The adoption of residential photovoltaic systems (PV) is seen as an important part of the sustainable energy transition. To facilitate this process, it is crucial to identify the determinants of solar adoption. This paper follows a meta-analytical structural equation modeling approach, presenting a meta-analysis of studies on residential PV adoption intention, and assessing four behavioral models based on the theory of planned behavior to advance theory development. Of 653 initially identified studies, 110 remained for full-text screening. Only eight studies were sufficiently homogeneous, provided bivariate correlations, and could thus be integrated into the meta-analysis. The pooled correlations across primary studies revealed medium to large correlations between environmental concern, novelty seeking, perceived benefits, subjective norm and intention to adopt a residential PV system, whereas socio-demographic variables were uncorrelated with intention. Meta-analytical structural equation modeling revealed a model (N = 1,714) in which adoption intention was predicted by benefits and perceived behavioral control (R2 = .280), and benefits in turn could be explained by environmental concern, novelty seeking, and subjective norm (R2 = .641). Our results imply that measures should primarily focus on enhancing the perception of benefits. Based on obstacles we encountered within the analysis, we suggest guidelines to facilitate the future aggregation of scientific evidence, such as the systematic inclusion of key variables and reporting of bivariate correlations.
This paper addresses two issues in spatial research on residential PV adoption: First, spatial analyses typically do not differentiate between households who have decision power over their rooftop, and households who do not, possibly causing indistinct results. Second, household characteristics like environmental attitudes and innovativeness are not accounted for sufficiently, despite their proven relevance for adoption decisions. We performed a statistical analysis with an innovative spatial data set of Saxony (Germany) in which households of (semi-) detached houses were analyzed separated from the remainder, and household characteristics were included through the Sinus-milieu model. PV saturation in subgroups of the sample was compared using the Mann-Whitney U test. Results show that PV saturation is high in affluent zip-codes with high shares of (semi-) detached houses, and is higher in rural than in urban areas. The presence of environmentally concerned households (Post-Materialists) is positively, and the presence of innovative households (Leaders) is negatively associated with PV saturation. Our results show that household characteristics beyond socio-economic measures are relevant to understand differences in PV saturation in the Saxonian zip-codes. The merits of distinguishing between decisive households and the remainder are less clear.
Although single empirical studies provide important insights into who adopts a specific LCT for what reason, fundamental questions concerning the relations between decision subject (= who decides), decision object (= what is decided upon) and context (= when and where it is decided) remain unanswered. In this paper, this research gap is addressed by deriving a decision framework for residential decision-making, suggesting that traits of decision subject and object are determinants of financial, environmental, symbolic, normative, effort and technical considerations preceding adoption. Thereafter, the decision framework is initially verified by employing literature on the adoption of photovoltaic systems, energy efficient appliances and green tariffs. Of the six proposed relations, two could be confirmed (financial and environmental), one could be rejected (effort), and three could neither be confirmed nor rejected due to lacking evidence. Future research on LCT adoption could use the decision framework as guidepost to establish a more coordinated and integrated approach, ultimately allowing to address fundamental questions.
This study derives the levelized cost of heat (LCOH) for exemplary post-fossil district heating (DH) scenarios. The DH system of Leipzig in 2040 under the assumption of a completely climate-neutral heat supply is considered. Accordingly, four generation scenarios (GS) are proposed based on different energy carriers that are characterized as follows: (1) natural gas with carbon capture and storage, (2) hydrogen, (3) diversified mix of biomass, waste heat and solar, and (4) electricity. In addition, the scenarios’ robustness toward commodity prices is investigated using a sensitivity analysis. A modeling environment was used to optimize the hourly economic dispatch. Based on this, levelized costs are determined. For the reference case, the LCOH of the GS 1 and 2 exceeds the LCOH of GS 3 and 4. Furthermore, the results indicate that relying on singular energy carriers as opposed to diversified generation portfolios leads to less robust LCOH regarding price sensitivities.
This article reviews models in the scientific literature for industrial final-energy generation system design to evaluate their applicability in practice. Since energy efficiency has top priority worldwide and industry accounts for half of the world's energy consumption, it would be important to use state-of-the-art methodologies in real life applications. The contribution of the presented article to this matter is threefold. First, to facilitate the understanding of existing models', we developed a model evaluation method based on the attributes of industrial energy systems. We applied it to the reviewed scientific energy system models, providing the reader an overview of considered modelling approaches, design process steps, energy types, technological, economic and ecological aspects. Second, we conclude based on the results that current models for industrial cases are not completely suitable for wide practical implementation, among others because of the way they evaluate system operation and they do not incorporate every discussed requirement aspect. Third, this article draws up a potential modelling approach to fill these gaps, considering the missing points and omissions.
