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Locational (In-)Efficiency of Renewable Power Generation Feeding in the Electricity Grid: A Spatial Regression Analysis

Author

Listed:
  • Höfer, Tim

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

  • Madlener, Reinhard

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract
This paper analyzes the negative external effects caused by the introduction of variable renewable energy sources into an inflexible power system. We investigate the costs that arise due to the need for temporarily reducing their output to relief grid overstress in the case of high electricity feed-in. The responsible system operator has to remunerate the power plant operator for this lost output. The resulting costs for the system operator, the so-called feed-in management costs, are passed on to the end-consumers in the respective region via the grid use tariff scheme. In this paper, we develop a two-part regression model that explains (i) the occurrence of feed-in management and (ii) the regional variation of feed-in management costs. In the first part, we use a logit model to explain why some regions experienced feed-in management in recent years and others did not. The second part covers an augmented spatial econometric model that investigates the interregional variability of feed-in management costs. The estimates of both models show that especially the installed capacity of wind energy connected to the medium and high voltage level have a negative impact on feed-in management and that high load in a region reduces the need for feed-in management measures. The augmented spatial model indicates for the case of four major DSOs in Germany that an increase by 1 MW of wind energy capacity at the medium and high voltage level lead to an increase in feed-in management costs by 1.9% and 0.9% in regions that already experienced feed-in management, respectively. The policy implication of this finding is that price signals for the reinforcement of the grid infrastructure or for the siting of VRES should be given.

Suggested Citation

  • Höfer, Tim & Madlener, Reinhard, 2018. "Locational (In-)Efficiency of Renewable Power Generation Feeding in the Electricity Grid: A Spatial Regression Analysis," FCN Working Papers 13/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised 01 Dec 2019.
  • Handle: RePEc:ris:fcnwpa:2018_013
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    References listed on IDEAS

    as
    1. Luc Anselin & Sergio J. Rey, 2010. "Perspectives on Spatial Data Analysis," Advances in Spatial Science, in: Luc Anselin & Sergio J. Rey (ed.), Perspectives on Spatial Data Analysis, chapter 0, pages 1-20, Springer.
    2. Schallenberg-Rodriguez, Julieta, 2013. "A methodological review to estimate techno-economical wind energy production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 272-287.
    3. Oliver W. Lerbs & Christian A. Oberst, 2014. "Explaining the Spatial Variation in Homeownership Rates: Results for German Regions," Regional Studies, Taylor & Francis Journals, vol. 48(5), pages 844-865, May.
    4. Kubik, M.L. & Coker, P.J. & Barlow, J.F. & Hunt, C., 2013. "A study into the accuracy of using meteorological wind data to estimate turbine generation output," Renewable Energy, Elsevier, vol. 51(C), pages 153-158.
    5. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    6. Hirth, Lion & Ueckerdt, Falko & Edenhofer, Ottmar, 2015. "Integration costs revisited – An economic framework for wind and solar variability," Renewable Energy, Elsevier, vol. 74(C), pages 925-939.
    7. Jonas Egerer & Clemens Gerbaulet & Richard Ihlenburg & Friedrich Kunz & Benjamin Reinhard & Christian von Hirschhausen & Alexander Weber & Jens Weibezahn, 2014. "Electricity Sector Data for Policy-Relevant Modeling: Data Documentation and Applications to the German and European Electricity Markets," Data Documentation 72, DIW Berlin, German Institute for Economic Research.
    8. Andresen, Gorm B. & Søndergaard, Anders A. & Greiner, Martin, 2015. "Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis," Energy, Elsevier, vol. 93(P1), pages 1074-1088.
    9. R. Kelley Pace & James P. LeSage, 2010. "Omitted Variable Biases of OLS and Spatial Lag Models," Advances in Spatial Science, in: Antonio Páez & Julie Gallo & Ron N. Buliung & Sandy Dall'erba (ed.), Progress in Spatial Analysis, pages 17-28, Springer.
    10. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    11. James P. LeSage, 2014. "What Regional Scientists Need to Know about Spatial Econometrics," The Review of Regional Studies, Southern Regional Science Association, vol. 44(1), pages 13-32, Spring.
    12. Engelhorn, Thorsten & Müsgens, Felix, 2018. "How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany," Energy Economics, Elsevier, vol. 72(C), pages 542-557.
    13. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    14. Haucap, Justus & Pagel, Beatrice, 2014. "Ausbau der Stromnetze im Rahmen der Energiewende: Effizienter Netzausbau und effiziente Struktur der Netznutzungsentgelte," DICE Ordnungspolitische Perspektiven 55, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    15. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    16. Sharp, Ed & Dodds, Paul & Barrett, Mark & Spataru, Catalina, 2015. "Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information," Renewable Energy, Elsevier, vol. 77(C), pages 527-538.
    17. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    18. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    19. Ueckerdt, Falko & Hirth, Lion & Luderer, Gunnar & Edenhofer, Ottmar, 2013. "System LCOE: What are the costs of variable renewables?," Energy, Elsevier, vol. 63(C), pages 61-75.
    20. Bird, Lori & Lew, Debra & Milligan, Michael & Carlini, E. Maria & Estanqueiro, Ana & Flynn, Damian & Gomez-Lazaro, Emilio & Holttinen, Hannele & Menemenlis, Nickie & Orths, Antje & Eriksen, Peter Børr, 2016. "Wind and solar energy curtailment: A review of international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 577-586.
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    Cited by:

    1. Wolff, Stefanie & Madlener, Reinhard, 2019. "Charged up? Preferences for Electric Vehicle Charging and Implications for Charging Infrastructure Planning," FCN Working Papers 3/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    2. Reinhard Madlener & Barbara Glensk & Lukas Gläsel, 2019. "Optimal Timing of Onshore Wind Repowering in Germany under Policy Regime Changes: A Real Options Analysis," Energies, MDPI, vol. 12(24), pages 1-33, December.
    3. Liu, Xueying & Madlener, Reinhard, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    4. Liu, Xueying & Madlener, Reinhard, 2019. "Get Ready for Take-Off: A Two-Stage Model of Aircraft Market Diffusion," FCN Working Papers 15/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).

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    More about this item

    Keywords

    FM; Spatial Econometrics; System Integration Cost; Grid-Related Cost; Renewable Energy;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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