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energies

Article

Designing a Model for the Glogbal Energy

System—GENeSYS-MOD: An Application of the

Open-Source Energy Modeling System (OSeMOSYS)

Konstantin Löffler 1,2,*ID , Karlo Hainsch 1, Thorsten Burandt 1ID , Pao-Yu Oei 1,2,3,

Claudia Kemfert 2,3,4 and Christian von Hirschhausen 1,2

1Workgroup for Infrastructure and Policy, TU Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany;

[email protected] (K.H.); [email protected] (T.B.); [email protected] (P.-Y.O.);

[email protected] (C.v.H.)

2Energy, Transport, and Environment, DIW Berlin, Mohrenstraße 58, 10117 Berlin, Germany;

ckemfert@diw.de

3German Advisory Council on Environment, SRU, Luisenstraße 46, 10117 Berlin, Germany

4Energy Economics and Sustainability, Hertie School of Governance, Friedrichstraße 180,

10117 Berlin, Germany

*Correspondence: [email protected]; Tel.: +49-30-314-25048

Received: 28 July 2017; Accepted: 18 September 2017; Published: 22 September 2017

Abstract:

This paper develops a path for the global energy system up to 2050, presenting a new

application of the open-source energy modeling system (OSeMOSYS) to the community. It allows

quite disaggregate energy and emission analysis: Global Energy System Model (GENeSYS-MOD) uses

a system of linear equations of the energy system to search for lowest-cost solutions for a secure energy

supply, given externally defined constraints, mainly in terms of CO

-emissions. The general algebraic

modeling system (GAMS) version of OSeMOSYS is updated to the newest version and, in addition,

extended and enhanced to include e.g., a modal split for transport, an improved trading system,

and changes to storages. The model can be scaled from small-scale applications, e.g., a company,

to cover the global energy system. The paper also includes an application of GENeSYS-MOD

to analyze decarbonization scenarios at the global level, broken down into 10 regions. Its main

focus is on interdependencies between traditionally segregated sectors: electricity, transportation,

and heating; which are all included in the model. Model calculations suggests that in order to achieve

the 1.5–2

◦

C target, a combination of renewable energy sources provides the lowest-cost solution,

solar photovoltaic being the dominant source. Average costs of electricity generation in 2050 are

about 4 €cents/kWh (excluding infrastructure and transportation costs).

Keywords:

decarbonization; energy system modeling; OSeMOSYS; renewables; energy policy;

energy transition

1. Introduction

Energy system modeling is an important tool to inform the scientific debate and the policy

discussion about different pathways available to reach certain objectives, such as environmental

objectives in terms of greenhouse gas emissions. Energy system models have been around for about

five decades, inspired by the combination of computer capacities and an increased interest in energy

issues in the wake of the first oil crisis (1973); since then, one observes a rapid increase in the number

of models and the complexity thereof (see for a survey Connolly et al. [1]).

In general, energy system models can be classified into two different classes of models:

techno-economic, also called process-orientated or bottom-up models, and macroeconomic models [

Energies 2017,10, 1468; doi:10.3390/en10101468 www.mdpi.com/journal/energies

Energies 2017,10, 1468 2 of 28

While the former can offer a respectable amount of resolution analyzing the impact of specific

technologies for their respective energy system, they lack in depicting relevant macroeconomic

coherence. Techno-economic energy system models saw a rise in the early 1970s after the first oil crisis

to analyze the possibilities of more efficient final energy use [

]. Since then, the focus shifted towards a

more long-term approach to identify challenges and developments in the broader picture of climate

change [

]. Some of today’s most known techno-economic models are from the MARKAL/TIMES

family of models, e.g., NEMS, PRIMES, or MESSAGE. While some of these models were originally

developed as pure optimization models, they already try to bridge the gap between techno-economic

and macroeconomic models [

–

]. These partial equilibrium models commonly focus on energy

demand and supply markets, allowing for a broader representation of technological aspects than

purely macroeconomic models [2].

Taking a rather top-down perspective, computable general equilibrium models (CGE) assume a

certain market structure, and dynamic of the economy, e.g., competitive or oligopolistic, and then add a

certain level of technological detail. Thus, the Emission Prediction and Policy Analysis (EPPA) -model

of Massachusetts Institute of Technology (MIT) is a CGE-model assuming a competitive economy and

covering a high level of sectoral and macroeconomic detail [8].

Adopting a more pragmatic approach, simulation models are designed to replicate the functioning

of specific energy markets, without being bound to some predefined, theoretical structural form.

Two examples of such simulation models are the World Energy Model (WEM; for more information

see: http://www.worldenergyoutlook.org/weomodel/) used by the International Energy Agency

(IEA), amongst others to calculate scenarios for the World Energy Outlook (WEO), and Prospective

Outlook on Long-term Energy Systems (POLES), developed by the University of Grenoble (France),

used extensively by the European Commission for long-term scenario work [

]. A simple form

of simulation models consists of the accounting framework models [

]. The long-range energy

alternatives planning model (LEAP), developed by the Stockholm Environment Institute, belongs

to this group. In fact, a link between OSeMOSYS and LEAP is established to extend the existing

accounting framework [11].

While the choice of the model structure is a very important issue, the choice of technical detail

and assumptions is another driver. For example, results of an energy model will largely diverge

depending on whether sector coupling is possible or not, whether certain technologies are available or

not, and whether price developments are properly anticipated. In that respect, one observes a critical

moment in energy system modeling of low-carbon futures, driven by the unexpected cost decrease of

renewable energies and storage technologies. Traditionally, energy system models relied on the trio

of fossil fuels with carbon capture, nuclear energy, and renewables; the two former ones providing

backup capacity in case of no wind and no sun. This pattern is now challenged by the availability

of low-cost storage technologies and other flexibility options (such as demand-side management,

high-voltage grid interconnections, etc.), providing the necessary flexibility to balance intermittent

renewables [

]. The recent controversy about renewables-based energy scenarios highlights this issue,

see Clack et al. [13] and Jacobson, et al. [14].

