 𝜋 𝐶 𝑝 ( 𝑣 ) 𝑟 2 𝑣 3 (1.11)
𝜙 𝑎𝑖𝑟 is the air densit y (assumed to b e 1 . 225 kg/m 3 , a standard v alue for German y
(Deutsc hes Institut für Bautec hnik, 2012)). 𝐶 𝑝 ( 𝑣 ) is the mec hanical efficiency and
dep ends on the wind sp eed. A ccording to Betz’ la w, it cannot p ossibly exceed ab out
59 p ercen t (Narb el et al., 2014). In mo dern turbines, factors of up 45- 52 % p ercen t
can b e observed, e.g. in Enercon (2012). Lastly , the wind sp eed 𝑣 en ters the form ula
in cubic form, demonstrating the imp ortance of fa v orable wind conditions for wind
p o w er generation.
Once the nominal p o w er 𝑃 𝑛𝑜𝑚 is reached, the actual pro duced amoun t of elec-

1.3. D A T A 25
tricit y , 𝑃 𝑊 , ceases to increase and sta ys at this maxim um un til reac hing its cut-out
sp eed at around 25 ms − 1 , as in Enercon (2012). Close to reac hing its nominal p o w er,
ho w ev er, a turbine’s mec hanical efficiency decreases, resulting in a c haracteristic den t
in the p o w er curv e. 11 In addition, turbines also p ossess cut-in sp eeds b elo w whic h
they cannot start running. I assume these to lie uniformly at 3 ms − 1 , as depicted in
Enercon (2012), where turbines ha v e v ery lo w efficiencies b elo w this lev el. Just ab o v e
this threshold their efficiency is not high y et, appro ximated b y a lo w mec hanical ef-
ficiency un til a wind sp eed of 4 ms − 1 is reac hed. Summarized, equation (1.12) sho ws
these calculations of the generated electricit y o v er diff eren t wind sp eeds, 𝑃 𝑊 .
𝑃 𝑊 =
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
0 if 𝑣 ≤ 3 𝑚𝑠 − 1 ,
0 . 9 * 𝑃 𝑝𝑜𝑡 if 𝑣 > 3 𝑚𝑠 − 1 and 𝑣 ≤ 4 𝑚𝑠 − 1 ,
𝑃 𝑝𝑜𝑡 if 𝑣 > 4 𝑚𝑠 − 1 and 𝑃 𝑝𝑜𝑡 ≤ 0 . 85 * 𝑃 𝑛𝑜𝑚 ,
𝑃 𝑝𝑜𝑡 − 0 . 85 * 𝑃 𝑛𝑜𝑚
2 + 0 . 85 * 𝑃 𝑛𝑜𝑚 if 1 . 15 * 𝑃 𝑛𝑜𝑚 ≥ 𝑃 𝑝𝑜𝑡 ≥ 0 . 85 * 𝑃 𝑛𝑜𝑚 ,
𝑃 𝑛𝑜𝑚 if 𝑃 𝑝𝑜𝑡 > 1 . 15 * 𝑃 𝑛𝑜𝑚 .
(1.12)
Hence, the p o w er curv es are based on the underlying tec hnology parameters.
This w a y , the mo del is flexible to accommo date for in v estmen t decisions not only
b et w een a few real-w orld turbines and their resp ectiv e p o w er curv es, but enables the
c hoice from 147 differen t turbines.
T w o extreme turbine configurations are depicted in figure 1-3. Both ha v e the
same price, as explained in section 1.3.1. The less system-friendly one’s nominal
p o w er is 4 MW and the diameter of its rotor blades is rather lo w at 90 m. The
second, more system-friendly tec hnology configuration has a m uc h smaller nominal
p o w er at 2 MW and the diameter of its rotor blades is considerably higher at 122 . 5 m.
Both configurations p ossess the same h ub heigh t of 80 m. F urther, the frequency of
wind sp eeds at a roughly t ypical German lo cation is displa y ed, namely the frequency
11 Here, I used a decrease in efficiency starting at 85 % of the rated output, approximated from
Enercon (2012). The results are robust to sensitivity analyses with respect to the exact sp ecification.

26 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-3: P o w er curv es of t w o exemplary tec hnology configurations
of wind sp eeds at the b enc hmark lo cation as defined in the German la w, cp. ap-
p endix 3.7. F rom this w e see that at most times, the system-friendly turbine actually
generates more electricit y . Here, it do es so in 72 % of the time, whereas the system-
unfriendly configuration only pro duces more 8 % of the time. Moreo v er, as most
German pro ject sites actually ha v e w orse wind conditions than this wind distribution
(Deutsc he WindGuard, 2014), this comparison w ould b e ev en more fa v orable for the
system-friendly configuration there.
It is easy to see that the less system-friendly turbine can pro duce more electricit y
at fairly strong winds and that the alternativ e turbine is more efficien t at medium-
strong winds. Figure 1-4 depicts the mo deled p ow er output of the t w o extreme turbine
configurations for t w o exemplary da ys at the same lo cation in Jan uary 2015: Whereas
the system-unfriendly turbine generates m uc h more p o w er in some of the hours, the
system-friendly configuration runs considerably more constan tly also in hours of lo w
wind sp eeds.
T ec hnology trade-off
In v estors face a trade-off b et w een the three tec hnology parameters h ub heigh t, rotor
blade length and nominal p ow er. An incremen t in eac h of these categories leads to
an increase in in v estmen t costs. In the mo del, the in v estor can opt for a range of
com binations co v ering v ery system-friendly and system-unfriendly turbines. Based

1.3. D A T A 27
Figure 1-4: Comparison of p o w er pro duction on Jan uary 13th and 14th 2015
on the three tec hnology parameters, the turbine’s p o w er curve is then calculated, as
describ ed in the previous section.
In v estmen t costs 𝛼 𝑖 are held constan t for this purp ose, so that an in v estor has
to optimize the relativ e costs and b enefits of the three parameters and cannot simply
opt for a configuration where all three are v ery large. Rough cost estimates from
Deutsc he WindGuard (2013) giv e e 1150 p er k W (kilo w att) in nominal p o w er and
e 410 p er m 2 in rotor sw ept area. A v eraging cost estimates for differen t h ub heigh ts
b et w een 80 and 140 m from Hau (2014) yield appro ximate costs of e 12 500 p er meter
h ub heigh t increase. The analyzed sp ecific p o w ers, i.e. the ratio b et w een nominal
p o w er and rotor-sw ept area, lie b et w een 167 and 629 W m − 2 , co v ering all new onshore
installations in 2014 (NREL, 2015). The generator’s nominal p o w er is b et w een 2
and 4 MW, co v ering 91 . 5 % of all sizes installed in 2014. The rotor blade length
is b et w een 45 and 61 . 7 m, co v ering ab out 74 % of new turbines, and finally the hub
heigh t lies b et w een 80 and 140 m, co vering 76 % of new onshore installations (Deutsc he
WindGuard, 2015). As it turns out, the priv ately optimal in v estmen t decision in
almost all cases lies on one tra jectory b et w een on the one hand a v ery high nominal
p o w er ( 4 MW), short rotor blades ( 45 m) and a lo w h ub heigh t ( 80 m) and on the

28 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-5: The turbines’ tec hnology configurations
other hand a fairly small generator ( 2 m), rather long rotor blades ( 56 . 8 m) and rather
high h ub heigh t ( 140 m). Figure 1-5 visualizes this tra jectory . F ollo wing Deutsc he
WindGuard (2013), quasi-fixed ann ual costs and v ariable op erations and main tenance
costs amoun t to 24.1 e /MWh for the first ten op erational y ears and 26.8 e /MWh
afterw ards.
1.3.2 Prices
The da y-ahead prices for 2013 and 2015 are obtained from Europ ean Energy Exc hange
(2016). 2013’s a v erage price sto o d at e 37 . 8 p er MWh, with a standard deviation of
e 16 . 5 p er MWh. There w ere 64 hours with negativ e prices and an av erage negativ e
price of - e 14 . 17 . In 2015, the n um b er of h ours had increased to 126, but at an a v erage
v alue closer to zero at - e 9 . 00 . Th e mean v alue decreased to e 31 . 6 p er MWh, with
a decreased standard deviation of e 12 . 7 p er MWh, i.e. a flatter p o w er price profile.
The mo del is implemen ted with a discoun t factor of 𝛿 = 0 . 95 . As turbines are eligible
for some kind of rem uneration for 20 y ears, the lifetime T is assumed to b e 20.
The underlying mo del REMix estimates electricit y prices in 2030 and w as dev el-
op ed b y Deutsc hes Zen trum für Luft-und Raumfahrt (2014) in order to ev aluate the

1.3. D A T A 29
long-term dev elopmen ts of p o w er plan ts, the p o w er grid, demand side managemen t,
energy storage as w ell as com bined heat and p o w er. The mo del co v ers W estern and
Northern Europ e, as w ell as parts of Eastern Europ e and North Africa. It assumes a
minim um share of renew able energies of 50 p ercen t of the total electricity generation
b y 2030. Wind p o wer in v estmen ts are endogenous and tak e place at the sites with the
b est wind resources first, using system-unfriendly turbines. 12 The mo deled years are
based on the wind sp eed patterns of 2006. The electricit y prices indicate the v alue
of p o w er generation in future energy systems with higher shares of renew ables. The
price v ariation increases, p ossibly due to the further-increasing share of fluctuating
renew able energies.
In the baseline scenario, national and in ternational grid in v estmen ts are as-
sumed to o ccur as en visioned b y the Europ ean T en-Y ear Netw ork Dev elopmen t Plans
(ENTSO-E, 2012) and in German y according to the national net w ork dev elopmen t
plans (50Hertz et al., 2013). All these grid connections b ecome op erational as planned
and on time. The results ha v e b een tested for robustness with a scenario with
additional, endogenous, optimized grid in v estmen ts, sho wn in app endix 1.6.2. As
exp ected, price v ariation and, th us, the case for system-friendly turbine designs de-
creases, but only sligh tly so. App endix 1.6.3 giv es some details on the resulting p o w er
price profile. Deutsc hes Zen trum für Luft-und Raumfahrt (2014) pro vides a detailed
explanation of the mo del.
1.3.3 Wind sp eed data
As input to the mo del, I use historic wind sp eed information pro vided b y Deutsc her
W etterdienst (2016). As a si te with v ery fa v orable wind conditions, Heligoland is ana-
lyzed, an island in the North Sea. As a site with medium wind resource, Boltenhagen
in the North-East is used. F or sensitivit y analyses, sites with generally unfa v orable
conditions (Hano v er) and sev eral other lo cations are scrutinized, see app endix 1.6.2.
12 If the mo del included more system-friendly turbines, the cov ariance b etw een the general wind
output and an y new system-friendly turbines would lie higher. Still, new system-friendly turbines
w ould pro duce at higher v alue and b e b eneficial as they shift pro duction aw a y from v ery windy
hours and pro duce more predictably .

30 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Results are robust across lo cations, unless stated otherwise.
F or the primary analysis, I emplo y data for 2013. As sensitivit y analysis, data
from 2015 has also b een used, with robust results. In order to compute inputs in to
the future energy systems whic h are based on the wind patterns of 2006, 2006 wind
sp eed data is also used. When measurements for individual hours w ere missing, the
arithmetic mean of the t w o adjacen t existing measurements w as tak en.
The data on wind sp eeds w as measured at differen t heigh ts than the turbines’
h ub heigh ts. This requires heigh t scaling. As commonly done, I assume a logarithmic
v ertical heigh t profile, describ ed in Hau (2014). Kno wing a wind sp eed at heigh t ℎ 2 ,
the sp eed at height ℎ 1 can b e calculated b y:
𝑣 ℎ 1 = 𝑣 ℎ 2 * ln ℎ 1
𝑧 0
ln ℎ 2
𝑧 0
(1.13)
𝑧 0 stands for the roughness length at the ground, i.e. if there are man y obstacles
lik e trees or buildings. It v aries b et w een lo cations. Urban places tend to ha v e more
obstacles and, th us, the roughness length is higher, e.g. ab out 0.5 for Hano v er. Rural
places ha v e lo w er v alues, e.g. Boltenhagen (German Baltic coast) rather has a v alue
of 0.1 (Silv a et al., 2000). Ev en if these v alues based on broader categorizations w ere
not exactly correct, it w ould not sp oil this analysis, as the fo cus of this study is not
where turbines are allo cated in the first place, but ho w the utilized tec hnologies differ
under differen t p olicy regimes.
1.3.4 Numerical application
I concen trate on the in v estmen t decision for a single turbine. The optimization prob-
lem is implemen ted in GAMS. Naturally , inv estmen ts can o ccur in lo cations with v ery
fa v orable wind conditions, so the analysis fo cuses on one suc h lo cation. Moreo v er,
the German supp ort sc heme incen tivizes installations also at sites with only fair wind
resources, th us suc h a site is also analyzed. Deplo ymen t at sites with v ery p o or wind
conditions is not incen tivized and therefore not relev an t.
The v olatilit y of the applied time series of p o w er prices matters for t w o cases:

1.3. D A T A 31
First, under the sliding feed-in premium, in v estors consider some p o w er price profile
for their optimization. Second, in order to deriv e an optimal decision from a regula-
tor’s p ersp ectiv e, the considered p o w er price profile of the regulator also matters. 13
In the n umerical application, in v estors and the regulator differ in their applied
time horizons: Whereas in v estors stress curren t flat p o w er price profiles, the regula-
tor also considers the implications of systems with a high share of v ariable renew able
energies. As commonly assumed, in v estors ha v e higher discoun t rates than the regu-
lator. Moreo v er, and extraordinarily for renew able energy pro jects, the v ast ma jorit y
of pro jects relies on (risk-a v erse) pro ject finance (Steffen, 2018). This implies that
in order to gain access to c heap capital, they are unable to incorp orate long-term
drastic c hanges in the p o w er mark et. Consequen tly , in v estors apply a p o w er price
profile whic h resem bles the curren t, rather flat profile with few price spik es.
The regulator uses a lo w er discoun t rate. Imp ortan tly , when the regulator is
committed to a future with a high(er) share of renew able energies, the regulator is
able to incorp orate the future implications of such a future p o w er system: Less base
load p o w er pro duction, more v olatile generation, and, th us, a more v ariable p o w er
price profile with more price spik es.
The exact time horizons are parametric c hoices. Sensitivit y analyses with re-
sp ect to these c hoices can b e found in app endix 1.6.2. In the base case, in v estors
only consider a curren t flat p o w er price profile, implemen ted for ev ery y ear of the
optimization once on the basis of the electricit y prices of 2013 and once based on the
prices of 2030. In sensitivities, the p o w er price profiles of 2015 w ere applied, as w ell
as a com bination of 2013’s prices for the first ten y ears of turbine lifetime and 2030’s
prices for the subsequen t ten y ears.
The regulator considers only the future v olatile p ow er price profile of 2030, on the
basis of whic h the optimal turbine is deriv ed. In a sensitivit y , the regulator assumes
the profile of 2013 to prev ail in the first ten y ears of turbine lifetime and 2030’s v olatile
profile afterw ards. With a lo w so cial discoun t rate of 2 %, this implies a w eigh t of
13 In b oth cases only the v olatility of the pow er price profile matters, not the absolute price lev el,
as suc h differences are canceled out by design under either rem uneration scheme.

32 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
55 % on 2013’s profile and 45 % on 2030’s.
The pro duction v alue-based b enc hmark approac h functions indep enden tly of the
sp ecific parameterization. The degree to which a regulator w an ts to commit to and
consider exclusiv ely suc h a future p o w er system in the end is a p olitical question
b ey ond the scop e of th is c hapter.
1.4 Results
In ev ery scenario, the in vestors tak e the p olicy regime as exogenously giv en and
optimize their net presen t v alue. I discuss the findings for one lo cation with medio cre
wind resources, Boltenhagen, and one lo cation with v ery fa v orable wind resources,
Heligoland.
Figure 1-6 pro vides an o v erview o v er the results for the site with medio cre wind
conditions. It depicts the rem uneration amoun ts under the differen t p olicies and the
lev elized cost of electricit y . The system-optim um is sho wn as the turbine c hosen when
accoun ting for future pro duction v alues, minimizing the difference b et w een costs and
mark et v alue. The system optimal turbine’s nominal p o w er is 2 . 4 MW. In compar-
ison, the fixed feed-in tariff leads to a relativ ely system-unfriendly configuration of
2 . 7 MW. 14
The feed-in premium leads to only v ery limited incen tiv es for a more system-
friendly design when in v estors’ foresigh t is limited; the slop e is barely distinguishable
from the feed-in tariff ’s. With b etter foresigh t and with higher price exp osure, the
in v estmen ts come closer to what is so cially-optimal. The results are detailed in the
follo wing.
14 Due to the default pro duction v olume-based b enc hmark approach, the FIT already distinguishes
b et w een the turbine designs to a limited exten t, with the result that the rem uneration line has a
sligh t do wn w ard slop e.

1.4. RESUL TS 33
Figure 1-6: Rem uneration and lev elized cost of electricit y for Boltenhagen. F or
comparabilit y , the lev el of the FIP is set to equal the fixed FIT for the turbine
that is c hosen under the fixed FIT. The rem uneration/costs refer to lev elized lifetime
rem uneration/costs p er MWh for the in v estor.
1.4.1 Fixed feed-in tariff
Under the fixed feed-in tariff, in v estors opt for a turbine whic h is not v ery system-
friendly , as sho wn in figure 1-6. This is in line with the empirical data w e can
observ e in German y: rather large generator capacities compared to the rotor blade
lengths. Suc h tec hnology com binations yield, under the fixed FIT, the largest returns
for in v estors. The c hosen turbine in this baseline scenario has a nominal p o wer of
2 . 7 MW and a rotor sw ept area of 8756 m 2 , based on a rotor blade length of 52 . 8 m.
The h ub heigh t lies at 118 m.
On more wind-ric h Heligoland, the priv ately-optimal turbine under the FIT is
naturally more sp ecialized in the generation at high wind sp eeds. It is considerably
less system-friendly than the priv ately-optimal one in Boltenhagen: It has a higher
nominal p o w er, shorter rotor blades and lo w er h ub heigh t. The nominal p o w er lies
32 % higher at 3 . 6 MW, visualized in figure 1-7.

34 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-7: Rem uneration and lev elized cost of electricit y for Heligoland. F or com-
parabilit y , the lev el of the FIP is set to equal the fixed FIT for the turbine that is
c hosen under the fixed FIT. The rem uneration/costs refer to lev elized lifetime rem u-
neration/costs p er MWh for the in v estor.
As exp ected, the mo del demonstrates th at more system-friendly turbines lead to
higher capacit y factors. The turbines’ n um b er of full load hours increases and they
pro duce a larger share of their pro duction in times of w eak and medium wind sp eeds.
1.4.2 Sliding feed-in premium
In v estors with limited foresigh t (“FIP 2013”) only sligh tly alter their in v estmen t de-
cisions under feed-in premia. In figures 1-6 and 1-7, w e can see that while the in-
v estmen t decision do es not c hange at all in Boltenhagen, there is a sligh t adjustmen t
in turbine configuration in wind-ric h Heligoland. Ho w ev er, this adjustmen t remains
w ell less system-friendly than what is iden tified as system-optimal. Where in v estors
p ossess greater foresigh t (“FIP 2030”), they opt for more system-friendly designs in
b oth Boltenhagen and H eligoland, as they consider a more v olatile price profile. In
Boltenhagen, the c hosen turbine has a nominal p o w er of sligh tly less than 2 . 5 MW,
its blades are 1 . 5 m longer than the ones under the FIT, and its to w er stands 8 m

1.4. RESUL TS 35
Figure 1-8: Pro duction of t w o turbine configurations and the a v erage wind p o w er
v alue in 2015, relativ e to mon thly a v erages.
taller at 126 m.
The p olicy design causes the incen tiv e for more system-friendly deplo ymen t to
b e w eak er than optimal. The theoretical analysis of section 1.2.3 holds at the hourly ,
daily and w eekly lev el, but not on a mon thly , ann ual or life-time lev el. The rem uner-
ation lev el is defined suc h that the a v erage rem uneration lev el is the same for ev ery
mon th, ev en though price lev els v ary b et w een mon ths. Hence, in v estors do not receiv e
higher rem uneration when turbines pro duce in mon ths with higher pro duction v alues.
Incen tiv es under the feed-in premium are hence not ev en aligned with the system-
optim um when b oth the in v estor and the regulator tak e in to accoun t the exact same
p o w er price profile, as sho wn in app endix 1.6.2. Figure 1-8 indicates the effect of this
in 2015: Both turbines pro duce – relativ ely sp eaking – a lot of energy in the windy
win ter mon ths. The system-unfriendly turbine exclusiv ely generates more electricit y
than the system-friendly turbine in mon ths where the price is b elo w a v erage. The
system-friendly turbine has a larger share of its pro duction in mon ths with relativ ely
lo w wind sp eeds, where prices are relativ ely high. Due to the p olicy design, this differ-
ence in pro duction v alues is not captured in the in v estors’ optimization, indep enden t
of the in v estor’s foresigh t.
This c hanges with ann ually-adjusted feed-in premia, where the a v erage rem u-

36 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-9: A v erage pro duction v alues in Boltenhagen, eac h data series normed to
100 % for the most system-friendly configuration
neration is based on the ann ual a v erage wind p o w er pro duction v alue; th us in vestors
are fully exp osed to sub-annual price fluctuations. Then, if in v estors ha v e no limited
foresigh t, in v estmen t incen tiv es are aligned with the system-optim um, as app endix
1.6.2 sho ws, with in v estors c ho osing significan tly more system-friendly turbines. Nat-
urally , in vestors tak e larger price risks under this design, inducing higher financing
risk premia.
The results suggest that under curren t flat p o w er price profiles in v estors can
barely deviate systematically from the o v erall German wind generation remuneration
lev el through their turbine tec hnology c hoice, ev en though it is theoretically conceiv-
able. Under flat p o w er price profiles, 𝐶 𝑜𝑣 ( 𝜔 𝑖,𝑡 , 𝑓 ( 𝑝 *
𝑡 , 𝛾 − 𝑖,𝑡 )) do es not differ strongly at
the sub-mon thly lev el for differen t turbine tec hnologies. Th us, the electricit y prices
𝑝 ( 𝛾 − 𝑖,𝑡 ) for differen t turbines lie to o close together to impact rev enues strongly enough
to incen tivize more system-friendly turbines when in v estors cannot incorp orate long-
term p o w er system c hanges in to their optimization.
Figure 1-9 sho ws that the additional v alue of system-friendly turbines material-
izes stronger in the future. Under the flat profile of 2013, system-friendly turbines’
pro duction is barely priced higher than other turbines’. Bet w een the most extreme
configurations, the difference is a mere 1 . 1 % of prices, or 0 . 6 % of total rem uneration.
Th us, the sliding feed-in premium’s slop es in figures 1-6 and 1-7 remain relativ ely

1.4. RESUL TS 37
flat. Y et, under a future, more v olatile price profile, system-friendly turbines pro duce
at considerably higher v alues than system-unfriendly ones, explaining the stronger
incen tiv es for system-friendly turbines when in v estors p ossess greater foresigh t. In
the example for Boltenhagen the a v erage v alue difference amoun ts to 14 % of prices.
These results hold under a v ariet y of assumptions regarding the in v estors’ fore-
sigh t and their abilit y to incorp orate long-term c hanges in the price profile in to their
optimization. App endix 1.6.2 la ys out a n um b er of sensitivities. In v estors utilize
a differen t base y ear for their optimization, foresee long-term c hanges in the price
profile, or en tirely optimize based on a long-term future price profile. Results v ary
sligh tly , but they share that without inducing additional rev en ue risks, the priv ate op-
tim um is not aligned with the system optim um, as higher generation in higher-priced
mon ths is not con v erted in to incen tiv es for in v estors, ev en if in v estors p ossessed p er-
fect foresigh t. Moreo v er, it remains questionable in ho w far in v estors are able to apply
long-term dev elopmen ts in their in vestmen t decisions.
1.4.3 Pro duction v alue-based b enc hmark approac h
The pro duction v alue-based b enc hmark approac h con v eys strong incen tiv es to in-
v estors to alter their in v estmen t decisions. As sho wn in Figures 1-6 and 1-7, the
rem uneration is more strongly differen tiated b et w een turbines. By design, in v estors
will alw a ys c ho ose the system-optimal turbine configurations.
F or Boltenhagen, compared to the baseline, the optimally c hosen turbine’s nomi-
nal p o w er is reduced b y 333 k W, whereas the rotor sw ept area increases b y 630 m 2 , the
rotor blade length rises b y 1 . 9 m, and the h ub heigh t go es up b y 10 m. The sp ecific
p o w er is reduced b y 18 %. This results in an increase in full load hours b y 11 % or
equiv alen tly an increase in capacit y factor b y 5 p ercen tage p oin ts. More imp ortan tly ,
the share of the pro duction shifts to times of lo w er wind sp eeds with c eteris p aribus
higher electricit y v alues, in particular in systems with high shares of in termitten t
renew able energies. F or lo wer wind sp eeds (< 5 m − 1 ) (o ccurring ab out 30 p ercen t of
the time in Boltenhagen), the (generally lo w) n um b er of full load hours increases b y

