JOURNAL ARTICLE

Can Distributed Intermittent Renewable Generation Reduce Future Grid Investments? Evidence from France.

  • Published In: Journal of the European Economic Association, 2023, v. 21, n. 1. P. 367 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Astier, Nicolas; Rajagopal, Ram; Wolak, Frank A 3 of 3

Abstract

This article examines the impact of investments in five distributed electricity generation technologies—solar photovoltaic (PV), wind, small hydro, renewable thermal, and non-renewable thermal—on hourly net electricity withdrawals from over 2,000 distribution substations in France between 2005 and 2018. Using comprehensive datasets and a fixed-effects regression framework, the study finds that distributed wind and solar PV have little to no effect on reducing peak net withdrawals that drive future transmission and distribution (T&D) grid investments, while distributed hydroelectric and thermal units significantly reduce these peaks. Additionally, distributed wind and solar increase the variability of net withdrawals, potentially raising operational challenges and costs. The analysis suggests that substantial battery storage—far exceeding current deployment levels—would be necessary alongside distributed wind and solar to achieve meaningful reductions in peak grid demand. Consequently, the findings imply that policies favoring distributed wind and solar over utility-scale installations in France cannot be justified by anticipated savings in future grid infrastructure investments.

Additional Information

  • Source:Journal of the European Economic Association. 2023/02, Vol. 21, Issue 1, p367
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:1542-4766
  • DOI:10.1093/jeea/jvac045
  • Accession Number:161830393
  • Copyright Statement:Copyright of Journal of the European Economic Association is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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