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Evolution of the electricity market in Germany: Identifying policy implications by an agent-based model

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  • Herrmann, Johannes
  • Savin, Ivan

Abstract

The diffusion of renewable electricity generating technologies is widely consid- ered as crucial for establishing a sustainable energy system in the future. However, currently the required transition is unlikely to be achieved by market forces alone. For this reason, many countries implement various policy instruments to support this process, also by re-distributing costs related to the policy instruments applied among all electricity consumers. This paper presents a novel history-friendly agent-based study aiming to explore efficiency of different mixes of policy instruments by means of a differential evolution algorithm. Special emphasis of the model is devoted to possibility of small scale renewable electricity generation without any further inputs, but also to storage of this electricity using small scale facilities being actively developed over the last decade. Both combined pose an important instrument to be used by electricity consumers to achieve partial or full autarky from the electricity grid, particularly after accounting for decreasing costs and increasing efficiency of both due to continuous innovation. Another distinct feature of this study is attention to stability of the electricity grid since more consumers becoming autarkic make, on the one hand, electricity in the grid more expansive, while on the other hand, supply of the electricity more vulnerable.

Suggested Citation

  • Herrmann, Johannes & Savin, Ivan, 2015. "Evolution of the electricity market in Germany: Identifying policy implications by an agent-based model," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112959, Verein für Socialpolitik / German Economic Association.
  • Handle: RePEc:zbw:vfsc15:112959
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    References listed on IDEAS

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    1. Volkmar Lauber & Lutz Mez, 2004. "Three Decades of Renewable Electricity Policies in Germany," Energy & Environment, , vol. 15(4), pages 599-623, July.
    2. del Río, Pablo & Bleda, Mercedes, 2012. "Comparing the innovation effects of support schemes for renewable electricity technologies: A function of innovation approach," Energy Policy, Elsevier, vol. 50(C), pages 272-282.
    3. Liu, Wen & Lund, Henrik & Mathiesen, Brian Vad, 2011. "Large-scale integration of wind power into the existing Chinese energy system," Energy, Elsevier, vol. 36(8), pages 4753-4760.
    4. Ivan Diaz‐Rainey & John K. Ashton, 2011. "Profiling potential green electricity tariff adopters: green consumerism as an environmental policy tool?," Business Strategy and the Environment, Wiley Blackwell, vol. 20(7), pages 456-470, November.
    5. Roe, Brian & Teisl, Mario F. & Levy, Alan & Russell, Matthew, 2001. "US consumers' willingness to pay for green electricity," Energy Policy, Elsevier, vol. 29(11), pages 917-925, September.
    6. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
    7. Ivan Savin & Dmitri Blueschke, 2013. "Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization," Jena Economic Research Papers 2013-051, Friedrich-Schiller-University Jena.
    8. Jacobsson, Staffan & Lauber, Volkmar, 2006. "The politics and policy of energy system transformation--explaining the German diffusion of renewable energy technology," Energy Policy, Elsevier, vol. 34(3), pages 256-276, February.
    9. Eric Guerci & Mohammad Ali Rastegar & Silvano Cincotti, 2010. "Agent-based modeling and simulation of competitive wholesale electricity markets," Post-Print halshs-00871063, HAL.
    10. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    11. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
    12. Blueschke, D. & Blueschke-Nikolaeva, V. & Savin, I., 2013. "New insights into optimal control of nonlinear dynamic econometric models: Application of a heuristic approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(4), pages 821-837.
    13. Cantner, Uwe & Graf, Holger & Herrmann, Johannes & Kalthaus, Martin, 2016. "Inventor networks in renewable energies: The influence of the policy mix in Germany," Research Policy, Elsevier, vol. 45(6), pages 1165-1184.
    14. Karolina Safarzyńska & Jeroen Bergh, 2013. "An evolutionary model of energy transitions with interactive innovation-selection dynamics," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 271-293, April.
    15. Bjarne Steffen, 2011. "Prospects for pumped-hydro storage in Germany," EWL Working Papers 1107, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Dec 2011.
    16. Bode, Sven & Groscurth, Helmuth-Michael, 2006. "The Effect of the German Renewable Energy Act (EEG) on "the Electricity Price"," HWWA Discussion Papers 358, Hamburg Institute of International Economics (HWWA).
    17. Malerba, Franco & Nelson, Richard & Orsenigo, Luigi & Winter, Sidney, 2008. "Public policies and changing boundaries of firms in a "history-friendly" model of the co-evolution of the computer and semiconductor industries," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 355-380, August.
    18. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    19. Colmenar-Santos, Antonio & Campíñez-Romero, Severo & Pérez-Molina, Clara & Castro-Gil, Manuel, 2012. "Profitability analysis of grid-connected photovoltaic facilities for household electricity self-sufficiency," Energy Policy, Elsevier, vol. 51(C), pages 749-764.
    20. Grau, Thilo & Huo, Molin & Neuhoff, Karsten, 2012. "Survey of photovoltaic industry and policy in Germany and China," Energy Policy, Elsevier, vol. 51(C), pages 20-37.
    21. Wiser, Ryan H., 2007. "Using contingent valuation to explore willingness to pay for renewable energy: A comparison of collective and voluntary payment vehicles," Ecological Economics, Elsevier, vol. 62(3-4), pages 419-432, May.
    22. Faber, Malte & Proops, John L. R., 1991. "The innovation of techniques and the time-horizon: A neo-Austrian approach," Structural Change and Economic Dynamics, Elsevier, vol. 2(1), pages 143-158, June.
    23. Garavaglia, Christian, 2010. "Modelling industrial dynamics with "History-friendly" simulations," Structural Change and Economic Dynamics, Elsevier, vol. 21(4), pages 258-275, November.
    24. Zahedi, A., 2006. "Solar photovoltaic (PV) energy; latest developments in the building integrated and hybrid PV systems," Renewable Energy, Elsevier, vol. 31(5), pages 711-718.
    25. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo, 2008. "The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany," Energy Policy, Elsevier, vol. 36(8), pages 3076-3084, August.
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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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