38 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
26 p ercen t. F or medium wind sp eeds ( ≥ 5 m − 1 and < 10 m − 1 , ab out 50 p ercen t of the
time), the n um b er of full load hours increases b y 19 p ercen t and for strong winds ( ≥
10 m − 1 , ab out 20 p ercen t of the time) it remains equal.
On wind-ric h Heligoland, rem uneration under feed-in tariff and sliding feed-in
premium are almost constan t across turbines and the v alue-based b enc hmark ap-
proac h establishes some differen tiation b et w een turbines. The resulting turbine has
a nominal p o w er of 3 . 3 MW. Its rotor blades are 1 . 7 m longer and the h ub heigh t is
increased b y 8 m compared to the baseline, resulting in the sp ecific p o w er, the ratio
of nominal p o w er to rotor-sw ept area, lieing 14 % lo w er at 422 W m − 2 . The n um b er
of full load hours increases b y 12 % and pro duction is shifted in to hours with higher
v alue.
1.5 Conclusion
The p o w er generation profile of wind turbines differs greatly from con v en tional ther-
mal plan ts’. Prices in times of s trong winds diminish, whereas electricit y prices in
lo w wind relativ ely increase. In line with previous literature, I argue that turbines
with a higher share of their pro duction in lo w and medium wind, i.e. system-friendly
wind turbines, pro vide higher v alues to the energy system. This is, on the one hand,
quan tifiable in the higher a v erage v alue of their pro duction and, on the other hand,
not quan tifiable in terms of their more constan t and predictable pro duction. In de-
tail, these are turbines with a lo w er nominal p o w er, longer rotor blades, and higher
to w ers.
I ev aluate the effectiv eness of differen t p olicies to encourage the deplo yment of
system-friendly turbines. Since other b enefits are hardly quan tifiable, I use the in-
creased a v erage pro duction v alue as main criterion. F or this purp ose, I define the
system-optimal turbine to minimize pro duction costs min us exp ected future mark et
v alue.
The in tro duction of the sliding feed-in premium sough t to c hange in v estors’ in-
v estmen t decisions b y incentivizing the alignmen t of wind energy supply and its de-

1.5. CONCLUSION 39
mand. I demonstrate through whic h mec hanism in v estors’ b eha vior can b e influenced,
namely through the co v ariance b et w een a turb ine’s p o w er generation and the o v erall
national wind p o w er generation. Ho w ev er, under mon thly-adjusted feed-in premia,
in v estors do not b enefit from pro duction in mon ths with higher prices, since the
mon thly rem uneration lev el is indep enden t of the mon thly price lev el. Based on a
wind p o w er in v estmen t mo del and assuming that in v estors are constrained b y imp er-
fect foresigh t and are b ound to conserv ativ e p o w er price profile dev elopments due to
their financing structure, I sho w that this sliding feed-in premium do es not strongly
align wind p o w er supply and demand. Both with b etter foresigh t and with higher
price risk exp osure, in v estmen t decisions come closer to the so cial optim um. Ho w ev er,
larger risk exp osure also p ossibly raises financing costs.
The pro duction v alue-based b enc hmark approac h tak es future energy systems’
prices in to accoun t and incen tivizes system-friendly deploym en t for in v estors. Based
on the fixed feed-in tariff or hourly-sliding feed-in premia, an adjustmen t to the re-
m uneration lev el – dep ending on the a v erage electricit y v alue at whic h a turbine is
exp ected to pro duce in the future – is implemented. Through this v ariation, in-
v estors strongly adjust their in v estmen t b eha vior, since they can fully in tegrate the
additional future v alue of system-friendly turbines in to their pro jected cash flo ws.
In v estmen ts are aligned with system-optimalit y . An additional requiremen t is that
regulators need to pro ject p o w er price profiles that are based on the hourly wind
patterns of a pre-defined y ear (or set of y ears), whic h needs to b e made a v ailable
to in v estors. The regulator then tak es o v er the p o wer price profile risk, i.e. that a
more or less v olatile profile dev elops. If a differen t price profile emerges, more or
less system-friendly turbines migh t b e optimal. An imp ortan t difference to feed-in
premia with high price exp osure is that under those, in v estors carry the price risk,
p ossibly inducing higher financing cost risk premia. Hence, in con trast to inv estmen ts
incen tiv es under feed-in premia, the pro duction v alue-based b enc hmark approac h is
a w a y to prev ent these additional risk-induced costs, but to still s et incen tiv es for
system-friendly deplo ymen t.
This analysis can b e expanded by exploring if adjustmen ts are necessary with

40 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
resp ect to the construction of wind parks. Losses in the wind energy con ten t through
shading migh t differ across wind turbine t yp es and th us justify further adjustmen ts.
F urthermore it needs to b e assessed ho w robust the results for wind turbine c hoices
are to c hanges in system parameters, to the question of whether it suffices to select
one historic reference y ear of wind output or whether m ultiple y ears are required, the
question ho w constrained in v estors are in their foresigh t and whether to use a 2030
pro jection of p o w er price profiles or a com bination of sev eral y ears.

1.6. APPENDIX 41
1.6 App endix
1.6.1 Pro duction v olume-based b enc hmark approac h
The pro duction volume-based b enc hmark approac h aims to div ersify the installation
lo cations of German wind turbin es. A cross a broad range of sites, it pro vides cost-
co v ering rem uneration. Because lo cal div ersification has b een, and still is, desired,
the pro duction v olume-based b enc hmark approac h has b een part of the fixed feed-in
tariff and prev ails under the feed-in premium of 2014 (Bundestag, 2014). 15 Without
it, in v estors would – ev en more than observ ed – only prefer those sites with the most
fa v orable wind conditions, largely in the north at the German coast. The pro duction
v olume-based b enc hmark approac h increases the attractiv eness of sites with p o orer
wind conditions.
The approac h sets a turbine’s a v erage remuneration lev el b y defining for ho w
long a turbine is eligible to the higher initial feed-in pa ymen t. Th us, it sets 𝜒 𝑖,𝑡
in equations (1.2) and (1.5). Figure 1-10 illustrates what the a v erage rem uneration
lev el is for in v estmen ts in one turbine t yp e at differen t lo cations. 16 It is eviden t that
in v estmen ts in a wind-ric h lo cation, suc h as Heligoland, require a lo w er feed-in tariff
than in v estmen ts in areas with low wind strengths, suc h as F rankfurt (Main).
The approac h w orks b y comparing an y turbine’s actual pro duction with its the-
oretical pro duction at a b enchmark lo c ation (Bundestag, 2014). The wind profile at
the latter lo cation follows a W eibull distribution with a shap e parameter of 𝛼 = 2
(whic h mak es it a Ra yleigh distribution), with an a v erage wind sp eed of 5 . 5 ms − 1 at a
heigh t of 30 m. V ertical extrap olation functions through a logarithmic heigh t profile.
Its roughness length 𝑧 0 is 0.1. In accordance with legislation, ev ery wind turbine
t yp e is theoretically installed at this b enc hmark lo cation and the ann ual b enchmark
15 The definition of the volume-based benchmark approac h has since changed (Bundestag, 2016),
but the k ey mec hanisms and incen tiv es con tin ue to function as b efore.
16 The cost curve is of an illustrativ e nature, scaled to equal the rem uneration for a turbine that
reac hes 80 p ercen t of the electricity yield at the benchmark location, as explained in the following.
F or the illustration, the costs p er MWh are assumed to dep end solely on the amoun t of energy
pro duced at a site.

42 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-10: A v erage rem uneration at differen t lo cations
yield is calculated. The actual electricit y yield where a wind turbine is installed,
𝜔 𝑖,𝑡 , is then set in relation to this b enchmark yield , whic h giv es the b enchmark r a-
tio 𝜁 𝑖 . The lo w er this b enc hmark ratio is, the longer a wind in v estor receiv es the
higher feed-in pa ymen t. Consequen tly , turbines at p o orer sites receiv e higher a v erage
rem unerations.
𝑙 𝑖 =
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
60 if 𝜁 𝑖 ≥ 130% ,
60 + 130 − 𝜁 𝑖
0 . 36 if 130% ≥ 𝜁 𝑖 ≥ 100% ,
60 + 130 − 𝜁 𝑖
0 . 36 + 100 − 𝜁 𝑖
0 . 48 if 100% ≥ 𝜁 𝑖 ≥ 80% ,
240 if 𝜁 𝑖 ≤ 80 .
(1.14)
The exact rem uneration dep ends on the b enchmark r atio and has c hanged o v er
time. Since an adjustmen t in August 2014, the calculation of the length extension
𝑙 𝑖 , measured in mon ths after installation, adheres to equation (1.14). The calculated
length extension 𝑙 𝑖 directly translates in to whether in a y ear 𝜒 𝑖,𝑡 is equal to zero, one,
or a v alue in b et w een. F or example, a v alue of 60 implies that 𝜒 𝑖 , 𝑡 is equal to one for
the initial fiv e y ears and zero ev er after.

1.6. APPENDIX 43
Figure 1-11: Rem uneration and lev elized cost of electricit y for Hano v er. F or com-
parabilit y , the lev el of the FIP is set to equal the fixed FIT for the turbine that is
c hosen under the fixed FIT. The rem uneration/costs refer to lev elized lifetime rem u-
neration/costs p er MWh for the in v estor.
1.6.2 Sensitivities
In the follo wing, the results of mo del runs for differen t lo cations and differen t opti-
mization parameters are giv en. In general, these sensitivities supp ort the findings of
the main analysis, unless stated there explicitly .
Lo cations
The results sho w similar patterns across lo cations. In wind-p o or Hano v er in cen-
tral northern German y , the priv ately-optimal turbine under the FIP is sligh tly more
system-friendly than under the FIT, as figure 1-11 depicts. Under the FIP , the nomi-
nal p ow er is 2 . 6 MW, whereas it is 2 . 7 MW under the FIT. The system-optimal turbine
is encouraged under the pro duction v alue-based b enc hmark approac h. It has a lo wer
nominal p o w er of 2 . 3 MW. Its to w er is 130 m and hence 10 m higher than under the
FIT. A t 55 . 0 m, its rotor blades are 1 . 9 longer. Consequen tly , its p o w er pro duction
is more constan t as its capacit y factor is 11 % higher and pro duction is shifted in to
hours with higher v alue.

44 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-12: Rem uneration and lev elized cost of electricit y for F eldb erg. F or com-
parabilit y , the lev el of the FIP is set to equal the fixed FIT for the turbine that is
c hosen under the fixed FIT. The rem uneration/costs refer to lev elized lifetime rem u-
neration/costs p er MWh for the in v estor.
F eldb erg is a wind-ric h lo cation in the south of German y . Both the FIT and
the FIP lead to a 3 . 9 MW turbine, as figure 1-12 visualizes. The pro duction v alue-
based b enchmark approac h incen tivizes a considerably more system-friendly turbine
at 3 . 5 MW, with an incremen t in rotor blade length b y 2 . 5 m to 48 . 0 m and of h ub
heigh t b y 13 m to 94 m. Consequently , the capacit y factor rises b y 20 . 2 %, equiv alen t
to an increase in full load hours b y 442 h.
The results in figure 1-13 for Kahler Asten, a lo cation in German y’s w est, sho w
a comparable pattern. Incen tiv es under FIT and FIP are fairly similar. Under the
FIT, in v estors opt for a turbine with 2 . 7 MW, whereas under the FIP they opt for a
2 . 6 MW turbine. Con v ersely , the rem uneration is strongly differen tiated according to
system-friendliness under the pro du ction v alue-based b enc hmark approac h. Th us, a
2 . 3 MW turbine is optimal, increasing h ub heigh t b y 14 m to 132 m and rotor blade
length b y 2 . 6 m to 55 . 4 m. This results in an increase in capacit y factor b y 16 . 0 % or
570 full load hours.

1.6. APPENDIX 45
Figure 1-13: Rem uneration and lev elized cost of electricit y for Kahler Asten. F or
comparabilit y , the lev el of the FIP is set to equal the fixed FIT for the turbine
that is c hosen under the fixed FIT. The rem uneration/costs refer to lev elized lifetime
rem uneration/costs p er MWh for the in v estor.
In v estor optimization
In v estors can utilize differen t p o w er price profiles for their optimization, whic h matter
particularly under the feed-in premium sc heme. Y et, due to the p olicy design, only
the price profile, i.e. its v olatilit y and correlation with high wind sp eeds, matters, but
not the absolute price lev el. The baseline scenario is based on the 2013 prices. Figure
1-14 sho ws sensitivities for differen t assumptions with resp ect to their optimization,
at the example of Boltenhagen. As alternativ e, the prices and wind sp eeds of 2015 are
applied. Moreo v er, in v estors could p ossess a greater abilit y to incorp orate exp ected
long-term dev elopmen ts in the p o w er mark et, for instance if they do not rely on
pro ject finance. In one scenario, in v estors assume 2013’s flat p o w er price profile to
b e represen tativ e for the first ten y ears of the in v estmen t. In the second half of the
turbine’s lifetime, a v olatile price profile based on 2030 is applied. F urthermore, a
pure FIP-based optimization based on the prices of 2030 is conducted. These results
can b e compared to the so cial optim um, whic h is deriv ed from 2030’s prices.
The results indicate that more forw ard-lo oking optimization can lead to more
system-friendly turbines, but do es not reac h the system-optim um. Optimization

46 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
Figure 1-14: Rem uneration and lev elized cost of electricit y for Boltenhagen. F or
comparabilit y , the lev el of the FIP is set to equal the fixed FIT for the turbine
that is c hosen under the fixed FIT. The rem uneration/costs refer to lev elized lifetime
rem uneration/costs p er MWh for the in v estor.
based on 2015’s price profile leads to a somewhat more system-friendly configura-
tion in Boltenhagen. Ho w ev er, this effect stems not only from the differen t v olatilit y
of the price profile, but also from the differen t w eather in 2015. While in Hano ver,
the effect is similar, in F eldb erg, Kahler Asten and Heligoland, optimization based
on 2015 only leads to insignifican t c hanges.
The priv ate optim um do es not reac h the system-optim um in an y of the scenarios,
ev en when in v estors apply the v ery same price profiles as the regulator. This is
the case b ecause inv estors will not see all additional v alue of more system-friendly
turbines in their optimization, ev en if they do p ossess p erfect foresigh t. Pro duction in
mon ths with higher pro duction v alues is not rew arded, as discussed in section 1.4.2.
Consequen tly , ev en with optimization based exclusiv ely on 2030’s price profile, the
system-optim um is not attained.

1.6. APPENDIX 47
Figure 1-15: Rem uneration and lev elized cost of electricit y for Boltenhagen. The
rem uneration/costs refer to lev elized lifetime rem uneration/costs p er MWh for the
in v estor.
Regulator optimization
The regulator’s c hoice of whic h time p erio d to consider influences the definition of the
so cially-optimal turbine and th us of the priv ately-optimal turbine under the pro duc-
tion v alue-based b enc hmark approac h. Again, only the v olatilit y and the correlation
with wind patterns impact the optimal c hoices. Figure 1-15 depicts the rem uneration
under the pro duction v alue-based b enc hmark approac h for differen t c hoices of the reg-
ulator, again at the example of Boltenhagen. The scenario “Pro duction v alue-based
b enc hmark approac h (2030, grid expansion as planned)” is the base scenario in the
main analysis. As previously , the optimal turbine’s nominal p o w er is 2 . 4 MW, its h ub
heigh t 128 m and its rotor blade length 54 . 7 m.
A dditional grid expansions (“Pro duction v alue-based b enc hmark approac h (2030,
grid expansion as planned + add. grid)”) b ey ond what is planned could lead to a
similar effect as the deplo ymen t of more system-friendly turbines: as the resulting
p o w er price profile is less v olatile, the optimal turbine is also less system-friendly . Its
nominal p o w er lies 133 k W higher, its h ub heigh t is 4 m lo w er and its rotor blades are
0 . 7 m shorter.

48 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
When the regulator c ho oses to apply a com bination of curren t flat (2013) and fu-
ture (2030) p o w er price profiles (“Pro duction v alue-based b enc hmark approac h (2013
and 2030)”, the optimal turbine is the same one as in the case with further additional
grid expansion. The rem uneration v aries sligh tly under the t wo differen t rem uner-
ation sc hemes, but is barely distinguishable, leading to the same chosen turbine.
Imp ortan tly , ev en when b oth the regulator and the in v estor (under the FIP) apply
a com bination of 2013 and 2030 prices, the so cially- and priv ately-optimal turbines
differ.
Design of the feed-in premium
Sliding feed-in premia can ha v e alternativ e rem uneration designs, exp osing in v estors
to the electricit y price to v arying degrees and impacting risks and in v estmen t incen-
tiv es differen tly . Bey ond the so-far discussed monthly arra ngemen ts, deviations from
the national wind p ow er feed-in can also b e calculated e.g. on an hourly or y early
basis, whic h c hanges the premium of equation (1.6). Hourly adjustmen ts as under the
UK’s Con tracts for Differences allo w for no deviations from the strik e price and, th us,
– b esides p oten tial balancing costs and related risks – ha ve v ery similar implications
as fixed feed-in tariffs.
Ann ual adjustmen ts exp ose the in v estor to the electricity price and erase only
the annual a v erage wind p o w er v alue v ariabilit y . In vestors are more exp osed to the
price than under mon thly approac hes. They face higher price risks, p ossibly inducing
higher financing costs. A t the same time, stronger incen tiv es for in v estmen ts in to
system-friendly turbines can b e exp ected.
Figure 1-16 sho ws the results for feed-in premium sc hemes with ann ually-sliding
premia. They naturally dep end on the y ear applied for the in v estor’s optimization.
Based on 2013, the optimal turbine is equal to the one under a fixed feed-in tar-
iff. With prices of 2015, incen tiv es for system-friendly turbines are stronger: The
priv ately-optimal turbine’s nominal p o w er lies 5 % lo w er at 2 . 6 MW, has longer rotor
blades and a higher to w er.

1.6. APPENDIX 49
Figure 1-16: Rem uneration and lev elized cost of electricit y for Boltenhagen. F or
comparabilit y , the lev el of the FIP is set to equal the fixed FIT for the turbine
that is c hosen under the fixed FIT. The rem uneration/costs refer to lev elized lifetime
rem uneration/costs p er MWh for the in v estor.
Ann ual premia based on 2030 yield the same strong incen tiv es for system-friendly
turbines as the regulator’s optimization: The system-optimal turbine is c hosen. F rom
a mo deling p ersp ectiv e, the t w o approac hes are v ery similar. The main differences
are that under ann ually-sliding feed-in premia, in vesto rs tak e o v er a larger part of
the price risk and require foresigh t with resp ect to future price patterns. Where
future price patterns are uncertain, in v estors p oten tially ha v e to pa y higher financing
costs. When fu ture price patterns are uncertain under the pro duction v alue-based
b enc hmark approac h, no additional financing costs are induced, but the regulator
risks to incen tivize configurations that are either to o system-friendly or not system-
friendly enough.
1.6.3 REMix mo del
The REMix mo del is used to mo del future p o w er price profiles for the y ear 2030. It
is a dynamic b ottom-up energy system mo del that fo cuses on the op erational opti-

50 CHAPTER 1. INTEGRA TING RENEW ABLE ENER GIES
mization of electricit y and heat-generating tec hnologies in conjunction with temp oral
and spatial load-balancing options.
F or German y , the a verage price and its v olatilit y increase compared to 2013 and
2015. The mean price is e 66 p er MWh, with an increased standard deviation of
e 27 p er MWh. In the sensitivit y with fu rther grid expansion, man y additional grid
connections are assumed to op erate in 2030. German y p ossesses additional 28 GW of
capacit y to its neigh b oring coun tries and an additional 8 GW of in ternal connections.
The resulting p o w er price profile is flatter, leading to a decreased standard deviation
of e 21 p er MWh.
The mo del do es not directly allo w for negativ e prices, but near-zero prices o ccur
frequen tly . In 492 hours, prices are b elo w e 5 in the base scenario, almost four times
as frequen tly as in 2013. These prices are obtained b y taking the a v erage of prices
from the six geographical regions in to whic h German y is divided in the mo del. Prices
are equal across regions unless there is congestion b et w een them. These prices are
w eighed b y the areas’ supply and demand and the a v erage of the supply-w eighed and
the demand-w eighed prices is tak en.
T able 1.1 indicates the installed capacities across generation tec hnologies in Ger-
man y in 2030. While n uclear energy has b een phased out, wind and solar p o w er ha v e
b een scaled up. Hard coal is assumed to cost e 59 p er MWh, lignite e 69 p er MWh
and gas e 43 p er MWh. The CO 2 price is e 45 p er ton. F urther details are giv en in
Deutsc hes Zen trum für Luft-und Raumfahrt (2014).