This paper contributes to the debate by presenting a new energy system model with a high level

of sectoral detail that can be used—among others—for global climate policy scenarios. The model,

called GENeSYS-MOD, is a full-fledged energy system originally based on the open-source energy

modeling system, called OSeMOSYS. The model uses a system of linear equations of the energy system

to search for lowest-cost solutions for a secure energy supply, given externally defined constraints on

greenhouse-gas (GHG) emissions. In particular, it takes into account increasing interdependencies

between traditionally segregated sectors, e.g., electricity, transportation, and heating. OSeMOSYS itself

is used in a variety of research to provide insights about regional energy systems and their transition

towards renewable energies (Moura et al. [

] implemented a version called SAMBA, where the South

American energy system is depicted. Others like Rogan et al. [

] tackle national energy system, in this

case analyzing the Irish one over the period 2009–2020. Recently, Lyseng et al. [

] modeled the Alberta

Energies 2017,10, 1468 3 of 28

power system, analyzing the impact of carbon prices, loads and costs getting a solution of how a

decarbonization until 2030 can be achieved). We provide a translation of the initial model, written in

GNU MathProg (GMPL), into the widely used and available GAMS software. We also extended the

code and implemented additional functionalities, e.g., a modal split for the transportation sector or

relative investment limits for the single model periods. Last but not least, both the code and the data

used by GENeSYS-MOD are open-access and freely available to the scientific community.

The paper is structured in the following way: the next section lays out the model and its

various aspects. Section 3presents the model implementation, and its global application. Fuels

and technologies, as well as their availabilities and limitations are described. Section 4presents

the results, and Section 5concludes (this paper results from a graduate study project convened by

Roman Mendelevitch, Pao-Yu Oei, and Franziska Holz, at Berlin University of Technology, in the

summer semester 2016, see Burandt et al. [

] for details. We are grateful for previous work in the

OSeMOSYS open-source community, without which our work would not have been possible. Earlier

versions of the paper were presented and discussed at the 10th TransAtlantic Infraday (November 2016,

Washington, DC, USA), the 40th International Conferences of the International Association for Energy

Economics (IAEE, Singapore, June 2017), the 10th Annual Internationale Energiewirtschaftstagung

IEWT (February 2017, Vienna, Austria), DIW’s internal cluster seminar on climate and energy policy

(December 2016), as well as the OSeMOSYS working group (June 2017). We thank participants at

these conferences, in particular Dawud Ansari, Wolf-Peter Schill, and Christian Breyer for comments;

the usual disclaimer applies).

2. GENeSYS-MOD: Model Description

GENeSYS-MOD has been developed by our team based on the OSeMOSYS, originally coded

in GNU MathProg. In addition to a full-fledged conversion of the current version of OSeMOSYS

into the GAMS software, we have extended the model significantly. This section describes both the

basic structure we have taken over, as well as the additions; we also provide the framework for the

application to the global energy system.

2.1. The Model

GENeSYS-MOD is based on the version of OSeMOSYS created by Noble [

], has been updated

to the newest version of OSeMOSYS, and will be regularly updated from there. GENeSYS-MOD

uses the CPLEX-solver (version 12.7.1.0) for its calculations. Just like OSeMOSYS, GENeSYS-MOD

consists of multiple blocks of functionality (see Figure 1), which work as separate entities that can

be changed or extended. To soften the limitations of a linear model, we implemented an additional

block, called ‘Transportation’, implementing a modal split for the distribution of passenger or freight

kilometers of a particular type of transportation (e.g., passenger road traffic). Additionally, we added

trade costs, losses and capacities for fuels between regions, changed the endogenous calculation of

storages, and reformulated the renewable energy target equations. A list of all sets, as well as all

relevant parameters, can be found in Appendix A.1.

The model calculates the optimal flows of energy carriers, services, or their proxies that are

produced in the production sector, and converted through a network of transformation technologies to

meet the set demands (energy carrier proxies are an abstract kind of energy carriers (e.g., passenger

kilometers).

To achieve this, the model distinguishes between fuels and technologies. Energy carriers and

services are called fuels in the model and hence are referred like this from this point on. Each fuel

represents a specific energy carrier, a group of similar ones or their proxies. Furthermore, fuels are

produced, transformed and used by technologies. Additionally, technologies represent all kinds of energy

using, producing or transforming techniques (e.g., plants, storages or residual fuel users).

Energies 2017,10, 1468 4 of 28

Energies 2017, 10, 1468 4 of 29

InputActivityRatio and OutputActivityRatio. Technologies with only one of these ratios defined are

either supply or demand nodes.

Figure 1. Blocks of functionality of GENeSYS-MOD (the illustration is based on Howells et al. [20]).

2.1.1. Objective Function

The objective function minimizes the net present cost of an energy system to meet the given

demands for energy carriers and services. This is done by summing up the total discounted costs of

each technology (t) in each year (y) and region (r). Furthermore, the total discounted trade costs of

importing fuels in each region are summed up and added to the objective value:

min =,,



+,



(1)

2.1.2. Costs

Costs incur when building new capacities of technologies (DiscountedCapitalInvestmenty,r,t),

maintaining capacities or using them (DiscountedOperatingCostty,r,t) (see Equation (2)):

,, =,,

+,,

+ℎ,,

−,, ∀,,

(2)

These parameters are defined for each year, technology, and region in the model. The operation of

and investment in a non-storage technology is specified by three kinds of costs. First, a technology has

a given capital cost. These costs are calculated on an annual basis and are determined by the level of

new installed capacity by a per-unit cost to determine the capital investment into new capacities.

