Solar policy and practice in Germany: How do residential households with solar panels use electricity?

Energy Research & Social Science 21 (2016) 199–211

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Energy Research & Social Science journal homepage: www.elsevier.com/locate/erss

Original research article

Solar policy and practice in Germany: How do residential households with solar panels use electricity? Inga Wittenberg ∗,1 , Ellen Matthies 1 Otto-von-Guericke-University Magdeburg, Institute for Psychology, Department of Environmental Psychology, Universitätsplatz 2, 39106 Magdeburg, Germany

a r t i c l e

i n f o

Article history: Received 26 February 2016 Received in revised form 17 July 2016 Accepted 21 July 2016 Keywords: Photovoltaic Electricity consumption Households Energy saving

a b s t r a c t A substantial amount of the over 1.5 million photovoltaic (PV) systems in Germany are installed in residential households. Among these households, those with the option of self-consumption, i.e., to consume self-generated electricity can reduce their electricity consumption, especially grid electricity, by load shifting and acting in an energy-efficient manner. We examined how electricity consumption is influenced by contextual and attitudinal factors. We administered an online questionnaire to 425 households with PV recruited from 15 PV-related web portals. The results showed that their electricity consumption was not lower than in other households, but environmental motivation was higher. Sufficiency attitudes and environmental motivation were predictors of engaging in energy-saving behaviors which in turn contributed to consumption reduction. Battery storage and automatic load shifting increased selfconsumption. Evidence was found for a distinction between households with PV installation before and after grid parity was achieved, especially concerning moderation effects of the economic framework. To reduce electricity consumption, a combination of efficient technologies and more environmentally motivated energy-saving behaviors should be supported. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction The transition of the energy system from conventional energy production (e.g., nuclear power plants) to energy production from renewable energy sources (e.g., solar photovoltaic systems) implies important changes in the realms of both technology and human behavior. As Sovacool [1] pointed out, there is a need for research on the human dimension in order to complete knowledge from, for example, engineering and economics, by offering insights into the influence of social components on energy use, the acceptance and use of technology, as well as communication. In particular, there is a need to investigate the factors that influence behaviors as well as behavioral change in energy use and the related technologies. Research on individuals and households in the energy system has tended to focus on their role as consumers, but they can also play a role as energy producers (e.g., Stern [2]). The role of households in energy production has changed across time: households contributed considerably to energy production in preindustrial

∗ Corresponding author. E-mail addresses: [email protected] (I. Wittenberg), [email protected] (E. Matthies). 1 Otto-von-Guericke-University Magdeburg, Institute for Psychology, Department of Environmental Psychology, Universitätsplatz 2, 39106 Magdeburg, Germany. http://dx.doi.org/10.1016/j.erss.2016.07.008 2214-6296/© 2016 Elsevier Ltd. All rights reserved.

societies, but in industrial societies, while they have continued to consume energy, major energy production usually takes place elsewhere. With the development of renewable energies and technologies such as photovoltaic (PV) systems, the role of the household is changing again, with a return to energy production [2]. The change from energy consumer to energy producer and consumer (the so-called prosumer) and its impact on the way individuals use energy has been addressed by several authors from different research areas (e.g., Keirstead [3], Stedmon et al. [4]) and policymakers (e.g., Department of Energy and Climate Change [5]). It is supposed that renewable energy production with microgeneration such as PV systems increases the awareness of energy consumption and thus favors a reduction in energy use or an increase in demand management (e.g., Bahaj and James [6], Dobbyn and Thomas [7], Haas et al. [8], Keirstead [3]). 1.1. General framework and diffusion of PV systems in Germany More than 1.5 million PV systems are currently registered in Germany [9],2 with a substantial proportion located in residential households. PV systems on the roofs of one- or two-family houses

2

BSW-Solar = Bundesverband Solarwirtschaft e.V. (German Solar Association).

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Fig. 1. EEG tariffs and electricity prices (own compilation of Vögele et al. [39] based on data from BMWi [10] and Bundesnetzagentur [40].

are generally small-scale systems with <10 kWp (kWp stands for kilowatt peak, which refers to the maximum power that can be generated by the system). Other residential households own systems <30 kWp, which are still small-scale systems (e.g., on the roof of a barn that offers more space than the roof of a typical one-family house). A market analysis of PV roof systems [10]3 reported estimates of a market share of 70% for systems <10 kWp and 25% for systems of 10–40 kWp, each representing 30% of the power production from rooftop PV systems in Germany. According to Corradini [11], in 2012, the diffusion of rooftop PV systems on one- or two family houses in Germany was of about 1–4% depending on the federal state, with a larger diffusion in the South (up to 4.1% in Baden-Württemberg) than in the North (0.7% in the federal city state Bremen). Compared with other European countries and even worldwide, Germany was still the country with the highest cumulative installed PV capacity in 2014 with 38.2 GW [12].4 But the situation was somewhat different for the annual capacity installed in 2014: Germany arrived in only fifth place with 1.9 GW after China (10.6 GW), Japan (9.7 GW), the US (6.2 GW), and the UK (2.3 GW; IEA [12]). In order to facilitate the diffusion of renewable energy in Germany, different policy measures have been adopted by the government. The feed-in tariffs constitute an important German policy that has been widely copied by other countries (e.g., Hoppmann et al. [13]) and has served as a good example of effective FIT [14]. Grid-connected PV systems have to be registered to be eligible for the feed-in tariffs fixed by the Erneuerbare Energien Gesetz (EEG; translated as renewable energy law). The legal framework and financial incentives have changed a lot over time (for an overview, see Wirth [15], or Fig. 1, which indicates the feed-in tariff for PV power [ct/kWh] for different periods) with adjustments in the policy design made by policy makers to consider factors such as technological developments and diffusion and market (Hoppmann et al. [13]). The tariff depends on the conditions fixed by the EEG on the date of the PV system’s installation and is guaranteed for a period of 20 years. For example, the feed-in tariffs decreased from more than 50 ct/kWh in 2006 to about 12 ct/kWh in 2014. From 2009 to 2012, additional incentives were paid for the direct consumption of PV-generated electricity by households (about 25 ct/kWh in 2009 decreasing stepwise to about 8–12.5 ct/kWp

3 BMWi = Bundesministerium für Wirtschaft und Energie (Federal Ministry for Economic Affairs and Energy, Germany). 4 IEA = International Energy Agency.

in 2012). In 2012, grid parity was achieved for households with PV systems <10 kWp (e.g., Moshövel et al. [16], Wirth [15]). This means that – for most of the households with new contracts – the price they had to pay per kWh was higher than the feed-in tariff, and thus, the self-consumption, i.e., consumption of self-generated electricity would lead to economic savings [17,18]. With the increasing number of PV systems feeding electricity in the grid, grid stability became an important concern. Since May, 2013, incentives have also been offered for the installation of battery storage systems. Batteries offer the opportunity to store electricity produced during the day for later use (e.g., in the evening), thus increasing the self-consumption and reducing the amount of electricity fed into the grid. The storage program of the KfW5 Bankengruppe and the Federal Ministry for the Environment aims towards an increase in self-consumption in order to ensure grid stability. The program provides low-interest loans and repayment subsidies for new PV installations if a fixed battery storage system is installed. PV systems installed since January, 2013, can also benefit from these conditions for the retrofitting of such a storage system. PV owners who receive funding oblige themselves to feeding maximum 60% of their nominal capacity into the grid. About half of the PV owners who invest in battery storage systems use this funding [16,19]. About 17,000 battery storage systems were installed between May, 2013, and March, 2015 [20]. 1.2. Households with PV systems: motivations for PV adoption and household characteristics Research on the acceptance and adoption of PV energy provides insight into the factors that motivate residential households to adopt PV systems and the characteristics of these households. Several factors have been identified, especially environmental protection/awareness [8,21–23], affinity with technology/technical interest [8,23,24], and autarky [22]. This last point refers to a certain degree of independence from grid electricity gained by means of PV-generated electricity in the household (e.g., Korcaj et al. [22]). Whereas some authors also found a financial motivation [8,21,22], others [21] did not identify such a factor as relevant. Social motivations (e.g., social status [22]), social networks [21], and peer effects [25] constituted another important dimension. According to the

