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L'architecture de marchés électriques : l'indispensable marché du temps réel d'électricité

Author

Listed:
  • Marcelo Saguan
  • Yannick Perez
  • Jean-Michel Glachant

Abstract

The introduction of competition into the electricity industry, organized a long time around a vertical integrated monopoly reveals problems of coordination which it is possible to treat by the appropriate combination of economic tools. The stake in terms of coordination of the electricity activities consists in replacing the preceding optimization tools of « command and control » by markets to manage the production and consumption of electricity in real time. The technico-economic characteristics of electricity lead to the implementation of complex architectures of markets, made up of several potential sub-markets being able to be built in several different manners. This article shows that, in spite of the great diversity of architectures, there is a common hard core to all designs. Our article shows that if one analyzes architectures of market from the point of view of the effort of the public authorities to build them or modify them, it is advisable to concentrate the efforts on the « real time market ».

Suggested Citation

  • Marcelo Saguan & Yannick Perez & Jean-Michel Glachant, 2009. "L'architecture de marchés électriques : l'indispensable marché du temps réel d'électricité," Revue d'économie industrielle, De Boeck Université, vol. 0(3), pages 69-88.
  • Handle: RePEc:cai:reidbu:rei_127_0069
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    Cited by:

    1. Rious, Vincent & Perez, Yannick & Roques, Fabien, 2015. "Which electricity market design to encourage the development of demand response?," Economic Analysis and Policy, Elsevier, vol. 48(C), pages 128-138.
    2. Roques, Fabien & Finon, Dominique, 2017. "Adapting electricity markets to decarbonisation and security of supply objectives: Toward a hybrid regime?," Energy Policy, Elsevier, vol. 105(C), pages 584-596.
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.

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