Furthermore, GENeSYS-MOD uses salvage costs to calculate the salvage value of technologies that

have exceeded their operational life or are being replaced. Thus, the salvage value is determined by

the year of installment, the operational life and a globally defined discount rate. OSeMOSYS offers

an implementation of a sinking fund deprecation method and a straight-line depreciation method

(the sinking fund deprecation method is an advanced deprecation method in which the estimated

salvage value from the deprecation is invested into a fund and the resulting discounted values are

used to calculate further salvage rates; the straight-line deprecation method is a simple, linear

deprecation method, allocating the same amount or percentage of an asset's cost to each year), our

model assumes the sinking fund deprecation method as default. Lastly, there are operational costs

for each technology, divided in variable and fixed costs. Furthermore, the total annual operating costs

Figure 1. Blocks of functionality of GENeSYS-MOD (the illustration is based on Howells et al. [20]).

The technologies can run in different modes of operation if applicable, e.g., a plant can be defined

to produce either electric power in one mode of operation, or heat in the other one. To simulate the loss

of energy when converting certain fuels into another type, technologies have a defined InputActivityRatio

and OutputActivityRatio.Technologies with only one of these ratios defined are either supply or

demand nodes.

2.1.1. Objective Function

The objective function minimizes the net present cost of an energy system to meet the given

demands for energy carriers and services. This is done by summing up the total discounted costs

of each technology (t) in each year (y) and region (r). Furthermore, the total discounted trade costs of

importing fuels in each region are summed up and added to the objective value:

minz=∑

∑

TotalDiscountedCostr,t,y+∑

∑

TotalDiscountedTradeCostsr,y(1)

2.1.2. Costs

Costs incur when building new capacities of technologies (DiscountedCapitalInvestment

y,r,t

maintaining capacities or using them (DiscountedOperatingCostty,r,t) (see Equation (2)):

TotalDiscountedCostr,t,y=DiscountedOperatingCostr,t,y

+DiscountedCapitalInvestmentr,t,y

+DiscountedTechnologyEmissionsPenaltyr,t,y

−DiscountedSalvageValuer,t,y∀r,t,y

(2)

These parameters are defined for each year,technology, and region in the model. The operation

of and investment in a non-storage technology is specified by three kinds of costs. First, a technology

has a given capital cost. These costs are calculated on an annual basis and are determined by the level

of new installed capacity by a per-unit cost to determine the capital investment into new capacities.

Furthermore, GENeSYS-MOD uses salvage costs to calculate the salvage value of technologies that

have exceeded their operational life or are being replaced. Thus, the salvage value is determined by

the year of installment, the operational life and a globally defined discount rate. OSeMOSYS offers

an implementation of a sinking fund deprecation method and a straight-line depreciation method

(the sinking fund deprecation method is an advanced deprecation method in which the estimated

salvage value from the deprecation is invested into a fund and the resulting discounted values are used

Energies 2017,10, 1468 5 of 28

to calculate further salvage rates; the straight-line deprecation method is a simple, linear deprecation

method, allocating the same amount or percentage of an asset

s cost to each year), our model assumes

the sinking fund deprecation method as default. Lastly, there are operational costs for each technology,

divided in variable and fixed costs. Furthermore, the total annual operating costs are discounted back

with a globally defined discount rate to the first year modeled to make costs comparable. A global

discount rate of 5% was assumed for the calculations of our model. The emission penalty can be

determined exogenously (e.g., a given carbon prices), or endogenously (by determining the shadow

price resulting from the CO

-emission constraints). The discounted operating costs are then summed

up with the discounted capital investment, emissions penalty, and salvage value.

2.1.3. Storage

The current implementation of storages in OSeMOSYS is based on general storage assumptions

described by Welsch et al. [

]. This implementation has been changed in order to facilitate

an endogenous calculation of storage capacities. Instead of setting a StorageMaxChargeRate,

an Energy-Power-Ratio has been implemented for storages, with the maximum storage capacity

being a variable instead. Different types of storage have different operation lifetimes, maximal and

minimal ratios, and costs. The model calculates the cost of investments per unit of storage capacity

and combines it with the salvage value that is computed for the end of the modeling period. Both costs

are used to incorporate the storage equations into the objective function. Equations (3) and (4) define

the rates for charging and discharging for each time slice:

∑

(RateOfActivityl,m,r,t,y·TechnologyFromStoragem,r,s,t

·Conversionlsl,ls·Conversionldl,ld·Conversionlhl,lh)

=RateOfStorageDischargeld,lh,ls,r,s,y∀ld,lh,ls,r,s,y

(3)

∑

(RateOfActivityl,m,r,t,y·TechnologyToStoragem,r,s,t

·Conversionlsl,ls·Conversionldl,ld·Conversionlhl,lh)

=RateOfStorageDischargeld,lh,ls,r,s,y∀ld,lh,ls,r,s,y

(4)

2.1.4. Transportation

The ‘Transportation’ block introduces a modal split for transportation technologies. First, the

demand of a certain fuel is split by the defined modal types into several demands per modal type.

Furthermore, technologies can be tagged with modal types to define which technology can cover this split

demand. Lastly, the tagged technologies must produce at least the amount of the split demand:

AccumulatedAnnualDemandf,r,y·ModalSplitByFuelAndModalTypef,mt,r,y

=DemandSplitByModalTypef,mt,r,y∀f,mt,r,y(5)

∑

(TagTechnologyToModalTypemt,t

·∑

(RateO f ProductionByTechnologyf,l,r,t,y∗YearSplitl,y))

=ProductionSplitByModalTypef,mt,r,y∀f,mt,r,y

(6)

ProductionSplitByModalTypef,mt,r,y

≥DemandSplitByModalTypef,mt,r,y∀f,mt,r,y(7)

The following example illustrates this idea: The model has two types of passenger cars and a

cheap rail technology defined. Each car is assigned to the modal type “passenger cars” and the rail

is assigned to the “rail” type. Furthermore, all technologies produce “passenger kilometers” as a fuel

(as mentioned before, everything a technology uses, produces, or transforms is considered a fuel in the

Energies 2017,10, 1468 6 of 28

model. This includes both regular fuels, such as power, but also services, such as passenger kilometers).