1.6. APPENDIX 51
T ec hnology Installed capacit y (GW)
Nuclear 0
Coal 21.4
Gas and oil 38.9
PV 87
Wind onshore 64.4
Wind offshore 29.2
Hydro 4.9
Biomass 10.9
Geothermal 1
Pump storage and other storage 6.5
T otal 264.2
T able 1.1: Installed capacities in 2030 (Deutsc hes Zen trum für Luft-und Raumfahrt,
2014)

Chapter 2
Financing P o w er: Impacts of Energy
P olicies in Changing Regulatory
En vironmen ts ∗
Abstract
P o w er systems with increasing shares of wind and solar p o w er generation ha v e higher
capital and lo w er op erational costs than p o w er systems based on fossil fuels. This in-
creases the imp ortance of the cost of financing for total system cost. W e quan tify ho w
renew able energy supp ort p olicies can influence the financing costs by addressing reg-
ulatory risk and facilitating hedging. W e use in terview data on wind p o w er financing
costs from the EU and mo del ho w long-term con tracts signed b et w een pro ject dev el-
op ers and energy suppliers impact financing costs in the con text of green certificate
sc hemes. W e find that b et w een the supp ort p olicies, the costs of renew able energy
deplo ymen t differ b y around 30 p ercen t.
∗ This c hapter is based on join t w ork with Karsten Neuhoff. W e thank Rob ert Brückmann, Olga
Chiappinelli, Ingmar Jürgens, Nolan Ritter, Marie Therese v on Schic kfus, Bjarne Steffen, Oliv er
Tietjen, and V era Zipp erer for their helpful commen ts and suggestions. W e also b enefited from
commen ts b y participan ts at the 11th Conference on The Economics of Energy and Climate Change
at the T oulouse School of Economics, the 23rd EAERE conference, the 12th A URÖ w orkshop, the
5th In ternational Symp osium on En vironmen t and Energy Finance Issues, the 39th conference of
the In ternational Asso ciation for Energy Economics, a seminar at UCL London, a seminar at the
Univ ersit y of St. Gallen, and in ternal seminars at DIW Berlin.
53

54 CHAPTER 2. FINANCING PO WER
2.1 In tro duction
The rising share of capital-in tensiv e assets increases the imp ortance of financing costs
for the total costs in p o w er systems. This applies particularly to renew able energies,
as opp osed to coal and gas p o w er plants, b ecause the costs of renew able energy
deplo ymen t are, to a large exten t, driv en b y the capital costs used to finance these
assets. Blo om b erg New Energy Finance (2016) pro ject in vestmen ts of $ 7.3 trillion
in to wind and solar p o w er b et w een 2017 and 2040 and a estimate a further $ 5.3
trillion in order to ac hiev e the goal of k eeping the global temp erature increase b elo w
t w o degrees.
The financing costs dep end on the risks faced b y inv estors, whic h hinge on the
regulatory framew ork. On the one hand, regulation impacts the mere risk allo cation,
for example regarding pro ject p erformance, whic h is usually b est left with in v estors
to a v oid adv erse incen tiv es. On the other hand, the regulatory framew ork can also
induce risks, for instance link ed to uncertain p olicy dev elopmen ts, or it can eliminate
risks, e.g. b y facilitating con tracts b et w een parties with complemen tary exp osure.
The regulatory regime can ha v e t wo main impacts on financing risks: regulatory risks
and mark et risks.
First, regulatory risks arise due to uncertain t y ab out the future rev en ues pro vided
b y supp ort p olicies lik e feed-in tariffs, feed-in premia, and green certificate sc hemes.
The p olicy design ma y shift regulatory risk b etw een parties, but where p olicy risk
can b e av oided altogether, p olicies can reduce, rather than shift, o v erall deplo ymen t
costs.
Second, mark et risks are in tro duced where supp ort mec hanisms do not comprise
explicit off-tak e guaran tees. In v estors then t ypically sign bilateral long-term con tracts
to secure these rev en ue streams. As Newb ery (2016) argues, some forms of long-term
con tracts b et w een generators and retailers are required to hedge against mark et risks
and to pro vide in v estors with sufficien t certain t y ab out their future cash flo ws. Dis-
cussing in v estmen ts in to p eak generators, Josk o w (2006) analyzes ho w the lac k of
long-term con tracts do es not necessarily deter in v estmen ts, but increases financing

2.1. INTR ODUCTION 55
costs. Both pro ducers and consumers are risk a v erse, preferring a stable price o v er an
uncertain price. Ho wev er, under lib eralized p o w er mark ets, individual and industrial
customers do not sign con tracts for durations exceeding a few y ears. This ma y re-
flect constrain ts on switc hing time-frames (or comp ensation pa ymen ts), coun terpart y
risks that are difficult to hedge, and asymmetric information ab out what w ould b e a
comp etitiv e price.
W e quan tify ho w m uc h the regulatory and mark et risks under differen t renew able
energy p olicies affect the ov erall deplo ymen t costs. T o this end, w e first analyze ho w
far regulatory risks under feed-in tariffs, sliding feed-in premia, and tradable green
certificates translate in to higher financing costs for renew able energy in v estors. W e
test this with a unique dataset on wind p o w er financing cost estimates for whic h
in v estors, bank ers, academics and utilities pro vide estimates of the w eigh ted a v erage
costs of capital in the EU. Second, w e analyze the effects of mark et risks on long-
term con tracts when p olicies do not pro vide explicit or implicit off-tak e guaran tees.
W e find structural reasons wh y the price renew able in v estors receiv e for long-term
con tracts is b elo w the exp ected v alue, reflecting increased financing costs incurred b y
their coun terparties when engaging in suc h con tracts.
Ov erall, our results indicate that p olicy design can c hange the lev el of financing
costs b y ab out 4.8 p ercen tage p oin ts o v erall, when comparing fixed feed-in tariffs with
green certificate sc hemes, whic h is equiv alen t to a c hange in the costs of renew able
energy deplo ymen t of ab out 29 p ercent. The c hange in costs is a result of, on the one
hand, reducing regulatory risk, and, on the other hand, eliminating mark et-related
risks b y facilitating implicit hedging b et w een pro ducers and consumers.
The remainder of this c hapter is structured as follo ws: After an o v erview o v er
p olicies supp orting renew able energy in section 2.2, w e estimate p olicy impacts on in-
v estors’ financing costs in section 2.3. Section 2.4 analyzes ho w incomplete long-term
con tracts incur additional costs for off-tak ers. The c hapter ends with a conclusion.

56 CHAPTER 2. FINANCING PO WER
2.2 In v estmen ts in to renew able energy
Three main p olicies that supp ort renew able energy inv estmen ts dominate globally:
Fixed feed-in tariffs (FIT), sliding feed-in premia (FIP) and tradable green certificates
(TGC). 1 In 2015, feed-in tariffs or feed-in premia existed in 82 coun tries, whereas
tradable green certificates w ere in place in 34 coun tries and man y US states (REN21,
2017). 2
Price-based supp ort p olicies, e.g. feed-in tariffs and feed-in premia, pro vide in-
v estors with a certain rem uneration lev el. Under feed-in tariffs, the regulator tak es the
electricit y output and guaran tees a rem uneration lev el, suc h that op erators face no un-
certain t y with resp ect to rem uneration p er k Wh. Under feed-in premia, in v estors sell
their output to priv ate off-tak ers, and receiv e an additional sliding premium, where
the sum of the t w o elemen ts on a v erage across all installations equals the feed-in tariff
rem uneration. F or an y individual plan t, there is some uncertain t y with resp ect to the
total rem uneration due to deviations from a v erage pro duction patterns (Ma y, 2017),
while additional balancing costs or c hanges of price zones can induce risks (Tisdale
et al., 2014), leading e.g. Couture and Gagnon (2010) to argue, based on theoretical
argumen ts, that feed-in premia en tail risk premia as compared to feed-in tariffs. Y et,
so far Klobasa et al. (2013) find no significan t c hanges in in v estmen t conditions when
analyzing descriptiv e statistics of the German exp erience after a shift in 2012 from a
feed-in tariff to a sliding premium, and Kitzing (2014) go es as far as classifying feed-
in tariffs and sliding feed-in premia as one, merely distinguishing higher risk fixed
feed-in premia.
T radable green certificates constitute quan tit y-based instrumen ts where in vestors
sell their electricit y output to priv ate coun terparties and further receiv e green certifi-
cates prop ortional to their output. Retail companies are obliged to obtain suc h cer-
1 Alternativ e names for FIPs are Market Pr emium and Contr acts for Differ enc es , while the main
difference is that under Con tracts for Differences, the con tractual obligation go es b oth wa ys, suc h
that the premium can b e negative, shielding consumers from high p o wer prices. TGC are also called
R enewable Portfolio Standar ds or Gr e en Quotas .
2 Since sliding feed-in premia dominate fixed feed-in premia globally , w e discuss only sliding feed-in
premia.

2.2. INVESTMENTS INTO RENEW ABLE ENER GY 57
tificates, creating demand for them; th us establishing a rev enue stream for renew able
energy op erators in addition to the sale of electricit y .
Man y authors raise concerns that under real w orld conditions, green certificates
induce additional in v estmen t risks. Butler and Neuhoff (2008) analyze the British
green certificate sc heme and the German feed-in tariff, finding that when correcting
for the coun tries’ differen t wind resources, the German system has b een more suc-
cessful, in the sense that it triggered considerably more in v estmen ts at lo w er cost to
consumers. Similarly , Haas et al. (2011) scrutinize descrip tiv e statistics on installation
n um b ers and general rem uneration costs for a small n um b er of Europ ean coun tries,
finding that feed-in tariffs ha v e b een more successful in b oth resp ects. In line, Bürer
and Wüstenhagen (2009) conduct a surv ey among in v estors and sho w, using a stated
preferences approac h, that these prefer feed-in tariffs o v er green certificates. A surv ey
of British in v estors suggests that the exp ected risk premium of the green certificates
compared to the newly-in tro duced feed-in premium amoun ts to 0.8-1.7 p ercen tage
p oin ts (NERA, 2013).
Y et, some authors also argue in fa v or of the efficiency of quan tit y instrumen ts.
Applying a real options in v estmen t mo del, Bo omsma and Linnerud (2015) argue
that in v estmen t incen tiv es do not differ strongly b et w een green certificates and feed-
in tariffs, meaning that additional risk premia under green certificates are small.
Sc hmalensee (2012) argues that so cial costs under feed-in tariffs are higher due to the
unkno wn installation quan tities.
Ho w ev er, studies on the impact of these p olicies on financing cost are based on
theoretical assessmen ts or on case studies for only v ery few coun tries. Analyzing a
surv ey on wind p o w er financing costs in 23 Europ ean coun tries, w e con tribute to the
literature b y pro viding empirical evidence on differences in financing costs b et w een
coun tries with differen t p olicies.

58 CHAPTER 2. FINANCING PO WER
2.3 Estimating in v estors’ financing costs
Renew able energy p olicies exp ose in v estors to v arying degrees of regulatory . W e test
the effects on financing costs with in terview data on the financing costs of wind p o w er
pro jects from the EU. W e estimate in ho w far wind p o w er p olicies can b e asso ciated
with higher risk premia for wind p o w er in v estors.
The risk premium is the difference b etw een the w eigh ted av erage cost of capital
(W A CC) and a coun try’s sp ecific risk-free rate 𝛾 𝑐 .
𝑟 𝑖𝑠𝑘 𝑝𝑟 𝑒𝑚𝑖𝑢𝑚 = 𝑊 𝐴𝐶 𝐶 − 𝛾 𝑐 (2.1)
2.3.1 Data
F or the analysis, w e deplo y in terview data of financing cost estimates b y pro ject dev el-
op ers, bank ers, and academics from 23 EU coun tries. 3 T able 2.1 pro vides descriptiv e
statistics for the v ariables.
The financing costs are represen ted b y the w eigh ted a v erage costs of capital,
whic h reflect the costs of b oth equit y and debt. Equit y naturally has higher required
returns than debt. The resp ectiv e ratio b et w een the t w o v ariables matters: higher
shares of equit y lead to higher w eigh ted a v erage cost of capital estimates. Details on
the data and the in terviews are in Diacore (2015).
W e obtain the wind p o w er risk premium b y subtracting the risk-free rate from
the w eigh ted a v erage cost of capital. This risk-free rate is commonly appro ximated b y
the yield on long-term go v ernmen t b onds, as it represen ts the v arying coun try risks
due to general p olitical and financial con texts. A t close to 10 p ercen t, Greek b onds
rank ed the highest, follo w ed b y Cypriot and P ortuguese ones, based on Eurostat
(2017a). A t the lo wer end, the b onds of German y , Denmark, and Finland paid the
lo w est returns with less than t wo percent. 4 Since the in terviews w ere conducted in
3 W e lack data for Luxem b ourg, Malta, Portugal and Slo venia. As explained in the follo wing, we
exclude Estonia due to its v ery particular FIT implementation.
4 W e also tested using official Eurostat data on firm lending rates. Y et, w e deemed the data unre-

2.3. ESTIMA TING INVESTORS’ FINANCING COSTS 59
T able 2.1: Descriptive statistics
V ariable N Mean Std. dev. Min. Max.
W A CC 53 8.22 2.81 2.5 13.5
W A CC appro ximated † 53 8.30 2.92 2.5 15
A vg gvt. b ond yields 01/14 53 3.73 2.53 1.59 9.81
Risk premium appro ximated ‡ 53 4.57 1.43 0.73 7.25
F eed-in tariff 53 0.57 0.50 0 1
Sliding feed-in premium 53 0.23 0.42 0 1
TGC w. price flo or 53 0.15 0.36 0 1
TGC w/o price flo or 53 0.06 0.23 0 1
T enders 53 0.08 0.27 0 1
Retroactiv e c hanges conducted 53 0.25 0.43 0 1
No p olicy in place 53 0.19 0.39 0 1
Consultan t/A cademic 53 0.32 0.47 0 1
Equit y in v estor 53 0.34 0.48 0 1
Utilit y emplo y ee 53 0.17 0.38 0 1
Bank er 53 0.17 0.38 0 1
Note: The p olicy dummies for feed-in tariff, sliding feed-in premium, TGC with price-flo or,
and TGC without price flo or are m utually exclusive. The same holds for the in terviewee
t yp es consultan t/academic, equit y inv estor, utilit y emplo y ee, and bank er. † F or relative
resp onses, “sligh tly higher” w as treated as 0.5 p ercen tage p oin ts higher, “higher” as 1.0
p ercen tage p oin t, and “m uc h higher” as 1.5 p ercen tage p oin ts
‡ appro ximated W A CC min us a verage go v ernmen t b ond yields
spring 2014, w e appro ximate the coun try risk with the a v erage yield in the six mon ths
b efore and after the b eginning of 2014, i.e. 07/2013-06/2014.
Based on Eclareon (2017) and González and Arán tegui (2015), w e iden tify whether
feed-in tariffs, sliding feed-in premia, or green certificate sc hemes prev ailed in early
2014 in the EU coun tries (see figure 2-1). When supp ort v aried with pro ject size, w e
classify the coun try using the p olicy for larger installations, as pro ject dev elop ers are
more lik ely to b e in v olv ed in larger settings.
Sev eral coun tries had particular p olicy implemen tations that distinguish their
liable, as in 2013 and 2014, lending rates for Spanish, Italian, and Greek firms seemed unrealistically
lo w, i.e. lo w er than, for example, the lending rate of British firms. Additionally , the resulting risk
premium for renew able pro jects w as partially negative, additionally casting doubts on this dataset’s
reliabilit y .

60 CHAPTER 2. FINANCING PO WER
sc hemes from those of other coun tries. In German y , in v estors could c ho ose b et w een
a feed-in tariff and a feed-in premium in early 2014. Div erging from Klobasa et al.
(2013), w e ev aluate this as a feed-in tariff, since in v estors were alw a ys able to c ho ose
the safe feed-in tariff. Estonia defines an annual limit of rem unerated generation.
Once this limit is reac hed, no further rem uneration is paid, as o ccurred in 2015, when
ab out 13 p ercen t of pro duction did not receiv e an y supp ort (Estonian Windp o w er
Asso ciation, 2015). This mec hanism in tro duces significan t rev en ue risks for op erators
and seems not comparable to the usual p olicies, suc h that w e drop the Estonian obser-
v ations (whic h sho w indeed v ery high risk premia). The Belgian regions and Romania
run green certificate sc hemes. Ho w ev er, price minima pro vide abs olute safet y against
lo w er returns, similar to feed-in tariffs. Th us, w e coun t their p olicies as feed-in tar-
iffs. F or sensitivit y analyses, w e drop this assumption and include them as a separate
class of p olicy sc heme. Only Denmark emplo y ed a fixed feed-in premium. Ho w ev er,
its pa y outs partially resem ble sliding premia, as total rem uneration is capp ed, simi-
lar to a strik e price under sliding premia. Explicitly treating Denmark as ha ving a
fixed feed-in premium do es n ot influence the results in the follo wing, suc h that w e
generally simply include it in the group of coun tries with sliding feed-in premia. The
Czec h Republic, Spain, and Latvia had implicitly abandoned an y rem uneration for
new pro jects, if not explicitly . Only Italy used tenders for large-scale wind p o w er
pro jects at that time.
In the in terview data, w e furthermore ha v e information on whether resp onden ts
think that retrosp ective cuts w ere conducted in their coun tries. Moreo v er, w e kno w
the in v estor t yp e, with roughly a third of consultan ts/academics and equit y in v estors,
and ab out a sixth of utilit y emplo y ees and bank ers.
2.3.2 Estimation strategy
W e aim to estimate the effect of wind p o w er p olicies on the wind p o w er risk premium,
i.e. the w eigh ted a v erage cost of capital min us the risk-free rate, estimated as sho wn
in equation (2.16). Imp ortan tly , our k ey explanatory v ariable, whose effect we aim to

2.3. ESTIMA TING INVESTORS’ FINANCING COSTS 61
Figure 2-1: Onshore wind p o w er p olicies in the EU in spring 2014. Source: Eclareon
(2017) and González and Arán tegui (2015)
assess, is the p olicy sc heme. Its co efficien ts are 𝛽 1 for sliding feed-in premia and 𝛽 2
for green certificate sc hemes, as compared to the baseline of a fixed feed-in tariff.
𝑟 𝑖𝑠𝑘 𝑝𝑟 𝑒𝑚𝑖𝑢𝑚 𝑖 = 𝛼 + 𝛽 1 𝐹 𝐼 𝑃 + 𝛽 2 𝑇 𝐺𝐶 + 𝑋 𝛿 + 𝑢 𝑖 (2.2)
F or eac h in terview-observ ation 𝑖 , w e con trol for additional factors through ex-
planatory v ariables con tained in 𝑋 . Our additional co-v ariates include dummies for
the implicit stop of renew able energy supp ort, retrosp ectiv e c hanges, tenders and
the t yp e of resp ondent. Retrosp ectiv e c hanges pla y a particularly imp ortan t role.
Some coun tries ha v e implicitly , if not explicitly , abandoned an y supp ort for renew-
able energies, for instance through the ab olition of rem uneration pa ymen ts or net w ork
op erators stopp ed grid connections for new wind p o w er plan ts due to net w ork sta-
bilit y concerns. Where go v ernmen ts ha v e retrosp ectiv ely c hanged rem uneration, the

62 CHAPTER 2. FINANCING PO WER
underlying risks for new installations ma y ha v e also shifted, resulting in additional re-
new able energy risk premia. Through suc h c hanges, some go v ernmen ts aim to reduce
their o wn or their constituen ts’ financial obligations to existing pro jects. Therefore,
w e also include information ab out whether suc h c hanges ha v e o ccurred. An additional
dimension are tenders. These are p oten tially implemen ted on top of the regular p ol-
icy regime, suc h that mark et actors ha v e to participate in tenders in order to b e
en titled to receiv e the normal rem uneration. The t yp e of resp onden t – pro ject de-
v elop er, bank er or academic – migh t also influence the results if these groups ha v e
systematically differen t p erceptions of financing parameters.
This simple sp ecification can b e estimated using ordinary least squares (OLS).
Ho w ev er, one ob vious necessit y for this estimator is that the dep endent v ariable con-
sists of individual v alues, e.g. a risk premium of 5.3 p ercen t. Ho w ev er, in sev eral
in terviews (23 p ercen t), resp onden ts did not pro vide p oin t estimates for the financing
costs, but ranges with an op en upp er or lo w er limit, e.g. “The w eigh ted a v erage cost
of capital is less than 5.3 p ercen t”. Consequen tly , in order to run an ordinary least
square regression, w e ha ve to appro ximate the exact v alue they mean. In a first step,
w e assume the decrease (increase) to b e .5 p ercentage points when the actual n um b er
w as “sligh tly lo w er" (higher), 1 p ercen tage p oin t when it w as “lo w er" (higher), and
1.5 p ercen tage p oin ts when it w as “m uc h lo w er" (higher).
2.3.3 Results
The results of our main sp ecification sho w that feed-in tariffs and sliding premia are
asso ciated with the same risk premium for in v estors, whereas green certificate sc hemes
are asso ciated with significan tly higher costs. The differences b et w een feed-in tariffs
and sliding feed-in premia are insignifican t (see column (1) of table 2.2). Under the
feed-in premium, the rev en ue risk remains as lo w as under the feed-in tariff, most
lik ely b ecause in v estors receiv e the sliding mark et premium on top of the electricit y
prices, with a particular, almost certain, strik e price. It app ears that mark ets ev aluate
the risks as lo w as under feed-in tariffs, or that they trust that the regulator w ould

2.3. ESTIMA TING INVESTORS’ FINANCING COSTS 63
bail-out an y stranded assets that migh t app ear due to e.g. the in tro duction of new
price zones. W e presen t an additional regression with all “safe p olicies” as baseline,
feed-in tariff and feed-in premium, sho wn in column (2). In b oth estimations (1) and
(2), significance of the explanatory v ariables remains the same.
Most imp ortan tly , tradable green certificates are asso ciated with an increase in
the risk premium b y 1.2-1.3 p ercen tage p oin ts, or 27-33 p ercen t in the logarithmic
sp ecification. This indicates that in v estors k eep some of the p o w er price risk. This
is also p ossibly the case when they sign l ong-term con tracts with off-tak ers, as these
off-tak ers migh t go bankrupt or ask for renegotiations of con tracts when sp ot mark et
prices fall (Finon, 2011).
Where regulators ha v e implicitly , if not officially , stopp ed implemen ting the p ol-
icy sc heme for new installations, financing costs are also increased. The results in-
dicate they are increased b y 2.3 p ercen tage p oin ts. One reason for this could b e the
additional uncertain t y with resp ect to administrativ e pro cesses and the significan t
rev en ue uncertain t y . Similarly , in the logarithmic sp ecifications, the co efficien ts are
statistically significan t at the one p ercen t lev el, implying an increase in financing costs
b y almost 50 p ercen t.
Somewhat surprisingly , retrosp ective c hanges ha v e no statistically significan t ef-
fect on the financing costs. One explanation is that the resp onden ts ev aluated their
coun try’s situation as if these c hanges had not tak en place. A dditionally , coun tries
that conducted retrosp ectiv e c hanges usually also c hanged their supp ort p olicies, fre-
quen tly b y implicitly abandoning supp ort pa ymen ts, whic h – as iden tified ab o v e –
increases financing costs b y around 2.3 p ercen tage p oin ts and migh t also capture the
effects of retrosp ectiv e c hanges, whic h w e cannot disen tangle where b oth are the case.
F urthermore, tenders do not decrease or increase rev en ue risks if they are imple-
men ted on top of the main p olicies. This means tenders set the price lev el, but once
in v estors ha v e w on them, regular feed-in tariffs/premia apply , i.e. no new rev en ue
risks are induced for in v estors at that stage. Where financing needs to b e secured
b efore the tenders, uncertain t y ab out the tender outcome can still induce risks at suc h
an early stage. The resp onses from the differen t t yp es of in v estors do not differ from

64 CHAPTER 2. FINANCING PO WER
one another. Compared to the baseline academic/consultan t, none of the in tervie-
w ee categories (equit y in v estors, utilit y emplo y ees and bank ers) ga v e systematically
differen t replies.
T able 2.2: OLS estimation results
(1) (2) (3) (4)
Lev el Lev el Log Log
Dep. v ar: risk premium
Sliding feed-in premium -0.290 -0.176
(0.501) (0.187)
T radable green certificates 1.209 ** 1.306 ** 0.269 ** 0.328 ***
(0.417) (0.389) (0.095) (0.087)
No p olicy 2.274 *** 2.341 *** 0.453 *** 0.494 ***
(0.438) (0.421) (0.097) (0.087)
Retrosp. changes -0.139 -0.082 -0.048 -0.013
(0.366) (0.361) (0.088) (0.083)
T enders 1.030 0.887 0.304 0.217
(0.608) (0.575) (0.156) (0.130)
Equit y in v estor -0.266 -0.293 -0.048 -0.065
(0.323) (0.320) (0.080) (0.074)
Utilit y emplo y ee -0.336 -0.316 -0.093 -0.080
(0.539) (0.528) (0.126) (0.118)
Bank er -0.708 -0.729 -0.263 -0.275
(0.507) (0.535) (0.192) (0.212)
𝑁 53 53 53 53
Robust standard errors in paren theses
* 𝑝 < 0 . 05 , ** 𝑝 < 0 . 01 , *** 𝑝 < 0 . 001
Fixed feed-in tariff and the Belgian and Romanian TGC systems with significan t price
flo ors are the baseline p olicy . In columns 2 and 4, also the feed-in premium is in the base-
line. A cademic/Consultants are the baseline resp onden t group.
Our results indicate that secure designs of sliding feed-in premia facilitates suc h
p olicies without inducing significan t additional rev en ue risks and, th us, without addi-
tional financing costs, at least in the short term. Ho w ev er, with p oten tially increasing
balancing costs and c hanges in the p o w er mark et design, in v estors migh t p erceiv e the
rev en ues under feed-in premia as more uncertain, whic h w ould lead to increases in
financing costs.
These results rest on sev eral assumptions. W e assume that b y con trolling for