5 KfW = Kreditanstalt für Wiederaufbau (Reconstruction Loan Corporation, Germany).

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literature, PV owners are usually early adopters or innovators (as defined by, e.g., Rogers [26]) with higher income and educational levels than the average population, a great deal of interest in technology (e.g., Fischer [27], Haas et al. [8], Keirstead [3]), and a high awareness of environmental issues [27,3]. 1.3. Electricity use and energy-saving in households with PV systems Like other households, households with PV systems can reduce their demand for energy through the efficient use of energy-consuming equipment and by investing in energy-efficient appliances. An additional option for households with PV is to influence the kind of electricity they use by influencing the respective shares of electricity from the grid and electricity from their PV system. The latter is also called the self-consumption and is defined as the electricity produced by the PV system that is directly consumed by the producer (e.g., Luthander et al. [28]). Two options can be used to increase the self-consumption: technical solutions such as battery storage systems and behavioral options by load shifting, also called demand side management (DSM; e.g., Luthander et al. [28]). Load shifting refers to shifting the demands for electricity consumption to the times during which electricity is produced. The two options can also be combined for a further increase in the self-consumption. Analyzing different studies on battery storage systems and DSM, Luthander et al. [28] concluded that the self-consumption can be increased by 13–24% with battery storage systems and 2–15% with DSM. Only a small number of studies have focused on the electricity consumption and energy-saving behaviors of residential households that own a PV system [6–8,29,3,28,4]. Most of these studies were conducted in the UK, the sample size was generally small, and the methodological approaches differed. Therefore, Luthander et al. [28] concluded that the existing research did not allow general conclusions to be drawn about the reactions of PV owners after the systems were installed. Nevertheless, most of the studies reported some kind of steps taken to save energy and load shifting [28]. With these limitations in mind, we conducted a review of the studies generally cited in the existing literature. It has been suggested that households who decide to install a PV system have already taken a number of energy-saving behaviors in their daily lives [7,8,3] and have invested in energy-efficient appliances [8]. Thus, there tends to be only a small window left for additional reductions in energy use of PV owners after PV installation. Haas et al. [8] found that the participants reported more investments in energy saving (e.g., in more efficient appliances) than the general population. A pre- and post-installation comparison revealed only two significant behavioral changes in the use of a green electricity tariff and efficient lighting [8]. When microgeneration was installed by the council or housing association (e.g., in the case of social housing), many households reported an increase in the awareness of energy use and behavioral changes after PV installation [7]. In a qualitative study of nine households in social housing with PV systems, Bahaj and James [6] found little evidence of energy-saving or load-shifting activities despite financial incentives. Schelly [30] pointed out that different policy measures can have important consequences on energy use in households before and after PV adoption. Indeed, Schelly’s study revealed that FIT measures favored more energy-efficient behaviors in households with PV systems, whereas other policy measures (e.g., size limitations on the PV system according to prior electricity consumption) can result in increased consumption both before and after PV installation. Keirstead [3] found a self-reported reduction of 6% in overall electricity use and an increase in general energy awareness. Loadshifting behaviors were indicated by 43% of the participants. These

201

were mainly households with facilitating contextual factors such as appliances that were equipped with timers or a family member who was at home during the day. Keirstead [3] concluded that there was evidence for what the author called a “double dividend” in reference to the production of renewable energy and a reduction in electricity use. Hondo and Baba [29] indicated that compared with their behavior before the PV installation, some households reported an increase in proenvironmental daily life behaviors, especially electricity-related behaviors. The most important changes were indicated for “Turn off the lights when going out even when going out for a short time” (30%) and “Unplug or switch off the main power of an electrical appliance when not using” (27%). Changes in some other daily life behaviors (electricity use, heating and other behaviors) were also mentioned by 9–19% of participants. The main reason behind the change indicated by participants was an increased interest in energy and electricity costs. Studies related to programs that were implemented to favor the diffusion of PV systems in residential households such as the 1000 roofs programme, followed by the 100,000 roofs programme, in Germany, or the 200 kWp-photovoltaic-rooftop programme in Austria have provided some insights into the electricity consumption of residential households with PV systems. The monitoring of these programs involves data collection on technical aspects as well as electricity production and consumption. For the 1000 roofs programme, no clear evidence of a reduction in electricity use was found [31]. In the Austrian programme, Haas et al. [8] compared the electricity consumption data of households before and after they installed a PV system. The authors concluded that the changes in energy consumption they found depended on the household’s initial consumption. Households with a high initial consumption (>3500 kWh/year) tended to reduce their consumption, whereas the reverse was observed for households with a low initial consumption. Comparing households who participated in the German 1000 roofs programme and German households without a PV system, Erge et al. [32] did not find any significant difference in annual electricity consumption. Unfortunately, the authors mentioned only the household size (four or more persons) but did not indicate whether the type of building (one- or two-family house or apartment) was considered. The electricity consumption data from households participating in different PV programs has not shown a clear trend toward reduced energy use in PV households. 1.4. Factors related to electricity consumption in households with PV systems Concerning electricity consumption after the PV system was installed, the way households were supported and informed about the PV system and their energy use was mentioned. For example, it was important that the microgeneration installation was accompanied by information about the amounts of electricity that the household produced and used [7]. The importance of continuous information and education after the installation was also emphasized by Bahaj and James [6]. In another study of households with PV systems, Keirstead [3] indicated high energy literacy amongst participants and highlighted the importance of monitoring devices, available information, and the location of the PV system in highvisibility areas (e.g., the kitchen). A relationship between awareness of the PV system, assessed by a PV-checking-index, and increased environmental behavior was observed in a study of Japanese households with PV systems [29]. Although the importance of visibility was highlighted in these studies [29,3], it did not appear to have an impact on electricity use in the study by Bahaj and James [6]. Nevertheless, all of these studies agreed that the visibility of energy use seemed to be relevant. Another factor influencing the environmental behavior of households owning PV systems was communication about

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environmental behaviors [29]. Those who reported that family members tended to discuss environmental behaviors more frequently after PV installation also indicated an increase in environmental behavior. Communication amongst PV owners in PV networks or in the neighborhood was also related to more PV checking behaviors and to changes in environmental behaviors [29]. In conclusion, only a few studies have explored the energy use and savings of households with PV systems. Given the diversity in methodology and research areas, the small sample sizes of some studies, and the diversity of national contexts, it is even more difficult to draw conclusions about energy consumption and underlying factors for households with PV systems. It seems necessary to further investigate both electricity consumption and the related factors in these households. Some indications of a role of environmental motivations for electricity consumption in households with PV can be found in previous studies. Thus, such motivations should be examined further. As mentioned earlier, the economic framework and policy measures related to PV systems in residential households can differ notably on both international and national levels. It appears important to consider the evolution of the economic framework to gain a better understanding of its potential impact on electricity consumption in households with PV systems. Two important aspects in this context are grid parity, achieved in 2012, and the diffusion of battery storage systems. They are closely linked to policy measures through FIT and financial incentives for self-consumption and storage installation. To our knowledge, the impact of battery storage systems on electricity consumption behaviors in households with grid-connected PV systems has yet to be investigated in the social sciences. From a technological point of view, storage options offer notable opportunities to increase self-consumption. Thus, policy, economic and technological changes deserve special attention. 1.5. The present study In this study, we investigated the extent to which different psychological and contextual factors influence the electricity use of residential households with PV systems. For this purpose, we paid special attention to environmental motivation and sufficiency attitudes as well as to the technical equipment involved in battery storage systems. We also considered the funding context. Indeed, recent studies (e.g., Hoppmann et al. [13]; Schelly [30]) suggest that policy measures and the implied financial incentives should be considered carefully in studies of energy use in households with PV systems to evaluate behavior in the correct context and to evaluate the impact of these measures. Therefore, we differentiated between households that installed a PV system before 2012 and in or after 2012. We addressed the following questions: 1) Does the electricity consumption of households with PV differ from the consumption of non-PV-owning households in Germany? Do they differ in environmental motivation? 2) Do environmental motivation and sufficiency attitudes encourage the efficient use of electricity, that is, an optimized use of solar power (saving electricity in the household or load shifting in order to use primarily self-produced electricity)? 3) Does storage capacity (batteries) support a more efficient use of electricity (load shifting)? 4) As grid parity implies important changes in the economic framework of households with PV systems, does the electricity use of households that installed their PV system before grid parity was achieved differ from the electricity use of households that installed their system after this important event? For the first question, we hypothesized that the overall electricity consumption of households with PV would not differ from

