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Optimal Policy Identification: Insights from the German Electricity Market

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  • Johannes Herrmann
  • Ivan Savin

Abstract

The diffusion of renewable electricity generating technologies is widely considered as crucial for establishing a sustainable energy system in the future. However, the required transition is unlikely to be achieved by market forces alone. For this reason, many countries implement various policy instruments to support this pro- cess, also by re-distributing related costs among all electricity consumers. This paper presents a novel history-friendly agent-based study aiming to explore the efficiency of different mixes of policy instruments by means of a Differential Evolution algorithm. Special emphasis of the model is devoted to the possibility of small scale renewable electricity generation, but also to the storage of this electricity using small scale facilities being actively developed over the last decade. Both combined pose an important instrument for 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. Among other things, we find that the historical policy mix of Germany introduced too strong and inflexible demand-side instruments (like feed-in tariff ) too early, thereby creating strong path-dependency for future policy makers and reducing their ability to react to technological but also economic shocks without further increases of the budget.

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  • Johannes Herrmann & Ivan Savin, 2016. "Optimal Policy Identification: Insights from the German Electricity Market," Working Papers of BETA 2016-16, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  • Handle: RePEc:ulp:sbbeta:2016-16
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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. 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.
    3. Karoline S. Rogge & Kristin Reichardt, 2015. "Going Beyond Instrument Interactions: Towards a More Comprehensive Policy Mix Conceptualization for Environmental Technological Change," SPRU Working Paper Series 2015-12, SPRU - Science Policy Research Unit, University of Sussex Business School.
    4. Cludius, Johanna & Hermann, Hauke & Matthes, Felix Chr. & Graichen, Verena, 2014. "The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications," Energy Economics, Elsevier, vol. 44(C), pages 302-313.
    5. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 67-116.
    6. Dr. Ulrike Lehr & Dr. Thomas Drosdowski, 2013. "Soziale Verteilungswirkungen der EEG-Umlage," GWS Discussion Paper Series 13-3, GWS - Institute of Economic Structures Research.
    7. Aghion, Philippe & David, Paul A. & Foray, Dominique, 2009. "Science, technology and innovation for economic growth: Linking policy research and practice in 'STIG Systems'," Research Policy, Elsevier, vol. 38(4), pages 681-693, May.
    8. Kalkuhl, Matthias & Edenhofer, Ottmar & Lessmann, Kai, 2012. "Learning or lock-in: Optimal technology policies to support mitigation," Resource and Energy Economics, Elsevier, vol. 34(1), pages 1-23.
    9. Nick Johnstone & Ivan Haščič & David Popp, 2010. "Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 45(1), pages 133-155, January.
    10. Lehmann, Paul, 2013. "Supplementing an emissions tax by a feed-in tariff for renewable electricity to address learning spillovers," Energy Policy, Elsevier, vol. 61(C), pages 635-641.
    11. Dani Rodrik, 2014. "Green industrial policy," Oxford Review of Economic Policy, Oxford University Press, vol. 30(3), pages 469-491.
    12. 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.
    13. Eric Guerci & Mohammad Ali Rastegar & Silvano Cincotti, 2010. "Agent-based modeling and simulation of competitive wholesale electricity markets," Post-Print halshs-00871063, HAL.
    14. Steffen, Bjarne, 2012. "Prospects for pumped-hydro storage in Germany," Energy Policy, Elsevier, vol. 45(C), pages 420-429.
    15. Lehmann, Paul & Gawel, Erik, 2013. "Why should support schemes for renewable electricity complement the EU emissions trading scheme?," Energy Policy, Elsevier, vol. 52(C), pages 597-607.
    16. 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.
    17. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    18. Ivan Savin & Dmitri Blueschke, 2016. "Lost in Translation: Explicitly Solving Nonlinear Stochastic Optimal Control Problems Using the Median Objective Value," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 317-338, August.
    19. 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.
    20. Elisa Lanzi & Ivan Haščič & Nick Johnstone, 2012. "The Determinants of Invention in Electricity Generation Technologies: A Patent Data Analysis," OECD Environment Working Papers 45, OECD Publishing.