2.3. ESTIMA TING INVESTORS’ FINANCING COSTS 65
coun tries’ general financing en vironmen ts, w e can con trol for national factors that
influence pro ject financing costs for wind p o w er pro jects or that suc h v ariations o ccur
randomly across coun tries. Moreo v er, w e rely on the resp onden ts’ kno wledge of the
financing costs in their coun try . If this kno wledge v aries with the prev ailing p olicy
sc heme, the results are biased.
2.3.4 Robustness c hec ks
W e conduct robustness c hec ks with resp ect to our assessmen t of financing costs of
observ ations, where resp onden ts only stated that the financing costs lie higher or
lo w er than some indicated threshold, but did not pro vide a sp ecific p oin t estimate.
W e can deriv e the unkno wn estimates conditional on the kno wn ones, assuming a
sp ecific functional form for the distribution of the risk premium estimates. W e ha v e a
v ector of lo w er b oundaries (in case of statemen ts where the upp er b oundary is op en)
and a v ector of upp er b oundaries (in case of statements where the lo w er b oundary
is op en). W e assume that the lo w er (upp er) b oundaries follo w normal distributions
and that the unkno wn v alues also adhere to these distributions. Consequen tly , a
maxim um lik eliho o d estimator is un biased: the in terv al regression estimator, whic h
is a generalized censored regression estimator. The un biasedness of this estimator
hinges on t w o assumptions: First, the lo w er (upp er) estimates need to follo w normal
distributions. Second, the unkno wn v alues ha v e to follo w the same normal distribu-
tion. W e can test only the first of these assumptions. Visual and n umerical chec ks of
this assumption state that normalit y of the kno wn estimates cannot b e rejected for a
sp ecification in lev els. As it is rejected in the logarithmic sp ecification, w e prefer the
lev el sp ecification o v er the logarithmic one. Details on the normalit y assumptions are
giv en in App endix 2.7.1.
The results from the in terv al regression are v ery similar to the OLS estimates,
indicating that neither estimator induces significan t biases, therefore confirming the
v alidit y of our initial approac h. T able 2.3 pro vides an o v erview of the results for the
in terv al regressions. As argued b efore, the lev el sp ecification in columns 1 and 2 are

66 CHAPTER 2. FINANCING PO WER
preferred o v er the logarithmic estimations in columns 3 and 4. The first estimation
indicates that the differences b et w een feed-in tariff and sliding feed-in premium are
again insignifican t.
Also under the in terv al regression, tradable green certificates are asso ciated with
a 1.2 p ercen tage p oin ts higher risk premium at a one p ercen t significance lev el. This
is, on a v erage, equiv alen t to an increase of the risk premium b y almost a third and,
th us, also significant economically . T urning to w ard the other explanatory v ariables,
their sign and statistical significance are similar to those of the OLS regressions.
Where p olicies hav e b een ab olis hed implicitly , financing costs strongly increase.
T able 2.3: Interv al regression estimation results
(1) (2) (3) (4)
Lev el Lev el Log Log
Dep. v ar: risk premium
Sliding feed-in premium -0.030 -0.130
(0.535) (0.228)
T radable green certificates 1.213 ** 1.222 ** 0.292 ** 0.333 **
(0.417) (0.414) (0.094) (0.108)
No p olicy 2.477 *** 2.484 *** 0.528 *** 0.557 ***
(0.458) (0.451) (0.105) (0.110)
Retrosp. changes -0.212 -0.207 -0.047 -0.023
(0.354) (0.354) (0.092) (0.092)
T enders 0.867 0.851 0.270 0.203
(0.604) (0.534) (0.177) (0.125)
Equit y in v estor -0.320 -0.323 -0.057 -0.069
(0.304) (0.311) (0.080) (0.078)
Utilit y emplo y ee -0.369 -0.366 -0.122 -0.107
(0.522) (0.516) (0.129) (0.119)
Bank er -0.592 -0.592 -0.229 -0.230
(0.496) (0.500) (0.198) (0.208)
𝑁 53 53 53 53
Robust standard errors in paren theses
* 𝑝 < 0 . 05 , ** 𝑝 < 0 . 01 , *** 𝑝 < 0 . 001
Fixed feed-in tariff and the Belgian and Romanian TGC systems with significan t price
flo ors are the baseline p olicy . In columns 2 and 4, also the feed-in premium is in the base-
line. A cademic/Consultants are the baseline resp onden t group.
The in terv al regression estimator relies on additional assumptions on asymptotic

2.4. LONG-TERM CONTRA CTS 67
c haracteristics of the data. Sp ecifically , it assumes that the unkno wn w eigh ted a v erage
cost of capital estimates are distributed according to the normal distributions deriv ed
from the kno wn estimates. Y et, esp ecially in the case of the unkno wn ones, one could
argue that they are lik ely to b e outliers as compared to those that are kno wn.
A dditional robustness c hec ks test ho w sensitiv e the OLS sp ecification is to the
necessary in terpretation of replies, as the un biasedness of OLS relies on the correct
in terpretation of these replies. The relev ance of this limitation can b e iden tified b y
comparing the results with differen t co dings. W e estimate the regression with differen t
absolute in terv al in terpretations and with relativ e in terpretations, i.e. “sligh tly lo w er"
(higher) implying fiv e p ercen t lo w er (higher) w eigh ted a v erage cost of capital, ten
p ercen t when it w as “lo w er" (higher), and 20 p ercen t when it w as “m uch lo w er"
(higher). These sensitivit y estimates are presen ted in App endix 3.7. They supp ort
the results of the main analysis, implying that the actual co ding-sp ecification has
some effect on the magnitude of the p oin t estimates, but do es not strongly affect
statistical significance and indicating that no significan t bias is in tro duced b y the
necessary resp onse interpretations under the OLS sp ecification.
2.4 Long-term con tracts
Long-term con tracts pla y a k ey role for renewable energy in v estmen ts under green
certificate sc hemes and fixed premia. Where p olicy design do es not comprise im-
plicit long-term con tract, w e observ e that mark et participan ts seek to sign bilateral
long-term con tracts as basis for pro ject financing of renew able energy pro jects. The
coun terpart y to the pro ject dev elop er, whic h w e will in the follo wing refer to as off-
tak er, ma y incur risks in signing suc h con tracts: the price to whic h the p o w er is
acquired via long-term con tract ma y exceed the price at whic h the off-tak er can sell
it in future y ears to customers. Suc h risks imply that the off-tak er, only offers prices
b elo w the exp ected v alue of the energy from the renew able pro ject to comp ensate for
its additional costs. This, in turn, implies that the pro ject needs to obtain additional
supp ort to break ev en, whic h directly translates in to additional deplo ymen t costs.

68 CHAPTER 2. FINANCING PO WER
While w e fo cus the subsequen t discussion on in v estmen ts through pro ject finance,
the most common financing arrangemen t e.g. in German y (Steffen, 2018), the anal-
ysis and results holds similarly for v ertically-in tegrated companies, as Finon (2008)
describ es ho w long-term con tracts b et w een generators and retailers are substitutes
with v ertical in tegration to establish the required long-term cash flo w securit y . Aïd
et al. (2011) argue that whether v ertical in tegration or long-term con tracts prev ails
dep ends on the degree of p o w er price uncertain t y .
2.4.1 Implications of long-term con tracts for priv ate off-tak ers
Pro ject in v estors seek long-term certain ty about their rev en ue streams; commonly se-
curing them b et w een ten and t w en t y y ears in to the future, in order to facilitate a high
share of debt relativ e to equit y and, th us, lo w capital costs for the in v estmen t. With
long-term con tracts and the according lo w v ariabilit y of pro ject rev en ues, lenders’
rev en ue requiremen ts lie lo w er, i.e. the pro ject’s financing costs (Mark o witz, 1952,
Ro ques et al., 2008). This is particularly imp ortan t since long-term financial hedging
is not a v ailable for electricit y , unlik e for ordinary commo dities. It is not storable
economically on a long-term at large scale and it is heterogeneous: its v alue v aries
with place and time of generation (Finon, 2011, Ro ques et al., 2008).
W e quan tify the additional risks for the long-term con tract’s off-tak er. This risk
is primarily that the off-tak er has con tracted the p o w er at long-term prices that turn
out to b e ab o v e sp ot mark et prices. Ho w ev er, the off-tak er, usually electricit y re-
tail companies, cannot sign equiv alen t long-term con tracts with priv ate households
for regulatory reasons and suc h con tracts p ose to o large obligations for most com-
panies, suc h that off- tak er cannot sign corresp onding long-term con tracts with final
customers. Therefore, the off-taker carries the price risk and, in a situation with lo w
sp ot prices, incurs losses. 5
This explains wh y , according to Baringa (2013) and Standard & P o or’s (2017),
rating agencies consider long-term con tracts as imputed debt in their credit rating b y
5 The off-tak er also incurs the risk that the pro ject fails to pro duce at times when the con tract
price is b elow the spot price level.

2.4. LONG-TERM CONTRA CTS 69
adding the v alue of the long-term con tract to the liabilities of a compan y . A ccordingly ,
an additional long-term con tract is treated equiv alen tly to additional debt, hence in-
creasing the debt-equit y ratio. The higher debt-equit y ratio reduces the credit rating,
resulting in higher default spreads for all debt raised and higher return requiremen ts
for equit y . 6
Consequen tly , the off-tak er will only sign long-term con tracts at a discoun t to the
exp ected p o w er price, whic h in comp etitiv e mark ets reflects the increased financing
costs. Pro ject dev elop ers will require comp ensating pa ymen ts through other c hannels,
e.g. b y bidding higher required rem uneration lev els under fixed premia, or requiring
higher green certificate prices.
W e appro ximate the cost incurred b y an off-tak er in signing a long-term con tract.
A firm’s total capital cost 𝐶 and comprises b oth the cost for debt 𝑑 and equit y 𝑒 at
the resp ectiv e return requiremen ts 𝑟 𝑑𝑒𝑏𝑡 and 𝑟 𝑒𝑞 𝑢𝑖𝑡𝑦 .
𝑐 ( 𝑑, 𝑒 ) = 𝑟 𝑑𝑒𝑏𝑡 𝑑 + 𝑟 𝑒𝑞 𝑢𝑖𝑡𝑦 𝑒 (2.3)
The return requiremen ts dep end on the rating grade 𝑔 ( 𝑑, 𝑒 ) , whic h is, in turn, a
function of the debt-equit y ratio. Th us, the total capital costs are
𝑐 ( 𝑑, 𝑒 ) = 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 )) 𝑑 + 𝑟 𝑒𝑞 𝑢𝑖𝑡𝑦 ( 𝑔 ( 𝑑, 𝑒 )) 𝑒 (2.4)
Priv ate off-tak ers’ balance sheets c hange for rating purp oses when they sign long-
term con tracts. The additional long-term liabilities are added to the companies’ debt
sto c k, w orsening their debt-equit y ratio and rating grade. F or simplicit y , w e analyze
only the c hanges in the costs of debt, rendering our estimates a lo w er b ound of the
costs of an increase in debt, as equit y can b e exp ected to b ecome more exp ensiv e as
w ell. The deriv ate is:
6 If rating agencies treat only part of the con tract v alue as liabilities, this reduces the estimated
costs. Y et, according to Standard & P o or’s (2017), ev en for companies not sub ject to retail comp e-
tition and with regulated cost reco v ery , half of the con tract v alue is counted, indicating ev en higher
n um b ers for companies in retail comp etition.

70 CHAPTER 2. FINANCING PO WER
0% Rating grade
𝐴𝑎𝑎
𝐴𝑎 1
𝐴𝑎 2
𝐴𝑎 3
𝐴 1 𝐴 2 𝐴 3
𝐵 𝑎𝑎 1
𝐵 𝑎𝑎 2
𝐵 𝑎𝑎 3
𝐵 𝑎 1
𝐵 𝑎 2
𝐵 𝑎 3
𝐵 1 𝐵 2 𝐵 3
𝐶 𝑎𝑎 1
𝐶 𝑎𝑎 2
𝐶 𝑎𝑎 3
𝐶 𝑎 𝐶 𝐷
2%
4%
6%
8%
10%
12%
14%
16% Sp eculativ e
In v estmen t In Default
𝑟 𝑑𝑒𝑏𝑡
Figure 2-2: Default spread as function of corp orate credit rating, based on
Damo daran (2017)
∂ 𝑐 ( 𝑑, 𝑒 )
∂ 𝑑 = ∂ 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 ))
∂ 𝑔
∂ 𝑔 ( 𝑑, 𝑒 )
∂ 𝑑 𝑑 + 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 )) (2.5)
The term ∂ 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑,𝑒 ))
∂ 𝑔
∂ 𝑔 ( 𝑑,𝑒 )
∂ 𝑑 𝑑 represen ts the increase in costs caused by the increase
in in terest rate, as this higher in terest rate is in the long run applied to the total sto c k
of debt 𝑑 . The term 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 )) represen ts the costs of an additional unit of debt
and simply equals the in terest rate. As describ ed in Standard & P o or’s (2017), the
long-term con tract is ev aluated as imputed debt, i.e. equiv alen t to an increase in
liabilities, hence, impacting the debt-equit y ratio. Debt is not formally increased, so
w e omit the term 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 )) in the follo wing.
W e analyze ho w the in terest rate resp onds to an incremen tal change in credit
rating using data pro vided b y Damo daran (2017) for all traded US companies. 7 An-
alyzing the link b et w een default spreads and ratings rev eals that the default spread
function is non-linear in rating: The w orse the rating, the stronger the impact of a
one step c hange in the credit rating on the default spread (see figure 2-2). 8
Moreo v er, the credit rating itself is appro ximately a linear function of debt. The
7 W e refer to the rating categories in Mo o dy’s nomenclature.
8 F or comparison, app endix 2.7.3 sho ws the estimation and results for a linear functional form,
whic h, how ever, has a lo wer R-squared (82 percent in the linear against 93 percent in the quadratic
case).

2.4. LONG-TERM CONTRA CTS 71
data b y Damo daran (2017) on the relationship b et w een another k ey financial metric,
the in terest co v erage ratio, and the credit rating indicates that the rating is roughly
linear in in terest co v erage ratio (and appro ximately corresp ondingly in debt-equity
ratio). This implies that the distances b et w een the otherwise ordinal rating grades 𝑔
are appro ximately equidistan t.
2.4.2 Estimation of off-tak ers’ costs
W e estimate off-tak ers’ costs of signing long-term con tracts b y parameterizing equa-
tion (2.5). T o this end, w e deriv e the default spread based on the credit rating and w e
parameterize function 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 )) . As argued b efore, the spread increases appro x-
imately exp onen tially , as confirmed b y Mo o dy’s (2005) and Elton et al. (2001). A
resp ectiv e non-linear function for the default spread 𝑟 𝑑𝑒𝑏𝑡 as function of credit grade
( 𝑔 ( 𝑑, 𝑒 )) is: 9
𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 )) = 𝑚 + 𝜆𝑔 ( 𝑑, 𝑒 ) 2 (2.6)
with slop e
∂ 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 ))
∂ 𝑔 ( 𝑑, 𝑒 ) = 2 𝜆𝑔 ( 𝑑, 𝑒 ) (2.7)
W e estimate equation (2.14) with the aggregated data b y Damo daran (2017).
Sp ecifically , w e regress the default spread on the according squared rating, using a
simple OLS estimator. F ollo wing the discussion in section 2.4, w e assume equidistan t
rating grades and co dify them as n umerical v alues 𝑛 , with the b est rating AAA as
1, the second b est rating AA1 as 2, and so forth. The term 𝑢 𝑔 represen ts the error
term.
𝑟 𝑑𝑒𝑏𝑡 𝑔 = 𝑚 + 𝜆𝑛 2
𝑔 + 𝑢 𝑔 (2.8)
9 W e estimate a function for the default spread, ev en though w e discussed the interest rate previ-
ously . Y et, we are only in terested in changes in the default spread, i.e. the slop e. The risk-free rate
w ould b e con tained in the constant and, th us, is not relev ant for our subsequen t analysis.

72 CHAPTER 2. FINANCING PO WER
T able 2.4: In terest rate as quadratic function of traded US companies’ credit ratings,
based on aggregate data b y Damo daran (2017)
Estimation results
(1)
Dep. v ar.: 𝑟 𝑑𝑒𝑏𝑡
g 2 0.000231***
(0.0000175)
m -0.000481
(0.00434)
N 15
Robust standard errors in paren theses
* p<0.10, ** p<0.05, *** p<0.010
The co efficien t 𝜆 is statistically significan t and is equal to 0.00023, while the
constan t is insignifican t, as table 2.4 sho ws. The equation describ es ho w the default
spread reacts to a c hange in credit grade. F or example, a do wngrade b y one rating
from Ba2 to Ba3 results in an increase in default spread from 2.8 to 3.3 p ercen t.
The rating grade 𝑔 ( 𝑑, 𝑒 ) is a function of the debt-equit y ratio. The function differs
b et w een industries, suc h that w e prefer deriving parameter v alues from a sample of
Europ ean utilities. The credit grade function can b e expressed as:
𝑔 ( 𝑑, 𝑒 ) = 𝑛 + 𝜖 𝑑
𝑒 (2.9)
where 𝑛 is a constan t and 𝜖 the effect of a one unit increase in the debt-equity
ratio on the credit grade. The function’s deriv ativ e with resp ect to 𝑑 is:
∂ 𝑔 ( 𝑑, 𝑒 )
∂ 𝑑 = 𝜖
𝑒 (2.10)
W e regress the credit rating on the debt-equit y ratio, applying an OLS estimator.
W e use aggregated ann ual data on a v erage debt-equit y ratios and credit ratings of
t w elv e large Europ ean utilit y companies o v er 11 y ears. The term 𝑢 𝑡 represen ts the
error term.
𝑛 𝑔 𝑡 = 𝑏 + 𝜖 ( 𝑑
𝑒 ) 𝑡 + 𝑢 𝑡 (2.11)

2.4. LONG-TERM CONTRA CTS 73
T able 2.5: Credit grade as function of debt-equit y ratio based on aggregated annual
a v erages of large EU utilities
Estimation results
(1)
Dep. v ar.: g
debt-equit y ratio 2.876032***
(0.5518585)
m 2.183433
(0.7326723)
N 11
Robust standard errors in paren theses
* p<0.10, ** p<0.05, *** p<0.010
The slop e 𝜖 is estimated as 2.88 and the constan t 𝑏 is 2.18, as sho wn in table
2.5. Hence, an increase in debt-equit y ratio b y one is asso ciated with a do wngrade of
almost three rating grades.
Com bined, w e can calculate the off-tak er’s cost of signing a long-term con tract
and holding it as liabilit y on the balance sheet for a y ear b y inserting the estimated
parameters in to equation (2.5).
∂ 𝑐 ( 𝑑, 𝑒 )
∂ 𝑑 = 2 𝜆 ( 𝑏 + 𝜖 𝑑
𝑒 ) 𝜖
𝑒 𝑑 (2.12)
Figure 2-3: Extra re-financing costs for priv ate off-tak ers as share of con tract v alue
Based on Europ ean utilities’ a v erage debt-equit y ratio of 2015 of 1.85, w e cal-

74 CHAPTER 2. FINANCING PO WER
culate these ann ual costs as 1.84 p ercent of con tract v alue. In order to obtain the
presen t v alue of the imputed debt o v er the con tract lifetime, w e need to calculate
the presen t v alue equiv alen t to lev elizing the cost of electricity according to equation
(2.13). The remaining outstanding liabilities decrease ev ery y ear, as captured in the
n umerator. F or an exemplary lifetime of 𝑇 of 20 y ears, the off-tak er p ossesses liabili-
ties for 20 more y ears in the first y ear, in the second y ear for another 19 y ears, and
so forth.
𝑐 𝑝𝑟 𝑒𝑠𝑒𝑛𝑡 = ∑︀ 𝑇
𝑡 =1 𝜁 𝑡 − 1 𝑐 𝑎𝑛𝑛𝑢𝑎𝑙 ( 𝑇 − 𝑡 − 1)
∑︀ 𝑇
𝑡 =1 𝜁 𝑡 − 1 (2.13)
Applying a discoun t factor 𝜁 of exemplary 0.96 p ercent, the lev elized a v erage
costs 𝑐 𝑝𝑟 𝑒𝑠𝑒𝑛𝑡 are 21.8 p ercen t of the con tract v alue. The costs are depicted in figu re
2-3 across a range of debt-equit y ratios of the off-taking compan y .
These costs lie lo w er for off-tak ers in more fa v orable financial p ositions: The
a v erage debt-equit y ratio of the 12 Europ ean utilities in 2005 w as 1.15. Inserting this
ratio and the parameter v alues yields a credit rating b et w een A1 and A2 and th us
extra costs of only 9.9 p ercen t.
2.4.3 Financial p osition of priv ate off-tak ers
In the absence of long-term financial hedges, utilities are commonly the sole mark et
actors that hold relativ ely stable long-term customer bases, whic h essen tially func-
tion as price hedges (Finon, 2011). 10 Moreo v er, utilities ha v e traditionally p ossessed
relativ ely strong financial p ositions and large p ortfolios, enabling them to commit to
long-term con tracts (Baringa, 2013), and exp erience with electricit y mark ets p ossi-
bly decreases their renew able energy risk premia compared to institutional in v estors
(Salm, 2018). Consequen tly , green certificate sc hemes generally dep end on utilities
with large stic ky customer bases and strong financial p ositions. Ho w ev er, the subse-
quen t analysis extends to other kinds of companies as w ell.
10 Sometimes, companies other than utilities aim to obtain renew able electricit y directly from
in v estors. In particular, in the US, large (IT) companies ha v e acted as off-tak ers to long-term
con tracts (Blo om b erg New Energy Finance, 2016).

2.4. LONG-TERM CONTRA CTS 75
Figure 2-4: A v erage debt-equit y ratio of t w elv e large EU utilities. Source: Own
calculations based on Datastream In ternational (2016) and V attenfall (2015)
Lib eralized electricit y mark ets mean new comp etition on the retail and wholesale
mark ets (T ullo c h et al., 2017), while the rise of renew able energies c hallenged incum-
b en ts’ business mo dels due to differen t risk-return profiles (Helms et al., 2015). This
resulted in reduced v aluations of con v entional pow er stations, reducing the equit y
v alue of companies. Figure 2-4 visualizes the dev elopmen t of utilities’ debt-equit y ra-
tios. The a v erage debt-equit y ratio of Europ e’s ten largest utilities, b y electricit y sales
according to R WE (2015), plus the UK’s Cen trica and SSE, has increased strongly
b et w een 2005 and 2015: Whereas the a v erage debt-equit y ratio sto o d at 116 p ercen t
in June 2005, it w as 184 p ercen t in Decem b er 2015, an a v erage ann ual increase of 6.5
p ercen tage p oin ts. A multitude of factors ma y underlie this: generally falling costs
of debt, write-do wns on thermal p o w er assets, and the increased comp etition due to
mark et lib eralization.
As a result, utilities’ credit ratings ha v e w orsened. As figure 2-5 indicates, the
credit ratings ha v e declined across the b oard o v er recen t y ears. On a v erage, b ond
ratings ha v e fallen more than 2.5 rating categories, e.g. from Aa1 to Aa2 or from A3
to Baa1.

76 CHAPTER 2. FINANCING PO WER
Figure 2-5: Credit ratings of large EU utilities. Source: Based on Mo o dy’s (2017)
2.5 A dditional costs under green certificate sc hemes
Due to regulatory and mark et risks, green certificates esp ecially increase the costs of
renew able energy deplo ymen t. F or an exemplary wind p o w er pro ject with lev elized
costs of electricit y of e 50 p er MWh under a feed-in tariff, 11 the a v erage tec hnology-
w eighed p o w er price of 2016 pa ys for ab out half of the costs, with the other half
required as additional supp ort. Under green certificates, the o v erall costs increase to
ab out e 65 p er MWh, increasing the required supp ort (o v erall rem uneration min us
p o w er price) b y roughly 75 p ercen t.
This increase stems from b oth additional regulatory risks, inducing higher financ-
ing costs for in v estors, and mark et risks, inducing costs for off-tak ers of long-term
con tracts. Firstly , incomplete hedging of regulatory risks increase in v estors’ financing
costs b y ab out 1.2 p ercentage points, as iden tified in section 2.3.3. This translates in to
an increase to e 53 p er MWh, as sho wn in figure 2-6. Secondly , the failure to hedge
mark et risks induce higher costs for off-tak ers of long-term con tracts, amoun ting to
ab out 21.8 p ercen t of the con tract v alue, as describ ed in section 2.4. This translate
11 W e apply rather lo w cost estimates of e 1080 p er k W and e 50 p er k W annually as operation
and main tenance costs com bined with a high capacit y factor of 33 p ercen t, based on Deutsc he
WindGuard (2013), and exemplary 4 p ercent financing costs.