the consumption of non-PV-owning households in Germany but that they would be more environmentally motivated. Indeed, previous studies considering electricity consumption in PV households did not find a clear trend of reduction, while these households seemed to be characterized by a high environmental motivation. We expected that environmental motivation and sufficiency attitude would encourage households to engage in energy-saving behaviors as well as in load-shifting activities. Furthermore, we postulated that battery storage systems would facilitate load shifting and would reduce the use of grid electricity, as a battery storage offers additional technological and behavioral options. We expected that grid parity would act as an important moderator of load shifting and related increases in the self-consumption (a reduction in the proportion of electricity from the grid). With grid parity, self-consumption becomes economically interesting because the consumption of self-generated electricity becomes cheaper than the consumption of electricity from the grid. 2. Materials and methods 2.1. Procedure A pre-study was conducted at the end of 2014. The main objective of the pre-study was to gain insight into the equipment of PV systems in residential households for the main study. For example, we were interested in the equipment with a battery storage system. The focus was on technical information, and psychological variables were not included. Participating PV owners responded to a questionnaire distributed online by photovoltaic-related web portals. For the main study, we administered an online questionnaire that was distributed by means of 15 photovoltaic-related web portals. These web portals were addressed to PV owners or interested persons, informing about PV systems in different ways (professional information, return on experience, exchange with other PV owners). Different types of web portals were included such as PV related web journals, associations and PV user internet forums. Households with PV systems were informed by posts on the web portals or newsletters sent out by some web portals. They were invited to take part in the study via a link to the online questionnaire. Participants did not receive any compensation for their participations, but a lottery was announced (and organized after the data were collected). The survey began in January, 2015, and ended in April, 2015. The main study comprised two phases of data collection. The results presented in this paper concern the first phase. The second collection of data took place in summer, 2015. 2.2. Sample The original sample in this study consisted of 425 residential households with PV. After excluding participants with too many missing values, 388 participants remained. Almost all participants were men (85.8%). Their average age was M = 51.03 years (SD = 11.21, Min = 20, Max = 80, 9.8% missing), which is comparable to the average in a representative study of German households (M = 51.31, UBA [33]).6 Monthly household income and educational level were higher than in the general population in Germany. Nearly half of the participants (46.7%) indicated a monthly household income over 3600D ; 40.2% reported holding a university or college degree. In the representative sample [33], around 70% reported a monthly household income under 2500D , and 14.9% reported holding a university or college degree. Household size ranged from one to eight people. About 40% (41.3%) were one- or two-person

6

UBA = Umweltbundesamt (Federal Environment Agency, Germany).

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Table 1 Overview of the installed PV systems of the sample. PV system characteristics Installation period of the PV system: 2005–2015 (2009–2014)

Installed power PV systems installed before 2012 PV systems installed in/after 2012 Equipment with Battery storage PV systems installed before 2012 PV systems installed in/after 2012 Installation period of the battery storage Installed storage capacity PV systems installed before 2012 PV systems installed in/after 2012

<2011 2011 2012 2013 2014 2015 1.2–99.0 kWp (1.2–40.5 kWp) 1.2–30.0 kWp (1.2–22.2 kWp) 1.3–99.0 kWp (1.3–40.5 kWp) 19.3% (12.8%) 10.6% (7.1%) 23.8% (18.1%) 2011–2014 (2012–2014) 2.0–30.0 kWp (2.0–24.0 kWp) 3.0–26.0 kWp (5.0–15.0 kWp) 2.0–30.0 kWp (2.0–24.0 kWp)

13.7% (14.9%) 19.8% (29.2%) 18.8% (22.6%) 26.3% (31.8%) 18.6% (1.5%) 2.8% (0.0%)

Note: Results for households with complete electricity consumption data in brackets.

households, whereas the households with three to four people represented 44.4%. Only 192 participants provided all of the information necessary for the analysis of electricity consumption data. Table 1 provides an overview of the characteristics of the PV systems installed by the households. The PV systems were installed between 2005 and 2015. Fewer than one in seven (13.7%) was installed before 2011, nearly 20% were installed in 2011 (19.8%) as well as in 2012 (18.8%). The year with the highest number of new installations was 2013 (26.3%). Another 18.6% was installed in 2014, and 2.8% in 2015. Installed power varied from 1.2 kWp up to 99.0 kWp. Roughly one in four (23.8%) of the participants that installed a PV system in 2012 or later indicated that their PV system included battery storage. In households that installed a PV system before 2012, only about 10% reported that they invested in battery storage. Battery storage units were installed between 2011 and 2014 with a storage capacity of 2 kWh up to 30 kWh. 2.3. Measures In this study, we assessed sufficiency attitudes, environmental motivation, energy-saving behaviors (see Table 2), as well as load shifting. We chose the short version of the sufficiency attitude scale by Henn [34] to measure sufficiency attitudes. Sufficiency attitudes that consider the environmental impact of consumption behaviors imply that a person tends to reduce consumption [34]. Example items are “With my lifestyle, I would like to use as little of the available resources (such as water, energy, wood) as possible,” “I think that there is no need for such a large choice/assortment of products in our supermarkets,” “In my opinion, all these new things proposed for sale all the time/incessantly are a waste of resources.” The six items on this scale were rated with a 5-point agreement scale (1 = do not agree, 2 = agree slightly, 3 = agree moderately, 4 = agree fairly, 5 = agree very much). Environmental motivation was assessed with seven items from the biennial panel study of the UBA [33]. The items included behaviors related to electricity consumption but also other aspects of consumption and waste management. One more item was added (“I attempt to use as little energy as possible”). They were assessed dichotomously (1 = yes, 2 = no). In this study, the scale was used to assess the general motivation for proenvironmental behavior, which we assumed would underlie the reported behaviors. For energy-saving behaviors, we chose four items from Nachreiner and Matthies [35]. They were related to different every-day electricity consumption behaviors in households (e.g., cooking) and were aimed at reducing electricity consumption. Load-shifting behavior

was measured with three items concerning manual load shifting, load shifting with a timer, and automatic load shifting. For example, for load shifting with a timer, the following item was used: “I use a timer or delayed start function (if available) to operate appliances.” These differ in personal implication (highest for manual load shifting) and required equipment (most complex for automatic load shifting). For example, manual load shifting implies that someone must be at home to turn appliances on when PV energy production is high. This kind of load shifting requires the person to be informed about PV production and the weather, but no additional equipment is necessary as meter reading and information about the weather are sufficient. By contrast, automatic load shifting involves equipment with a suitable data logger and remote-controlled sockets or equipped appliances. A 5-point frequency scale (1 = [almost] never, 2 = rarely, 3 = sometimes, 4 = most of the time, 5 = [almost] always) was used to assess the items concerning energy-saving behaviors and load-shifting behaviors. For these items, the option do not know/does not apply was proposed. It was treated as missing in the analyses. We were interested in total electricity consumption and the proportion of total electricity consumption represented by grid electricity. Therefore, additional questions concerned electricity production, electricity fed into the grid, and electricity drawn from the grid (kWh per settlement period) as well as the settlement period with both the grid operator and the power supply company. For these data, in order to get indications that were as accurate as possible, participants were invited to refer to documents provided by their grid operator and power supply company. The amount of self-generated electricity that was consumed was calculated by subtracting the amount of electricity that was fed into the grid from the total amount of electricity that was self-generated. The total consumption of electricity was calculated by adding the amount of electricity that came from the grid to the amount of self-consumption. Moreover, different incentive systems for households with PV in Germany were recorded. The following aspects were analyzed: ratio of feed-in tariff and grid electricity tariff, remuneration for the self-consumption as well as high or low levels of feed-in tariff, grid electricity tariff, and remuneration for the self-consumption. 3. Results Data analyses were conducted with SPSS. For moderation effects, the additional PROCESS package by Hayes [36] was also used.

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Table 2 Scales and items. Households that installed PV system in/after 2012

Households that installed PV system before 2012

Scale of sufficiency attitudes [34] With my lifestyle, I would like to use as little of the available resources (such as water, energy, wood) as possible. I think that it is worthwhile to own few things. My comfort is more important to me than a plain living. I think that there is no need for such a large choice/assortment of products in our supermarkets. I think that it is worthwhile to cultivate or produce as much as possible on your own/yourself. In my opinion, all these new things proposed for sale all the time are a waste of resources. Environmental motivation index [33] I minimize water and electricity consumption. I minimize the consumption of heating costs. I use green electricity. I turn unnecessary appliances or lights off. I buy energy-efficient appliances. I attempt to consume as little electricity as possible. I avoid waste. I separate waste and give it to the different waste systems separately. Energy-saving behavior index [35] In your household, are you opening the doors/lids of cooling appliances as shortly as possible? How often do you behave this way personally? In your household, do you usually disconnect electronic entertainment devices (such as television, set-top boxes, games consoles but also computers and monitors) completely from the grid, when they are not in use? How often do you behave this way personally? In your household, do you usually turn on the washing machine when it is completely filled with laundry? In your household, are you usually turning off hot plates and/or the oven a few minutes before the end of the cooking/baking time in order to use the residual heat?

