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

Open access

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.

Albrecht, J., Laleman, R., & Vulsteke, E. (2015). Balancing demand-pull and supply-push measures to support renewable electricity in Europe. Renewable and Sustainable Energy Reviews; 49 (Sep 2015) 267–277, https://doi.org/10.1016/j.rser.2015.04.078

Bedsworth, L.W., & Hanak, E. (2013). Climate policy at local level: Insights from California. Global Environmental Change - Human and Policy Dimensions; 23 (3), 667–677.

Bellekom, S., Arentsen, M., & Van Gorkum, K. (2016). Prosumption and the distribution and supply of electricity. Energy, sustainability and society, 6(1), 22, http://dx.doi.org/10.1186/s13705-016-0087-7

Bobinaite, V., & Tarvydas, D. (2014). Financing instruments and channels for the increasing production and consumption of renewable energy: Lithuanian Case. Renewable and Sustainable Energy Reviews; 38 (Oct 2014) 259–276, http://dx.doi.org/10.1016/j.rser.2014.05.039

Bousquet, F., & Le Page, C. (2004). Multi-agent simulations and ecosystem management: a review. Ecological Modelling, 176 (3-4), 313-332, http://dx.doi.org/10.1016/j.ecolmodel.2004.01.011

Cai, K., Niu, J.Z., & Parsons, S. (2014). On the effects of competition between agent-based double auction markets. Electronic Commerce Research and Applications, 13 (4), 229-242, http://dx.doi.org/10.1016/j.elerap.2014.04.002

Cermak, P., Zimmermannova, J., Lavrincik, J., Pokorny, M., & Martinu, J. (2015). The Broker Simulation Model in the Emission Allowances Trading Area. International Journal of Energy Economics and Policy, 5 (1), 80-95. ISSN: 2146-4553.

Chen, J.J. Tan, L., & Zheng, B. (2015). Agent-based model with multi-level herding for complex financial systems. Scientific Reports, 5 (article no. 8399), http://dx.doi.org/10.1038/srep08399

Fiosins, M., Fiosina, J., Muller, J.P., & Gormer, J. (2011). Agent-Based Integrated Decision Making for Autonomous Vehicles in Urban Traffic. Advances on Practical Applications of Agents and Multi-Agent Systems. Edited by: Demazeau, Y., Pechoucek, M. Corchado, J.M., & Bajo, J. Book Series: Advances in Intelligent and Soft Computing, vol. 88, 173-178, http://dx.doi.org/10.1007/978-3-642-19875-5_22

Flaute, M., Großman, A., Lutz, C., & Nieters, A. (2017). Macroeconomic Effects of Prosumer Households in Germany. International Journal of Energy Economics and Policy 7(1), 146-155.

Gontis, V., & Kononovicius, A. (2014). Consentaneous Agent-Based and Stochastic Model of the Financial Markets. Plos One, 9 (7), article no. e102201, http://dx.doi.org/10.1371/journal.pone.0102201

Hunkin, S., Barsoumian, S., Krell, K., Severin, A., & Corradino, G. (2014). Thematic Study on Energy Efficiency and Renewable Energies. CENTRAL EUROPE Programme, April 2014.

Janda, K., Krška, Š., & Průša, J. (2014). Czech Photovoltaic Energy: Model Estimation of the Costs of its Support. Politická ekonomie; 62 (3) 323-346.

Janssen, M. A., & Ostrom, E. (2006). Empirically based, agent-based models. Ecology and Society, 11 (2), art.37. Available from https://www.ecologyandsociety.org/vol11/iss2/art37/

Lagorse, J., Paire, D., & Miraoui, A. (2010). A multi-agent system for energy management of distributed power sources. Renewable Energy. 35 (1), 174-182. http://dx.doi.org/10.1016/j.renene.2009.02.029

Lengnick, M., & Wohltmann, Hw. (2013). Agent-based financial markets and New Keynesian macroeconomics: a synthesis. Journal of Economic Interaction and Coordination, 8 (1), 1-32, http://dx.doi.org/10.1007/s11403-012-0100-y

Marques, A.C., & Fuinhas, J.A. (2012). Are public policies towards renewables successful? Evidence from European countries. Renewable Energy; 44 (Aug 2012) 109–118.