2.5. ADDITIONAL COSTS UNDER GREEN CER TIFICA TE SCHEMES 77
Figure 2-6: A dditional costs under green certificates
in to a cost increase to e 65 p er MWh, equiv alen t to an increase in in v estors’ financing
costs b y another 3.6 p ercen tage p oin ts. In total, this cost increase is equiv alen t to an
increase in in v estors’ financing costs b y 4.8 p ercen tage p oin ts.
With higher initial pro ject costs, the additi onal costs increase prop ortionally .
Initial costs of e 89 p er MWh under a feed-in tariff rise to e 116 p er MWh under
green certificates. 12 This divides in to additional costs of e p er MWh for the new
regulatory risks and additional costs of e 20 p er MWh for the new mark et risks.
In general, the same extra costs for long-term con tracts are in tro duced when
all p olicy supp ort is ab olished and in v estmen ts are conducted based on a significan t
carb on price. This price w ould ha v e to b e high enough that the exp ectation of the
resulting p o w er price is sufficien t to supp ort in v estmen ts in to renew able energies.
Then, in v estors w ould still hedge their resulting price risks and liabilities, implying
similar cost increases.
Under fixed premia, the cost increase applies only to a part of the o v erall costs of
renew able energies. In v estors sell their electricit y and receiv e additional, fixed premia,
so they only need to sign long-term con tracts for the p ow er v alue, as the premium is
guaran teed b y the regulator. If, as in the previous example, the p o w er price mak es
12 This scenario grounds on the same cost assumption as previously , but higher inv estment costs
of e 1500 p er k W and a lo w er capacit y factor of 23 p ercen t.

78 CHAPTER 2. FINANCING PO WER
up ab out half of the total rem uneration, then the extra costs of 21.8 p ercen t only
applies to this half. Th us, the additional costs for the off-tak ers increase the o v erall
costs b y around elev en p ercen t.
2.6 Conclusion
P o w er systems with increasing shares of wind and solar generation ha v e high capital
and lo w op erational costs. This increases the imp ortance of the cost of financing for
total system cost. W e estimate ho w differen t risk factors affect in v estors’ financing
costs.
First, based on a surv ey on wind p o w er financing cost estimates from 23 EU coun-
tries, w e find that sliding feed-in premia do not increase financing costs in comparison
with fixed feed-in tariffs. With ev olving p o w er mark et designs, how ev er, in v estors are
exp osed to additional risks under feed-in premia, e.g. in relation to balancing costs,
suc h that risk premia migh t increase in the future.
T radable green certificates can b e asso ciated with increases in the wind p o w er
risk premium b y ab out 1.2 p ercen tage p oin ts. Capital pro viders require higher risk
premia b ecause of the higher rev en ue v ariabilit y . These results hold under ordinary
least square sp ecifications as well as with in terv al regressions, whic h tak e in to accoun t
the sp ecific nature of resp onses, with sev eral replies in relativ e terms.
Second, w e mo del the implicit long-term hedge that renew able supp ort mec ha-
nisms can offer to mark et participan ts. In principle, b oth renew able pro ject dev elop ers
and final consumers w ould lik e to hedge against price uncertaint y . In practice, mark et
design rules and coun terpart y risks inhibit suc h long-term con tracts b et w een pro ject
dev elop ers and final consumers. In the absence of suc h long-term con tracts, pro ject
dev elop ers commonly sign long-term con tracts with electricit y retail companies in
order to secure rev en ue streams for financing purp oses. Y et, signing suc h long-term
con tracts constitutes imputed debt on the balance sheets of the retail companies. W e
estimate b y ho w m uc h suc h con tracts increase retail companies’ re-financing costs.
Ultimately , these costs are passed on to consumers — resulting in around 20 p ercen t

2.6. CONCLUSION 79
additional costs of renew able energy deplo ymen t.
The com bined increases in financing costs for the in v estor and for the priv ate off-
tak ers of long-term con tracts render renew able energy depl o ymen t ab out 30 p ercen t
more exp ensiv e under green certificate sc hemes compared to feed-in tariffs, increasing
the costs of an illustrativ e wind p o w er plan t from e 50 p er MWh to e 65 p er MWh.
With increasing shares of renew able energies and higher con tracted v olumes, this cost
premium increases.
Com bining the effects of risk for pro ject in v estors and risk for coun terparties
signing long-term off-tak e con tracts ma y also explain a parado x of previous assess-
men ts. Studies lik e Ragwitz et al. (2012) and Butler and Neuhoff (2008) sho w that
significan tly higher supp ort lev els are required where p olicy design in v olv es green cer-
tificate systems, but no equiv alen t discrepancy in financing cost has b een iden tified in
surv eys of in v estors. Ample space for future researc h remains with resp ect to c hanges
in financing costs o v er time. When sales represen t a larger share of rev en ues, then
the extra costs of p olicies with a higher p o w er price exp osure migh t induce higher
extra costs. Information on renew able energy financing cost o v er time w ould allo w
for iden tification of suc h effects. F uture researc h could also in v estigate the role of
additional dimensions of renew able energy supp ort lik e preferen tial public loans and
priorit y dispatc h on in v estors’ financing costs.

80 CHAPTER 2. FINANCING PO WER
2.7 App endix
2.7.1 Normalit y of w eigh ted a v erage cost of capital estimates
W e test the normalit y of the estimates of the w eigh ted a v erage cost of capital, pro vided
b y the in terview ees. A rough initial visual c hec k of this assumption can b e made b y
plotting the existing resp onses against normal distributions and ev aluating if the data
app ears to adhere to the distribution. Figure 2-7 sho ws the risk premium in lev els,
figure 2-8 sho ws it in logarithms. The lev el sp ecification app ears lik e a b etter matc h,
as the data is less sk ew ed to wards a v ery narro w in terv al and has few er outliers.
Figure 2-7: Normalit y assumption for lo w er and upp er estimates of risk premium
in lev els
Numerically , w e c hec k normalit y through a Shapiro-Wilk test. It tests the n ull
h yp othesis that certain data is normally-distributed (Shapiro and Wilk, 1965). The
test of the lo w er b ound yields a W-v alue of 0.954, with a resulting p-v alue of 0.0578.
Hence, the n ull h yp othesis of the data follo wing a normal distribution cannot b e
discarded at a fiv e p ercen t significance lev el, y et is rejected at a ten p ercen t significance
lev el. The resp ectiv e test of the upp er b ound yields a W-v alue of 0.963 and a p-v alue

2.7. APPENDIX 81
of 0.161. Th us, w e cannot reject the n ull h yp othesis of a normal distribution for the
upp er b ound at an y reasonable significance lev el. Summarizing, some doubts remain
with resp ect to the normalit y of the lo w er risk premium b oundary , whereas the upp er
b oundary app ears normally distributed.
The same tests for the logarithm of the risk premium clearly reject the n ull
h yp otheses of normalit y: The lo w er b oundary’s W-v alue is 0.719, with a p-v alue of
0.000. The upp er b oundary’s W-v alue is 0.776, with a resulting p-v alue of 0.000.
Hence, w e prefer the lev els-estimation o v er the log-sp ecification, as the latter will b e
biased.
Figure 2-8: Normalit y assumption for lo w er and upp er estimates of risk premium
in logarithms

82 CHAPTER 2. FINANCING PO WER
2.7.2 Sensitivit y analyses regarding the co ding of resp onses
T able 2.6: OLS estimation results w. alternativ e co ding
(1) (2) (3) (4)
Lev el Lev el Log Log
Dep. v ar: risk premium
Sliding feed-in premium -0.467 -0.241
(0.599) (0.225)
T radable green certificates 1.585 ** 1.741 ** 0.372 ** 0.453 ***
(0.533) (0.507) (0.125) (0.119)
No p olicy 2.622 *** 2.729 *** 0.568 *** 0.623 ***
(0.591) (0.572) (0.146) (0.140)
Retrosp. c hanges 0.033 0.125 -0.027 0.021
(0.559) (0.569) (0.147) (0.148)
T enders 1.214 0.984 0.415 * 0.296
(0.677) (0.634) (0.187) (0.149)
Equit y in v estor -0.377 -0.421 -0.095 -0.118
(0.473) (0.467) (0.128) (0.125)
Utilit y emplo y ee -0.552 -0.519 -0.144 -0.128
(0.613) (0.605) (0.159) (0.156)
Bank er -0.567 -0.601 -0.159 -0.176
(0.534) (0.556) (0.194) (0.212)
𝑁 53 53 53 53
Robust standard errors in paren theses
* 𝑝 < 0 . 05 , ** 𝑝 < 0 . 01 , *** 𝑝 < 0 . 001
Fixed feed-in tariff and the Belgian and Romanian TGC systems with significan t price
flo ors are the baseline p olicy . In columns 2 and 4, also the feed-in premium is in the base-
line. A cademic/Consultants are the baseline resp onden t group.
The in terview replies are in terpreted in some cases where the replies do not yield a
p oin t estimate, but pro vide ranges ab o v e or b elo w a certain threshold. Consequen tly ,
for OLS regressions, w e m ust mak e assumptions ab out what in terview ees p ossibly
mean t. In the baseline scenario, w e coun t “sligh tly higher” as 0.5 p ercen tage p oin ts
higher, “higher” as 1.0 p ercen tage p oin t, and “m uc h higher” as 1.5 p ercen tage p oin ts.
In the first sensitivit y , w e c hange these in terpretations to 1, 2, and 3 p ercen tage
p oin ts, resp ectiv ely . T able 2.6 sho ws the results. Statistical significance lev els are the
same as previously . The only relev an t difference is that the effect of green certificates
is ev en more pronounced: They are asso ciated with an increase of financing costs of

2.7. APPENDIX 83
1.6-1.7 p ercen tage p oints.
Another in terpretation of the resp onses is in relativ e terms: “sligh tly higher” im-
plies a fiv e p ercen t higher v alue, “higher” ten p ercen t, and “m uc h higher” 20 p ercen t.
T able 2.7 sho ws the results. Statistical significance lev els are the same as b efore.
Ho w ev er, tradable green certificates are only significan t at the fiv e p ercen t signifi-
cance lev el. Their co efficien t is also sligh tly smaller and lies at 1.1-1.2 in the lev els-
sp ecification, implying an increase in financing costs b y 1.1-1.2 p ercen tage p oin ts.
T able 2.7: OLS estimation results w. alternativ e co ding I I
(1) (2) (3) (4)
Lev el Lev el Log Log
Dep. v ar: risk premium
Sliding feed-in premium -0.380 -0.231
(0.516) (0.203)
T radable green certificates 1.122 * 1.249 ** 0.242 * 0.319 **
(0.434) (0.402) (0.100) (0.092)
No p olicy 2.052 *** 2.140 *** 0.406 *** 0.460 ***
(0.495) (0.472) (0.109) (0.098)
Retrosp. changes -0.072 0.003 -0.056 -0.010
(0.453) (0.455) (0.105) (0.102)
T enders 1.012 0.824 0.320 0.206
(0.628) (0.606) (0.168) (0.137)
Equit y in v estor -0.106 -0.141 -0.004 -0.025
(0.376) (0.369) (0.088) (0.081)
Utilit y emplo y ee -0.267 -0.241 -0.079 -0.063
(0.547) (0.534) (0.131) (0.119)
Bank er -0.764 -0.791 -0.317 -0.333
(0.544) (0.580) (0.214) (0.241)
𝑁 53 53 53 53
Robust standard errors in paren theses
* 𝑝 < 0 . 05 , ** 𝑝 < 0 . 01 , *** 𝑝 < 0 . 001
Fixed feed-in tariff and the Belgian and Romanian TGC systems with significan t price
flo ors are the baseline p olicy . In columns 2 and 4, also the feed-in premium is in the base-
line. A cademic/Consultants are the baseline resp onden t group.

84 CHAPTER 2. FINANCING PO WER
T able 2.8: In terest rate as linear function of traded US companies’ credit ratings,
based on aggregate data b y Damo daran (2017)
Estimation results
(1)
Dep. v ar.: 𝑟 𝑑𝑒𝑏𝑡
g 0.0052989***
(0.0009123)
m -0.02087331**
(0.0096336)
N 15
Robust standard errors in paren theses
* p<0.10, ** p<0.05, *** p<0.010
2.7.3 F unctional form of the in terest rate function
Assuming a linear functional form for the in terest rate function 𝑟 𝑑𝑒𝑏𝑡 𝑔 , w e form ulate
the follo wing function:
𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 )) = 𝑚 + 𝜆𝑔 ( 𝑑, 𝑒 ) (2.14)
with slop e
∂ 𝑟 𝑑𝑒𝑏𝑡 ( 𝑔 ( 𝑑, 𝑒 ))
∂ 𝑔 ( 𝑑, 𝑒 ) = 𝜆 (2.15)
Equiv alen t to the previous estimation, w e estimate:
𝑟 𝑑𝑒𝑏𝑡 𝑔 = 𝑚 + 𝜆𝑛 𝑔 + 𝑢 𝑔 (2.16)
In this case, the constan t is -.0208 and the slop e is 0.0052, as depicted in 2.8.
The additional costs for long-term con tracts under green certificates differ ac-
cordingly , as depicted in figure 2-9. Using the exemplary cost parameters laid out
in section 2.5, an installation based on the a v erage debt-equit y ratio of the large EU
utilities of 1.85 sees additional costs of ab out 33.4 % , i.e. considerably more than
in the quadratic case. With w orse debt-equit y ratios, the costs under the quadratic
functional form increase more sharply .

2.7. APPENDIX 85
Figure 2-9: Extra re-financing costs for priv ate off-tak ers as share of con tract v alue
with linear in terest rate

Chapter 3
T o o Go o d to Be T rue? Ho w
Time-Inconsisten t Renew able Energy
P olicies Can Deter In v estmen ts ∗
Abstract
The transition to w ards lo w-carb on economies requires massiv e in v estmen ts in to re-
new able energies, whic h are commonly supp orted through regulatory framew orks.
Y et, go v ernmen ts can ha v e incen tiv es – and the abilit y – to deviate from previously-
announced supp ort once those inv estmen ts ha v e b een made, which can deter in v est-
men ts. W e analyze a renew able energy regulation game, apply a mo del of time-
inconsistency to renew able energy p olicy and deriv e under what conditions go v ern-
men ts ha v e incen tiv es to deviate from their commitmen ts. W e analyze the effects of
v arious supp ort p olicies and deplo ymen t targets and explain wh y Spain conducted
retrosp ectiv e c hanges in the p erio d 2010-2013 whereas German y stuc k to its commit-
men ts.
∗ This c hapter is based on joint w ork with Olga Chiappinelli. W e thank Sasc ha Drahs, Karsten
Neuhoff, and Jörn Ric hstein for their helpful comments and suggestions. W e also b enefited from
commen ts b y participan ts at the join t energy mark et researc h semin ar b y TU Berlin, the Ifo Institute
for Economic Researc h, TU Munich, the Univ ersity of Nurem b erg, and DIW Berlin.
87

88 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
3.1 In tro duction
In 2016, global in v estmen ts in to renew ables-based p o w er capacit y outpaced in v est-
men ts in to coal and gas p o w er plan ts, amoun ting to e 297 billion (IEA, 2017). In-
v estmen t needs remain high o v er the next decades, supp orting coun tries’ transitions
to not just lo w-carb on electricit y but also other energy sectors. The v ast ma jorit y of
renew able energy pro jects are facilitated through supp ortiv e regulatory framew orks
and p olicies. These p olicies offset the usually not-in ternalized negativ e externalities
of thermal p ow er plan ts and supp ort the learning of tec hnologies (Edenhofer et al.,
2013).
Go v ernmen ts supp ort renew able energy in v estmen ts b y promising in v estors cer-
tain p olicy framewo rks and rem uneration lev els. Time-inconsistency can arise as
regulators follo w a m ulti-ob jectiv e agenda: They pursue long-term decarb onization
tra jectories, y et these ma y conflict with short term distributional concerns regarding
the costs of energy (Chiappinelli and Neuhoff, 2017). Moreo v er, in addition to the
desire to do so, regulators can ha v e the ability to deviate from previously-announced
supp ort lev els since renew able energy in v estmen ts are irrev ersible and op erate at v ery
lo w marginal costs: Regulators in terested in renew able energy announce supp ort lev-
els to b e paid via levies on electricit y , based on whic h inv estors resp ond b y in v esting
in to new capacit y . Ho w ev er, regulators then p ossibly deviate, not pa ying out the
promised supp ort, b enefiting from b oth the no w-existing renew able energy capacit y
and the lo w costs of electricit y . Firms an ticipate this opp ortunistic b eha vior and do
not in v est in the first place. Ho w ev er, when the game is rep eated, there is scop e for
compliance dep ending on p olicies and tec hnology parameters.
The monetary p olicy literature first applied time-inconsi stency concepts to infla-
tion and economic gro wth. In their ground-breaking article, Kydland and Prescott
(1977) analyze unemplo ymen t and inflation and la y out the problem that rational
agen ts optimizing at differen t p oin ts in time adjust their b eha vior simply due to the
differen t timing, leading to sub-optimal outcomes when agen ts are rational. Barro
and Gordon (1983) underpin the argumen t that rules for go v ernmen t b eha vior can

3.1. INTR ODUCTION 89
ha v e fa v orable outcomes rather than the discretion to adjust p olicies when optimal.
Subsequen tly , the concept has b een used to analyze broader climate p olicies.
Helm et al. (2003) demonstrate that emission pricing faces similar problems since
agen ts foresee that incen tiv es for emiss ion reductions that are optimal ex-ante b e-
come sub optimal ex-p ost , th u s diminishing their credibilit y in the first place. This is
extended for differen t cases, e.g. where go v ernmen ts and firms are uncertain ab out
future go v ernmen ts’ preferences, inducing argumen ts for researc h gran ts (Ulph and
Ulph, 2013). Brunner et al. (2012) discuss solutions to the commitmen t problem
with a fo cus on delegation to an indep enden t climate agency , long-term planning via
targets and securitization through legal righ ts. In this con text, they men tion feed-
in tariffs for renew able energies as fa v orable example and dismiss the retrosp ectiv e
c hanges that Spain had just initiated as “unlik ely to affect in v estors’ prop ert y” (p.16),
whic h has since turned out to b e incorrect. Rem uneration lev els were cut b y around
25 p ercen t on a v erage (Comisión Nacional de los Mercados y la Comp etencia, 2014,
2015).
Renew able energy p olicy has some common features with general en vironmen-
tal regulation: Demand for renew able energy is driv en and affected b y go v ernmen t
regulations. Due to their high initial capital in tensit y and lo w marginal costs, in vest-
men ts in to wind and solar p o w er are p oten tially exp osed to time-inconsistency issues
as once in v estmen ts are made, op erators will usually op erate the assets indep enden t
of rem uneration. Consequen tly , go v ernmen ts migh t face incen tiv es to deviate b ecause
existing installations will run in an y case.
W e con tribute to the literature b y scrutinizing time-inconsistency problems of
renew able energy p olicies in detail. Building on the analysis of time-inconsistency of
en vironmen tal regulation b y Chiappinelli and Neuhoff (2017), w e sho w that renew able
energy p olicies can b e affected b y time-inconsistency and consider differen t p olicy
regimes and ho w they affect regulatory compliance. As renew able energy supp ort is
usually not paid out as capacit y supp ort, but rather o v er the pro jects’ lifetimes as
pa ymen ts for output, w e mo del the in teraction b et w een firms and the go v ernmen t
as a dynamic game where the effects of past p erio ds’ supp ort commitmen ts and

90 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
in v estmen ts last in to the presen t.
W e analyze commitmen t devices in the field of renew able energy . While Borgh-
esi (2011) argues that renew able energy targets, lik e the Europ ean 2020 renew able
energy targets can incen tivize commitmen t, w e sho w that targets only do so under
certain conditions. Hab ermac her and Lehmann (2017) analyze more generally ho w
uncertain t y ab out en vironmen tal b enefits leads to c hanging optimal supp ort lev els
o v er time, but do not fo cus on the classical commitmen t problem where the optimal
regulatory supp ort differs ov er time ev en in the absence of new information.
Our analysis can explain wh y some coun tries deviate from their announced re-
new able energy supp ort p olicies whi le others do not. F or example, Spain, then a
global fron trunner in renew able energies, cut its renew able energy supp ort o v er the
2010 to 2013 p erio d, while German y , another fron trunner, did not.
The c hapter is structured as follo ws: Section 3.2 describ es the dynamic regulatory
game. Section 3.3 c haracterizes optimal regulatory and firm b eha vior. W e discuss
the effects of v arious renew able energy p olicies and targets in section 3.4. Next, in
section 3.5, w e pro ceed to apply the mo del to the situations facing Spain and German y
around 2012 to deriv e reasons for their differing b eha vior. The c hapter ends with a
conclusion.
3.2 Setup of the regulation game
Regulatory supp ort for renew able energies can b e mo deled as a game where the
regulator announces and sets supp ort lev els, while firms form exp ectations ab out the
an ticipated supp ort lev els and c ho ose to in v est or not. A dynamic regulatory game is
a useful mo del of renewable energies supp ort p olicies for the following reasons: First,
in v estmen ts in to renew able energies are capital in tensiv e up-fron t, whic h implies that
exp ectations ab out lifetime earnings formed at the in v estmen t stage define capital
costs and, th us, the required supp ort lev el (see, among others, Couture et al., 2010,
Haas et al., 2011, Ma y, 2017). Due to the dep endence of in v estmen ts on supp ort
p olicies, in v estors emphasize the imp ortance of stable regulation with out unexp ected

3.2. SETUP OF THE REGULA TION GAME 91
c hanges (Lüthi and Wüstenhagen, 2012). Only a small fraction of the costs is incurred
after the in v estmen t stage, suc h that installations will op erate (almost) indep enden t
of actual rev en ues. This matters b ecause, secondly , renew able supp ort is t ypically
paid out throughout the lifetime of the assets as supp ort p er output to incen tivize
efficien t pro ject planning and managemen t. Consequen tly , o v er the lifetime of the
assets, there is ample time for regulators to b eha v e strategically and to deviate of
their initial supp ort commitments because the assets will con tin ue generating p o w er
regardless.
The go v ernmen t acts as Nash leader, suc h that it announces a renew able energy
regime and supp ort lev el that a represen tativ e firm, as Nash follo w er, can observ e and
tak e in to accoun t for its in v estmen t decision. The firms in v est in to renew able energy
dep ending on the remuneration they exp ect.
The game mo difies the more general setup in tro duced b y Chiappinelli and Neuhoff
(2017) to tak e in to accoun t dynamic asp ects of the in teraction b et w een the go v ern-
men t and the firms, depicting the renew able energy setting in more detail. Figure
3-1 visualizes the general setup. First, the go v ernmen t announces its supp ort for
renew able energy . The rem uneration is financed as a levy on the electricit y price, to
b e paid b y all electricit y consumers, th us decreasing the demand for electricit y , as is
implemen ted in most Europ ean coun tries (Eclareon, 2017). Second, the firms c ho ose
to in v est in to renew able energy capacit y , generating renew able energy . Third, the
go v ernmen t observ es the firms’ in v estmen t decisions and sets the actual rem unera-
tion lev el. This mirrors that regulators can, if the p olicy design allo ws them to, alter
the supp ort leve l after pro ject completion, effectiv ely c hanging the rem uneration o v er
the en tire lifetime of the pro ject.
T o reflect that renew able energy supp ort is almost univ ersally paid out p er unit
of output, rather than installed capacit y , the mo del is dynamic and actions directly
affect future p erio ds. Supp ort promised in p erio d 𝑡 lasts in to the next p erio d 𝑡 + 1
and in v estmen ts undertak en in 𝑡 still reduce emissions in 𝑡 + 1 , after whic h they are
assumed to cease op erating.
W e adopt a linear direct demand function 𝑄 𝑡 where electricit y prices increase with

92 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
Regulator announces supp ort level p
Firms c ho ose in v estmen t lev el, generating x
Regulator sets actual supp ort lev el p and pa y offs are realized
Figure 3-1: Timing of the p erio d game, whic h is rep eated indefinitely
renew able energy supp ort: Without supp ort, demand is equal to 𝑎 and decreases b y 𝑏
for ev ery Euro of supp ort p er megaw att-hour (MWh). Supp ort for renew able energies
𝑝 is promised for t w o p erio ds, 1 suc h that b oth the past p erio d’s promised supp ort
𝑝 𝑡 − 1 and the presen t supp ort 𝑝 𝑡 influence d emand in the presen t.
𝑄 𝑡 = 𝑎 − 𝑏𝑝 𝑡 − 1 − 𝑏𝑝 𝑡 (3.1)
In ev ery p erio d, the go v ernmen t optimizes w elfare 𝑊 𝑡 b y setting the renew able
energy levy 𝑝 𝑡 . The regulator represen ts the in terests of electricit y customers and,
th us, cares ab out consumer surplus, comparable to the mo del setup in Salan t and
W oro c h (1992), but also cares ab out en vironmental pollution, as in Chiappinelli and
Neuhoff (2017). As sho wn in equation (3.2), p er-p erio d w elfare dep ends on the con-
sumer surplus from the consumption of electricit y (first term) and the en vironmen tal
damage caused b y the pro duction of non-renew able electricit y (second term). 2 Dam-
age dep ends on emissions from co vering the demand and are reduced b y generation
from renew able energies. These renew able energies are the sum of generation across
1 While this can easily b e increased to longer supp ort horizons, it do es not alter the nature of the
results; suc h that we stic k to tw o p erio ds for notional simplicity .
2 W e are interpreting the alternativ e to inv estments in to renewable energy as running existing
thermal plan ts, e.g. coal and gas p o w er plan ts. The a v oided costs of running these, the so-called
merit-order effects (Ketterer, 2014), implicitly damp en the costs for renew able energy supp ort. In
the n umerical application in section 3.5, w e subtract these costs from the renew ables’ supp ort costs.

