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Public Support of Solar Electricity and its Impact on Households - Prosumers

Author

Listed:
  • Zimmermannová Jarmila

    (Moravian University College Olomouc, tr. Kosmonautu 1288/1, 77900Olomouc, Czech Republic)

  • Pawliczek Adam

    (Moravian University College Olomouc, tr. Kosmonautu 1288/1, 77900Olomouc, Czech Republic)

  • Čermák Petr

    (Moravian University College Olomouc, tr. Kosmonautu 1288/1, 77900Olomouc, Czech Republic)

Abstract

Background and Purpose: Currently, the idea of households - prosumers is broadly discussed in public governments, mainly in connection with both the energy security issues and the environmental issues. Therefore, the main goal of this paper is to present new agent model of household - prosumer and to compare two scenarios – “off grid household” and “on grid household”. The additional goal is to evaluate the impact of public support of solar electricity on the economic efficiency of household – prosumer projects (systems).Design/Methodology/Approach: The model is structured as a micro-level agent model, representing one household – prosumer. The model has the following general characteristics: one household with own electricity generation (photovoltaic panels), battery and in case of “on grid household” also connection to the grid. The main goal of the agent is to cover electricity consumption in household with minimal costs. The agent model of prosumer is tested and validated, using the empirical data.Results: The highest level of subsidy has significant impact on the economic indicators of selected scenarios. It causes lower investment costs at the beginning of the project and consequently shorter payback period (3-4 years earlier), positive cumulative cash flow, net present value and IRR in earlier period (approximately 5-10 years earlier, depending on the scenario).Conclusion: We can recommend to the government to continue with current system of subsidies, since it contributes to better economic indicators of particular solar electricity projects. On the other hand, the level of subsidy should be at least the same as in current year 2017, for the purposes of representing the significant part of the investment costs. Low level of subsidy has negligible impact on the economic indicators of households – prosumers projects. The developed agent model is suitable for the evaluation of economic impact of public support on households – prosumers.

Suggested Citation

  • Zimmermannová Jarmila & Pawliczek Adam & Čermák Petr, 2018. "Public Support of Solar Electricity and its Impact on Households - Prosumers," Organizacija, Sciendo, vol. 51(1), pages 4-19, February.
    • Handle: RePEc:vrs:organi:v:51:y:2018:i:1:p:4-19:n:1
    DOI: 10.2478/orga-2018-0001
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    References listed on IDEAS

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    1. Ortega, Margarita & del Río, Pablo & Montero, Eduardo A., 2013. "Assessing the benefits and costs of renewable electricity. The Spanish case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 294-304.
    2. Zamfir, Andreea & Colesca, Sofia Elena & Corbos, Razvan-Andrei, 2016. "Public policies to support the development of renewable energy in Romania: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 87-106.
    3. V. Gontis & A. Kononovicius, 2014. "Consentaneous agent-based and stochastic model of the financial markets," Papers 1403.1574, arXiv.org, revised Jul 2014.
    4. Vygintas Gontis & Aleksejus Kononovicius, 2014. "Consentaneous Agent-Based and Stochastic Model of the Financial Markets," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-12, July.
    5. Daigneault, Adam J. & Morgan, Fraser, 2012. "Estimating Impacts of Climate Change Policy on Land Use: An Agent Based Modeling Approach," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124973, Agricultural and Applied Economics Association.
    6. Markus Flaute & Anett Gro mann & Christian Lutz & Anne Nieters, 2017. "Macroeconomic Effects of Prosumer Households in Germany," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 146-155.
    7. Lagorse, Jeremy & Paire, Damien & Miraoui, Abdellatif, 2010. "A multi-agent system for energy management of distributed power sources," Renewable Energy, Elsevier, vol. 35(1), pages 174-182.
    8. Matthias Lengnick & Hans-Werner Wohltmann, 2013. "Agent-based financial markets and New Keynesian macroeconomics: a synthesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 1-32, April.
    9. Jun-Jie Chen & Lei Tan & Bo Zheng, 2015. "Agent-based model with multi-level herding for complex financial systems," Papers 1504.01811, arXiv.org.
    10. Petr Cermak & Jarmila Zimmermannova & Jan Lavrincik & Miroslav Pokorny & Jiri Martinu, 2015. "The Broker Simulation Model in the Emission Allowances Trading Area," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 80-95.
    11. Jan Prusa & Andrea Klimesova & Karel Janda, 2013. "Consumer Loss in Czech Photovoltaic Power Plants," CAMA Working Papers 2013-50, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Tang, Ling & Wu, Jiaqian & Yu, Lean & Bao, Qin, 2015. "Carbon emissions trading scheme exploration in China: A multi-agent-based model," Energy Policy, Elsevier, vol. 81(C), pages 152-169.
    13. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    14. Marques, António Cardoso & Fuinhas, José Alberto, 2012. "Are public policies towards renewables successful? Evidence from European countries," Renewable Energy, Elsevier, vol. 44(C), pages 109-118.
    15. Průša, Jan & Klimešová, Andrea & Janda, Karel, 2013. "Consumer loss in Czech photovoltaic power plants in 2010–2011," Energy Policy, Elsevier, vol. 63(C), pages 747-755.
    16. Bobinaite, Viktorija & Tarvydas, Dalius, 2014. "Financing instruments and channels for the increasing production and consumption of renewable energy: Lithuanian case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 259-276.
    17. Albrecht, Johan & Laleman, Ruben & Vulsteke, Elien, 2015. "Balancing demand-pull and supply-push measures to support renewable electricity in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 267-277.
    18. Magdalena Zajaczkowska, 2016. "Prospects For The Development Of Prosumer Energy In Poland," Oeconomia Copernicana, Institute of Economic Research, vol. 7(3), pages 439-449, September.
    19. Fraser J Morgan & Adam J Daigneault, 2015. "Estimating Impacts of Climate Change Policy on Land Use: An Agent-Based Modelling Approach," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)

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