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

  • Special Issue Paper
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Computer Science - Research and Development

Abstract

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.

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Notes

  1. “EEG-Umlage”: According to §60 EEG electricity utilities have to pay an EEG apportionment to the transmission system operators (TSO) for each delivered kilowatt hour of electricity to final consumers. With these payments, the difference between the revenue and expenditure of the TSO will be covered in the EEG implementation.

  2. The payback period is the amount of time (typically, years) it takes for a system to pay for itself.

  3. Deflection is a measure of effectiveness based on ACT.

References

  1. Adepetu A, Keshav S (2016) Understanding solar pv and battery adoption in ontario: an agent-based approach. In: Sixth ACM international conference on future energy systems ACM e-Energy

  2. AECOM (2015) AECOM Australia: energy storage study. http://arena.gov.au/files/2015/07/AECOM-Energy-Storage-Study.pdf. Accessed 6 Apr 2016

  3. Bass FM (2004) Comments on a new product growth for model consumer durables the bass model. Manag Sci 50(12_supplement):1833–1840. doi:10.1287/mnsc.1040.0300

  4. BDEW (2015a) Bdew: German household electricity costs fall 1% in 2015—3,500 kwh/year for EUR 84.02/month or 28.81 ct/kwh. Elec Grid (2015). http://www.germanenergyblog.de/?p=18391

  5. BDEW (2015b) Statistische Zahlen der deutschen solarstrombranche (photovoltaik). http://www.solarwirtschaft.de

  6. BMWI (2014) EEG ’Gesetz für den ausbau erneuerbarer energien’. http://www.bmwi.de/BMWi/Redaktion/PDF/G/gesetz-fuer-den-ausbau-erneuerbarer-energien,property=pdf,bereich=bmwi2012,sprache=de,rwb=true.pdf

  7. BNetzA (2015a) Bundesnetzagentur: Entwicklung des deutschen PV-Marktes, “PV-Meldedaten Jan.–Feb. 2015”. https://www.solarwirtschaft.de/fileadmin/media/pdf/BNetzA-Daten_Jan_2015_kurz.pdf. Accessed 2 May 2016

  8. BNetzA (2015b) Photovoltaikanlagen: Datenmeldungen sowie eeg-verguetungssaetze” [monthly reported new installations of pv systems and current feed-in tariffs of the German renewable energy act. http://www.bundesnetzagentur.de/

  9. Bronski P, Creyts J, Guccione L, Madrazo M, Mandel J et al (2014) The economics of grid defection: when and where distributed solar generation plus storage competes with traditional utility service. Rocky Mountain Institute, Boulder, CO

    Google Scholar 

  10. BSW-Solar (2015) Entwicklung des deutschen PV-Marktes. https://www.solarwirtschaft.de/fileadmin/media/pdf/bnetza_0214_kurz.pdf

  11. Crowdflower (2016) Crowdflower—make your data useful. http://www.crowdflower.com. Accessed 6 Apr 2016

  12. Efron B, Hastie T, Johnstone I, Tibshirani R et al (2004) Least angle regression. Ann Stat. 32(2):407–499

    Article  MathSciNet  MATH  Google Scholar 

  13. Energiewende (2016) http://energytransition.de/

  14. Eurostat (2016) Energy price statistics. http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_price_statistics. Accessed 16 Apr 2016

  15. FinanceFormulas (2016) Finance Formulas. Discounted payback period. http://www.financeformulas.net/Discounted-Payback-Period.html. Accessed 7 June 2016

  16. Fraunhofer (2015) Aktuelle Fakten zur Photovoltaik in Deutschland. https://www.ise.fraunhofer.de/de/veroeffentlichungen/veroeffentlichungen-pdf-dateien/studien-und-konzeptpapiere/aktuelle-fakten-zur-photovoltaik-in-deutschland.pdf

  17. Fraunhofer I, Energiewende A (2015) Current and future cost of photovoltaics; long-term scenarios for market development, system prices and lcoe of utilityscale pv-systems. Agora Energiewende

  18. Ghiassi-Farrokhfal Y, Keshav S, Rosenberg C (2015) Toward a realistic performance analysis of storage systems in smart grids. Smart Grid IEEE Trans 6(1):402–410