3.2. SETUP OF THE REGULA TION GAME 93
all firms 𝑗 , ∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 and ∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 − 1 , 3 again reflecting that previous in v estmen ts im-
pact later p erio ds. The sum is multiplied with a p ollution parameter 𝑒 , indicating
ho w p olluting the non-renew able p o w er supply is. 4
𝑊 𝑡 = ∫︁ 𝑝 ′
𝑡
𝑝 𝑡
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒 ( 𝑄 𝑡 −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑡 −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑡 − 1 ) (3.2)
The maxim um total supp ort, at whic h electricit y demand drops to zero, is giv en
b y 𝑎
𝑏 . Consequently , the maxim um lev el for 𝑝 𝑡 is 𝑝 ′
𝑡 = 𝑎
𝑏 − 𝑝 𝑡 − 1 .
In the long-run, w elfare is the sum of future w elfare, discoun ted b y the discoun t
factor 𝛿 ∈ [0 , 1] .
𝑊 =
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 [︂ ∫︁ 𝑝 ′
𝑠
𝑝 𝑠
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒 ( 𝑄 𝑠 −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑠 −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑠 − 1 ) ]︂ (3.3)
The firms are iden tical comp etitiv e price-tak ers, represen ting the in v estmen t b e-
ha vior of renew able energy in v estors and co v ering all demand. Ev ery individual firm’s
renew able generation 𝑥 𝑖 𝑡 is very small compared to the sum of all firms’ generation
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 . F or simplicit y , the con v ex cost function 𝑐 ( 𝑥 𝑖 𝑡 ) is assumed to b e quadratic
in in v estmen ts, dep ending on some factor 𝛼 > 0 , as sho wn in equation (3.4). Costs
o ccur up-fron t and increase with deplo ymen t b ecause pro ject dev elop ers migh t only
ha v e capacities to implemen t a limited n umber of pro jects at a time and b ecause
suitable sites are scarce. Marginal op erational costs are zero.
𝑐 ( 𝑥 𝑖 𝑡 ) = 𝛼
2 𝑥 2
𝑖 𝑡 (3.4)
Eac h firm’s short-run profits 𝜋 𝑖 𝑡 are influenced b y the firm’s presen t and past re-
new able energy in v estmen ts 𝑥 𝑖 𝑡 and 𝑥 𝑖 𝑡 − 1 and the supp ort p er unit of output. The o v er-
all supp ort pa ymen ts divided b y the o v erall new generation from renew ables, 𝑝 𝑡 𝑄 𝑡
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡
,
3 Precisely , 𝑥 stands for renew able energy generation, so when discussing “inv estments”, w e refer
to “the in v estmen ts necessary to generate 𝑥 ”.
4 Not all non-renew able electricit y is equal. With increasing renew able energy shares, thermal
p o w er plan ts with higher marginal costs, e.g. gas p o wer plan ts in most of Europ e, might be replaced
first, while other plan ts are replaced later. W e abstract from this differentiation and assume one
single p ollution parameter.

94 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
represen t the supp ort p er unit of output. Stated differently , the ratio 𝑥 𝑖 𝑡
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡
𝑝 𝑡 𝑄 𝑡
indicates ho w m uc h of the ov erall supp ort for renew able energies for new generation
in p erio d 𝑡 , 𝑝 𝑡 𝑄 𝑡 , is generated b y firm 𝑖 . 5
𝜋 𝑖 𝑡 = 𝑥 𝑖 𝑡
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡
𝑝 𝑡 𝑄 𝑡 + 𝑥 𝑖 𝑡 − 1
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 − 1
𝑝 𝑡 − 1 𝑄 𝑡 − 𝑐 𝑡 (3.5)
In the long-run, firms’ profits are the sum of all future profits, discoun ted b y the
discoun t factor 𝛿 .
𝜋 𝑖 =
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 [︂ 𝑥 𝑖 𝑠
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑠
𝑝 𝑠 𝑄 𝑠 + 𝑥 𝑖 𝑠 − 1
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑠 − 1
𝑝 𝑠 − 1 𝑄 𝑠 − 𝑐 𝑖 𝑠 ]︂ (3.6)
3.3 Regulatory optima
The go v ernmen t maximizes w elfare b y setting the supp ort lev el, while the firms max-
imize profits b y c ho osing in v estmen t levels. In the commitmen t b enc hmark, the
go v ernmen t has to set exactly the lev el it has previously announced, whereas in the
absence of commitmen t, it has the abilit y to deviate. In the absence of renew able en-
ergy in v estmen ts, electricit y demand is co v ered b y con v en tional tec hnologies, leading
to prop ortional en vironmen tal damage. 6
3.3.1 Commitmen t b enc hmark
When the go v ernmen t can commit to a particular supp ort lev el 𝑝 𝑡 , it can tak e the
firms’ reaction function ∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 ( 𝑝 𝑡 ) in to accoun t for its optimization of w elfare 𝑊 .
A ccordingly , w e solv e the game b y bac kw ard induction, starting with the firms’ re-
action function. When optimizing, an y firm’s pro duction is v ery small compared to
5 T o ensure that levy paymen ts b y electricit y consumers and pa y-offs for renew able energy output
matc h exactly , indep enden t of demand fluctuations, balancing accoun ts facilitate this in ter-temp oral
exc hange, cp. for example the German “renew able energy accoun ts” (Bundestag, 2016). F or sim-
plicit y and as the managemen t of these balancing accoun ts is not our fo cus, w e abstract from them
and assume that levy pa yments and pa youts matc h exactly in every perio d.
6 W e assume that in the absence of any renew able inv estments, the go v ernmen t will alw a ys set
a supp ort level of zero, i.e. curbing demand is not an end in itself. App endix 3.7.1 sp ells out this
condition.

3.3. REGULA TOR Y OPTIMA 95
o v erall pro duction suc h that it tak es the sum of pro duction as constan t. This implies
that an y one firm’s action do es not alter the supp ort p er output, whereas all firms’
collectiv e in v estmen ts ma y w ell c hange the supp ort p er output.
Deriving an y firm’s profit function yields that its marginal costs, 𝛼𝑥 𝑖 𝑡 , m ust in
the optim um equal its marginal rev en ues, 𝑝 𝑡 ( 𝑄 𝑡 + 𝛿 𝑄 𝑡 +1 )
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡
, whic h are comp osed of the
rev en ues in in the in v estmen t p erio d and in the discoun ted subsequen t p erio d. The
optimal in v estmen ts are th us:
𝑥 *
𝑖 𝑡 = 𝑝 𝑡 ( 𝑄 𝑡 + 𝛿 𝑄 𝑡 +1 )
𝛼 ∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡
(3.7)
Therefore, the sum of renew able energy generation from new in v estmen ts is:
𝐽
∑︁
𝑗 =1
𝑥 *
𝑗 𝑡 = 𝑝 𝑡 ( 𝑄 𝑡 + 𝛿 𝑄 𝑡 +1 )
𝛼 (3.8)
Consequen tly , the regulator tak es the firms’ in v estmen t decisions in to accoun t
when setting the optimal supp ort lev el 𝑝 𝑠 whic h maximizes w elfare:
𝑊 =
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 [︂ ∫︁ 𝑝 ′
𝑠
𝑝 𝑠
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒 ( 𝑄 𝑠 − 𝑝 𝑠 ( 𝑄 𝑠 + 𝛿 𝑄 𝑠 +1 )
𝛼 − 𝑝 𝑠 − 1 ( 𝑄 𝑠 − 1 + 𝛿 𝑄 𝑠 )
𝛼 ) ]︂ (3.9)
In the optim um, the regulator s ets the supp ort lev el in ev ery p erio d suc h that on
the margin, the additional costs of an increase in the levy in terms of reduced consumer
surplus equal the en vironmen tal b enefits of the increase of the levy . The regulator is
able do this under commitmen t as it kno ws the firms’ reaction functions and p ossesses
p erfect foresigh t. Consequen tly , in this simple setup without new information during
the game, the regulator will optimally set the same levy in ev ery p erio d, 𝑝 𝑡 − 1 =
𝑝 𝑡 = 𝑝 𝑡 +1 . An y deviation w ould decrease w elfare. When increasing the levy from this
optimal levy , the costs of a higher levy w ould not b e w orth the resulting en vironmen tal
b enefits. When decreasing the levy , w elfare w ould b e lo w er due to the forsak en
en vironmen tal b enefits. 7 Imp osing the steady state yields:
7 In our setup, a p oten tially optimal alternativ e to the steady state exists: Consumer surplus is

96 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
𝑝 * = 𝑎𝛼 − 𝑏𝑒𝑐 − 𝑎𝑒 − 𝑎𝑒𝛿
𝑏 (2 𝑐 + 2 𝑐𝛿 − 3 𝑒 − 8 𝑒𝛿 − 4 𝑒𝛿 2 ) (1 + 𝛿 ) (3.10)
Ov erall, in v estmen ts are:
𝐽
∑︁
𝑗 =1
𝑥 *
𝑗 = 𝑝 * 𝑎 − 2 𝑏𝑝 *
𝛼 (1 + 𝛿 ) (3.11)
Ev ery firm in v ests:
𝑥 *
𝑖 = 𝑝 * 𝑎 − 2 𝑏𝑝 *
𝛼 ∑︀ 𝐽
𝑗 =1
(1 + 𝛿 ) (3.12)
3.3.2 Dynamic optimization: no commitmen t case
Without commitmen t, the regulator deviates from the announced supp ort lev el if that
is optimal. Whether or not it is optimal dep ends on the strategies the pla y ers are
follo wing: Under op en lo op strategies, they only consider the curren t p erio d’s pa y-
offs, while under trigger strategies, past actions impact future actions. W e explore
ho w far the commitmen t outcome can b e attained under these strategies and what
factors attainmen t relies on.
Op en lo op strategies
When the go v ernmen t cannot commit, it can observ e the firms’ inv estmen t decisions
and c ho ose to set the rem uneration lev el to the lev el that optimizes welfare, indep en-
den t of its initial announcemen t. The go v ernmen t optimizes, taking the renew able
energy output as exogenously giv en.
decreased b y the sum of the levy of the current and the previous perio d. Thus, the levy do es not
actually ha v e to b e equal in ev ery individual p erio d in order to set the marginal costs of an increase
of the levy equal to the marginal b enefits of an increase of the levy; it suffices that the total p er-
p erio d pa ymen ts are the same. The regulator could constan tly switc h b et w een a high levy in one
p erio d and a lo w levy in the next p erio d, such that the total per-p erio d levy paymen ts are the same
in all p erio ds. Ho w ev er, this decreases w elfare due to the con v exit y of costs in com bination with
linear demand and linear en vironmen tal damages. Besides, this seems unrealistic, as real-options
theory indicates that p otential in vestors w ould delay their in vestmen ts until levies are high, see e.g.
Ritzenhofen and Spinler (2016).

3.3. REGULA TOR Y OPTIMA 97
∂ 𝑊
∂ 𝑝 𝑡
=
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 [︂ ∂
∂ 𝑝 𝑡 ∫︁ 𝑝 ′
𝑠
𝑝 𝑠
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒 ∂ 𝑄 𝑠
∂ 𝑝 𝑡 ]︂ (3.13)
An y p ositiv e rem uneration lev el curbs demand, whic h reduces consumer surplus.
The only b enefits are the a v oided emissions due to the lo w er demand. Ho w ever,
w e assumed that the regulator will not set a levy only in order to curb demand,
see app endix 3.7.1. Th us, the go v ernmen t sets the supp ort lev el to zero. The firms
an ticipate this and optimally c ho ose not to in v est – a classical expropriation argumen t
(see e.g. Williamson (1975)).
Grim trigger strategies
With trigger strategies, pla y ers are able to observ e the others’ past b eha vior and
react. A grim trigger means that once the pa y ers deviate, they are punished forev er.
In our setup, only the regulator can deviate o wing to the timing of output-based
supp ort pa ymen ts. The firms can punish b y not making further in v estmen ts after the
regulator has deviated from its previously-announced supp ort. In order to sustain a
subgame p erfect Nash equilibrium, a deviation ma y not b e profitable for the regulator
at an y time. F or a detailed formal definition of suc h trigger strategies, see Chiappinelli
and Neuhoff (2017).
The regulator ev aluates the o v erall b enefits of deviating against the threat of no
new in v estmen ts in the future. T o this end, the gov ernmen t compares the w elfare of
compliance 𝑊 𝑐 to the w elfare if it deviates 𝑊 𝑑 .
𝑊 𝑐 ≥ 𝑊 𝑑 (3.14)
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 𝑊 ( 𝑝 𝑠 = 𝑝 * , 𝑥 𝑖 𝑠 = 𝑥 *
𝑖 ) ≥
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 𝑊 ( 𝑝 𝑠 = 0 , 𝑥 𝑖 𝑠 = 𝑡 = 𝑥 *
𝑖 , 𝑥 𝑖 𝑠 
= 𝑡 = 0) (3.15)
T ransforming yields as compliance condition:

98 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 2 𝑏𝑒𝑝 * +
∞
∑︁
𝑠 = 𝑡 +1
𝛿 𝑠 − 𝑡 2 𝑒
𝛼 (1 + 𝛿 ) 𝑝 * 𝑄 ≥
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 ( 𝑎𝑝 * − 𝑏𝑝 * 2 ) (3.16)
The left hand side of equation (3.16) represen ts the total discoun ted en vironmen-
tal b enefits that o ccur under compliance. Demand is reduced when the supp ort lev el
is p ositiv e, whic h o ccurs in ev ery p erio d with some en vironmen tal b enefits captured
b y 2 𝑏𝑒𝑝 * . In con trast, only future inv estmen ts are decision-relev an t b ecause the in-
v estmen ts in p erio d 𝑡 ha v e b een made regardless. Th us, the en vironmen tal b enefits
from renew able energies 2 𝑒
𝛼 (1 + 𝛿 ) 𝑝 * 𝑄 only accrue from p erio d 𝑡 + 1 on w ard. Naturally ,
past in v estmen ts’ en vironmen tal b enefits also do not pla y a role.
The righ t hand side sho ws the b enefits of deviating: consumer surplus is increased
since no more supp ort is paid. Imp ortan tly , this already b egins in p erio d 𝑡 , ev en
though new in v estmen ts ha v e p oten tially still b een triggered in p erio d 𝑡 , to whic h the
regulator is not pa ying the promised supp ort. Equiv alen tly , the regulator also do es
not pa y an y longer for generation from in v estmen ts made in p erio d 𝑡 − 1 , whic h are
also still eligible to supp ort in p erio d 𝑡 .
3.4 The role of p olicies and targets
W e analyze the effects that differen t supp ort p olicies and explicit renew able energy
deplo ymen t targets ha v e on compliance, costs, and renew able energy in v estments.
3.4.1 Time-inconsistency under differen t p olicy regimes
P olicy regimes can supp ort commitmen t equilibrium outcomes ev en though no go v ern-
men tal action is able to rule out deviations altogether. Y et, some p olicy framew orks
allo w for easier c hanges to announced supp ort than others. An example that is eas-
ily in tegrated in to the mo del are prohibitiv ely high costs in case of “full” deviations:
Bey ond some threshold, e.g. a deviation on more than a certain share (1 − 𝛾 ) with

3.4. THE R OLE OF POLICIES AND T AR GETS 99
𝛾 ∈ [0 , 1] of commitmen ts, firms in other sectors will also fear deviations b y the
regulator, whic h out w eighs the gains from full deviations. Dep ending on the p olicy
regime, this threshold can differ. The fear of con tamination of other sectors’ in v est-
men ts migh t b e larger when the initial promise of the go v ernmen t is stronger, as the
necessary p olitical and legal barriers are harder to o v ercome. If the go v ernmen t is
willing to get o v er large barriers to deviate from its renew able energy commitmen ts, it
migh t app ear more lik ely to do so in other sectors as w ell. In con trast, when supp ort
lev els are not clearly defined, deviations are harder to detect and understand, and
deviations migh t b e less lik ely to spread to other sectors.
The mo del is easily extended to incorp orate limited deviations. Since the pa y-
offs under compliance remain unaltered, the optimal supp ort lev el 𝑝 * and the optimal
in v estmen t lev el 𝑥 *
𝑖 remain the same. Ho w ev er, the left hand side of the compliance
inequalit y (3.16) – the en vironmental benefits of compliance – decrease. Whereas
the second term on the left hand side of equation (3.17), the en vironmen tal b enefits
of new in v estmen ts, do es not c hange, the en vironmen tal b enefits from a decrease
in demand actually decrease with limited deviations compared to full deviations, as
sho wn in equation (3.17). Th e simple reason is that in the first t w o p erio ds after the
deviation, the levy for commitmen ts made un til the time of deviation remains at 𝛾 𝑝 *
rather than falling to zero. As of the second p erio d after deviation, the levy is zero
once again and the b enefits accrue just as with full deviations. App endix 3.7.2 details
the calculations.
Similarly , the b enefits of deviating in terms of reduced levy pa ymen ts decrease
compared to full deviations. As of the second p erio d after a deviation, the pa y-offs are
the same as under full deviations. After deviating, some levy 𝛾 𝑝 * remains, rendering
deviations less attractiv e.
In total, compliance b ecomes more attractiv e when deviations are limited than
when regulators can fully deviate. Demand is depressed after the deviation, leading
to some en vironmen tal b enefits on its o wn, but larger negativ e impacts on consumer
surplus (whic h holds indep enden tly of the levy 𝑝 and the remaining share 𝛾 by our
assumption of negativ e impacts on w elfare in the absence of in vestmen ts; see app endix

100 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
3.7.1). The v alue of the left hand side decreases l ess than the v alue of the righ t hand
side b et w een equations (3.16) and (3.17).
𝑒𝑏𝛾 𝑝 * (2 + 𝛿 ) +
∞
∑︁
𝑠 = 𝑡 +2
𝛿 𝑠 − 𝑡 2 𝑏𝑒𝑝 * +
∞
∑︁
𝑠 = 𝑡 +1
𝛿 𝑠 − 𝑡 2 𝑒
𝛼 (1 + 𝛿 ) 𝑝 * 𝑄 ≥
𝛾 (1 + 𝛿 ) 𝑎𝑝 * − 𝛾 2 (1 + 𝛿
2 𝑏 ) 𝑝 * 2 +
∞
∑︁
𝑠 = 𝑡 +2
𝛿 𝑠 − 𝑡 ( 𝑎𝑝 * − 𝑏𝑝 * 2 ) (3.17)
W e differen tiate b et w een three p olicy framew orks: Those where deviations are
difficult for go v ernmen ts to implemen t as they m ust also c hange the constitution,
framew orks where a – simpler – c hange of la w suffices, and, lastly , suc h framew orks
where only some rules need to b e adjusted in order to deviate.
First, there are p olicy framew orks where the renew able energy p olicy itself stresses
that it represen ts a rem uneration stream that will not and cannot b e altered o v er time
and where, additionally , the constitution pro vides in v estmen t securit y through strong
grandfathering rules. In order to c hange the constitution, whic h explicitly protects
existing assets from legal c hanges, usually a qualified ma jorit y is required in parlia-
men t, p osing a high threshold for retrosp ectiv e c hanges of the renew able energy p olicy
(Jak ob and Brunner, 2014). This can b e the case b oth for feed-in tariffs and feed-in
premia. Ho w ev er, under feed-in premia, balancing costs remain with the in v estors,
rendering the rules around balancing cost assignmen t prone to time-inconsistency
issues (Neuhoff et al., 2016). Th us, with constitutional righ ts and resp ectiv e p oli-
cies, no full deviations are p ossible and 𝛾 in equation (3.17) is increased, rendering
compliance more attractiv e.
Second, p olicy framew orks pro vide in v estors with an in v estmen t en vironmen t
where regulators legally guaran tee some supp ort, but this supp ort is not bac k ed b y
constitutional grandfathering righ ts. Regulators can explicitly guaran tee a sp ecific
rem uneration lev el or a sense can prev ail that o v erall profitabilit y of pro jects is guar-
an teed, but not sp ecific supp ort lev els. Without constitutional grandfathering rights
and sp ecified supp ort lev els, 𝛾 in equation (3.17) is lo w er than under the first set of

3.4. THE R OLE OF POLICIES AND T AR GETS 101
p olicies.
Based on suc h argumen ts, Spanish in vestors who had in v ested b efore 2010 lost
their case against the Spanish go v ernmen t that had retrosp ectiv ely cut their supp ort
pa ymen ts. The Spanish supreme court judges argued that the in v estors w ere en titled
to profitabilit y of their in v estmen ts, but not necessarily the exact lev el they had
initially b een promised. In particular, they stated that the adjustmen ts w ere in line
with “legitimate exp ectations” (El P aís, 2014). This has since b een explicitly co dified
in the Spanish rem uneration sc heme and in v estors are guaran teed a certain markup
o v er the returns of go v ernmen tal long-term b onds (Spanish Ministry of Industry and
Energy and T ourism, 2014). As early as b et w een 2004 and 2007, the p olicy regime
explicitly adjusted supp ort levels based on the wholesale electricit y price (Spanish
Ministry of Economic Affairs, 2004), indicating a more subtle promise of supp ort
stabilit y in comparison to explicit long-term stabilit y of sp ecific supp ort lev els.
Third, p olicy regimes lik e tradable green certificates promise rem uneration streams
that can b e adjusted o v er time without retrosp ectiv e legal c hanges. Under green cer-
tificate sc hemes, the n um b er of certificates in the system defines the v alue of these
certificates and, th us, the rem uneration lev els that renew able energies receiv e. Regula-
tors can dev alue certificates b y flo o ding the mark et, e.g. b y pro viding new certificates
to foster new tec hnologies. Alternativ ely , the regulator can decrease the demand for
certificates b y lo w ering (or not increasing) the n um b er of certificates that p o w er sup-
pliers need to obtain, as w as the case in P oland b et w een 2010 and 2012 (Sk arżyński,
2016) and in Romania in 2017 (Business Review, 2016). In an y of these cases, the
an ticipated rem uneration lev el is lo w ered during the lifetime of pro jects without the
need for explicit retrosp ective legal c hanges. Therefore, 𝛾 in equation (3.17) is ev en
lo w er compared to the other discussed p olicies.
These three p olicy framew orks hold implications for the o verall co sts of renew able
energy deplo ymen t. When firms ha ve imperfect foresigh t, the in v estmen t costs 𝛼
increase when retrosp ectiv e c hanges are easier to implemen t. In turn, this renders
retrosp ectiv e c hanges more lik ely , whic h increases the in v estmen t costs ev en further,
creating a vicious cycle. This implies that c eteris p aribus p olicies lea ving more space

102 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
for regulators to deviate lead to higher costs of deplo ymen t. 8
3.4.2 T argets as commitmen t devices
National targets monitored or enforced b y a supranational en tit y or the public can
impact go v ernmen tal b eha vior and can act as commitmen t devices. They do not
directly affect the p oss ibilit y of deviations, but they increase the costs at stak e when
considering deviations. T argets from a supranational lev el cannot usually b e c hanged
through c hanges of la w at the national lev el. As Jak ob and Brunner (2014) outline,
suc h costs can b e in terms of reputation, describ ed b y Barro and Gordon (1983), or
financially .
The commitmen t b enc hmark c hanges. The firms’ profit functions are not touc hed
in the first stage and the reaction functions remain the same as b efore. W elfare under
commitmen t is altered, incorp orating the p oten tial fine from deviation if the target
expansion ¯ 𝑥 is not reac hed. The fine 𝑓 is m ultiplied with the deviation from the
in v estmen t tra jectory target, ¯ 𝑥 .
𝑊 𝑡 = ∫︁ 𝑝 ′
𝑡
𝑝 𝑡
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒 ( 𝑄 𝑡 −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑡 ( 𝑝 ) −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑡 − 1 ( 𝑝 )) − 𝑓 [ ¯ 𝑥 −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑡 ( 𝑝 ) −
𝐽
∑︁
𝑗 =1
𝑥 𝑗 𝑡 − 1 ( 𝑝 )]
(3.18)
with 𝑓 = 0 if ∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 ( 𝑝 ) + ∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 − 1 ( 𝑝 ) ≥ ¯ 𝑥 . As long as the renew able energy
target is not reac hed, the fine 𝑓 w orks similarly to the increased en vironmen tal b en-
efits of renew able energies, with the only exception that reducing demand do es not
inheren tly decrease p oten tial fines. 9 Solving for the optimal levy in the steady state
yields as optimal levy:
𝑝 *
𝑡𝑎𝑟 𝑔 𝑒𝑡 = 𝑎𝛼 − 𝑏𝑒𝑐 − 𝑎 ( 𝑒 + 𝑓 )(1 + 𝛿 )
𝑏 [2 𝑐 (1 + 𝛿 ) − ( 𝑒 + 𝑓 )(3 + 8 𝛿 + 4 𝛿 2 )] (1 + 𝛿 ) (3.19)
8 Citizen o wnership is another means to alleviate time-inconsistency issues. When the regula-
tor represen ts the interests of electricit y consumers and those consumers also constitute electricity
pro ducers, the regulator also weighs producer surplus, reducing time-inconsistency issues.
9 When targets are set relativ e to demand, then the additional effect o ccurs that reducing demand
lo w ers the renew able energy target, whic h is not captured b y our mo del.