3.1. Comparison of households that installed a PV system and non-PV-owning households representative of Germany In a first step, we investigated whether and to what extent the electricity consumption data in households with PV that we collected differed from the data collected in a representative study [37].7 As shown in Table 3, compared with the electricity consumption of households in Germany living in one- or two-family houses in general [37], the households with PV who participated in this study had medium to high electricity consumption. Especially the two-person households indicated a higher mean consump(Mhouseholds that installed a PV system before 2012 = 4537.60 kWh; tion Mhouseholds that installed a PV system in or after 2012 = 3901.09 kWh) than the households in the general study. The sample size for each household size was small; no significant difference was observed between the electricity consumption of households that installed a PV system before versus in/after 2012. Table 4 shows that the two-person households were older (M = 56.67 years) than the other households (average age under 50 years), and one third (33.1%) of the two-person households said they were retired. In order to determine whether households with PV differed from the German representative households concerning their environmental motivation, we compared their environmental motivations to those reported by a German representative sample [33]. As shown in Table 5, compared with households without PV [33], the households with PV systems in the present study indicated higher rates of environmental motivation. Differences were observed for all of the included behaviors: those related to energy consumption and those concerning other aspects of environmental behaviors. The differences were especially high for two items, “I use green elec-

7 BMUB = Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety, Germany).

M

SD

N

M

SD

N

18.81

3.75

126

18.60

4.12

239

6.99

1.26

115

7.09

1.06

214

16.74

2.43

121

16.20

2.43

226

tricity” and “I buy energy-efficient appliances.” But these were also the items with the lowest percentages in the representative UBA study. No significant differences were observed between households with PV installation before versus in/after 2012. The average education level and household income in our sample were higher than in the representative sample. In addition, in our sample, over 85% were male, whereas in the UBA sample, only about 49% were male. A comparison of subsamples of households that installed a PV system before versus in/after 2012, in which we controlled for these three variables, showed results that were similar to the percentages presented above. No significant differences were obtained between the subsamples except for the minimization of water and electricity consumption and the consumption of heating costs. 3.2. Factors influencing the electricity consumption of households that installed a PV system In order to investigate whether environmental motivation and sufficiency attitudes encourage the efficient use of electricity (selfreported energy consumption behavior assessed by energy-saving behaviors and the objective/metered consumption of electricity), correlational analyses were computed. As shown in Table 6, in both subsamples, energy-saving behaviors were significantly (p < 0.01) correlated with sufficiency attitudes (r = 0.25 and r = 0.48, respectively) and environmental motivation (r = 0.35 and r = 0.30, respectively). The analysis revealed that, for households that installed a PV system in or after 2012, manual load shifting was correlated with energy-saving behaviors (r = 0.20, p < 0.01), whereas load shifting with timers was correlated with both environmental motivation (r = 0.25, p < 0.01) and energy-saving behaviors (r = 0.16, p < 0.01). A correlation between load shifting with a timer and environmental motivation was obtained for households that installed a PV system before 2012 (r = 0.25, p < 0.01). As shown in Table 7, reported

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Table 3 Reported electricity consumption of households with PV system compared to the electricity consumption of German households in general. Households that installed a PV system before 2012

Households that installed a PV system in/after 2012

Household size

N

M

SD

Min

Max

N

M

SD

Min

Max

1 2 3 4 5 and more

1 32 13 23 10

2 884.90 4 537.60 4 738.67 4 638.26 5 779.03

– 2 451.42 1 788.99 1639.68 3 102.87

– 1 630.87 2 506.87 2 005.49 1 787.90

– 10 723.38 8 623.63 9 894.11 12 734.89

7 34 22 23 15

2 913.84 3 901.09 4 813.51 5 015.23 6 951.22

1 142.64 1 609.70 2 132.32 2 020.12 3 097.61

1 921.26 1 674.59 1 704.67 2 336.40 2 719.45

4 368.97 8 322.80 10 582.00 9 990.37 14 670.19

t-test German house- holds 2700 (3100) 3200 (3900) 4000 (5000) 4400 (5600) 5500 (7200)

t = 1.25 n.s. t = 0.28 n.s. t = −0.70 n.s. t = −0.93 n.s.

Note: Household size is indicated in number of persons. For the consumption in German households, consumption of households with electrical water heating is indicated in brackets. Table 4 Age of participants, number of persons <18 years old and percentage of retired by household size. Household size

1 2 3 4 5 or +

Age

Number of persons <18 years

M

SD

Min

Max

49.00 56.67 47.94 43.42 46.07

15.50 11.06 9.67 7.05 8.91

33 32 33 34 30

73 80 70 61 65

1

2

62.9% 17.0% 14.8%

% of retired participants 3

74.5% 11.1%

4

40.7%

11.1%

25.0% 33.1% 17.5% 5.4% 4.0%

Table 5 Comparison of environmental motivation of households with PV and a German representative samples. Households that installed a PV system before 2012

Households that installed a PV system in/after 2012

Item

% UBA

% PV-sample

Chi2

Phi

Odds ratio

% PV-sample

Chi2

Phi

Odds ratio

I minimize water and electricity consumption. I minimize the consumption of heating costs. I use green electricity. I turn unnecessary appliances or lights off. I buy energy-efficient appliances. I avoid waste. I separate waste and give it to the different waste systems separately.

84.2% 80.6% 21.0% 73.3% 55.1% 58.4% 76.5%

88.8% 90.5% 55.7% 95.3% 96.0% 86.1% 97.7%

1.90 n.s. 7.59** 78.09** 30.43** 81.33** 36.60** 31.07**

−0.030 n.s. −0.060** −0.192* * −0.120** −0.196** −0.131** -0.121**

.672 .437 .212 .136 .051 .228 .078

92.7% 93.2% 54.1% 96.6% 97.4% 83.0% 97.9%

12.03** 22.76** 121.93** 63.09** 156.65** 51.99** 57.54**

−0.073 ** −0.101** −0.234** −0.168** −0.265** −0.153** -0.160**

.417 .302 .226 .096 .032 .289 .071

Note: ** p < 0.01. The results did not show any significant difference between households that installed a PV system before versus in/after 2012.

Table 6 Correlations between sufficiency attitudes, environmental motivation, self-reported energy-saving behaviors and load shifting. Households that installed a PV system before 2012 1 1 Sufficiency attitudes 2 Environmental motivation

3 Energy- saving behavior

4 Manual load shifting

5 Load shifting with timer

6 Automatic load shifting

– 0.25** (.20**) N = 114 0.25** (0.23*) N = 120 −0.06 n.s. (−0.00 n.s.) N = 125 0.06 n.s. (0.06 n.s.) N = 104 0.10 n.s. (0.04 n.s.) N = 81

2

3

4

Households that installed a PV system in/after 2012 5

6

–

0.35** (0.26**) N = 111 −0.02 n.s. (0.02 n.s.) N = 115 0.25** (0.26*) N = 97 −0.11 n.s. (−0.17 n.s.) N = 76

–

0.06 n.s. (0.08 n.s.) N = 121 0.03 n.s. (−0.00 n.s.) N = 102 0.04 n.s. (−0.03 n.s.) N = 80

–

−0.02 n.s. (−0.14 n.s.) N = 105 −0.37** (−0.30**) N = 82

–

0.09 n.s. (0.19 n.s.) N = 81

–

1 – 0.44** (0.43**) N = 213 0.48** (0.46**) N = 226 0.06 n.s. (0.10 n.s.) N = 231 0.03 n.s. (0.03 n.s.) N = 206 0.10 n.s. (0.06 n.s.) N = 171

2

3

4

5

6

–

0.30** (0.26**) N = 204 0.06 n.s. (0.08 n.s.) N = 209 0.19** (0.21**) N = 188 0.06 n.s. (0.05 n.s.) N = 157

–

0.20** (0.21**) N = 220 0.16* (0.13 n.s.) N = 197 −0.01 n.s. (−0.04 n.s.) N = 167

–

−0.23** (−0.26**) N = 205 −0.35** (−0.37**) N = 169

–

.23** (0.26**) N = 165

–

Note: ** p < 0.01, * p < 0.05, Spearman’s Rho in brackets.

total electricity consumption was not correlated with environmental motivation. Energy-saving behaviors were only correlated with reported total electricity consumption for households that installed a PV system in or after 2012 (r = −0.27, p < 0.01). To go one step further in the analysis, and to determine whether the relations suggested by the correlations could be confirmed, we computed regression analyses (see Table 8). For households that installed a PV system before versus in/after 2012, sufficiency

attitudes predicted self-reported energy behaviors, adj. R2 = 0.06, F(1, 109) = 7.51, p < 0.01, and, adj. R2 = 0.24, F(1, 202) = 61.86, p < 0.01, respectively. Adding environmental motivation increased the explained variance by 9% and 1%, respectively (R2 = 0.09 and R2 = 0.01, respectively). For households that installed a PV system before 2012, sufficiency attitudes no longer contributed to the explanation of energy-saving behaviors when environmental motivation were added to the model. The opposite finding was

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Table 7 Correlations between environmental motivation, self-reported energy-saving behavior and electricity consumption. Households that installed a PV system before 2012 1 1 Environmental motivation 2 Energy-saving behavior

3 Total electricity consumption (TEC)

4 Proportion of TEC represented by grid electricity

2

– 0.34** (0.19 n.s.) N = 77 −0.13 n.s. (−0.18 n.s.) N = 77 0.09 n.s. (0.16 n.s.) N = 77

3

Households that installed a PV system in/after 2012 4

1

–

−0.19 n.s. (−0.22*) N = 82 −0.08 n.s. (−0.13 n.s.) N = 82

–

0.20 n.s. (0.23*) N = 84

2

– 0.18 n.s. (0.12 n.s.) N = 91 −0.00 n.s. (−0.01 n.s.) N = 93 0.03 n.s. (0.01 n.s.) N = 93

–

3

4

–

−0.27** (−0.32**) N = 97 −0.10 n.s. (−0.09 n.s.) N = 97

–

0.25* (0.20*) N = 102

–

Note: ** p < 0.01, * p < 0.05, Spearman’s Rho in brackets.

