Analysis and model-based predictions of solar PV and battery adoption in Germany: an agent-based approach

In order to tackle energy challenges faced in Germany, a Feed-in Tariff program was created in 2004 to aid the adoption of solar PhotoVoltaic (PV) systems where owners of such systems are paid a certain amount for each unit of electricity generated. Solar PV electricity generation is limited due to its intermittency but this can be managed using batteries. In this paper, we study the adoption of PV and battery (PV-battery) systems in Germany, and evaluate policies that could improve the adoption of these systems and their impact on the electric grid. To do this, we create an agent-based model that is simulated to estimate the impacts of different policies; this model is informed by an online survey with respondents from Germany. Simulating adoption over a period of 10 years, the results show that increasing electricity prices could result in improved PV-battery adoption in Germany better than reducing PV-battery system prices could. In addition, given the high level of affinity of people towards PV systems in Germany, disconnection from the grid would be a viable option within the next 10 years.

[1]  Reinhard Madlener,et al.  Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-Based Simulation , 2013 .

[2]  Tomoyuki Murakami Agent-based simulations of the influence of social policy and neighboring communication on the adoption of grid-connected photovoltaics , 2014 .

[3]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[4]  David R. Heise Expressive Order: Confirming Sentiments in Social Actions , 2006 .

[5]  Charles M. Macal,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[6]  Varun Rai,et al.  GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion , 2013 .

[7]  Johannes Palmer,et al.  Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-Based Simulation , 2013 .

[8]  Catherine Rosenberg,et al.  Toward a Realistic Performance Analysis of Storage Systems in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[9]  Igor Nikolic,et al.  A method for developing agent-based models of socio-technical systems , 2011, 2011 International Conference on Networking, Sensing and Control.

[10]  Andrea Borghesi,et al.  Agent Based Simulation of Incentive Mechanisms on Photovoltaic Adoption , 2015, AI*IA.

[11]  Frank M. Bass,et al.  Comments on "A New Product Growth for Model Consumer Durables The Bass Model" , 2004, Manag. Sci..

[12]  Anand Sivasubramaniam,et al.  Energy storage in datacenters: what, where, and how much? , 2012, SIGMETRICS '12.

[13]  Igor Nikolic,et al.  Agent-Based Modelling of Socio-Technical Systems , 2012, Agent-Based Social Systems.

[14]  Yevgeniy Vorobeychik,et al.  Predicting Rooftop Solar Adoption Using Agent-Based Modeling , 2014, AAAI Fall Symposia.

[15]  Nurcin Celik,et al.  Hybrid agent-based simulation for policy evaluation of solar power generation systems , 2011, Simul. Model. Pract. Theory.

[16]  Varun Rai,et al.  Agent-Based Modeling of Energy Technology Adoption: Empirical Integration of Social, Behavioral, Economic, and Environmental Factors , 2014, Environ. Model. Softw..

[17]  Koen H. van Dam,et al.  Capturing socio-technical systems with agent-based modelling , 2009 .

[18]  Srinivasan Keshav,et al.  Understanding solar PV and battery adoption in Ontario: an agent-based approach , 2016, e-Energy.

[19]  Elizabeth James Kistin Keller,et al.  Agent Based model of residential Solar PV diffusion. , 2014 .

[20]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[21]  Yevgeniy Vorobeychik,et al.  Data-driven agent-based modeling, with application to rooftop solar adoption , 2015, Autonomous Agents and Multi-Agent Systems.