3.4. THE R OLE OF POLICIES AND T AR GETS 103
Therefore, the levy under commitmen t is larger if the renew able energy target
is not already reac hed without target. Similarly , based on the same reaction func-
tion as b efore, the in v estmen t lev el under commitmen t increases. If the renew able
target is not y et reac hed without target, but the optimal levy and in v estmen t lev els
with target yield an expansion b ey ond the renew able target, then the corner solution
∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 ( 𝑝 ) + ∑︀ 𝐽
𝑗 =1 𝑥 𝑗 𝑡 − 1 ( 𝑝 ) = ¯ 𝑥 maximizes welfare.
As a result, p oten tial costs of not-ac hiev emen t are added to the left hand side of
equation (3.16) if the expansion target ¯ 𝑥 is not reac hed. Inequalit y (3.20) pro vides
the compliance condition. The second term on the left hand side, the en vironmen tal
b enefits of renewable energy in v estmen ts, is increased b y the p oten tial b enefits of
a v oided fines.
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 2 𝑏𝑒 +
∞
∑︁
𝑠 = 𝑡 +1
𝛿 𝑠 − 𝑡 2( 𝑒 + 𝑓 )
𝛼 (1 + 𝛿 ) 𝑄 ≥
∞
∑︁
𝑠 = 𝑡
𝛿 𝑠 − 𝑡 ( 𝑎𝑝 *
𝑡𝑎𝑟 𝑔 𝑒𝑡 − 𝑏𝑝 * 2
𝑡𝑎𝑟 𝑔 𝑒𝑡 ) (3.20)
Therefore, with target, compliance b ecomes more lik ely than without targets due to
the increased b enefits of renew able energies if the target is high enough. With a lo w
target that is o v er-ac hiev ed in an y case, the target do es not increase the levy , the
in v estmen t lev el, or the attractiv eness of compliance.
The Europ ean Union’s renewable energy deplo ymen t targets for 2020 are p oten-
tially relev an t for national decision-making. In 2009, the Europ ean Union in tro duced
the Renew able Directiv e. It included binding targets for the share of energy stem-
ming from renew able energies b oth at the Europ ean level and at the individual coun-
try lev el. It sp ecifies the share out of total energy use, incorp orating the transp ort,
heating, co oling and the electricit y sectors.
The Europ ean targets are binding at the national level. These are based on
eac h coun try’s initial share of renew able energy in 2005 and their wealth, requiring
stronger actions from w ealthier mem b er states. In 2005, Malta w as the coun try with
the lo w est share of renew able energy , 0.2 p ercen t, whic h it has to increase to ten
p ercen t b y 2020, whereas the coun try with the second-lo w est share, the UK at 1.4

104 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
p ercen t, m ust increase its share to 15 p ercen t. On the other hand, Sw eden with the
highest initial share of 40.6 p ercen t, has to increase it to 49 p ercen t and Latvia with
the second-highest share of 32.3 p ercen t needs to reac h 40 p ercen t. Bet w een 2009
and 2020, there are additional non-binding targets, indicating whether coun tries are
on trac k to reac hing their 2020 targets.
These national targets influence the regulatory incen tiv es for compliance as they
migh t b e fined b y the Europ ean Commission. Alternativ ely , coun tries missing their
targets can directly pa y other EU coun tries that o v er-ac hiev e their targets for sta-
tistical transfers of renew able energies, of whic h the first deals w ere closed in 2017,
tec hnically transferring renew able energy pro duction from Lith uania and Estonia to
Luxem b ourg (Europ ean Commission, 2017a,b). Equiv alen tly , this implies a direct
financial cost for coun tries that do not ac hiev e their targets through their o wn renew-
able energy generation.
The 2020 targets only function as commitmen t devices when coun tries do not
p ossess sufficien t alternativ es. The sustainabilit y of biomass matters less in some
coun tries (Rataro v a et al., 2012). Using biomass seems to ha v e b een c heap er than
wind and solar p o w er in man y Eastern Europ ean coun tries. Consequen tly , on the one
hand, the Europ ean targets made biomass in v estments more attractiv e. On the other
hand, they did not render compliance more lik ely for wind and solar p o w er in these
coun tries, as they can reac h their targets more or less regardless of wind and solar
p o w er deplo ymen t.
Figure 3-2 sho ws the gro wth in renew able energy generation since the baseline
y ear of 2005 in Bulgaria. 10 Starting at an initial share of 9.4 p ercent of renew able
energies, it sto o d at 18.2 p ercen t in 2015, an increase of 8.8 p ercen tage p oin ts. The
share of biomass has gro wn significan tly and together with a small uptak e in h ydro
p o w er generation almost suffices to fulfill the Bulgarian 2020 target of 16 p ercen t
(an increase of 6.6 p ercen tage p oin ts since 2005). 11 Th us, targets do not function as
10 Assuming that the growth in renew able energies for heating and co oling came from biomass.
11 Still, some wind and solar p o wer ha ve been installed. Ho w ev er, as no sustained gro wth in
their capacities is required to reac h the 2020 target, compliance is not attractiv e to the Bulgarian
go v ernmen t. In 2013, it retrosp ectively in tro duced a 20 p ercent rev enue tax for wind and solar

3.4. THE R OLE OF POLICIES AND T AR GETS 105
Figure 3-2: New generation from renew able energies since 2005 in Bulgaria, based
on Eurostat (2017b)
commitmen t devices when alternativ e, preferred tec hnologies exist or if targets are
reac hed ahead of time.
In most W estern Europ ean countries, electricit y from wind and solar p o w er are
the main viable renew able energy tec hnologies to reac h the 2020 targets. 12 Figure
3-3 depicts the increase in renew able energy in German y since 2005. 13 Ev en though
biomass and other renew able energies, particularly energy from m unicipal waste, also
pla y a role, new wind and solar p o w er is considerably more prominen t than it is in
Bulgaria.
Figure 3-4 sho ws the gro wth in generation from renew able energies in Spain
since 2005. Starting at 8.4 p ercen t in 2005, the coun try initially strongly increased
its renew able energy share. How ev er, after retrosp ectiv e cuts b et w een 2010 and 2013
and the passing of a moratorium for new installations, the renew able energy share
stagnated, suc h that the 20 p ercen t target for 2020 is more difficult to ac hiev e.
p o w er, y et not for biomass installations (F ouquet and Nysten, 2015). In parallel, it announced a
moratorium for all new wind and solar p ow er installations. The national constitutional court has
since ruled that the retrosp ective rev enue tax w as unconstitutional (F ouquet and Nysten, 2015).
12 Finland and Sweden represen t interesting exceptions as they ha v e strongly increased their shares
of biomass fuels in the transp ort sector.
13 Approximating the generation and installation data from BSW-Solar (2016), w e assume that
t w o-thirds of the solar thermal pro duction of 2015 was installed after 2005, in roughly equal ann ual
amoun ts. The remainder of renew able energy growth in the heating and cooling sector is assumed
to come from biomass.

106 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
Figure 3-3: New generation from renew able energies since 2005 in German y , based
on Eurostat (2017b)
Coun tries that can only reac h their targets through long-term fa v orable in v est-
men t en vironmen ts for sp ecific tec hnologies are more lik ely to sta y committed to these
tec hnologies. If they ha v e viable alternativ e tec hnologies or reac h their targets ahead
of time, the p oten tial fines 𝑓 in equation (3.4.2) diminish. Th us, commitmen ts are
more credible in coun tries that are endo w ed with the p oten tial for few alternativ e
tec hnologies and that lag b ehind enforceable, supranational targets.
Figure 3-4: New generation from renew able energies since 2005 in Spain, based on
Eurostat (2017b)

3.5. WHY DID SP AIN DEVIA TE WHEN GERMANY DID NOT? 107
3.5 Wh y did Spain deviate when German y did not?
W e ev aluate the time-inconsistency mo del for 2012 b y n umerically mo deling the exam-
ples of Spain and German y as these coun tries w ere fron trunners in renew able energy ,
but only Spain deviated. German y in 2012 w as the global n um b er three in terms of
installed wind p o w er capacit y and n um b er one for solar, Spain was num b er four for
wind p o w er and n um b er fiv e for solar p o w er (IRENA, 2017a). With its renew able
energy pa ymen ts at ab out e 34 p er MWh in 2012 (Comisión Nacional de Energia,
2012), Spain conducted retrosp ectiv e c hanges b et w een 2010 and 2013. Germany did
not, ev en though it had also exp erienced unpreceden ted gro wth in PV installations,
increasing the levy three-fold from e 11.3 p er MWh in 2009 to e 36.9 p er MWh in
2012 (and subsequen tly to e 52.7 p er MWh in 2013) (Bundesnetzagen tur, 2017). Wh y
did and could Spain tak e these measures, whereas German y did not?
As this analysis fo cuses on time-inconsistency , all past in v estmen ts in to renew able
energies are tak en for gran ted and not accoun ted for in terms of en vironmen tal b enefits
b ecause they accrue in an y case. This is also where p oten tial time-inconsistency
arises from: As the in v estmen ts b efore 2012 ha ve already been made, only their
disadv an tages, namely their costs, p ersist and matter for the decision-mak er.
3.5.1 P arameters in 2012
In 2012, Spain had 22.8 GW of wind p o w er and 6.6 GW of solar capacit y , highligh ting
a fo cus on (then c heap er) wind p o w er. German y had 31.3 GW of wind p o w er capacit y ,
o wing to rather constan t gro wth in wind p o w er capacities, and rather erratic gro wth
in PV b et w een 2009 and 2012, when 22 GW of the PV total of 32.8 GW w ere installed
(IRENA, 2017a).
The p olicy framew ork sets the bac kdrop against whic h in v estors in v est and sp ec-
ifies ho w easily retrosp ectiv e c hanges can b e conducted. After some p olicy c hanges
in the 2000s, Spain had a feed-in tariff for wind p o w er, photo v oltaics and concen-
trated solar p o w er. Bet w een 2004 and 2007, ins tallations w ere not gran ted sp ecific

108 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
rem uneration lev els, but m ultiples of the p o w er price. This p olicy , based on a more
general sense of profitabilit y as laid out in section 3.4.1, allo w ed some deviations.
Spain cut ab out 25 p ercen t of its renew able energy pa ymen ts from a total of e 9.1
billion to e 6.6 billion (Comisión Nacional de los Mercados y la Comp etencia, 2014,
2015). Since then, there ha v e b een conflicting court rulings. As detailed in section
3.4.1, the Spanish Supreme Court decided that Spanish in vestors w ere not eligible
for comp ensation. In ternational in v estors hav e b een deplo ying the Energy Charter
T reat y , whic h also led to differen t outcomes: On the one hand, one firm successfully
argued that its rev en ues fell b y t w o-thirds and it w as a w arded comp ensation from
the Spanish go v ernmen t. On the other hand, a similar case of another firm that had
in v ested somewhat later w as dropp ed on the grounds that “no reasonable in v estor
could ha v e the exp ectation that this framew ork w ould not b e mo dified in the future
and w ould remain unc hanged" (Stibb e, 2017). Therefore, w e assume that the Span-
ish regulators w ere able to cut around t w en t y p ercen t of supp ort pa ymen ts, setting
𝛾 = 0 . 8 in equation (3.17), sligh tly less than the actual 25 p ercen t, whic h, at least in
some cases, seems to ha v e infringed on in v estors’ legal righ ts.
German y had a feed-in tariff that allo w ed firms the option to shift to a feed-in
premium. As describ ed in section 3.4.1, the fo cus of the legislation and the consti-
tutional grandfathering righ ts w as on pro viding stable supp ort. This enables only
small deviations. In order to compare factors other than the p olicy regime differences
b et w een Spain and German y , w e set 𝛾 = 0 . 8 for German y as w ell, noting that this is
an upp er b ound of the attractiv eness of deviating.
The regulator compares the curren t and future b enefits and costs of compliance
and deviation based on equation (3.17). The pa y outs are calculated for all future
p erio ds un til 2050, summing up the discoun ted w elfare of the individual y ears.
Costs of renew able energy
W e need to kno w ho w renew able energy generation 𝑥 , costs 𝑐 , p o w er d emand 𝑄 , and
the renew able energy supp ort 𝑝 dev elop in the future under compliance. W e use a
detailed mo del created by Ök o-Institut (2017) that pro vides estimates for the German

3.5. WHY DID SP AIN DEVIA TE WHEN GERMANY DID NOT? 109
renew able energy levy , whic h uses installation tra jectories, p o w er demand, and costs
as inputs. In order to calculate the levy under compliance for Spain, w e calculate ho w
large the levy w ould ha v e b een without deviation and add appro ximated extra costs
of new renew ables installed after 2012 under compliance. Details of the calculations
can b e found in app endix 3.7.3.
Discoun t factor
The discoun t factor 𝛿 determines the relativ e w eigh t of curren t and future p erio ds and
affects time-(in)consisten t b eha vior to a large exten t. Spain w as at the heigh t of the
financial crisis in 2012. The go v ernmen t passed austerit y measures in man y sectors of
the econom y . Whereas the go v ernmen t previously filled the utility companies’ tariff
deficit, it was sev erely constrained to do so. W e use the a v erage Spanish ten-year
go v ernmen tal b ond rate, a standard indicator of the regulatory discoun t rate of 2012,
whic h w as 5.9 p ercen t (Eurostat, 2017a). In German y , this rate w as considerably
lo w er at 1.5 p ercen t (Eurostat, 2017a).
Demand
In line with the mo del setup, w e assume that demand Q is linear. W e need to assume
v alues for 𝑎 and 𝑏 . F or b oth coun tries, w e assume a demand slop e of 𝑏 = 30 MWh 2 / e .
This is based on long-run inelastic demand elasticities of .16 in German y and .3 in
Spain, obtained from Madlener et al. (2011). F or example, for German y in 2012, this
yields that a levy of e 36 p er MWh and at an exemplary household electricit y price
without levy of e 230 p er MWh reduces demand b y ab out 2.5 p ercen t. Based on this,
w e can calculate the increase in demand after a deviation, i.e. with a lo w ered levy .
W e use the same demand slop e for Spain as for German y b ecause Spanish demand
is lo w er, but the demand elasticit y is almost t wice as high. F or Spain, this implies
that the Spanish levy of e 34 p er MWh decreases demand without levy b y ab out 4.3
p ercen t. Moreo v er, based on realized demand v olumes and the demand elasticities,
w e deriv e 𝑎 , the demand without an y levy . F or German y , this demand in 2017 w as

110 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
ab out 361 TWh, 14 whereas it sto o d at 239 TWh in Spain. Details on the calculations
can b e found in app endix 3.7.4.
W e treat the costs of renew able energies as levy in Spain as w ell, ev en though
this is only partially correct: Utilit y companies b ore the costs, but w ere only partially
reim bursed through customers’ electricit y bills. They w ere stuc k with a considerable
c h unk of the costs. This accum ulated in a tariff deficit, whic h accum ulated to e 30
billion (Linden et al., 2014). These costs w ere ev en tually securitized and offered on the
financial mark et. Ultimately , these costs are b orne b y electricity consumers (Reuters,
2010), suc h that the link b et w een costs of new in v estmen ts and levy is less direct than
in German y , but nev ertheless exists.
En vironmen tal b enefits
The en vironmen tal b enefits 𝑒 from renew able energies (and to a m uc h smaller exten t
from the reduction in demand) are the main factor driving and justifying renew able
energy supp ort. F or b oth coun tries, w e apply a shado w carb on price of e 50 p er ton
of CO 2 . This b y far exceeds the observ ed emission prices of the Europ ean emission
trading sc heme, for whic h man y authors argue that they insufficien tly reflect the true
costs of p ollution, e.g. Grubb (2012) and Edenhofer et al. (2017).
The carb on price is then m ultiplied with the carb on in tensit y for eac h coun try .
In Spain, the a v erage carb on in tensit y in 2013 sto o d at 303 grams of carb on dio xide
equiv alen ts p er MWh (Moro and Lonza, 2017), yielding 𝑒 = 15 . 15 e /MWh, whic h
w e apply for ev ery p erio d after 2012. In German y , the in tensit y was 567 grams p er
ton (Moro and Lonza, 2017), implying that the en vironmen tal b enefits of reduced
con v en tional generation of 𝑒 = 28 . 35 e /MWh exceed S pain’s.
In v estmen t v olumes after 2012
In Spain, w e observ e a stand-still follo wing the deviation and the sim ultaneously-
in tro duced moratorium. Th us, w e do not kno w what installation v olume a regulator
14 In addition, there are around 150 TWh of industry demand and self-consumption of electricit y
that are mostly exempted from the renew able levy .

3.5. WHY DID SP AIN DEVIA TE WHEN GERMANY DID NOT? 111
Figure 3-5: Driv ers of the Spanish deviation. P ositiv e v alues indicate that compli-
ance is (rendered) more attractiv e, negativ e v alues that deviating is (rendered) more
attractiv e.
w ould ha v e assumed. W e choose 1500 MW for wind p o w er, 750 MW for photo v oltaics,
and no new concen trated solar p o w er. F or German y , w e use the realized installation
v olumes for 2013-2016, ev en though those, particularly the PV b o om b et w een 2010
and 2012, certainly w ere not exactly exp ected.
T argets
As detailed in section 3.4.2, Spain and German y w ere on a rather similar trac k to w ard
reac hing their 2020 obligations in 2012. Spain’s renew able energy share w as 14.3
p ercen t in 2012, well on its w a y to w ard its target of 20 p ercen t b y 2020 (Eurostat,
2018). German y’s share in 2012 w as 12.1 p ercen t, similarly far on its path to reac hing
18 p ercen t b y 2020 (Eurostat, 2018).
3.5.2 Results
In Spain, deviation w as more attractiv e than compliance b y ab out e 2 billion, whereas
in German y compliance w as more attractiv e b y ab out e 10 billion. Figure 3-5 visu-
alizes the effects of the main driv ers: Spain’s higher discoun ting of future p erio ds,

112 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
Figure 3-6: Spanish renew able energy levy , where compliance includes b enefits of
the merit-order effect of renew able energies installed after 2012
German y’s relativ ely dirt y fleet of con v en tional p o w er plan ts, and Spain’s higher elec-
tricit y prices implying lo w er extra costs for renewable energies.
The higher Spanish discoun t factor is able to explain a significan t part of the dif-
ference b et w een German y and Spain. Had Germany c onducted the same discoun ting
as Spain, deviating w ould ha v e b een more attractiv e also there. The higher discoun t
rate w ould ha v e made deviating e 45 billion more promising. The high Spanish dis-
coun t factor, for example, implies that pa y-offs in 2020 only coun t 60 p ercen t of the
pa y-offs in 2012, whereas in German y , 2020-pa y offs still coun t 86 p ercen t as m uc h.
The dirt y German thermal p o w er plan t fleet turns out as v ery relev an t as w ell.
German y with the Spanish thermal p o w er plan t fleet w ould ha v e had large incen tiv es
to deviate, as deviating w ould ha v e b een e 27 billion more attractiv e than with the
German p o w er plan t. This also implies that Spain w ould not ha v e deviated, had it
p ossessed the dirty German p o w er plan t fleet.
The extra costs of renew ables are considerably lo w er in Spain, almost en tirely
driv en b y the higher wholesale electricit y price. With the lo w Spanish extra costs
of renew ables, compliance pa y-offs w ould ha v e b een around e 60 billion higher in
German y . Figures 3-6 and 3-7 sho w the renewable energy levies in Spain and German y
under compliance and deviation, also taking in to accoun t the induced merit-order of

3.5. WHY DID SP AIN DEVIA TE WHEN GERMANY DID NOT? 113
Figure 3-7: German renew able energy levy , where compliance includes b enefits of
the merit-order effect of renew able energies installed after 2012
new installations. In Spain, the levy after deviating is the actual Spanish levy , whic h
w e can observ e b et w een 2012 and 2017. The levy under compliance lies higher since
on the one hand, costs ha v e not b een cut b y 20 p ercen t in 2012 and, on the other
hand, b ecause new installations after 2012 are supp orted. The levy do es not increase
an ymore after 2013 ev en under compliance. The reason is the Spanish wholesale
electricit y price, whic h sto o d at e 44 p er MWh in 2012 and increased to ab out e 51
p er MWh in 2017 (OMIE, 2018), reducing supp ort costs as the difference b et w een
p o w er price lev el and supp ort lev el decreases. In German y , the opp osite happ ened.
P o w er prices declined strongly from e 55 p er MWh in 2012 to around e 30 p er MWh
in 2017 and the levy for new in v estmen ts, despite their relativ ely lo w costs, increases
somewhat un til the early 2020s and only falls thereafter. Therefore, the extra costs for
renew able energies, i.e. the difference b et w een the costs of new installations and the
p o w er price, is m uc h smaller in S pain, rendering compliance more attractiv e there.
Due to the high Spanish price lev el and the falling costs of renew able energies,
renew able energies b ecome cost-comp etitiv e when considering their en vironmen tal
b enefits as early as 2020 for wind p o w er and 2029 for solar p o w er. The date for
solar p o w er is later since the costs consist of a com bination of utilit y and small-
scale installations, where utilit y-scale solar b ecomes c heap er than the p o w er price

114 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
b eforehand already . In German y , p o w er prices are lo w er but en vironmen tal b enefits
are larger. Wind p o w er is cost-comp etitiv e in 2020 and the mix of utilit y- and small-
scale solar p ow er in 2027. 15
When adding a fine for breac hing the coun tries’ 2020 targets, b oth coun tries b e-
come more lik ely to comply , but the effect is considerably larger in German y . Through
German y’s lo w discoun t rate, the p oten tial fines – only starting in 2020 – w eigh 25
p ercen t hea vier than in Spain. While the pa y-offs after 2020 represen t almost three
quarters of all pa y-offs in German y , they only represen t 58 p ercen t in Spain.
3.6 Conclusion
Time inconsistency can arise for renew able energy in v estmen ts and deter in v estmen ts.
In ligh t of large in v estmen t needs, it is crucial that p olicy-mak ers address time incon-
sistency issues through p olicy framew orks.
W e dev elop a dynamic regulatory game where the regulator optimizes w elfare
b y announcing and setting renew able energy supp ort, while firms in v est in renew-
able energies. While, with op en lo op strategies, the regulator alw a ys deviates, trigger
strategies can induce compliance if en vironmen tal b enefits of future in v estmen ts out-
w eigh the costs of old and new in v estmen ts. Go v ernmen tal b eha vior, th us, hinges on
the en vironmen tal b enefits of future in v estmen ts. If the exp ected renew able energy
output and its en vironmen tal pa y-offs are sufficien tly large and firms do not in v est
after regulatory deviations, go v ernmen ts do not deviate from their announced p olicies.
Some p olicies mak e it easier for the regulator to deviate, whereas others tie the
hands of the regulator more tigh tly . Firstly , sliding premia and feed-in tariffs that
stress sp ecific supp ort lev els together with grandfathering rules guaran teed b y the
constitution lea v e the least space for regulatory deviations. Secondly , sliding premia
and feed-in tariffs with a more v ague exp ectation of general profitabilit y based on
certain p o w er mark et c haracteristics, lik e the wholesale electricit y price, giv e the
15 Assuming that due to simultaneit y of supply , renew able energies pro duce at only 80 p ercen t of
the wholesale p ow er price.