    21. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
    22. 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.
    23. Unruh, Gregory C., 2000. "Understanding carbon lock-in," Energy Policy, Elsevier, vol. 28(12), pages 817-830, October.
    24. 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.
    25. 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).
    26. 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.
    27. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    28. 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.
    29. Lopolito, A. & Morone, P. & Taylor, R., 2013. "Emerging innovation niches: An agent based model," Research Policy, Elsevier, vol. 42(6), pages 1225-1238.
    30. 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.
    31. 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.
    32. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    33. 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.
    34. Steinmueller, W. Edward, 2013. "The pre-industrial energy crisis and resource scarcity as a source of transition," Research Policy, Elsevier, vol. 42(10), pages 1739-1748.
    35. 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.
    36. Safarzyńska, Karolina & van den Bergh, Jeroen C.J.M., 2017. "Integrated crisis-energy policy: Macro-evolutionary modelling of technology, finance and energy interactions," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 119-137.
    37. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.
    38. 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.
    39. 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.
    40. 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.
    41. Fischer, Carolyn & Newell, Richard G., 2008. "Environmental and technology policies for climate mitigation," Journal of Environmental Economics and Management, Elsevier, vol. 55(2), pages 142-162, March.
    42. Sundt, Swantje & Rehdanz, Katrin, 2015. "Consumers' willingness to pay for green electricity: A meta-analysis of the literature," Energy Economics, Elsevier, vol. 51(C), pages 1-8.
    43. Winkler, Ralph, 2005. "Structural change with joint production of consumption and environmental pollution: a neo-Austrian approach," Structural Change and Economic Dynamics, Elsevier, vol. 16(1), pages 111-135, March.
    44. Paul Grauwe, 2011. "Animal spirits and monetary policy," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 47(2), pages 423-457, June.
    45. 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.
    46. Safarzyńska, Karolina & Frenken, Koen & van den Bergh, Jeroen C.J.M., 2012. "Evolutionary theorizing and modeling of sustainability transitions," Research Policy, Elsevier, vol. 41(6), pages 1011-1024.
    47. Dr. Ulrike Lehr & Dr. Thomas Drosdowski, 2015. "Soziale Verteilungswirkungen der EEG-Umlage unter Berücksichtigung von Einkommensklassen," GWS Discussion Paper Series 15-1, GWS - Institute of Economic Structures Research.
    48. Hadjipaschalis, Ioannis & Poullikkas, Andreas & Efthimiou, Venizelos, 2009. "Overview of current and future energy storage technologies for electric power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1513-1522, August.
    49. Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
    50. 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.
    51. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    52. Garavaglia, Christian, 2010. "Modelling industrial dynamics with "History-friendly" simulations," Structural Change and Economic Dynamics, Elsevier, vol. 21(4), pages 258-275, November.
    53. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    54. Flanagan, Kieron & Uyarra, Elvira & Laranja, Manuel, 2011. "Reconceptualising the 'policy mix' for innovation," Research Policy, Elsevier, vol. 40(5), pages 702-713, June.
    55. Hoppmann, Joern & Huenteler, Joern & Girod, Bastien, 2014. "Compulsive policy-making—The evolution of the German feed-in tariff system for solar photovoltaic power," Research Policy, Elsevier, vol. 43(8), pages 1422-1441.
    56. Kverndokk, Snorre & Rosendahl, Knut Einar, 2007. "Climate policies and learning by doing: Impacts and timing of technology subsidies," Resource and Energy Economics, Elsevier, vol. 29(1), pages 58-82, January.
    57. Arthur, W. Brian, 2006. "Out-of-Equilibrium Economics and Agent-Based Modeling," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 32, pages 1551-1564, Elsevier.
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    More about this item

    Keywords

    differential evolution; electricity storage; energy grid; feed-in tariff; renewable energy.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • 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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