Table 8 Regression analysis for self-reported energy-saving behavior. Households that installed a PV system before 2012

Model 1 Constant Sufficiency attitudes Model 2 Constant Sufficiency attitudes Environmental motivation

Households that installed a PV system in/after 2012

B

SE B

␤

p

B

SE B

ˇ

p

13.71 0.16

1.13 0.06

0.25

0.000 0.007

11.22 0.29

0.69 0.04

0.48

0.000 0.000

10.62 0.11 0.57

1.43 0.06 0.17

0.18 0.30

0.000 0.056 0.001

10.02 0.26 0.24

1.04 0.04 0.16

0.44 0.11

0.000 0.000 0.124

Note: For households that installed a PV system before 2012 Model 1 R2 = 0.06, adj.R2 = 0.06, F(1,109) = 7.51, p < 0.01; R2 = 0.09 for Model 2; Model 2 R2 = 0.15, adj.R2 = 0.14, F(2,108) = 9.55, p < 0.01. For households that installed a PV system in/after 2012 Model 1 R2 = 0.23, adj.R2 = 0.23, F(1,202) = 61.86, p < 0.01; R2 = 0.01 for Model 2; Model 2 R2 = 0.24, adj.R2 = 0.24, F(2,201) = 32.34, p < 0.01.

Table 9 Regression analysis for the reported total electricity consumption for households that installed a PV system in/after 2012.

Constant Energy-saving behavior

B

SE B

ˇ

p

9419.17 −282.47

1705.93 102.12

−0.27

p = 0.000 p = 0.007

Note: R2 = 0.08, adj.R2 = 0.07, F(1,95) = 7.65, p < 0.01.

observed for households that installed a PV system in or after 2012. Sufficiency attitudes remained the only predictor of energy-saving behaviors. Table 9 shows that energy-saving behaviors contributed significantly toward explaining reported total electricity consumption, adj. R2 = 0.07, F(1, 95) = 7.65, p < 0.01, for households that installed a PV system in or after 2012. 3.3. Battery storage systems, load shifting, and electricity consumption in residential households that installed a PV system To determine whether battery storage systems support a more efficient use of electricity, we computed correlations between storage, load shifting, and electricity consumption (see Table 10). For households that installed a PV system before or in/after 2012, the results indicated significant correlations between storage and automatic load shifting (r = 0.32, p < 0.05 and r = 0.25, p < 0.05, respectively) as well as between storage and the proportion of reported total electricity consumption represented by grid electricity (r = −0.50, p < 0.01 and r = −0.57, p < 0.01, respectively). For households that installed a PV system in or after 2012, Pearson’s r indicated additional significant correlations between storage and manual load shifting (r = −0.20, p < 0.05) as well as reported total electricity consumption and automatic load shifting (r = 0.26, p < 0.05). On the basis of the correlation between battery storage and automatic load shifting, we wanted to verify the hypothesized moderation effect of battery storage on load shifting. Differentiating the

subsamples by PV installation period, the results shown in Table 11 indicate a significant main effect of the equipment with a storage system (p < 0.05 and p < 0.01) in both subsamples. The interaction between the equipment with a storage system and automatic load shifting was significant only for households that installed a PV system before 2012 (p < 0.05). The explained variance was 45%, adj. R2 = 0.45, F(3, 50) = 15.23, p < 0.01, for households that installed a PV system before 2012 and 32%, adj. R2 = 0.32, F(3, 65) = 12.97, p < 0.01, for households that installed a PV system in or after 2012.

3.4. Economic framework Depending on the installation period, the policy measures and the economic framework of households with PV systems differed notably. As indicated earlier, when grid parity was achieved in 2012, the ratio of feed-in tariffs and grid electricity tariffs for residential households was reversed. The remuneration for the self-consumption was suppressed. By consequence, the economic situation changed notably. Therefore, we compared the economic framework for households that installed a PV system before versus in/after 2012. The comparison of high and low levels of feed-in tariffs and grid electricity tariffs were based on median dichotomization. The results are presented in Table 12. As expected, for most of the households that installed a PV system before 2012 (85.8%), the feed-in tariff was higher than the grid electricity tariff, whereas the opposite ratio was found for the large majority of households that installed a PV system in or after 2012. The former usually received remuneration for the selfconsumption (85.6%) in contrast to the latter (5.4%). As the distinction between installation periods seems to reflect quite clearly the differences concerning the feed-in tariff and the remuneration for the self-consumption while the grid electricity tariff conditions were similar in both subsamples, we decided to include these two variables in order to verify the hypothesized moderation effect of the economic framework on the proportion

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207

Table 10 Correlations between battery storage, load shifting and electricity consumption in PV households. Households that installed a PV system before 2012 1 1 Manual load shifting

–

2 Load shifting with timer

−0.08 n.s.

3 Automatic load shifting

4 Total electricity consumption (TEC)

5 Proportion of TEC represented by grid electricity

6 Storage

2

3

4

Households that installed a PV system in/after 2012 5

6

1

2

3

4

5

6

– –

−0.29**

–

(−0.16 n.s.) N = 70 −0.39**

0.08 n.s.

–

(−0.33**) N = 88 −0.18 n.s.

0.35**

–

(−0.34*) N = 54 0.03 n.s.

(0.17 n.s.) N = 53 0.10 n.s.

0.19 n.s.

–

(−0.27*) N = 72 −0.14 n.s.

(0.31**) N = 69 −0.03 n.s.

0.26*

–

(0.06 n.s.) N = 82 0.02 n.s.

(0.04 n.s.) N = 70 0.05 n.s.

(0.26 n.s.) N = 54 −0.41**

0.20 n.s.

–

(−0.18 n.s.) N = 100 0.06 n.s.

(−0.03 n.s.) N = 85 −0.07 n.s.

(0.20 n.s.) N = 69 −0.27*

0.25*

–

(−0.04 n.s.) N = 82 −0.04 n.s. (0.00 n.s.) N = 83

(0.06 n.s.) N = 70 0.02 n.s. (0.02 n.s.) N = 70

(−0.16 n.s.) N = 54 0.32* (0.34*) N = 54

(0.23*) N = 84 0.20 n.s. (0.15 n.s.) N = 84

−0.50** (−0.32**) N = 84

(0.02 n.s.) N = 100 −0.20* (−0.17 n.s.) N = 103

(−0.05 n.s.) N = 85 −0.04 n.s. (−0.03 n.s.) N = 88

(−0.21 n.s.) N = 69 0.25* (0.25*) N = 72

(0.20*) N = 102 0.14 n.s. (0.18 n.s.) N = 102

−0.57** (−0.49**) N = 102

–

–

Note: ** p < 0.01, * p < 0.05, Spearman’s Rho in brackets.

Table 11 Moderation effects of storage system on the relationship between automatic load shifting and the proportion of total electricity consumption represented by grid electricity for households that installed a PV system before versus in/after 2012. Households that installed a PV system before 2012

Constant Storage Automatic load shifting Storage x automatic load shifting

Households that installed a PV system in/after 2012

B

SE B

t

p

B

SE B

t

p

0.61 [0.58,0.64] −0.15[−0.26,−0.03] −0.01 [−0.05,0.02] −0.12[−0.19,−0.05]

0.015 0.056 0.018 0.036

42.08 −2.63 −0.70 −3.42

<0.01 <0.05 n.s. <0.01

0.59 [0.57,0.62] −0.15 [−0.22,−0.08] −0.01 [−0.03,0.01] −0.04[−0.09,−0.006]

0.013 0.034 0.012 0.024

44.96 −4.44 −0.87 −1.74

<0.01 <0.01 n.s. n.s.

