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A Framework for Assessing Electricity Market Performance under Different Bidding Zone Configurations

Author

Listed:
  • Haoke Wu

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

  • Tao Huang

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

  • Stefania Conti

    (Department of Electrical, Electronic and Computer Engineering, Università di Catania, 95123 Catania, Italy)

  • Ettore Bompard

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

Abstract

Improper configuration of bidding zones can lead to market efficiency losses, hinder the integration of renewable energy sources (RESs), and reduce grid security. To evaluate the impact of different bidding zone configurations on market performance, we developed a multi-dimensional evaluation framework containing a series of indicators covering aspects of market efficiency, grid security, and sustainability. These indicators facilitate the comparisons among different market dispatch mechanisms. To validate the proposed framework, the reconfiguration of the Italian bidding zones has been applied to a simplified Italian grid model to compare the market performance under different bidding zone configurations. The simulation results indicate that the implemented reconfiguration has led to enhanced market efficiency and security in the Italian power system. However, the reconfiguration shows a comparatively lower reduction in greenhouse gas (GHG) emissions, suggesting a weaker sustainable performance.

Suggested Citation

  • Haoke Wu & Tao Huang & Stefania Conti & Ettore Bompard, 2024. "A Framework for Assessing Electricity Market Performance under Different Bidding Zone Configurations," Energies, MDPI, vol. 17(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2743-:d:1408472
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    References listed on IDEAS

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    4. Ambrosius, Mirjam & Grimm, Veronika & Kleinert, Thomas & Liers, Frauke & Schmidt, Martin & Zöttl, Gregor, 2020. "Endogenous price zones and investment incentives in electricity markets: An application of multilevel optimization with graph partitioning," Energy Economics, Elsevier, vol. 92(C).
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