energies
Article
An Empirical Study of How Household Energy Consumption Is
Affected by Co-Owning Different Technological Means to
Produce Renewable Energy and the Production Purpose
Lucas Roth 1,*, Jens Lowitzsch 1and Özgür Yildiz 2,3
Citation: Roth, L.; Lowitzsch, J.;
Yildiz, Ö. An Empirical Study of How
Household Energy Consumption Is
Affected by Co-Owning Different
Technological Means to Produce
Renewable Energy and the
Production Purpose. Energies 2021,14,
3996. https://doi.org/10.3390/
en14133996
Academic Editors: Sergey Zhironkin
and Manuela Tvaronaviˇcien˙
e
Received: 6 May 2021
Accepted: 22 June 2021
Published: 2 July 2021
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4.0/).
1Kelso-Professorship for Comparative Law, East European Economic Law and European Legal Policy,
Faculty of Business Administration and Economics, European University Viadrina, Grosse Scharrnstr. 59,
2Department of Environmental Economics and Economic Policy, Technische Universität Berlin, Str. des 17.
3Advyce GmbH, Brunnstraße 7, 80331 München, Germany
*Correspondence: r[email protected]; Tel.: +49-(0)335-5534-2560
Abstract:
The transition from fossil fuel-based to renewable energy sources is one of the main
economic and social challenges of the early 21st century. Due to the volatile character of wind and
solar power production, matching supply and demand is essential for this transition to be successful.
In this context, the willingness of private consumers to use energy flexibly has gained growing
attention. Research indicates that a viable driver to motivate consumers to be demand flexible is to
make them (co-)owners of renewable energy production facilities. However, existing research has
only analyzed this question from an aggregated perspective. This article analyses whether behavioral
changes triggered by (co-)ownership in renewables differ according to the type of installation; be it
solar, wind, or bioenergy. In addition, the prosumption options self-consumption/self-consumption
and sale/sale are considered. To do so, we collected 2074 completed questionnaires on energy
consumption that entered an econometric model using propensity score matching to control for
estimation biases. We find significant differences in the willingness to consume electricity in a flexible
manner for (co-)owners of solar installations. However, only the usage of household appliances
proves to be statistically significant (p-value = 0.04). Furthermore, the results show that within
the group of (co-)owners of solar installation, the choice between self-consumption and sale of the
produced energy has a significant effect on the inclination to become demand flexible (
p-value ≤0.001
;
p-value = 0.003).
Keywords:
renewable energy; consumer ownership; demand flexibility; demand side management;
propensity score matching; solar energy
1. Introduction
Producing energy from renewable sources has gained increasing support in Europe
and throughout the world. The authors of [
1
] predict that the largest share of capacity
increase in the years to come will be shouldered by photovoltaic (PV) installations and
wind turbines. In their market analysis and forecast from 2018 to 2023, PV and wind power
alone will account for approximately 75 percent of growth in the renewable energy (RE)
sector. (This article focuses solely on electric energy). The abbreviation RE therefore means
exclusively renewable energy in terms of electricity production.
The volatile nature of both technologies poses numerous technical and economic
challenges. From an infrastructure perspective, the predominantly centralized electricity
grid systems run into difficulties when loads vary in short intervals and with significant
values, e.g., rapid declines or increases in power production, for instance, when wind
turbines or PV installations cannot produce as forecasted due to unexpected weather
Energies 2021,14, 3996. https://doi.org/10.3390/en14133996 https://www.mdpi.com/journal/energies
Energies 2021,14, 3996 2 of 38
changes. A constant baseload from fossil energy sources can stabilize the grid system
and mitigate critical events triggered by volatile renewable energy sources (vRES) that
threaten to impair grid stability [
2
]. However, this solution conflicts with the declared
political aim of an ever-increasing market penetration of renewables in the European
Union [
3
]. Furthermore, under this approach, coping with an increasing amount of vRES
in the electricity mix also requires the capability of quick reaction which strongly increases
the overall system cost due to a combination of low operation times and high per unit
production cost of the most flexible backup generation technologies, for instance, of modern
gas turbines [
4
]. From an economic perspective, cumulated production amounts of vRES
cause market-related challenges. If the share of available energy in domestic or international
energy markets fluctuates unexpectedly, energy prices follow common market laws. The
invert relationship between price and supply results in dramatically depleting, sometimes
even negative, electricity prices in cases of unexpected oversupply caused by weather
changes, that are often difficult to predict. Regulation to incentivize renewables, such as
priority dispatch of renewables into the grid, worsens the resulting economic inefficiency [
5
].