Note: For households that installed a PV system before 2012 R2 =0.48, adj.R2 = 0.45, F(3,50) = 15.23, p < 0.01. For households that installed a PV system in/after 2012 R2 =0.35, adj.R2 = 0.32, F(3,65) = 12.97, p < 0.01. Storage and automatic load shifting were mean centered prior to analysis.

Table 12 Economic framework (Feed-in tariff, grid electricity tariff and remuneration for self-consumption) for households that installed PV system before versus in/after 2012.

Ratio of feed-in tariff and grid electricity tariff Remuneration for self-consumption Feed-in tariff (Mdn = 17.50 cts/kWh)

Grid electricity tariff (Mdn = 25.00 cts/kWh)

Feed-in tariff > grid electricity tariff <17.50 cts/kWh ≥17.50 cts/kWh M SD <25.00 cts/kWh ≥25.00 cts/kWh M SD

Households that installed a PV system before 2012

Households that installed a PV system in/after 2012

85.8% (83.8%) 85.6% (94.1%) 8.9% (8.4%) 91.1% (91.6%) 30.67 (30.58) 8.22 (7.64) 53.7% (51.8%) 46.3% (48.2%) 24.56 (25.15) 4.08 (3.50)

8.1% (9.1%) 5.4% (5.7%) 70.9% (60.2%) 29.1% (39.8%) 15.90 (17.19) 4.33 (3.97) 55.4% (53.0%) 44.8% (47.0%) 24.39 (24.63) 3.30 (3.05)

Note: Results for households with complete electricity consumption data in brackets.

of reported total electricity consumption represented by grid electricity. The first variable “installation period” included economic factors that were stable over a long period (20 years) for one household, whereas the second variable “grid electricity tariff” changed more frequently. Therefore, we expected that the moderation effect of the former would be moderated by the latter. We tested the model shown in Fig. 2 (an arrow pointing on another arrow indicated an expected moderation). The results presented in Table 13 showed a moderated moderation effect (p < 0.05) but no main effects or other interaction effects: The moderation of the PV system’s installation period on the relationship between automatic load shifting and the proportion of electricity from the grid was moderated by the grid electricity

Fig. 2. Moderation effects of economic framework on the grid electricity share of total electricity consumption.

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Table 13 Moderation effects of economic framework on the relationship between automatic load shifting and the proportion of total electricity consumption represented by grid electricity.

Constant Installation period (Install) Automatic load shifting (ALS) ALS × Install Grid electricity tariff (GET) ALS × GET Install × GET ALS × Install × GET

B

SE B

t

p

0.61 [0.58,0.63] −0.03 [−0.08,0.02] −0.02 [−0.06,0.01] −0.04 [−0.12,−0.04] −0.04 [−0.09,0.01] −0.04 [−0.11,0.02] 0.01 [−0.09,0.11] 0.16 [0.01,0.30]

0.013

47.69

<0.01

0.026

−1.15

n.s.

0.019

−1.28

n.s.

0.040

−0.95

n.s.

0.025

−1.60

n.s.

0.035

−1.27

n.s.

0.051

0.21

n.s.

0.074

2.09

<0.05

Note: R2 =0.18, adj.R2 = 0.13 F (110, 7) = 3.46, p < 0.01. R2 ALS × Install × GET = 0.03. Moderator and predictor variables were mean centered prior to analysis.

tariff. The model explained 13% of the variance, adj. R2 = 0.13, F(7, 110) = 3.46, p < 0.01. 4. Discussion The present study yields some interesting insights into the use of electricity in German households with a PV system. While the electricity consumption of the households with PV systems was not lower than in other households in Germany, the results showed that psychological, technological as well as economic and policy factors play a role for the energy use in PV households. Psychological factors contributed to explaining energy-saving behaviors, which had a significant impact on reducing electricity consumption. Technological factors as well as economic and policy factors were moderators of the relationship between load shifting and the selfconsumption but did not contribute to reducing total consumption. Environmental motivation or sufficiency attitudes seemed to be necessary to translate the potential of technology, supported by policy measures, into an efficient use of energy. 4.1. Comparison of households with a PV system and representative German households Compared with the electricity consumption of households in Germany living in one- or two-family houses in general [37], the results of the current study showed that the electricity consumption of the households with PV systems who participated in this study reported medium to high electricity consumption. This finding is in line with previous research on electricity consumption in households with PV systems that did not find lower energy consumption in households with a PV system compared with the general population [32,31,8]. Several reasons for the observed lack of difference in electricity consumption between households with PV compared with households without PV have already been mentioned in the literature: the high level of energy-saving behaviors aimed at reducing total consumption and investments in energyefficient appliances. Another explanation might be that individual daily life behaviors (e.g., the energy-saving behaviors assessed in this study and manual load shifting or shifting with a timer) that are related to environmental motivations are so-called intent-oriented behaviors (e.g., Stern [38]). The households might have a strong intention to act in an environmentally friendly manner but their efforts might fail to have a strong impact compared with so-called

impact-oriented behaviors (e.g., investing in a battery storage system). No differences were observed between households that installed their PV system before versus after grid parity was achieved in 2012. In two-person households with PV, the consumption was particularly high. This result could be explained by additional characteristics of these participants. They were older and one third was retired. Thus, they probably spent more time at home. The fact that households with a PV system did not report low but rather medium to high electricity consumption could be partly due to the fact that several of the households with unusually high consumption had some kind of electric mobility (e.g., electric bike, electric car). The comparison between households without a PV system [33] and our sample indicated higher environmental motivation in the PV households (with no difference between PV installation periods). As one motivation of PV adoption identified by previous research [8,21–23] was environmental awareness, and as such studies found that PV owners had a high awareness of environmental issues [27,3], this finding supports the importance of environmental motivations in households with a PV system. Among the environmental behaviors of the scale from the [33], those with the most important differences between the samples consisted of the use of green electricity and the purchasing of efficient appliances. It is interesting that these were similar to the only behaviors (i.e., use of green electricity and use of efficient lighting) that showed changes before and after the PV system was installed as reported by Haas et al. [8]. Even if these were the behaviors that were engaged in by the smallest percentages in the general population [33], thus leaving the largest space for differences, it seems that PV households went one step further in engaging in energy-saving behaviors than the general population. We can note that the consumption of green electricity is independent from the ownership of microgeneration as there are several energy supply companies proposing green electricity in Germany. 4.2. Factors related to electricity consumption Sufficiency attitudes predicted self-reported energy-saving behaviors. Adding environmental motivation increased the explained variance in households that installed a PV system before 2012, but sufficiency attitudes lost their predictive power. By contrast, for households that installed a PV system in or after 2012, sufficiency attitudes remained the sole predictor of energy-saving behaviors. Thus, sufficiency attitudes seem to be more relevant than environmental motivation for explaining the energy-saving behaviors of the former, whereas the inverse seems to hold for the latter. As grid parity made the self-consumption more interesting from an economic point of view because it led to economic savings, it may have increased the importance of sufficiency attitudes in electricity consumption for the households that installed a PV system in or after 2012. Despite the fact that sufficiency attitudes are supposed to include environmental considerations, the motivation to engage in energy-saving behaviors might be more economic for such households and more environmental for households that installed a PV system before 2012. Only self-reported energy-saving behaviors were directly linked to reported total electricity consumption and only for households that installed a PV system in or after 2012. Thus, such behaviors could contribute to reducing reported total electricity consumption. In both subsamples, neither sufficiency attitudes nor environmental motivation or energy-saving behavior were correlated with the proportion of reported total electricity consumption represented by grid electricity. One explanation for the absence of correlations between the proportion of electricity from the grid and these variables could be that sufficiency attitudes and environ-