Without further restrictions, the model would only use the rail technology due to its lowest costs. This,

however, is unrealistic as cars will still be the best option for less frequented routes which cannot be

properly included in the model due to a relatively broad geographical coverage. If our modal split

function is now included in the model, we can define that at least 20% of the “passenger kilometers”

must be fulfilled by the modal type “passenger cars”. Thus, we are not implementing strict limitations of

the use of technologies, but set up lower bounds. Therefore, we can reproduce a more realistic result of

the transportation sector and overcome some of the inherent disadvantages of a Linear Program (LP).

2.1.5. Trade

To implement trade costs in our model, we had to split the pre-existing trade variable into separate

export and import variables. The total trade costs for each time slice,year and region are then calculated

by summing up the trade costs for each fuel that is imported into a given region from another region,

as seen in Equation (8). To incorporate these costs into the objective function, they are furthermore

discounted back to the starting year of the model run and then added to the total discounted costs.

Also, trade losses, as well as maximum trade capacities for power trade have been implemented in the

model equations. Equation (9) demonstrates the inclusion of losses that occur on exports, equation 10

presents the maximum capacity constraint for an electricity trade route, which has to be satisfied for

all time slices:

∑

rr(Importf,l,r,rr,y·TradeRoutef,r,rr,y·TradeCostsf,r,rr)

=TotalTradeCostsl,r,y∀l,r,y

(8)

∑

rr (Exporty,l,f,r,rr·(1+TradeLossBetweenRegionsy,f,r,rr)−Importy,l,f,r,rr)

=NetTradey,l,f,r∀y,l,f,r(9)

Importy,l,Power,r,rr+Exporty,l,Power,r,rr

CapacityToActivityUnit·YearSplitl,y

=TradeCapacityy,Power,r,rr ∀y,l,r,rr (10)

3. Model Application and Implementation

GENeSYS-MOD includes a multitude of supply and transformation technologies to satisfy the

different demand needs that, in combination, form the global energy system. Its possible flows,

technologies (symbolized by boxes), and demands (shaded boxes) are illustrated in Figure 2.

Energies 2017, 10, 1468 6 of 29

lowest costs. This, however, is unrealistic as cars will still be the best option for less frequented routes

which cannot be properly included in the model due to a relatively broad geographical coverage. If

our modal split function is now included in the model, we can define that at least 20% of the

“passenger kilometers” must be fulfilled by the modal type “passenger cars”. Thus, we are not

implementing strict limitations of the use of technologies, but set up lower bounds. Therefore, we can

reproduce a more realistic result of the transportation sector and overcome some of the inherent

disadvantages of a Linear Program (LP).

2.1.5. Trade

To implement trade costs in our model, we had to split the pre-existing trade variable into

separate export and import variables. The total trade costs for each time slice, year and region are then

calculated by summing up the trade costs for each fuel that is imported into a given region from

another region, as seen in Equation (8). To incorporate these costs into the objective function, they are

furthermore discounted back to the starting year of the model run and then added to the total

discounted costs. Also, trade losses, as well as maximum trade capacities for power trade have been

implemented in the model equations. Equation (9) demonstrates the inclusion of losses that occur on

exports, equation 10 presents the maximum capacity constraint for an electricity trade route, which

has to be satisfied for all time slices:

,,,, ·

,,, ·

,,

=

,, ∀,,

(8)

,,,, ·1+,,,−,,,,

 =,,, ∀,,



,

(9)

,,,, +,,,,

·,

=,,, ∀,,,

(10)

3. Model Application and Implementation

GENeSYS-MOD includes a multitude of supply and transformation technologies to satisfy the

different demand needs that, in combination, form the global energy system. Its possible flows,

technologies (symbolized by boxes), and demands (shaded boxes) are illustrated in Figure 2.

Figure 2. Layout of GENeSYS-MOD.

Energies 2017,10, 1468 7 of 28

3.1. Regional Disaggregation and Trade

In its current form, GENeSYS-MOD addresses global energy issues, and for this purpose it splits

the world into ten regions: Africa, China, Europe, Former Soviet Union, India, Middle East, North

America, Oceania, Rest of Asia and South America (see Appendix A.3 for a list of countries in each

of the regions). These regions represent geographical clusters of countries in which energy is both

produced and consumed (see Figure 3for a graphical representation). At the same time, the regions

act as nodes connecting with other regions to allow for trading. All parameters, e.g., on demand and

production potentials (e.g., such as the potential area in which onshore wind generators could be built),

and other parameters such as costs and efficiency are defined for each region. Regions are able to trade

fuels via the set TradeRoutes, which define which regions are able to trade a certain type of fuel with

one another. Because of the large distances between regions, we disabled the trading of power for our

model calculations.

Energies 2017, 10, 1468 7 of 29

3.1. Regional Disaggregation and Trade

In its current form, GENeSYS-MOD addresses global energy issues, and for this purpose it splits

the world into ten regions: Africa, China, Europe, Former Soviet Union, India, Middle East, North

America, Oceania, Rest of Asia and South America (see Appendix A.3 for a list of countries in each

of the regions). These regions represent geographical clusters of countries in which energy is both

produced and consumed (see Figure 3 for a graphical representation). At the same time, the regions

act as nodes connecting with other regions to allow for trading. All parameters, e.g., on demand and

production potentials (e.g., such as the potential area in which onshore wind generators could be

built), and other parameters such as costs and efficiency are defined for each region. Regions are able

to trade fuels via the set TradeRoutes, which define which regions are able to trade a certain type of fuel

with one another. Because of the large distances between regions, we disabled the trading of power

for our model calculations.