    Article  Google Scholar 

  19. Heise DR (2007) Expressive order: confirming sentiments in social actions. Springer, US

    Google Scholar 

  20. HOMER (2016) The HOMER, Microgrid Software. http://www.homerenergy.com/software.html

  21. Iachini V, Borghesi A, Milano M (2015) Agent based simulation of incentive mechanisms on photovoltaic adoption. In: AI* IA 2015. Advances in artificial intelligence. Springer, pp 136–148

  22. Investopedia (2016) Return on investment—ROI. http://www.investopedia.com/terms/r/returnoninvestment.asp. Accessed 20 June 2016

  23. KIT (2015) Karlsruher Instituts für Technologie: Strompreise steigen bis 2025 um 70 prozent. http://www.welt.de/wirtschaft/energie/article106310031/Strompreise-steigen-bis-2025-um-70-Prozent.html. Accessed 6 Apr 2016

  24. Macal CM, North MJ (2010) Tutorial on agent-based modelling and simulation. J Simul 4(3):151–162

    Article  Google Scholar 

  25. Murakami T (2014) Agent-based simulations of the influence of social policy and neighboring communication on the adoption of grid-connected photovoltaics. Energy Convers Manag 80:158–164

    Article  Google Scholar 

  26. Nikolic I, Ghorbani A (2011) A method for developing agent-based models of socio-technical systems. In: 2011 IEEE international conference on networking, sensing and control (ICNSC), IEEE, pp 44–49

  27. Palmer J, Sorda G, Madlener R (2013) Modeling the diffusion of residential photovoltaic systems in italy: An agent-based simulation. Institute for Future Energy Consumer Needs and Behavior

  28. Paschotta R (2015) Stromtarif im RP-Energie-Lexikon. https://www.energie-lexikon.info/stromtarif.html. Accessed 7 June 2016

  29. Rai V, Robinson SA (2015) Agent-based modeling of energy technology adoption: empirical integration of social, behavioral, economic, and environmental factors. Environ Model Softw 70:163–177

    Article  Google Scholar 

  30. Robinson SA, Stringer M, Rai V, Tondon A (2013) Gis-integrated agent-based model of residential solar pv diffusion. In: 32nd USAEE/IAEE North American Conference, pp 28–31

  31. Statista Inc (2016) Statista—Das Statistik-Portal. http://www.statista.com/

  32. Van Dam KH (2009) Capturing socio-technical systems with agent-based modelling. PhD thesis, TU Delft, Delft University of Technology

  33. Van Dam KH, Nikolic I, Lukszo Z (eds) (2012) Agent-based modelling of socio-technical systems, vol 9. Springer, Netherlands

  34. Wang D, Ren C, Sivasubramaniam A, Urgaonkar B, Fathy H (2012) Energy storage in datacenters: what, where, and how much? In: ACM SIGMETRICS performance evaluation review, ACM, pp 187–198

  35. Zhang H, Vorobeychik Y, Letchford J, Lakkaraju K (2014) Predicting rooftop solar adoption using agent-based modeling. In: 2014 AAAI Fall Symposium Series

  36. Zhang H, Vorobeychik Y, Letchford J, Lakkaraju K (2015) Data-driven agent-based modeling, with application to rooftop solar adoption. In: Proceedings of the 2015 international conference on autonomous agents and multiagent systems, International Foundation for Autonomous Agents and Multiagent Systems, pp 513–521

  37. Zhao J, Mazhari E, Celik N, Son YJ (2011) Hybrid agent-based simulation for policy evaluation of solar power generation systems. Simul Model Pract Theory 19(10):2189–2205

    Article  Google Scholar 

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Acknowledgments

This work has been partially carried out within both Internet-Kompetenzzentrum Ostbayern (IKZ_Ostbayern) project and the European project DC4Cities (FP7-ICT-2013.6.2). Furthermore, the authors of this paper would like to show their gratitude to Mr. Franz-Josef Feilmeier and Prof. S. Keshav, Univ. of Waterloo, for their significant contributions in this paper.

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Correspondence to Ammar Alyousef.

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Alyousef, A., Adepetu, A. & de Meer, H. Analysis and model-based predictions of solar PV and battery adoption in Germany: an agent-based approach. Comput Sci Res Dev 32, 211–223 (2017). https://doi.org/10.1007/s00450-016-0304-9

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