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mental motivation as well as energy-saving behaviors are aimed at reducing the consumption of electricity in general. Instead, the proportion of electricity from the grid could be linked to the technical equipment of the PV system rather than to motivation and self-reported behaviors. 4.3. Load-shifting activities The results revealed some differences between households that installed a PV system before versus in/after 2012 concerning loadshifting behaviors. For households that installed a PV system before 2012, load shifting with a timer was related to environmental motivation. For households that installed a PV system in or after 2012, manual load shifting was correlated with self-reported energysaving behaviors and environmental motivation. The latter was also related to load shifting with a timer. No correlation between environmental motivations and automatic load shifting was observed. Therefore, the type of load shifting seems to depend on different motivations. For households with corresponding equipment (storage, monitoring devices with relevant information for load shifting and an automatic load shifting option), technology might be more decisive. The results regarding automatic load shifting and electricity consumption showed a positive correlation between load shifting and reported total electricity consumption (for households that installed a PV system in or after 2012) and a negative correlation with the proportion of electricity from the grid (in both subsamples), the latter being moderated by the storage capacity of households that installed a PV system before 2012. In addition, equipment with battery storage systems was related to a reduction in grid electricity but not to reported total electricity consumption. The use of these technologies supports the self-consumption of the PV-generated electricity, but the impact seems to be on the quality of the electricity rather than on the quantity. Further research should investigate the motivation behind the investment in battery storage and the automatic load shifting option to determine whether there are, for example, environmental or economic drivers. It should be mentioned that battery storage systems and technology that supports automatic load shifting often go together. Households with battery storage should thus be more likely to have the opportunity to use automatic load shifting options. Furthermore, households that receive funding for a battery storage system are obligated to limit the amount of electricity they feed into the grid.The fact that the moderation effect of battery storage was observed only in households that installed a PV system before 2012 is surprising and might be due to the very small sample size. The other types of load shifting were not significantly correlated with the proportion of electricity from the grid. This might be explained by the expected impact on the self-consumption that was based on simulations or technical studies (e.g., Luthander et al. [28]; Moshövel et al. [16]), which tends to be higher for battery storage than for load shifting. 4.4. Role of the economic framework Finally, grid parity and thus PV installation before versus in/after 2012 seem to be important. The results revealed that whether the PV system was installed before or after grid parity moderated the relationship between automatic load shifting and the proportion of electricity from the grid. This moderation was moderated by a high or low grid electricity tariff. Thus, economic savings appeared to play a role in the attempt to reduce grid electricity consumption by increasing self-consumption. This study addressed several questions related to the energy use of households with PV. There has been only a little previous

209

scientific research on electricity consumption in such households. The current study also took into account the rather new technology of battery storage systems. 4.5. Limitations Some limitations of this study should be mentioned. Even if our sample was larger than most samples from existing studies on energy use in PV households, it was still quite small and was limited to a national context. Additional research with larger samples in different countries should be conducted to complement the current results. This is especially true for the electricity data. Furthermore, the design of the study did not allow us to access the electricity data from meter readings or direct information from the energy supplier or the grid operator. If we asked participants to report technical information and data on their consumption and production from their meters or electricity bills, for example, the data provided by the participants was self-reported. Moreover, it would be interesting to compare self-reported electricity consumption of households with and without PV system in order to find out if there are differences in accuracy. More generally, a comparative study of households with and without PV system would also be worthwhile as differences may occur regarding other aspects as well. Some variables were assessed with only a few items (e.g., load shifting). They should be measured in greater detail in future research and should be augmented by additional information concerning the use of the PV system and battery storage. 5. Conclusion Concerning the comparison of households with a PV system and German households in general, the results showed that households with a PV system did not report lower electricity consumption but higher environmental motivations than other German households. These results are coherent with previous research. Regarding the role of environmental motivations and sufficiency attitudes as well as storage capacities and grid parity, the results of the present research highlight the complexity of electricity use in households with PV. As expected, on the one hand, there was evidence that psychological factors (sufficiency attitudes and environmental motivation) contribute to explaining energy-saving behaviors, which had a significant impact on reducing electricity consumption. On the other hand, technological factors such as battery storage systems and economic factors represented by the ratio of the feed-in tariff and the grid electricity tariff, remuneration for the self-consumption, and grid electricity tariffs were moderators of the relationship between load shifting and the self-consumption but did not contribute to reducing total consumption. Moreover, the results concerning load shifting activities indicated that, linked to environmental motivation, load shifting with a timer was also correlated with automatic load shifting. Based on these results, if the PV system is generally expected to increase the awareness of energy use, sufficiency attitudes and environmental motivation seemed to be necessary to translate such an awareness into an intention to reduce not only electricity consumption from the grid but also the total electricity use of the household. An increase in people’s awareness of their energy consumption (and energy literacy) could be achieved with microgeneration such as PV and the implied change in the role of households as they become prosumers. But personal implication in electricity use through behavioral changes related to environmental motivations seems necessary to translate this awareness into reductions in consumption. The results of the present study illustrated the importance of such psychological factors for energy-

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saving behaviors in households with PV system. Similarly these psychological factors could be helpful to transform the potential of technology, supported by policy measures, into an efficient use of energy, for example by additional policy measures and education. The energy transition implies several challenges. First, there was the challenge of the diffusion of renewable energy resources such as wind and solar power. The different financial incentives proposed with the EEG favored the diffusion of PV systems. With increases in the amount of energy coming from renewable energy resources and the decentralization of energy generation, a new challenge emerges: to preserve grid stability. Therefore, a recent strategy used by the German energy policy is to favor the self-consumption by funding battery storage systems. Our results show that the participating households with storage systems increased their selfconsumption but did not report lower electricity consumption than those without storage systems. Nevertheless, if policy makers want to reduce electricity consumption, they might consider that additional efforts should be made in this direction. Special attention should be paid to combining efficient technology with energysaving behaviors in future policy measures. Indeed, the energy transition implies both a reduction in electricity consumption and an increase in renewable energy produced for example by PV systems. To achieve these aims, interdisciplinary research could be very helpful for combining insights from different research areas, especially the social sciences and technology, to investigate how the barriers between intent- and impact-oriented behaviors or efficient technology and individual behaviors, could be reduced. For example, load shifting with a timer might be an interesting tool for the energy transition. More precisely, load shifting with a timer could bridge the gap between intention and impact or environmental motivation and efficiency. Indeed, there is a need for support for intention-related behaviors to increase their impact on the selfconsumption (load shifting) and in particular on a reduction in consumption related to energy-saving behaviors. The German feed-in tariffs constitute a widely copied policy measure (e.g., Hoppmann et al. [13]) and have served as a good example [14]. Results related to both diffusion of PV systems and related technologies as well as applied policy measures to handle the amount of renewable energy generated in households (e.g. financial incentives for battery storage systems) in Germany can be of interest for other countries, learning from examples. Indeed, if the policy strategies adopted in Germany seemed efficient to regulate the diffusion of technologies and the grid, efficiency of energy use in households deserves further attention in policy measures. In this context, the results showed that psychological factors seem to be a key factor that should not be neglected.

Acknowledgements This research was conducted as part of the project “Determinants of Household Decisions and Behaviour” within the Helmholtz Alliance ENERGY TRANS. As such the study was funded by the German Helmholtz Association and the German federal state of Saxony-Anhalt. We would like to thank Bernhard Weyres-Borchert, Stefan Vögele and Paul Stern for valuable comments on our analyses.

References [1] B.K. Sovacool, What are we doing here? Analyzing fifteen years of energy scholarship and proposing a social science research agenda, Energy Res. Soc. Sci. 1 (2014) 1–29, http://dx.doi.org/10.1016/j.erss.2014.02.003. [2] P.C. Stern, Individual and household interactions with energy systems. Toward integrated understanding, Energy Res. Soc. Sci. 1 (2014) 41–48, http://dx.doi.org/10.1016/j.erss.2014.03.003.