Community electricity storage systems for multiple applications promise benefits over household electricity storage systems. More economical flexibility options such as demand response and sector coupling might reduce the market size for storage facilities. This paper assesses the economic performance of community electricity storage systems by taking competitive flexibility options into account. For this purpose, an actor-related, scenario-based optimization framework is applied. The results are in line with the literature and show that community storage systems are economically more efficient than household storage systems. Relative storage capacity reductions of community storage systems over household storage systems are possible, as the demand and generation profiles are balanced out among end users. On average, storage capacity reductions of 9% per household are possible in the base scenario, resulting in lower specific investments. The simultaneous application of demand-side flexibility options such as sector coupling and demand response enable a further capacity reduction of the community storage size by up to 23%. At the same time, the competition between flexibility options leads to smaller benefits regarding the community storage flexibility potential, which reduces the market viability for these applications. In the worst case, the cannibalization effects reach up to 38% between the flexibility measures. The losses of the flexibility benefits outweigh the savings of the capacity reduction whereby sector coupling constitutes a far greater influencing factor than demand response. Overall, in consideration of the stated cost trends, the economies of scale, and the reduction possibilities, a profitable community storage model might be reached between 2025 and 2035. Future work should focus on the analysis of policy frameworks.
Little is known about the role of active peer effects (interpersonal contact) compared to passive peer effects (noticing or seeing) regarding residential photovoltaic (PV) diffusion. Recent literature suggests that peer effects are determined by the perceived credibility of one’s active peer connections regarding PV and passive peer effects. Utilising data from a survey of German house owners who either own a PV or indicate purchase intention (N = 1,165), this paper explores the underlying mechanisms of active peer effects during different stages of PV adoption decision-making. Our findings suggest that the perceived credibility of peers on PV related issues is positively associated with the reported number of adopters in the decision-maker’s peer group (passive peer effect) and also with the progress in the PV adoption decision process. Furthermore, we find a relationship between the perceived credibility of peers regarding PV and the reported influence strength of peer interactions throughout the decision-making processes of German householders, suggesting that subjective evaluations of peers play a role in active peer effects. Finally, we observe an association between the rate of self-initiated peer interactions about PV for respondents with a higher reported number of peers that have adopted PV, suggesting that passive effects (knowing peer PV adopters) play a role in initiating active peer interactions. These results are significant for all sub-groups, regardless of the decision progress. From a policy-making perspective, “bottom-up” efforts such as peer consultation and community-led outreach should be promoted in Germany to empower potential adopters.
This paper explores the role of social interactions in residential photovoltaic (PV) adoption. Our survey data from Germany indicate that residential PV decision makers are influenced primarily by stakeholders to whom they ascribe beneficial attributes. The data further show that key attributes vary along the decision making process: integrity and likeability demonstrate the strongest association with influence strength at the awareness stage, while availability and trustworthiness have the strongest association at the planning stage. The perception of the competence of the stakeholder is associated with greater influence across all stages.
Competition Effects of Simultaneous Application of Flexibility Options Within an Energy Community
(2018)
As part of an increased diffusion of decentralized renewable energy technologies, an additional need for flexibility arises. Studies indicate that operating battery storage systems for multiple uses as community electricity storage system (CES) promises superior benefits. This seems decisive, since cheaper flexibility options such as demand response (DR) are more applicable and might further reduce the market size for storage facilities. This research paper aims to analyze the competition effects of CES with simultaneous application of DR. The optimization results of the synthetic case studies provide insights in the profitability level, the service provision and the flexibility potential. While even under requested legal circumstances a CES is only partially profitable, the economic situation improves in terms of an optimal storage utilization. This, however, is reduced through competition effects with DR.