Figure 3. Regional disaggregation of GENeSYS-MOD.

3.2. Demand and Fuel Disaggregation

GENeSYS-MOD distinguishes three groups of final demand: electricity, heat, and mobility. They

are then split up into low temperature heat (used for water and room heating and cooling) and high

temperature heat (process heat over 100 °C) in the heat sector, and passenger and freight transport

demands in the mobility sector. Other fuels in the model are used for transformation purposes (e.g.,

hydrogen or biomass), serve as an input (such as the conventional fossil fuels coal, natural gas, or

oil), or are used to define certain technical restrictions. These ‘area input fuels’ can be used to limit

the use of certain technologies (such as PV cells) by available suitable land on a regional basis, which

may serve as a superior indicator to capacity-based calculations.

As such, we defined the following fuels for our final demands (see Table 1).

Table 1. Fuel disaggregation.

Electricity [in PJ] Heat [in PJ] Mobility [in Gkm]

Power Heat low Heat high Mob. Passenger Mob. Freight

3.3. Modeling Period and Investment Restrictions

The modeling period covers the years 2020 to 2050 in 5-year-steps. The year 2015 is used as base

year with existing capacities. There are no fixed investment limits for technologies. Instead, we opted

Figure 3. Regional disaggregation of GENeSYS-MOD.

3.2. Demand and Fuel Disaggregation

GENeSYS-MOD distinguishes three groups of final demand: electricity, heat, and mobility.

They are then split up into low temperature heat (used for water and room heating and cooling)

and high temperature heat (process heat over 100

◦

C) in the heat sector, and passenger and freight

transport demands in the mobility sector. Other fuels in the model are used for transformation purposes

(e.g., hydrogen or biomass), serve as an input (such as the conventional fossil fuels coal, natural gas,

or oil), or are used to define certain technical restrictions. These ‘area input fuels’ can be used to limit

the use of certain technologies (such as PV cells) by available suitable land on a regional basis, which

may serve as a superior indicator to capacity-based calculations.

As such, we defined the following fuels for our final demands (see Table 1).

Table 1. Fuel disaggregation.

Electricity [in PJ] Heat [in PJ] Mobility [in Gkm]

Power Heat low Heat high Mob. Passenger Mob. Freight

3.3. Modeling Period and Investment Restrictions

The modeling period covers the years 2020 to 2050 in 5-year-steps. The year 2015 is used as base

year with existing capacities. There are no fixed investment limits for technologies. Instead, we opted

Energies 2017,10, 1468 8 of 28

for a percentage-based approach in order to reproduce investment rates more realistically. Therefore,

the investment is limited by the total amount invested, as well as the maximum capacity potential.

3.4. Time Disaggregation

GENeSYS-MOD presents most results on an annual basis, but it offers a much more disaggregated

approach with respect to time periods and time dependent data, such as, for example, the power

demand per region or the use of storages. This is accomplished by dividing the year into several time

slices, which can be defined by the model user to suit the needs of the application. One year is thus

divided into seasons, which then contain day types (e.g., weekday/weekend) and daily time brackets

(e.g., day/night), all defined as fractions of a year.

For this model specification, we chose to use three seasons (intermediate, summer, winter—with

intermediate combining the seasons of autumn and spring), one day type, and two daily time brackets

(day, night). The daily time bracket “day” is set to 16 h (

2/3

of one day), while “night” is 8 h long

(

1/3

). Multiplying these fractions for each combination (calculation example:

Summer day =

year ×

(season “summer”)×

(daily time bracket “day”) =

0.1667) gives us a total of six different time

slices. Table 2presents the fraction per year for all the time slices used (given in % of one year).

Table 2. Time disaggregation (% of one year).

Winter Day Winter Night Intermediate Day Intermediate Night Summer Day Summer Night

17% 8% 33% 17% 17% 8%

3.5. Emissions

GENeSYS-MOD is mainly targeted at greenhouse gas emissions from the energy sector, and

therefore monitors CO

in particular detail. CO

constraints can be defined at the regional level,

but also at the global level. For the applications used in this paper, we choose the global approach with

a CO

budget corresponding to the 1.5–2

◦

C target; according to IPCC [

], about 550–1300 Gt CO

may be emitted between 2011 and 2050. Considering the global emissions between 2011–2014, as well

as taking into account non-energy emissions (such as from industry, or land use and land-use change),

we opted for a budget of 650 Gt of CO2for the GENeSYS-MOD global model calculations.

The emission values per energy carrier per petajoule have been calculated, based on NEI [

Edenhofer et al. [

] (for nuclear energy production) and EIA [

] (for coal, gas and oil). All emissions

can then be calculated for each technology based on their fuel consumption.

3.6. Storage

GENeSYS-MOD has been designed with attention to storage requirements, in particular in the

electricity sector. Storages are connected on a technology basis, meaning each technology that wants to

store or use stored energy must be connected by defining the link between them. Also, storages do not

store specific fuels, but are generic “energy depots”, whose input is defined by the output fuel of the

technology. A list of all implemented technologies and storage technologies can be found in Appendix A.2.

3.7. Modal Split for Transportion

The modal split for the transportation sector is exogenously given, and based on calculations that

are based on data from the 450 ppm scenario from the World Energy Outlook [

], using a regional

differentiation. While the modal split is strictly defined for 2015, these bounds are consecutively

lowered to let the model find the optimal solution.

3.8. Input Data

This section provides the main data sources required for the subsequent model calculations.

As the scenarios focus on low-carbon technologies, particular weight is placed on renewable sources in

Energies 2017,10, 1468 9 of 28

this section; this will be different when we address other questions using GENeSYS-MOD, e.g., the

optimal selection of coal vs. natural gas utilization.