[3] J. Keirstead, Behavioural responses to photovoltaic systems in the UK domestic sector, Energy Policy 35 (8) (2007) 4128–4141, http://dx.doi.org/10. 1016/j.enpol.2007.02.019. [4] A.W. Stedmon, R. Winslow, A. Langley, Micro-generation schemes: user behaviours and attitudes towards energy consumption, Ergonomics 56 (3) (2013) 440–450, http://dx.doi.org/10.1080/00140139.2012.723140. [5] Department of Energy and Climate Change, Feed-in Tariffs: Government’s Response to the Summer 2009 Consultation, 2010, Retrieved from http:// webarchive.nationalarchives.gov.uk/20121217150421/http://www.decc.gov. uk/en/content/cms/consultations/elec financial/elec financial.aspx [24.02.16]. [6] A.S. Bahaj, P.A.B. James, Urban energy generation: the added value of photovoltaics in social housing, Renew. Sustain. Energy Rev. 11 (9) (2007) 2121–2136, http://dx.doi.org/10.1016/j.rser.2006.03.007. [7] J. Dobbyn, G. Thomas, Seeing the light: the impact of microgeneration on the way we use energy, in: Hub Research Consultants, 2005, Retrieved from http://www.sd-commission.org.uk/data/files/publications/Microgenerationreport.pdf [24.02.16]. [8] R. Haas, M. Ornetzeder, K. Hametner, A. Wroblewski, M. Hübner, Socio-economic aspects of the Austrian 200 kWp-photovoltaic-rooftop programme, Sol. Energy 66 (3) (1999) 183–191, http://dx.doi.org/10.1016/ S0038-092X(99)00019-5. [9] Bundesverband Solarwirtschaft e.v., Statistic data on the German Solar power (photovoltaic) Industry. Retrieved from https://www.solarwirtschaft.de/ fileadmin/media/pdf/2015 6 BSW Solar Faktenblatt Photovoltaik.pdf, 2015 [24.02.16]. [10] Bundesministerium für Wirtschaft und Energie, Marktanalyse Photovoltaik-Dachanlagen, Retrieved from https://www.bmwi.de/BMWi/ Redaktion/PDF/M-O/marktanalyse-photovoltaikdachanlagen,property=pdf,bereich=bmwi2012,sprache=de,rwb=true.pdf, 2014 [24.02.16]. [11] R. Corradini, Solarthermie im Gebäudebestand: Notwendiger Schlüssel Energiewende, 2015, Retrieved from: https://www.ffe.de/download/article/ 542/Solarthermie Notwendiger Schluessel zur Energiewende.pdfhttps:// www.ffe.de/download/article/542/Solarthermie Notwendiger Schluessel zur Energiewende.pdf. [12] International Energy Agency, 2014 Snapshot of Global PV Markets: Report IEA PVPS T1-26:2015. Retrieved from http://www.iea-pvps.org/fileadmin/dam/ public/report/technical/PVPS report - A Snapshot of Global PV - 1992-2014. pdf, 2015 [09.05.16]. [13] J. Hoppmann, J. Huenteler, B. Girod, Compulsive policy-making-The evolution of the German feed-in-tariff system for solar photovoltaic power, Research Policy 43 (2014) 1422–1441, http://dx.doi.org/10.1016/j.respol.2014.01.014. [14] B.K. Sovacool, The importance of comprehensiveness in renewable electricity and energy-efficiency policy, Energy Policy 37 (2009) 1529–1541, http://dx. doi.org/10.1016/j.enpol.2008.12.016. [15] H. Wirth, Recent Facts About Photovoltaics in Germany. Freiburg: Fraunhofer Institut für Solare Energiesysteme, 2015, Retrieved from https://www.ise. fraunhofer.de/en/publications/veroeffentlichungen-pdf-dateien-en/studienund-konzeptpapiere/recent-facts-about-photovoltaics-in-germany.pdf [24.02.16]. [16] J. Moshövel, K.-P. Kairies, D. Magnor, M. Leuthold, M. Bost, S. Gährs, E. Szczechowicz, M. Cramer, D.U. Sauer, Analysis of the maximal possible grid relief from PV-peak-power impacts by using storage systems for increased self-consumption, Appl. Energy 37 (2015) 567–575, http://dx.doi.org/10. 1016/j.apenergy.2014.07.021. [17] Bundesministerium für Wirtschaft und Energie, Energy Data, Retrieved from http://bmwi.de/EN/Topics/Energy/Energy-data-and-forecasts/energy-data. html, 2015 [24.02.16]. [18] KfW, KfW and Federal Environment Ministry Launch Programme to Promote use of Energy Storage in Solar PV Installation, 2013, Retrieved from https:// www.kfw.de/KfW-Group/Newsroom/Aktuelles/Pressemitteilungen/ Pressemitteilungen-Details 107136.html [24.02.16]. [19] M. Sterner, F. Eckert, M. Thema, F. Bauer, Der positive Beitrag dezentraler Batteriespeicher für eine stabile Stromversorgung. Bundesverband Erneuerbare Energie e.V. (BEE) & Forschungsstelle Energienetze und Energiespreicher (FENES), 2015, Retrieved from http://www.bee-ev.de/ fileadmin/Publikationen/BEE HM FENES Kurzstudie Der positive Beitrag von Batteriespeichern 2015.pdf [24.02.16]. [20] K.-P. Kairies, D. Haberschusz, D. Magnor, M. Leuthold, J. Badeda, D.U. Sauer, Wissenschaftliches Mess- und Evaluierungsprogramm Solarstromspeicher: Jahresbericht 2015, 2015, Retrieved from http://www.speichermonitoring.de/ fileadmin/user upload/Speichermonitoring Jahresbericht 2015 web.pdf [24.02.16]. [21] W. Jager, Stimulating the diffusion of photovoltaic systems: a behavioural perspective, Energy Policy 34 (14) (2006) 1935–1943, http://dx.doi.org/10. 1016/j.enpol.2004.12.022. [22] L. Korcaj, U.J. Hahnel, H. Spada, Intentions to adopt photovoltaic systems depend on homeowners’ expected personal gains and behavior of peers, Renew. Energy 75 (2015) 407–415, http://dx.doi.org/10.1016/j.renene.2014. 10.007. [23] J. Leenheer, M. de Nooij, O. Sheik, Own power: motives of having electricity without the energy company, Energy Policy 39 (9) (2011) 5621–5629, http:// dx.doi.org/10.1016/j.enpol.2011.04.037.

I. Wittenberg, E. Matthies / Energy Research & Social Science 21 (2016) 199–211 [24] C. Schelly, Residential solar electricity adoption: what motivates, and what matters? A case study of early adopters, Energy Res. Soc. Sci. 2 (2014) 183–191, http://dx.doi.org/10.1016/j.erss.2014.01.001. [25] A. Palm, Local factors driving the diffusion of solar photovoltaics in Sweden: a case study of five municipalities in an early market, Energy Res. Soc. Sci. 14 (2016) 1–12, http://dx.doi.org/10.1016/j.erss.2015.12.027. [26] E.M. Rogers, Diffusion of Innovations, Free Press, New York, 1962. [27] C. Fischer, From consumers to operators: the role of micro cogeneration users, in: M. Pehnt, M. Cames, C. Fischer, B. Praetorius, L. Schneider, K. Schumacher, J.P. Voß (Eds.), Micro Cogeneration. Toward Decentralized Energy Systems, Springer, Berlin, 2006, pp. 117–143. [28] R. Luthander, J. Widén, D. Nilsson, J. Palm, Photovoltaic self-consumption in buildings: a review, Appl. Energy 142 (2015) 80–94, http://dx.doi.org/10. 1016/j.apenergy.2014.12.028. [29] H. Hondo, K. Baba, Socio-psychological impacts of the introduction of energy technologies: change in environmental behavior of households with photovoltaic systems, Appl. Energy 87 (1) (2010) 229–235, http://dx.doi.org/ 10.1016/j.apenergy.2009.05.009. [30] C. Schelly, Implementing renewable energy portfolio standards: the good, the bad, and the ugly in a two state comparison, Energy Policy 67 (2014) 543–551, http://dx.doi.org/10.1016/j.enpol.2013.11.075. [31] B. Genning, V.U. Hoffmann, Sozialwissenschaftliche Begleituntersuchung Zum Bund-Länder-1000 Dächer Photovoltaik-Programm, Umweltinstitut Leipzig, Leipzig, 1996. [32] T. Erge, V.U. Hoffmann, K. Kiefer, The German experience with grid-connected PV-systems, Sol. Energy 70 (6) (2001) 479–487, http://dx.doi.org/10.1016/ S0038-092X(00)00143-2.

211

[33] Umweltbundesamt, Umweltbewusstsein in Deutschland 2012: Ergebnisse einer repräsentativen Bevölkerungsumfrage, 2013, Retrieved from https:// www.umweltbundesamt.de/sites/default/files/medien/publikation/long/ 4396.pdf [24.02.16]. [34] L. Henn, Ressourcenschonung durch maßvollen Konsum, in: Entwicklung eines Maßes zur Erfassung suffizienten Verhaltens, Albert-Ludwigs-Universität Freiburg, 2013, Unpublished Master’s Thesis. [35] M. Nachreiner, E. Matthies, Enhancing informational strategies for supporting residential electricity saving: identifying potential and household characteristics in Germany, Energy Res. Soc. Sci. 11 (2016) 276–287, http://dx. doi.org/10.1016/j.erss.2015.10.007. [36] A.F. Hayes, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach, Guilford Press, New York, 2013. [37] Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit, Stromspiegel für Deutschland, Retrieved from http://www.diestromsparinitiative.de/fileadmin/bilder/Stromspiegel/Brosch%C3%BCre/ Stromspiegel2014web final.pdf, 2014 [24.02.16]. [38] P.C. Stern, Toward a coherent theory of environmentally significant behavior, J. Soc. Issues 56 (3) (2000) 407–424, http://dx.doi.org/10.1111/0022-4537. 00175. [39] S. Vögele, P. Kunz, T. Naegeler, Technology-infrastructure transitions: potentials of technologies and concepts, in: Poster Session Presented at Statuskolloquium Helmholtz-Allianz Energy-Trans, Hannoversch Münden, 2015, September. [40] Bundesnetzagentur, PV data and Feed-in Tariffs, Retrieved from http://www. bundesnetzagentur.de, 2015 [24.02.16].