Feasible and profitable business models to better integrate and harness decentrally generated renewable energy are expected to constitute a key element for the energy transition in Germany. Until now, generated electricity of decentralized systems is to the largest extent only used by the property owner directly or fed into the public grid. To make better use of the generated electricity, it is necessary to find business models that provide an opportunity for different market actors, such as municipal utilities and residential prosumers. Due to the importance, yet low-anticipated monetary potential of such solutions, the legislator encourages their implementation by exemption of statutory fees, levies and taxes as well as by offering public remunerations, premiums and compensations in some cases. Capitalizing on these benefits, however, is only feasible under compliance with the legal requirements. In the light of the considerations above, this work states and analyzes the legal and regulatory framework of different business models within the German energy landscape. The major aim is to identify opportunities and challenges for the implementation of the business models self consumption, direct consumption, direct marketing, demand response, community electricity storage and net metering at the municipal level. The findings show that the profitability of various decentralized on-site business models depends primarily on the current statutory cost exemptions and compensations. At the same time, the regulation is characterized by unsystematic specific exemptions which leads to uncertainty regarding long-term planning. Additionally, although not directly privileged by the existing legal framework, municipal utilities are better suited to handle the legal burdens due to their experience and their administrative infrastructure.
With the suitable infrastructure of information and communication technologies in place, customers are able to perform demand response (DR), meaning that they can decrease or increase their electricity consumption in response to changes in their electricity tariff. In this research, different variable electricity tariffs are designed taking both customer and utility preferences into account. Subsequently, a model-based analysis on the basis of optimization model IRPopt (Integrated Resource Planning and Optimization) is carried out. Electricity customers are exposed to the designed tariffs in order to find out whether variable electricity tariffs are a suitable instrument for municipal energy utilities to exploit the potential laying in residential DR. Loads considered for DR in this work are those of selected electric household appliances and the loads of electric heat pumps. One major contribution of this work is that the assessment differentiates between different types of energy utilities, whose specific generation profiles are taken into account in the design of the variable tariffs. The results show that variable electricity tariffs have a small economic potential. However, customers only benefit if the design of the business model includes a proper compensation mechanism. In this context, successful business models require the direct cooperation of different municipal energy market actors. Furthermore, taking the specific generation profiles of municipal energy utilities into account in the design of variable electricity tariffs helps to increase the energy autonomy of municipalities.
The reliable integration of decentralized energy technologies and the associated system transformations represent a challenging task. Taking into account existing cross-sectoral demand and supply structures, diverse communities require specific solutions. With an appropriate business model, municipal utilities might be capable to transform themselves in a successful way. For better decision-making, they need to investigate under what conditions certain business cases might represent a sustainable part of the future system and their future portfolio. On the basis of an innovative multi-model and cross-sector approach, this research paper aims to assess opportunities of such business models in terms of four strategic targets: affordability, profitability, autarky and ecology. The results of the combined evaluation of synthetic case studies provide insights under what conditions different business models show positive performance.
Determining the development of Germany’s energy system is the subject of a series of studies. Since their results play a significant role in the political energy debate for understanding the role of hydrogen and synthetic energy carriers, a better discussion is needed. This article provides an assessment of published transition pathways for Germany to assess the role and advantages of hydrogen-based carriers. Twelve energy studies including 37 scenarios for the years 2030 and 2050 were evaluated. Despite the variations, the carrier will play an important role. While their deployment is expected to have only started by 2030 with a mean demand of 91 TWh/a (4% of the final energy demand) in Germany, they will be an essential part by 2050 with a mean demand of 480 TWh/a (24%). The outcome of the scenarios depends on the chosen methods and assumptions. A moderately positive correlation (0.53) between the decarbonisation targets and the share of hydrogen-based carriers in final energy demand underlines the relevance for reaching the climate targets. Additionally, value creation effects of about 16 billion EUR/a in 2050 can be expected for hydrogen-based carriers. Hydrogen is expected to be produced domestically while synthetic fuels are projected to be mostly imported.
Despite the intensive research on residential photovoltaic adoption, there is a lack of understanding regarding the social dynamics that drive adoption decisions. Innovation diffusion is a social process, whereby communication structures and the relations between sender and receiver influence what information is perceived and how it is interpreted. This paper addresses this research gap by investigating stakeholder influences in household decision-making from a procedural perspective, so-called stakeholder dynamics. A literature review derives major influence dynamics which are then synthesized based on egocentric network maps for distinct process stages. The findings show a multitude of stakeholders that can be relevant in influencing photovoltaic adoption decisions of owner-occupied households. Household decision-makers are mainly influenced by stakeholders of their social network like family, neighbors, and friends as well as PV-related services like providers and civil society groups. The perceived closeness and likeability of a stakeholder indicate a higher level of influence because of greater trust involved. Furthermore, the findings indicate that social influence shifts gradually from many different stakeholders to a few core stakeholders later on in the decision-making process. These insights suggest that photovoltaic (PV) adoption may be more reliably predicted if a process perspective is taken into account that not only distinguishes between different stakeholders but considers their dynamic importance along the process stages. In addition, especially time- and location-bound factors affect the influence strength. This clearly shows the importance of local and targeted interventions to accelerate the uptake.