Meixnerová, L., Menšík, M., & Pászto, V. (2017). Economic analysis and spatial arrangements of engineering SMEs performance in Olomouc region of Czech Republic. Journal of International Studies, 10(1), 135-145, http://dx.doi.org/10.14254/2071-8330.2017/10-1/9

MIT (Ministry of Industry and Trade of the Czech Republic). (2015). National Action Plan for Smart Grids (NAP SG). Available at: www.mpo.cz

MIT (Ministry of Industry and Trade). (2017). Renewable energy sources in 2015; Results of the survey. Available at: https://www.mpo.cz/assets/cz/energetika/statistika/obnovitelne-zdroje-energie/2017/2/Obnovitelne-zdroje-energie2015.pdf

Morgan, F.J., & Daigneault, A.J. (2015). Estimating impacts of climate change policy on land use: An agent-based modelling approach. PLoS ONE, 10 (5), 21 May 2015, article number e0127317.

Olkkonen, L., Korjonen-Kuusipuro, K., & Grönberg, I. (2017). Redefining a stakeholder relation: Finnish energy “prosumers” as co-producers. Environmental Innovation and Societal Transitions, 24, 57-66.

Ortega, M., Del Rio, P., & Montero, E.A. (2013). Assessing the benefits and costs of renewable electricity. The Spanish case. Renewable and Sustainable Energy Reviews; 27 (Nov 2013), 294 – 304, https://doi.org/10.24084/repqj14.527

Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93 (2), 314-337, http://dx.doi.org/10.1111/1467-8306.9302004

Pawliczek, A. (2011). Czech Photovoltaic Business and Sustainable Development. The International Conference Hradec Economic Days 2011. Peer-Reviewed Conference Proceedings, Hradec Králové: Gaudeamus, 2011, 214-218, ISBN 978-80-7435-101-3.

Průša, J., Klimešová, A., & Janda, K. (2013). Consumer loss in Czech photovoltaic power plants in 2010–2011. Energy Policy; 63 (2013) 747–755, http://dx.doi.org/10.1016/j.enpol.2013.08.023

Ryvolová, I., & Zemplinerová, A. (2010). The Economics of Renewable Energy – Example of Wind Energy in the Czech Republic. Politická ekonomie; 58 (6) 323–346.

Tang, L., Wu, J., Yu, L., & Bao, Q. (2015). Carbon emissions trading scheme exploration in China: A multi-agent-based model. Energy Policy, 81 (1 June 2015), 152-169, http://dx.doi.org/10.1016/j.enpol.2015.02.032

Zajaczkowska, M. (2016). Prospects for the development of prosumer energy in Poland. Oeconomia Copernicana, 7(3), 439-449.

Zamfir, A., Colesca, S.E., & Corbos, R.A. (2016). Public policies to support the development of renewable energy in Romania: A review. Renewable and Sustainable Energy Reviews; 58 (May 2016) 87–106, http://dx.doi.org/10.1016%2Fj.rser.2015.12.235

Zimmermannová, J. (2017). Is Current Institutional Environment Suitable for Renewable Electricity Generation in the Czech Republic? Current Trends in Public Sector Research. Proceedings of the 21st International Conference. Masaryk University, Brno-Šlapanice. 2017, 434 – 442. ISBN 978-80-210-8448-3, ISSN 2336-1239

Zimmermannová, J., & Čermák, P. (2014). Possibilities of Multiagent Simulation Model Application in the Emission Allowances Trading Area. Procedia Economics and Finance, 2014, vol. 12, 788-796, http://dx.doi.org/10.1016/S2212-5671(14)00406-7

Zimmermannová, J., & Jílková, E. (2016). Do Economic Instruments in the Czech Republic Support Generation of Renewable Energy? Economics Management Innovation; 8 (2) 16-30.

Organizacija

Journal of Management, Informatics and Human Resources

Journal Information

CiteScore 2018: 0.87

SCImago Journal Rank (SJR) 2018: 0.179
Source Normalized Impact per Paper (SNIP) 2018: 0.529

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 293 190 14
PDF Downloads 199 133 10