3.8.1. Fossil Fuel Availability and Prices

Current energy systems are mainly based on conventional resources like coal, gas, oil, and

nuclear power [

]. GENeSYS-MOD can use conventional fuels and their corresponding technologies,

and invest into new capacities. Existing capacities of conventional and renewable technologies are

considered by the model as residual capacities, and phased out as their lifetime expires [

]. The annual

production of the conventional energy resources published in the World Energy Outlook [

] is taken

as a constant limit in the model. Carbon capture is not being considered, since it is not commercially

available and is unlikely to be so in the future.

3.8.2. Renewable Technologies and Potentials

Solar

With worldwide average annual growth rates of solar power supply of 46.2% since 1990,

solar power it is one of the main drivers of any low-carbon transformation [

]. The technical potential

of solar power is very high, but it is highly dependent on regional and temporal circumstances.

We consider two different technologies for power generation purposes: photovoltaics (PV) and

concentrated solar power (CSP). The former makes use of direct radiation as well as radiation reflected

by the clouds, and therefore results in a steadier energy inflow. Similar to Jacobson et al. [

we consider residential and commercial photovoltaic panels on the one hand, and utility plants

at open areas on the other hand. The potentials of these technologies with respect to different regions

are adopted from Jacobson et al. [

] and illustrated in Table 3. Sites with less than 4kWh/m

/d are

excluded, as are sites with too high slope, urban areas, or protected areas.

Table 3. Solar PV—Regional Potential.

Region PV–Residential

[100 km2]

PV–Commercial

[100 km2]

PV–Utility [100

km2]Total [100 km2]

Africa 4.20 2.31 10.55 17.06

Asia Rest 3.67 2.69 2.99 9.35

China 5.61 6.56 4.69 16.86

Europe 3.34 4.16 2.76 10.26

India 2.82 3.89 1.49 8.2

Middle East 2.39 1.76 3.50 7.65

North America 4.61 4.56 10.01 15.39

Oceania 0.82 1.31 4.26 6.39

FSU 0.84 1.17 10.01 12.02

South America 2.59 2.59 8.85 14.03

Total 30.89 31 59.11 130

The conditions for using concentrated solar, on the other hand, are more constraining.

CSP requires a high intensity of direct radiation, and produces low efficiency values with

lower radiation. We consider sites with more than 2000 kWh/(m

2·

year), corresponding to capacity

factors of about 20–25% [

]. This occurs mainly in regions such as Africa, the Middle East and Oceania.

Wind

The availability of wind can vary strongly, both during the day but also seasonally,

with availabilities up to 50% higher in winter months, e.g., in Europe [

]. In addition, the availability

of wind power can also be constrained by environmental factors, such as the exclusion of high altitude

Energies 2017,10, 1468 10 of 28

winds [

]. As a result, we only consider locations where the average wind speed in 10 meters height

exceeds 4 m/s [33].

Our model differentiates between onshore and offshore wind. Onshore wind technology is

already reasonably mature compared to other renewable energy sources, with its efficiency being close

to the theoretical optimum. Most wind turbine systems have hub heights around 100 meters with

rotor diameters of 50–100 m [

]. The potentially suitable area for onshore plants is directly given

by the calculation of Jacobson et al. [

]. However, the potentially suitable area for offshore plants

was calculated by a reverse calculation of the total GW (Gigawatt) potential given by Arent et al. [

Therefore, we used their assumption of a power density of 5 MW/km

. The latter, in combination with

the stated regional potential for wind power in GW, allows the calculation of the possible suitable area,

which is shown in Table 4.

Table 4. Wind—Regional Potential.

Region Wind Onshore [100 km2] Wind Offshore [100 km2] Total [100 km2]

Africa 125.2 1.1 126.3

Asia Rest 41.0 2.9 43.9

China 2.3 2.1 4.4

Europe 10.9 4.2 15.1

India 1.1 0.1 1.2

Middle East 27.5 0.1 27.6

North America 21.2 4.7 25.9

Oceania 9.7 5.2 14.9

FSU 10.9 2.7 13.6

South America 23.5 8.3 31.8

Total 273.3 31.4 304.7

Biofuels

If all possible sources of residues and waste would be used, the world’s total technical annual

potential is estimated to be more than 100 EJ per year [

]. Since it is difficult to estimate the regional

potential of residues and forest products, we refer to solid biomass waste. This includes renewable

urban waste, but also food wastes that are produced at the first stages of the supply chain. Furthermore,

we only consider second-generation biomass for energy production. Compared to first-generation

energy crops (e.g., wheat, corn, beet or palm oil), they have an important advantage because they are

non-food materials. This means that agricultural by-products like cereal straw, sugarcane bagasse,

or forest residues are used and biofuels do not compete directly with food production.

The share of food losses and waste (inclusive animal excrements) of the total energetic biomass

potential is 42% [

]. Therefore, food losses and waste offer a potential of around 11.7 EJ. Some

important differences exist between different regions depending on their grade of industrialization.

In highly developed countries like North America or Europe, the annual per capita food loss and waste

is 280–300 kg. On the other hand, in less-developed countries, the figure is lower, at 120 and 170 kg [

In accordance with the IEA [

], we assume that 40% of the collected waste could be used for power

production. Thus, we calculated different regional potentials based on data from Gustavsson [

Cost assumptions for biomass and their evolution from 2015 to 2050 are adopted from Sims et al. [

]

and Havlík et al. [

]. The resulting potentials are shown in Table 5. Costs are reduced from 14.5

€

/GJ

in 2015 to 2.2 €/GJ in 2050 due to technical improvements.

Energies 2017,10, 1468 11 of 28

Table 5. Biomass—Regional Potential.