Although there is a clear indication that stages of residential decision making are characterized by their own stakeholders, activities, and outcomes, many studies on residential low-carbon technology adoption only implicitly address stage-specific dynamics. This paper explores stakeholder influences on residential photovoltaic adoption from a procedural perspective, so-called stakeholder dynamics. The major objective is the understanding of underlying mechanisms to better exploit the potential for residential photovoltaic uptake. Four focus groups have been conducted in close collaboration with the independent institute for social science research SINUS Markt- und Sozialforschung in East Germany. By applying a qualitative content analysis, major influence dynamics within three decision stages are synthesized with the help of egocentric network maps from the perspective of residential decision-makers. Results indicate that actors closest in terms of emotional and spatial proximity such as members of the social network represent the major influence on residential PV decision-making throughout the stages. Furthermore, decision-makers with a higher level of knowledge are more likely to move on to the subsequent stage. A shift from passive exposure to proactive search takes place through the process, but this shift is less pronounced among risk-averse decision-makers who continuously request proactive influences. The discussions revealed largely unexploited potential regarding the stakeholders local utilities and local governments who are perceived as independent, trustworthy and credible stakeholders. Public stakeholders must fulfill their responsibility in achieving climate goals by advising, assisting, and financing services for low-carbon technology adoption at the local level. Supporting community initiatives through political frameworks appears to be another promising step.
Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach
(2018)
Against the backdrop of a changing political, economic and ecological environment, energy utilities are facing several challenges in many countries. Due to an increasing decentralization of energy systems, the conventional business could be undermined. Yet the reliable integration of small-scale renewable technologies and associated system transformations could represent an opportunity as well. Municipal energy utilities might play a decisive role regarding successful transition. For better decision-making, they need to investigate under which conditions certain novel business cases can become a sustainable part of their future strategy. The development of the strategy is a challenging task which needs to consider different conditions as business portfolio, the customer base, the regulatory framework as well as the market environment. Integrated Multi-Modal Energy System (IMMES) models are able to capture necessary interactions. This research introduces a model-driven decision support system called Integrated Resource Planning and Optimization (IRPopt). Major aim is to provide managerial guidance by simulating the impact of business models considering various market actors. The mixed-integer linear programming approach exhibits a novel formal interface between supply and demand side which merges technical and commercial aspects. This is achieved by explicit modeling of municipal market actors on one layer and state-of-the-art technology components on another layer as well as resource flow relations and service agreements mechanism among and between the different layers. While this optimization framework provides a dynamic and flexible policy-oriented, technology-based and actor-related assessment of multi-sectoral business cases, the encapsulation in a generic software system supports the facilitation. Based on the actor-oriented dispatch strategy, flexibility potential of community energy storage systems is provided to demonstrate a real application.
Adoption of new technologies necessitates changes in behaviours, attitudes, and social norms that cannot be sufficiently explained by a single predictor or group of predictors, particularly in heterogeneous populations. Aiming to add a greater understanding of intention to adopt Rooftop Photovoltaic (PV), this paper utilised an established population segmentation tool, the Sinus-Milieus and the Theory of Planned Behaviour (TPB) to account for population heterogeneity of the sample of German homeowners (n = 1800). By employing partial least squares structural equation modelling, we confirmed that attitudes, social norms, and financial evaluation significantly influence adoption intention at the population level. A multi-group analysis revealed noteworthy variations in the relative impact of attitudes and social norms on adoption intention across different milieugroups. Notably, even milieugroups sharing the same broad social status and/or values, as defined by the Sinus-Milieu tool, exhibited significant differences in the influence of attitudes and social norms on adoption intention. Moreover, a Kruskal–Wallis H-test indicated significant differences in the reported level of adoption intention among milieugroups, with the Leading milieus showing the highest intention and the Deprived the lowest. Differences were also observed for the determinants of social norms, attitudes, environmental concern, and novelty-seeking behaviour. Our findings confirm the relevance of considering potential adopters as a heterogeneous group in terms of overall lifestyle characteristics. Tailoring communication strategies to target the underlying motivations of specific milieus can lead to more effective interventions.