Given the latest political endorsement for PV and wind energy in major economies, both
effects are expected to worsen if not controlled for as the share of vRES is on the rise in the
foreseeable future [1].
One possibility to mitigate both effects described above is to apply a broad spectrum
of demand side management (DSM). DSM comprises all measures (e.g., flexibilization of
demand) undertaken on the consumption side to stabilize the grid system. Strategies for
promoting sustainable behavior and flexible demand are manifold.
The involvement of citizens in community energy projects is often mentioned as a
particularly promising approach. The underlying rationale is that the direct participation
of citizens in energy projects through (co-)ownership deepens the personal involvement
with questions of energy consumption and thus promotes sustainable behavior suitable to
support the infrastructure system.
Initial findings from research in this context show that there is a link between the
role of being a (co-)owner in energy projects and personal energy consumption behav-
ior [
6
,
7
]. However, these studies share that the characteristic of (co-)ownership is only
examined without differentiating according to the underlying technology used to produce
RE. Nonetheless, the type of installation deserves special attention as available technologies
differ regarding background characteristics, such as the technical complexity of the opera-
tion or the spatial proximity between energy production and consumption. For example,
solar energy systems are easier to handle during operation than wind power plants and
production as a rule takes place closer to consumption in particular with rooftop systems [
8
].
This paper takes up those technological differences. The research goal is to deepen the
growing body of literature on demand side management and analyze the question whether
there is a relationship between (co-)ownership of renewable energy production facilities,
the applied technology, and energy consumption behavior. To do so, an empirical analysis
with propensity score matching methodology is applied.
2. Literature Review
The sustainability and security of economic development around the globe are based
on the smooth and uninterrupted supply and demand mechanisms of energy sources [
9
].
The current stage of the international energy sector transformation is characterized by a
growth in demand for energy supply and intensified use of renewable energy sources
(RES) resulting in a higher fluctuation of energy supply and technical challenges stemming
thereof [
10
]. To deal with the challenge of providing stability and flexibility to energy
systems that include a high proportion of vRES, several strategies involving utilities, grid
operators, regulatory bodies, policy-makers, and consumers are pursued. These strategies
can be distinguished into three main categories: energy efficiency (EE), on-site back-up
systems, and flexibilization. In this context, flexibilization, i.e., measures that encourage
consumers to load shifting, is considered as particularly attractive as it mainly relies on
Energies 2021,14, 3996 3 of 38
capacities that already exist and therefore is theoretically easy to implement [
11
]. While the
effectiveness of flexibilization at the demand side in general is debated, given an increasing
share of vRES, the bulk of research and practical experience supports its advantages and
suitability to contribute to support infrastructure stability [
12
–
15
]. Approaches to systemat-
ically characterize DSM measures identify strategies which include among others technical
strategies such as remote control or in-home displays for load and consumption trans-
parency [
16
,
17
], dynamic pricing strategies to promote demand response [
18
], information
campaigns for energy consumers, e.g., the diffusion of good practices, and regulatory
measures, like utility obligations, product standards, and product labeling [
15
] with all of
these strategies being able to operate on the household and the industrial sector level.
When focusing on DSM at the household level, various factors can be used as trig-
gers to bring private consumers to apply demand side measures. A widely discussed
approach in the scientific discourse and energy industry practice is to equip households
with smart appliances and face them with variable electricity prices. These variable pricing
schemes can be time-based (see, e.g., in [
19
–
21
]) or schemed to optimize distribution grid
services [22,23]
. A second approach focuses on the provision of information through tradi-
tional communication channels such as information brochures and billing letters or smart
meter devices in order to promote flexible behavior (see, e.g., in [
24
,
25
]). The provision
of information has in general been proven to be an effective measure to induce changes
in energy consumption behavior and people’s opinion towards renewable energy [
26
].