Region 2015 [PJ] 2050 [PJ]

Africa 154 401

Asia Rest 192 798

China 713 1165

Europe 504 504

India 170 737

Middle East 371 1061

North America 514 633

Oceania 232 232

FUS 409 527

South America 667 1258

Total 3926 7316

Hydropower

Hydropower is the energy transported by the water on its way from a higher to a lower level, and

therefore has the highest density in regions with high slope and a constant supply of water. The greater

the amount of water the river transports and the steeper the gradient of its stream course, the higher

the potential in this area. Most of hydropower potentials are located in mountainous regions [

The global annual amount of water transported this way is estimated to be 47.000 km

, of which

28.000 km

is on the surface. This sums up to around 40.000 TWh/year theoretical hydropower

generation [

]. Regions like Asia, especially China, South America, and Africa show the most

hydropower resources. Compared to solar and wind, hydropower is more predictable and constant

over the years, but there are seasonal fluctuations caused by rain or melting snow. The regional

potentials of hydropower are calculated using data from [

] and are represented in Table 6. For the

resulting potentials, an even distribution of small and large-scale hydro has been assumed.

Table 6. Hydropower—Regional and Economical Potential.

Region Hydropower (Small) [GW] Hydropower (Large) [GW] Total [GW]

Africa 130.8 130.8 261.6

Asia Rest 85.0 85.0 170

China 185.9 185.9 371.8

Europe 129.0 129.0 258

India 99.2 99.2 198.4

Middle East 39.0 39.0 78

North America 107.8 107.8 215.6

Oceania 42.1 42.1 84.2

FUS 121.6 121.6 243.2

South America 165.7 165.7 331.4

Total 1106.1 1106.1 2212.2

Geothermal

Geothermal energy can provide a regular supply, but it is relatively expensive compared to other

sources, although advances in drilling technologies and more effective reservoir management have

been lowering costs significantly in the recent past [

]. The technical potential of geothermal energy

is abundant, and it is broadly available [

]. The geothermal resources are caused by three important

components: (i) the energy flow within the Earth crust (magma, water, steam, gases); (ii) the heat flow

due to conduction; and (iii) the energy that is stored in rocks and fluids within the earth crust [

The most promising geothermal sources are located near plate margins and geologically active regions.

Most of the existing geothermal plants for power plants are located in regions with high temperatures

of the crust surface, high rock permeability, or a naturally existing water-steam resource [43,45,46].

Energies 2017,10, 1468 12 of 28

Some early research projects indicate a high potential of geothermal recourses for the energy sector.

The EPRI [

] stated that within the first three kilometers of the continental earth crust exists sufficient

heat to provide sufficient energy for the next 100,000 years. Nevertheless, not all the theoretically

existing energy can be used directly in terms of heat or to generate electricity, both for technical and

economic reasons. The resulting regional potentials for geothermal power generation, based on [

]

are shown in Table 7.

Table 7. Geothermal—Regional Potential.

Region Regional Potential [GW]

Africa 12.8

Asia Rest 25.7

China 3.5

Europe 6.8

India 0.6

Middle East 1.4

North America 25.4

Oceania 13

FUS 3.7

South America 44.9

Total 137.8

Others

Renewable, synthetically produced gas, such as hydrogen, can be used to provide low and

high-temperature heat, as well electric power. Furthermore, liquefied hydrogen gas can be used as fuel

in the transportation sector. Thus, hydrogen can play a major role in a low-carbon transformation.

Hydrogen gas can be used in combined heat and power plants (CHP) to produce electricity and

heat [

]. Renewable hydrogen gas CHPs are modeled according to the characteristics of the natural

gas CHP technology, with hydrogen as input instead. Because hydrogen can only be generated by

using expensive electrolysis technologies, power and heat generated by hydrogen is rather expensive.

In the transportation sector, hydrogen can be used in fuel-cell-driven electric vehicles (FCEV) via

gaseous hydrogen. FCEV provide long range services up to 900 km per refueling [

]. However, this

long range is achieved through a decreased overall efficiency in comparison to battery-driven electric

vehicles. Additionally, other long-range transportation technologies can use liquefied hydrogen,

such as freight cargo trucks or aircraft.

3.9. Cost Assumptions

Since the model identifies the least-cost combination for the energy system, cost parameters,

especially their assumptions for the future, are crucial and a main driver of the results. Hence, it is

essential to understand the relations and implications of those costs and verify results by testing for

their sensitivity. The different types of cost considered in GENeSYS-MOD are: (1) cost for building

capacities and running those, (2) emission penalties, and (3) costs for trading fuels between regions.

Most of our cost assumptions and data originate from Schröder et al. [

], Gulagi et al. [

and Breyer et al. [

]. Also, price estimates from the 450 ppm scenario of the World Energy Outlook [

]

are taken as fuel prices for fossil fuels in our model. Emission penalties are currently not considered

in this model setup, as we opted for a global carbon budget instead. For more information about the

different costs consult Burandt et al. [

]. The capex of different electricity generating technologies can

be found in Appendix A.4.

Energies 2017,10, 1468 13 of 28

4. Scenario Definition and Results

4.1. Scenario Definition

GENeSYS-MOD was designed to develop and compare different scenarios for the global energy

system, but for our first application, we have chosen a rather simple structure: we are interested in the

cost-optimal energy mix that respects a global CO

-target calibrated for a 1.5–2

◦

C world, as explained

above: defined here as a CO

-budget of 650 Gt for 2015 to 2050 (consistent with a 1.5

◦

C scenario).

All technologies described in the previous section are available.

Also, a sensitivity analysis concerning various parameters and assumptions has been made (costs

of renewable power generating technologies, storage costs, fossil fuel costs, and demand growth),

showing that our results are robust and that the model behaves accordingly.

4.2. Results

4.2.1. The Global Energy System

Figure 4shows the results for the basic run of GENeSYS-MOD, applied to the global energy

system. Our investigation of whether a globally sustainable 100% renewable energy supply is possible

by 2050 results in the finding that it is technically and economically feasible, with a resulting shadow

price for CO

of about 32

€

per ton CO

. This shows that a switch towards 100% renewables can be

achieved with very low costs, as renewable technologies become increasingly competitive.