PVactVal: A Validation Approach for Agent-based Modeling of Residential Photovoltaic Adoption
(2022)
Agent-based simulation models are an important tool to study the effectiveness of policy interventions on the uptake of residential photovoltaic systems by households, a cornerstone of sustainable energy system transition. In order for these models to be trustworthy, they require rigorous validation.However, the canonical approach of validating emulation models through calibration with parameters that minimize the difference of model results and reference data fails when the model is subject to many stochastic influences. The residential photovoltaic diffusion model PVact features numerous stochastic influences that prevent straightforward optimization-driven calibration.From the analysis of the results of a case-study on the cities Dresden and Leipzig (Germany) based on three error metrics (mean average error, root mean square error and cumulative average error), this research identifies a parameter range where stochastic fluctuations exceed differences between results of different parameterization and a minimization-based calibration approach fails.Based on this observation, an approach is developed that aggregates model behavior across multiple simulation runs and parameter combinations to compare results between scenarios representing different future developments or policy interventions of interest.
The uptake of residential photovoltaic systems is essential for energy system transformation towards carbon neutrality and decentralization. However, despite numerous campaigns to incentivize their uptake, adoption by residential homeowners is lacking behind. While countless drivers and barriers have been identified, the decision process is not fully understood. To address this gap, we developed an agent-based residential rooftop photovoltaic adoption model called PVact. Our model analyzes the interactions of potential household adopters based on their utility functions and social network, with a focus on the role of monetary evaluation and social pressure in adoption behavior. In this paper, we aim to assess the influence of monetary evaluation and social pressure in an abstract case study based on real-world data from the municipality of Leipzig, Germany. We consider stochastic dynamics through scenario analysis to investigate the influence of these factors on adoption behavior. Our results show that monetary evaluation and social pressure have a significant impact on adoption behavior. Specifically, we find shifting adoption patterns with an increased requirement for monetary returns and higher level of normative pressure required for households to act. Higher resistance against these pressure shows more stochastic variations, more pronounced tipping points and stronger run-away effects.
Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.
The seasonal storage of natural gas is a recognized and reliable technology in the energy industry. Salt caverns are particularly suitable for storing alternative gaseous fuels such as hydrogen. Germany has a great technical potential for expanding its cavern storage capacity, which exceeds the expected demand for hydrogen many times. Regarding the projected long-term decline in natural gas use, the question arises as to whether existing caverns can meet future storage requirements. To this end, a techno-economic model is presented to meet electricity and hydrogen demand in a cost-optimal solution. This analysis focused on the utilization of hydrogen storage in terms of energy throughput and maximum storage capacity. To link the outcome of economic dispatch to the literature, the fundamental assumptions are based on comprehensive capacity expansion models. This study advances the state of the art by evaluating key input parameters of the future energy system. By conducting 192 model runs, the analysis revealed the range of uncertainty in terms of storage use. This indicates a strong dependence of the systemic and economic value of hydrogen storage on boundary conditions such as a consideration of dark doldrums, a flexible hydrogen demand profile, hydrogen import restrictions and a larger electrolyzer capacity. The uncertainty ranged from 0 to 67 TWhH2 for the storage capacity, with an average of 36.6 TWhH2 across all scenarios, and from 0 to 190 TWhH2 for the annual energy throughput. These results are significant for gas storage operators who derive transformation strategies and policymakers evaluating financial funding requirements.
Chlor-alkali electrolysis indicates significant demand response potential, accounting for over 2% of Germany’s total electricity demand. To fully analyze this potential, digital models or digital twins are necessary. In this study, we use the IRPopt modeling framework to develop a digital model of an electrolysis process and examine the cost-optimal load shifting application in the day-ahead spot and balancing reserve market for various price scenarios (2019, 2030, 2040). We also investigate the associated CO 2 emissions. Combined optimization at both markets results in greater and more robust cost savings of 16.1% but cannibalizes the savings that are possible through optimization separately at each market. In future scenarios, the shares of savings from spot and reserve market could potentially reverse. CO 2 savings between 2.5% and 9.2% appear only through optimization at the spot market and could even turn negative if optimized solely at the reserve market.