Another factor that can be used to bring private consumers to apply demand side measures
is the appeal to norms. In this context, aspects such as environmental concern are tested
to enable load-shifting (and other energy conservation behaviors) among end users with
mixed results on the actual efficacy of such measures (see, e.g., in [
27
–
29
]). In line with this
rather psychological approach, Frederiks et al. [
30
] explained energy consumption behavior
with biased perception, consumer heuristics, and other irrational inclinations. Furthermore,
other cognitive and contextual factors can have an influence on an individual’s behavior
in the context of energy-related topics and therefore also in the context of electricity con-
sumption and demand flexibility. These factors include the awareness of the problem, e.g.,
an individual’s understanding and knowledge on energy-related questions such as grid
stability and supply security; nuances of media coverage, e.g., the variety of information
sources and its evaluation; and finally, trust in the energy system’s stability [
31
]. Accord-
ingly, much more attention should be given to these aspects when analyzing the behavioral
aspects of energy consumption [
32
]. In this context, business models involving citizens as
(co-)owners of RE and thus as prosumers are of particular interest as they combine several
possible influences on an individual’s energy consumption. These business models, often
referred to in the literature as community energy (see, e.g., in [
33
]) or citizen energy (see,
e.g., in [
34
]), either involve citizens in project planning and financing with self-consumption
having a subordinate role, or they explicitly foster prosumership and address several of
the above-mentioned triggers to promote demand flexibility [
35
]. Therefore, the behavior
of energy consumers involved in citizen energy projects has been the focus of various
scientific papers. Among others, Anda and Temmen [
36
] find that community-based social
marketing has shown to be very effective at inducing behavioral change towards a more
efficient and flexible energy use. Another study from Hoppe et al. [
37
] shows that in
addition to psychological and socio-demographic variables, the characteristic framework
of an energy cooperative, as a particular business model for citizen energy, can contribute
to more engagement in energy-saving actions and reported energy conservation. Another
approach by Goulden et al. [
38
] revealed that citizen participation in energy production
and energy consumption holds great potential for various kinds of DSM measures in a
non-commercial context. Further, the authors of [
7
] showed that different prosumption
options for produced electricity impact demand flexible behavior as well, depending on
whether produced electricity can be used for self-consumption, sale, or both.
However, all these papers have a restriction in common, i.e., the participants of citizen
energy projects are analyzed as a homogenous group in comparison to non-participants
Energies 2021,14, 3996 4 of 38
without any further distinction of the type of RE conversion technology they are invested
in, be it PV, wind, or bioenergy. Kubli et al. [
6
] discuss the technological background to a
limited extent when they showed that people who own PV installations or electric vehicles
are more likely to adapt DSM, indicating a relationship between energy technologies and
consumption behavior. This study, however, focuses on three different application areas
of RE technologies, being photovoltaic heat pumps and electric vehicles, i.e., electricity
production, heating, and transportation. Extending and deepening this analysis with
regard to other types of RE is important, as the preceding studies show that the application
of green technologies is expected to allow for and trigger different behavior. Moreover,
conversion technologies have inherently different characteristics that hold potential for
various, behavioral implications. For instance, PV systems when compared to wind farms
(in particular, offshore), are generally located closer to the prosumer, are smaller in terms
of installed capacity requiring smaller investments per unit and offer individual control.
The authors derive the leading research hypotheses on this basis.
When looking at RE conversion technologies, biomass, hydropower, geothermal,
tidal/wave, solar, and wind energy are identified as technologies with the highest potential
to substitute fossil fuels [
39
]. Among those technologies, biomass, wind, and solar energy
have the largest potential in terms of installed capacity and are expected to be the most
prevalent in the future [
40
], which is why the following analysis focuses on these tech-
nologies. Drawing on results of the data analysis the authors combine the two aspects of
conversion technology and prosumption options to refine the picture further. Availability
of data permitting, each conversion technology is analyzed in association with the three
options to use electricity produced, i.e., (i) self-consumption, (ii) sale to third parties, and
(iii) the choice between self-consumption and sale.
Regarding the conversion technology, in line with the work in [
6
], it is assumed that
people who (co)-own solar power plants are inclined to be more willing to exercise demand
flexible behavior than the other groups. Further, in line with Goulden et al. [
38
], a general
involvement in RE in other technologies is expected to trigger a more flexible consumption
as compared to no involvement.
For further analyzation, the following abbreviations for group affiliation were used
throughout the manuscript:
•Non-owners People who are not involved with RE;
•Solar-owners People who (co-)own a solar energy power plant;
•Wind-owners People who (co-)own a wind turbine;
•Biogas-owners People who (co-)own a biogas power plant.