3.6. CONCLUSION 115
regulator some flexibilit y with resp ect to rem uneration lev els. Through legal c hanges,
rem uneration lev els can b e adjusted to a certain exten t. Thirdly , green certificate
sc hemes pro vide regulators with the abilit y to adjust supp ort lev els ex-p ost more
easily still. The rules of the system, lik e the n um b er of certificates for new installations
or the obligations for electricit y retailers, can b e adjusted relativ ely easily without
explicitly in terfering with firms’ prop ert y righ ts.
Moreo v er, w e sho w that national, binding deplo ymen t targets stemming from a
supranational en tit y lik e the EU can mak e compliance more attractiv e for go v ern-
men ts as their stak es are increased. Ho w ev er, w e also demonstrate that this holds
exclusiv ely when only limited renew able resources are a v ailable to reac h those targets
as go v ernmen ts can otherwise simply shift the fo cus to other renewables.
In a n umerical application, we iden tify the reasons wh y Spain conducted retro-
sp ectiv e cuts to its renew able energy supp ort, while German y did not. The mo del
suggests that on the one hand, the extra costs of renew able energies w ere actually
considerably lo w er in Spain due to the higher wholesale electricit y price, rendering
compliance more attractiv e in Spain. Ho w ev er, on the other hand, this is out w eighed
b y the dirtier German con v en tional p o w er plan ts, whic h increase the en vironmen tal
b enefits of renew able energies in German y . Most imp ortan tly , the larger m y opia of
the Spanish regulator, caused b y high discoun ting during the financial crisis of future
b enefits of sustained renew able energy deplo ymen t, rendered deviating more attrac-
tiv e. These factors w ere com bined with an enabling p olicy regime that left some space
for retrosp ectiv e c hanges.
Questions remain ho w to mak e compliance more attractiv e for regulators. One
approac h migh t b e Con tracts for Differences – existing in the UK – where firms ha v e to
pa y bac k the regulator when p o w er prices lie ab o v e supp ort lev els. If the general p o w er
price lev el increases while costs of renew ables decrease, regulators ha v e incen tiv es
to k eep renew ables inside supp ort sc hemes and deviating b ecomes less attractiv e.
A dditionally , the effects of differing discoun t rates b et w een the regulator and firms
migh t extend the existing analysis and allo w for in teresting scenario analyses.

116 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
3.7 App endix
3.7.1 Levy condition
W e assume that in the absence of in v estmen ts in to renew able energies, the regulator
optimally sets the levy to zero since an y p ositive support lev el w ould decrease w elfare.
The rational is that suc h additional levies on the electricit y price migh t w ell b e in
place, but they exist indep enden tly of the renew able energy levy . A simple example
is an energy efficiency levy , whic h seeks to curb demand, that is implemen ted inde-
p enden tly of the renew able energy levy . Ho w ev er, the renew able energy levy still also
curbs demand, but the regulator will only set a p ositiv e lev el if the in v estmen ts into
renew able energies “are w orth it”.
This assumption means that an y increase in the levy 𝑝 𝑡 that is not accompanied
b y an increase in the in v estmen t lev el at some p oin t has a negativ e impact on w elfare.
As this m ust hold in ev ery individual p erio d, it also holds in the (discoun ted) sum of
p erio d pa y-offs. W e assume the follo wing holds in ev ery p erio d 𝑡 :
∂ 𝑊 𝑡
∂ 𝑝 𝑡
| ( ∂ 𝑥
∂ 𝑝 𝑡
= 0) = ∂
∂ 𝑝 𝑡 ∫︁ 𝑝 ′
𝑡
𝑝 𝑡
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒 ∂ 𝑄 𝑠
∂ 𝑝 𝑡
< 0 (3.21)
It follo ws that without in v estments in to renew ables, curbing demand decreases
consumer surplus more than it increases en vironmen tal b enefits. W elfare from setting
a zero levy 𝑊 𝑡 ( 𝑥 = 0 , 𝑝 𝑡 = 0) is larger than w elfare with an y p ositiv e levy 𝑊 𝑡 ( 𝑥 =
0 , 𝑝 𝑡 

= 0) when there are no in v estmen ts into renew able energies.
∫︁ 𝑝 ′
𝑡
0
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒𝑎 > ∫︁ 𝑝 ′
𝑡
𝑝 𝑡
𝑄 ( 𝑧 ) 𝑑𝑧 − 𝑒𝑄 𝑡 (3.22)
3.7.2 Limited deviations
With limited deviations, as of p erio d 𝑡 + 2 , the pay-offs are the same as under full
deviations and only the pa y-offs in 𝑡 and 𝑡 + 1 are altered. The impact of renew able

3.7. APPENDIX 117
energy in v estmen ts remains the same as b efore in an y case as no new inv estmen ts
tak e place after a deviation.
As some share of the levy remains in place in p erio d 𝑡 and p erio d 𝑡 + 1 , the
left hand side of equation (3.16) decreases b ecause of the en vironmen tal b enefits of
reduced demand. In p erio d 𝑡 , these b enefits are 2 𝑒𝑏𝛾 𝑝 * , i.e. p er p erio d the same as
b efore but m ultiplied with 𝛾 and, th us, lo w er than under full deviations. In 𝑡 + 1 ,
only the effect of 𝑝 𝑡 prev ails, which is discoun ted and, therefore, accrues to 𝛿 𝛾 𝑏𝑒𝑝 * .
Analyzing the righ t hand side of equation (3.17), in p erio d 𝑡 , the regulator pa ys
out 𝛾 𝑝 𝑡 for in v estmen ts from p erio d 𝑡 and 𝛾 𝑝 𝑡 − 1 for in v estmen ts from the previous
p erio d. Therefore, the b enefits of deviating decrease as some costs remain. The
negativ e effect on consumer surplus is 𝛾 𝑎𝑝 * − 𝛾 2 𝑝 * 2 . In p erio d 𝑡 + 1 , 𝛾 𝑝 𝑡 − 1 con tin ues
to depress demand b y 𝛾 𝑎𝑝 * − 1
2 𝛾 2 𝑝 * 2 . Demand reac hes the same lev el as under full
deviations as of p erio d 𝑡 + 2 .
3.7.3 Levy calculation
T o calculate the levy under compliance for German y , w e adjust the mo del in sev eral
w a ys: First, w e up date the cost estimates for renew able energies in ligh t of the coun-
try’s most recen t auction results. Generally , this implies lo w er costs than previously
assumed. W e assume wind onshore to cost e 41 p er MWh b y 2025, PV on a v erage
b et w een small-scale ro of-top installations and ground-moun ted installations e 67 p er
MWh and offshore wind p o w er e 45 p er MWh. F ollo wing Ök o-Institut (2017), the
new installations follo w the national target corridors of 2500 MW of onshore wind
p o w er ann ually , 2500 MW photo volta ics, and a total of 14 GW of offshore wind p o w er
capacit y b et w een 2017 and 2030. W e assume that after 2035 supp ort is no longer re-
quired, simplifying the calculations and resem bling the curren t cost trends. Kno wing
the supp ort lifetimes of the new installations through 2035, w e can deriv e the supp ort
lev els un til 2050.
F or Spain, w e com bine data from m ultiple sources to deriv e the p otential renew-
able energy levy un til 2050: w e assume a simplistic capacit y expansion of 1500 MW

118 CHAPTER 3. TIME-INCONSISTENCY & RENEW ABLE ENER GIES
of new wind p ow er and of 750 MW of PV p er y ear, roughly reflecting the higher
national w eigh t on wind p o w er deplo yment and the lo w er o v erall installation v olume
than in German y . The deplo ymen t costs are derived from differences in costs in 2012:
New wind p o w er installations w ere ab out four p ercen t more exp ensiv e p er k Wh in
Spain than in German y , PV w as 16 p ercen t more exp ensiv e. Spanish wind and solar
conditions are sup erior to German y’s, but the p olicy regime lea v es more ro om for
retrosp ectiv e c hanges, th us increasing the costs of financing.
Renew able energy generation reduces the o v erall p o w er price. W e impute this
implicit merit order effect, whic h damp ens the renew able energy levy . Cludius et al.
(2014) sho w that in 2015, German y’s 176 TWh of renew able energy generation damp-
ened the p o w er price b y ab out e 15. This effect v aries with the generation mix and the
flexibilit y of the system. F or simplicit y , w e assume the merit order effect of e 0.085
p er TWh as constan t. 16 W e add this merit-order effect to the renew able energy levy
in order to capture the en tire electricit y price effect of renew able energies.
3.7.4 Demand calculation
F ollo wing Ök o-Institut (2017), German demand dev elops until 20 21 according to the
TSO’s trend scenario (Energy Brainp o ol, 2014, Leipziger Institut für Energie, 2016,
Prognos, 2014); i.e. the demand paying the renew able levy decreases sligh tly from 356
TWh in 2016 to 322 TWh in 2021. W e assume subsequen t demand remains constan t
due to the coun ter-acting effects of increased energy efficiency and self-consumption
of PV p ow er, on the one hand, and electrification of the transp ort and heating and
co oling sectors, on the other hand. Ho w ev er, these v alues are realized demand v alues
that tak e in to accoun t the existing renew able energy levy . A ccordingly , w e calculate
the demand lev el without renew able energy levy , 𝑎 .
In 2015 and, th us, after the deviation, Spanish demand w as 232 TWh (Eurostat,
2018), whic h w e for simplicity assume as constan t in the follo wing y ears. Calculating
16 Spain’s electricity sector is smaller than German y’s, thus, w e would expect a larger merit-order
effect there. Ho w ev er, to the authors’ b est knowledge, no existing researc h analyzed b oth coun tries’
merit-order effects with one approac h. Analyses with different methodologies reach v astly differen t
results, e.g. Gelabert et al. (2011) and Sáenz de Miera et al. (2008), who find m uch larger effects.

3.7. APPENDIX 119
the electricit y demand under compliance needs to b e done vice v ersa as Spain did
deviate in the early 2010s, suc h that the realized demand is based on a levy that
is already reduced b y 20 p ercen t. W e start with the realized demand v alues, based
on Eurostat (2018), whic h giv es us the demand Q after deviation. Kno wing the
renew able energy levy 𝑝 under deviation and compliance and 𝑏 , w e can th us calculate
ho w large demand w ould ha v e b een without deviation. F or example, Q w ould ha v e
b een 229 TWh in 2015.

General Conclusion
This dissertation addresses the financing and in tegration of renew able energies. Chap-
ter 1 demonstrates that system-friendly wind p o w er facilitates an easier in tegration
of large shares of in termitten t renew able energies and analyzes the incen tives that
in v estors ha v e to install suc h tec hnologies. Chapter 2 finds empirical evidence that fi-
nancing costs differ across renew able energy supp ort p olicies and iden tifies additional
deplo ymen t costs that o ccur with priv ate long-term con tracts for renewable energy .
Chapter 3 analyzes p oten tial time-inconsistency issues, addresses ho w p olicy design
and deplo ymen t targets can alleviate them and scrutinizes the reasons wh y Spain had
incen tiv es to conduct retrosp ectiv e c hanges around 2012 wh ile German y did not.
Chapter 1 finds that system-friendly renew able energy tec hnologies b ecome more
imp ortan t in p o w er systems with higher shares of in termitten t renew ables. With more
wind and solar p o w er, the tec hnology with the lo w est costs ma y no longer minimize
o v erall costs. Electricit y prices decrease when it is windy in p o w er systems designed
around thermal p o w er plan ts. System-friendly tec hnologies coun ter this effect, as they
ha v e a larger share of their pro duction in times of lo w wind. Therefore, in energy
systems with high shares of in termitten t renew ables, more system-friendly turbines
are system-optimal, i.e. minimize o v erall costs, than in systems with lo w er shares of
renew able energies.
The German sliding feed-in premium pro vides some incen tiv es for system-friendly
c hoices, as sho wn b oth analytically and n umerically , but do es not transmit sufficien t
incen tiv es to install system-optimal tec hnologies. On the one hand, system-friendly
tec hnologies that run less sim ultaneously with all other wind p o w er receive a higher
121

122 GENERAL CONCLUSION
total supp ort. On the other hand, though, mark et price signals remain suppressed in
order to k eep additional risks from price exp osure in c hec k. Moreo v er, when in v estors
lac k p erfect foresigh t, incen tiv es are even less system-optimal.
Based on the system-optimalit y criterion, a p olicy designed to align priv ate in-
cen tiv es with system-optimalit y while k eeping rev en ue risks lo w is analyzed. Supp ort
lev els are adjusted b y installation-sp ecific adjustmen t factors, represen ting the re-
sp ectiv e exp ected pro duction v alues. System-friendly installations receiv e b on uses,
whereas con v en tional installations receive penalties. Th us, in v estors can consider the
en tire additional exp ected pro duction v alues of system-friendly technologies without
facing additional rev en ue risks during the installations’ lifetimes. This requires the
regulator to pro vide an exp ected p o w er price profile. F urther discussions of this ap-
proac h, including an extension to facilitate also system-optimal lo cational c hoices, can
b e found i n Neuhoff et al. (2017). Questions of p olitical acceptance of the prop osed
reference-yield mo del arise, whic h future researc h migh t address. Large-scale p o w er
system mo dels that are detailed enough to include the sp ecific actions of individual
in v estors, e.g. regarding differen tiated turbine tec hnology in v estmen t c hoices, w ould
facilitate comparing the differences to curren t and alternativ e p olicies in more detail.
Ho w ev er, suc h large-scale energy system mo dels are usually not detailed enough to
analyze the incen tiv es of individual pla y ers, let alone mo del imp ortan t asp ects lik e
imp erfect foresight, risk-a v ersion, sp ecific financing costs, or in v estors’ heterogeneous
requiremen ts in terms of risk-return profiles. A dditionally , large-scale mo dels com-
monly do not allo w actors to c ho ose b et w een sev eral in v estmen t alternativ es in terms
of tec hnologies, as in v estmen t mo dels lik e the approac h in c hapter 1 do, or in terms
of timing of the in v estmen t, as the real-options literature do es.
The analysis in c hapter 2 consists of t w o parts: an empirical analysis fo cusing
on empirically testing the impact of renew able energy supp ort p olicies on wind p o w er
financing costs and an analytical approac h concen trating on the implications of long-
term con tracts b et w een in v estors and priv ate off-tak ers. The empirical analysis finds
evidence that green certificate sc hemes are asso ciated with financing costs that are
around 1.2 p ercentage p oin ts higher than those for feed-in tariffs and sliding feed-

123
in premia. The main reasons, frequen tly discussed in the literature, are the h igher
rev en ue risks due to the uncertain t y of wholesale p o w er prices and certificate v alues.
The additional risks of sliding feed-in premia compared to feed-in tariffs app ear to
b e ev aluated as rather lo w, whic h migh t, ho w ev er, c hange in the future. The analysis
do es not supp ort a statistical difference in financing costs b et w een the t w o p olicies,
ev en though sliding feed-in premia exp ose in v estors to some rev en ue risks and risks
link ed to the future dev elopmen t of p o wer mark ets, e.g. the dev elopmen t of balancing
costs.
In the absence of implicit long-term con tracts b et w een in v estors and consumers,
priv ate long-term con tracts induce additional deploymen t costs. Sliding feed-in pre-
mia and feed-in tariffs represen t implicit long-term con tracts b et w een in v estors and
consumers, bac k ed and facilitated b y the regulator. Under green certificate sc hemes,
as w ell as with in v estmen ts based solely on the electricit y price, no suc h implicit con-
tracts exist. Priv ate long-term con tracts b et w een in v estors and off-tak ers, frequen tly
large utilit y companies, aim to tak e o v er their role. Credit rating agencies coun t suc h
con tracts as liabilities on off-tak ers’ balance sheets, suc h that the off-takers’ financial
ratios w orsen. Consequen tly , their re-financing costs increase, leading to p oten tially
substan tial implicit costs of the long-term con tracts. This reduces their demand for
long-term con tracts, lo w ers the price of these con tracts b elo w the exp ected p o w er
price, increases the rev en ues required b y in v estors additionally to the con tracts, and,
consequen tly , increases o v erall deplo ymen t costs. These effects are larger when the
off-tak ers, t ypically large utility companies, are financially constrained. Considering
Europ ean utilities’ financial situations, w e estimate that the o v erall costs of green
certificate sc hemes are around 30 p ercen t higher than the costs under feed-in pre-
mia and feed-in tariffs. F uture researc h could estimate the extra costs of priv ate
long-term con tracts empirically when more data on con tracted v olumes and con tract
conditions b ecomes av ailable. Moreo v er, if more data on financing costs, e.g. o v er
time, w as kno wn, also c hanges in effects of supp ort p olicies on financing costs could
b e in v estigated.
Chapter 3 demonstrates, analyzing the dynamic game b et w een the regulator and

124 GENERAL CONCLUSION
renew able energy in v estors, that time-inconsistency issues can arise for renew able en-
ergy in v estmen ts. Regulators can ha v e incen tiv es to rev ok e previous supp ort promises
after in v estmen ts ha v e b een made in order to b enefit from new installations and lo w
electricit y prices. In an op en lo op game, where pla y ers only consider the current pe-
rio d, no compliance can b e attained since the regulator an ticipates no disadv an tages
when deviating. As firms foresee this, no in v estmen ts are made in the first place.
T rigger strategies, i.e. where pla y ers react to other pla y ers’ previous actions and tak e
future consequences in to accoun t, can sustain commitmen t outcomes dep end ing on
a v ariet y of factors, including the regulator’s discoun t rate, costs of old installations,
costs of new installations, the wholesale electricit y price lev el, and the en vironmen tal
b enefits of renewable energy deplo ymen t.
W e sho w that supp ort p olicies and deploymen t targets can supp ort the attain-
men t of commitmen t outcomes. P olicies gran ting in v estors legal righ ts to sp ecific
supp ort lev els, com bined with constitutional grandfathering righ ts, decrease the reg-
ulator’s pa y-offs when deviating and th us render compliance more attractiv e. F eed-in
premia, unlik e feed-in tariffs, lea v e some limited space for adjustmen ts, as structural
c hanges to balancing costs and general p ow er mark et design questions can impact re-
new able energy op erators. Moreo v er, systems where “general profitability” is stressed,
rather than sp ecific supp ort lev els, lea v e more space for retrosp ectiv e c hanges. Green
certificate sc hemes mak e adjustmen ts ev en easier as they can b e implemen ted through
rather simple c hanges to the n um b er of certificates in circulation or the obligations
for retail companies. Besides, deplo ymen t targets supp ort commitmen t when they
are tec hnology-sp ecific or few alternativ e tec hnologies exist.
A n umerical example iden tifies the reasons wh y Spain conducted retrosp ectiv e
c hanges around 2012 while German y did not. On the one hand, in Spain, the regulator
v alued the long-term b enefits of sustained renew able energy expansion m uc h less due
to its high discoun t rate as the coun try w as sev erely financially constrained. On the
other hand, German y faced v ery lo w in terest rates, leading to high v aluations of future
pa y-offs. A dditionally , the en vironmen tal b enefits differ: German y’s existing p o w er
plan t fleet emitted considerably more carb on dio xide than Spain’s, increasing the

125
b enefits of renew able energies in German y . Ho w ev er, these differences are partially
coun tered b y the m uc h higher Spanish p o w er prices, implying lo wer extra costs for
renew able energies. In sum, the mo del confirms that retrosp ectiv e c hanges w ere
more attractiv e than compliance in Spain, whereas in German y , compliance w as more
attractiv e than deviating. Once more cases against the Spanish go v ernmen t ha v e b een
closed, future researc h could analyze ho w profitable the retrosp ective c hanges ha v e
after all b een for the Spanish go v ernmen t.
With falling deplo ymen t costs, the costs of renew able energies come closer to
p o w er prices and in some p erio ds and coun tries ev en lie b elo w them, raising a set of
op en researc h questions. When the reference price under con v en tional sliding feed-in
premia falls b elo w the long-term exp ected p o wer price, an increasing share of rev en ues
is sub ject to price risks. Costs co v ered through reven ues from these uncertain rev en ues
ab o v e the reference price are difficult to finance through debt. Consequen tly , pro ject
dev elop ers migh t need to secure priv ate long-term con tracts with off-takers for these
rev en ues or use equit y to finance these costs, b oth increasing o v erall deplo ymen t
costs and p ossibly p osing barriers to en try for small pla y ers. Alternativ ely , t w o-sided
sliding premia, lik e the UK’s Con tracts for Differences, oblige op erators to repa y
the regulator when p o w er prices are high, suc h that rev en ues are fixed in adv ance.
Ho w ev er, pro ject dev elop ers then p oten tially ha v e incen tiv es to abstain from an y
supp ort and sell their output via priv ate long-term con tracts. Questions arise as to
ho w these t w o systems, installations b oth within and outside the supp ort system, can
co-exist, as w ell as ho w these systems influence eac h other, e.g. ho w to steer v olumes
in order to fulfill deplo ymen t targets. Another op en question is ho w large the demand
for suc h long-term con tracts is; i.e. ho w large a mark et outside the supp ort system
can b ecome.
The ev olution of the p o w er price signal leads to another set of op en researc h
questions. On the one hand, in the long-term, p o w er prices migh t increase, as de-
mand for electricit y pic ks up due to the coupling of sectors, higher carb on prices and
p o w er systems b ecoming more flexible. On the other hand, the v alue of wind and so-
lar p o w er pro duction migh t con tin ue to fall b ecause of their sim ultaneous pro duction,

126 GENERAL CONCLUSION
aggra v ated b y the increasing shares of renew able energies. In an y case, if the p o w er
price fluctuates more strongly b etw een times with wind and sun and times without,
the questions of time and lo cation of pro duction b ecome ev er more imp ortan t. This
also raises the question of ho w the lo cational c hoices of in v estors should b e managed:
A dministrativ ely , as curren tly the case for wind p o wer in German y , through the ex-
p ected v alue of output at differen t lo cations, as sk etched in Neuhoff et al. (2017), not
at all as the case in most coun tries, or via the splitting of price zones and resp ective
exp osure to p o w er prices. This sho w cases that with ev olving p o w er mark ets and with
tec hnologies that exhibit learning, b oth renew able energy supp ort p olicies and p o w er
mark et design need to b e addressed in order to facilitate the transformation to a
carb on-neutral energy system.

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List of publications already published
Chapter 1: The Impact of Wind P o w er Supp ort Sc hemes on T ec hnology Choices
• Published in Ener gy Ec onomics (2017), June 2017, V olume 65: 343-354, DOI:
h ttps://dx.doi.org/10.1016/j.eneco.2017.05.017
• This is the p ost-print v ersion
Chapter 2: Financing P o w er: Impacts of Energy P olicies in Changing Regulatory
En vironmen ts
• Co-author: Karsten Neu hoff (DIW Berlin, TU Berlin)
• Published as: DIW Discussion P ap er 1684, 2017,
h ttps://www.diw.de/do cumen ts/publik ationen/73/diw_01.c.565302.de/dp1684.p df
• This is the p ost-print v ersion
Chapter 3: T o o go o d to b e true? Ho w time-inconsisten t renew able energy p olicies
can deter in v estmen ts
• Co-author: Olga Chiappin elli (DIW Berlin)
• Published as: DIW Discussion P ap er 1726, 2018,
h ttps://www.diw.de/do cumen ts/publik ationen/73/diw_01.c.580373.de/dp1726.p df
• This is the p ost-print v ersion
139

Why institutions use Plag.ai for originality review, entry 31

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