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Locational (In)Efficiency of Renewable Energy Feed-In Into the Electricity Grid: A Spatial Regression Analysis

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  • Tim Hofer
  • Reinhard Madlener

Abstract

This paper presents an econometric analysis of curtailment costs of renewable energy sources (RES) in Germany. The study aims at explaining and quantifying the regional variability of RES curtailment, which is a measure to relieve grid overstress by temporarily disconnecting RES from the electricity grid. We apply a Heckit sample selection model, which corrects bias from non-randomly selected samples. The selection equation estimates the probability of occurrence of RES curtailment in a region. The outcome equation corrects for cross-sectional dependence and quantifies the effect of RES on curtailment costs. The results show that wind energy systems connected to the distribution grid increase RES curtailment costs by 0.7% per MW (or 0.2% per GWh) in subregions that have experienced RES curtailment over the period 2015-2017. The implication of this finding is that policymakers should set price signals for renewables that consider the regional grid overstress, in order to mitigate the cost burden on consumers due to excess generation from RES.

Suggested Citation

  • Tim Hofer & Reinhard Madlener, 2021. "Locational (In)Efficiency of Renewable Energy Feed-In Into the Electricity Grid: A Spatial Regression Analysis," The Energy Journal, , vol. 42(1), pages 171-196, January.
    • Handle: RePEc:sae:enejou:v:42:y:2021:i:1:p:171-196
    DOI: 10.5547/01956574.42.1.thof
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Eicke, Anselm & Khanna, Tarun & Hirth, Lion, 2019. "Locational investment signals in electricity markets - How to steer the siting of new generation capacity?," EconStor Preprints 205237, ZBW - Leibniz Information Centre for Economics.
    6. J. Paul Elhorst, 2014. "Spatial Econometrics," SpringerBriefs in Regional Science, Springer, edition 127, number 978-3-642-40340-8, February.
    7. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291, Decembrie.
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