At the meta-level, the authors formulate two main hypotheses (A and B) regarding
the used conversion technology and prosumption options:
Hypothesis A (HA).
(Co-)ownership in renewable energy production facilities positively impacts
a consumers’ willingness to use electricity in a flexible manner. Solar installations hold the largest
potential to trigger flexibility in this regard.
This breaks down into six sub-hypotheses:
Hypothesis 1 (H1). Solar-owners are more willing to be demand flexible than Non-Owners.
Hypothesis 2 (H2). Wind-owners are more willing to be demand flexible than Non-Owners.
Hypothesis 3 (H3). Biogas-owners are more willing to be demand flexible than Non-Owners.
Hypothesis 4 (H4). Solar-owners are more willing to be demand flexible than Wind owners.
Hypothesis 5 (H5). Solar-owners are more willing to be demand flexible than Biogas owners.
Energies 2021,14, 3996 5 of 38
Hypothesis 6 (H6). Wind-owners are more willing to be demand flexible than Biogas owners.
Hypothesis B (HB).
The consecutive hypotheses regarding the technology in association with
different options to use the produced energy depend on the results of H1–H6 as well as the availability
of data. Therefore, the respective hypotheses system will be built up after analyzing H1–H6.
3. Methodology
The following chapters give a brief overview of the sampling process, specifications
on measurement, and statistical procedure.
3.1. Deliberations on Data Collection
The study was conducted in cooperation with the website www.immobilienscout24.de
(accessed on 14 April 2020). The sample was obtained via an online survey using the data
base of this website. ImmobilienScout24 is the biggest online real estate platform in
Germany. The user base of 14.8 million monthly visitors [
41
] provides perfect conditions to
obtain a representative high-quality sample containing participants holding demographics
close to those of the German population. At the same time, it was important to reach a
sufficient number of people of interest to this analysis, i.e., (co-)owners of RE production
facilities, to enable the researchers to apply the underlying econometric approach which,
in this instance, requires a large sample size (cf. Section 3.4). As a real estate platform,
the ImmobilienScout24 data base is likely to hold a large share of users which own real
estate. The probability of homeowners being involved with energy related questions (e.g.,
production, self-sufficiency, and energy efficiency) is higher than with typical consumer
panels holding less people who live in own property [42].
The participants were invited via e-mail to take part in the study. The invitation
text specifically mentioned purposes and involved institutions in the analyzation of the
data. In total, the sample consisted randomly chosen users of 135,000 users who have an
account with ImmobilienScout24. 4315 potential participants had to be excluded because
of personal e-mail settings or technical restriction (Users of the website can restrict their
e-mail settings as to not receive any e-mails from ImmobilienScout24. In this case the e-mail
addresses had to be excluded. Technical problems can be, for instance, a full e-mail inbox,
not existing or deactivated domain). To motivate the participants to complete the whole
questionnaire, 5 Amazon vouchers were raffled. Doing so increases the response rate and
statistical power in terms of representativity [
43
,
44
]. Ultimately 130,685 invitation e-mails
were sent which resulted in 2074 completed questionnaires which entered the analysis.
The response rate is in line with ordinary expectation for online surveys where people do
not expect an e-mail invitation for a study [
45
–
47
]. Answers from participants who did
not finish the questionnaire might yield bad data quality [
48
]. Therefore, only completed
questionnaires were included in the analysis.
3.2. Deliberations on Measurement
To analyze the hypothesis, in a first step, the groups of interest must be distinguished.
To do so, the questionnaire included questions regarding a participant’s involvement in RE
production (the Questionnaire flow, i.e., a summary of all the questions the participants
had to go through is depicted in Appendix A. The full version of the questionnaire is
depicted in Appendix B. Please note that the original questionnaire was in German. Under
each questionnaire screenshot an English translation can be found). Further, to determine
a personal level of demand flexibility the participants had to rate their willingness to
consume energy in a flexible way for three different electricity consumption settings on a
Likert scale possible answers being graded from 1 for “I strongly disagree” to 5 “I strongly
agree”. The electricity consumption settings were chosen after a literature analysis that the
questionnaire was based on. Possibilities for demand flexibility are discussed in several
domains. For instance, Firth et al. [
49
], Naus et al. [
50
], and Moser [
51
] highlight the
importance of household appliances and mobile electrical devices. Further, Kubli et al. [
6
]
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