Energies 2017, 10, 1468 13 of 29

4. Scenario Definition and Results

4.1. Scenario Definition

GENeSYS-MOD was designed to develop and compare different scenarios for the global energy

system, but for our first application, we have chosen a rather simple structure: we are interested in

the cost-optimal energy mix that respects a global CO2-target calibrated for a 1.5–2 °C world, as

explained above: defined here as a CO2-budget of 650 Gt for 2015 to 2050 (consistent with a 1.5°C

scenario). All technologies described in the previous section are available.

Also, a sensitivity analysis concerning various parameters and assumptions has been made

(costs of renewable power generating technologies, storage costs, fossil fuel costs, and demand

growth), showing that our results are robust and that the model behaves accordingly.

4.2. Results

4.2.1. The Global Energy System

Figure 4 shows the results for the basic run of GENeSYS-MOD, applied to the global energy

system. Our investigation of whether a globally sustainable 100% renewable energy supply is

possible by 2050 results in the finding that it is technically and economically feasible, with a resulting

shadow price for CO2 of about 32€ per ton CO2. This shows that a switch towards 100% renewables

can be achieved with very low costs, as renewable technologies become increasingly competitive.

The global energy system shifts from a world almost entirely reliant on the fossil fuel sources

oil, coal, and natural gas, to a fully decarbonized energy system in 2050. While starting out slowly,

the growth of renewables, especially biomass, solar and wind quickly picks up and reaches a stage

of about 50% of the energy mix being renewable as soon as 2030. This transformation varies strongly

from sector to sector, as well as on a regional basis.

Figure 4. Development of the global energy mix (final energy supply) with a CO2-budget of 650 Gt.

4.2.2. Electricity

The energy system experiences a very strong sector coupling of the power with both the heat

and transportation sectors. This can be observed via the vastly rising generation of power, more than

tripling by 2050 compared to 2015 values. Figure 5 shows the development of the power generation

mix between 2015–2050 at the global level. While conventional sources still account for over 2/3 of

consumption in 2015, and even over 80% when including hydropower, the energy mix changes

structurally from 2025 on, mainly due to solar photovoltaics becoming economically competitive.

100

150

200

250

300

350

400

2015 2020 2025 2030 2035 2040 2045 2050

Biomass

Wind

Onshore

Wind

Offshore

Solar

Hydro

Figure 4. Development of the global energy mix (final energy supply) with a CO2-budget of 650 Gt.

The global energy system shifts from a world almost entirely reliant on the fossil fuel sources

oil, coal, and natural gas, to a fully decarbonized energy system in 2050. While starting out slowly,

the growth of renewables, especially biomass, solar and wind quickly picks up and reaches a stage of

about 50% of the energy mix being renewable as soon as 2030. This transformation varies strongly

from sector to sector, as well as on a regional basis.

4.2.2. Electricity

The energy system experiences a very strong sector coupling of the power with both the heat

and transportation sectors. This can be observed via the vastly rising generation of power, more than

tripling by 2050 compared to 2015 values. Figure 5shows the development of the power generation

mix between 2015–2050 at the global level. While conventional sources still account for over 2/3

Energies 2017,10, 1468 14 of 28

of consumption in 2015, and even over 80% when including hydropower, the energy mix changes

structurally from 2025 on, mainly due to solar photovoltaics becoming economically competitive. Since

low-carbon electricity generation technologies are available at low costs, the electricity sector is the

first to decarbonize, and freeing up CO

-emissions for the heat and transportation sectors. Natural gas

loses market shares relatively early (2025), and the use of coal is also significantly reduced. Due to

possible sunk costs, rising fossil fuel prices (especially for natural gas), and increased competitiveness

of renewables, no new fossil-fueled power plants are constructed. Instead, existing capacities are being

utilized, depending on their remaining lifetimes. Coal remains the largest fossil fuel source for power

generation, although still quickly declining in overall amounts after 2020.

Energies 2017, 10, 1468 14 of 29

Since low-carbon electricity generation technologies are available at low costs, the electricity sector is

the first to decarbonize, and freeing up CO2-emissions for the heat and transportation sectors. Natural

gas loses market shares relatively early (2025), and the use of coal is also significantly reduced. Due

to possible sunk costs, rising fossil fuel prices (especially for natural gas), and increased

competitiveness of renewables, no new fossil-fueled power plants are constructed. Instead, existing

capacities are being utilized, depending on their remaining lifetimes. Coal remains the largest fossil

fuel source for power generation, although still quickly declining in overall amounts after 2020.

Somewhat surprisingly, wind picks up market shares relatively late, i.e., in the 2030s (onshore

wind) or even after 2035 for offshore wind. This is due to optimal solar potentials being exhausted,

which gives wind power the opportunity to enter the mix. The contribution of hydropower increases

slightly, with most optimal potentials already being utilized beforehand. Hydropower makes up a

share of about 13% of the final power generation profile.

Figure 5. Development of global power generation.

Figure 6 presents the regional power generation mixes in 2050, demonstrating regional

differences in our model results.

Figure 6. Power generation profiles in 2050.

4.2.3. Heat

The energy mix in the heating sector shows quite a different decarbonization pathway. Figure 7

shows the model results for the high temperature heat production from 2015–2050. After a first

10000

20000

30000

40000

50000

60000

70000

2015 2020 2025 2030 2035 2040 2045 2050

TWh

Figure 5. Development of global power generation.

Somewhat surprisingly, wind picks up market shares relatively late, i.e., in the 2030s (onshore

wind) or even after 2035 for offshore wind. This is due to optimal solar potentials being exhausted,

which gives wind power the opportunity to enter the mix. The contribution of hydropower increases

slightly, with most optimal potentials already being utilized beforehand. Hydropower makes up a

share of about 13% of the final power generation profile.

Figure 6presents the regional power generation mixes in 2050, demonstrating regional differences

in our model results.