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Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment

Author

Listed:
  • Wei Liao

    (Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China)

  • Yi Yang

    (Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518000, China)

  • Qingwei Wang

    (Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China)

  • Ruoyu Wang

    (Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China)

  • Xieli Fu

    (Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China)

  • Yinghua Xie

    (Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China)

  • Junhua Zhao

    (Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518000, China)

Abstract

The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy.

Suggested Citation

  • Wei Liao & Yi Yang & Qingwei Wang & Ruoyu Wang & Xieli Fu & Yinghua Xie & Junhua Zhao, 2023. "Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment," Energies, MDPI, vol. 16(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4819-:d:1175085
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    References listed on IDEAS

    as
    1. Ekaterina Bayborodina & Michael Negnevitsky & Evan Franklin & Alison Washusen, 2021. "Grid-Scale Battery Energy Storage Operation in Australian Electricity Spot and Contingency Reserve Markets," Energies, MDPI, vol. 14(23), pages 1-21, December.
    2. Aqsa Rana & Gyula Gróf, 2022. "Assessment of the Electricity System Transition towards High Share of Renewable Energy Sources in South Asian Countries," Energies, MDPI, vol. 15(3), pages 1-23, February.
    3. David Borge-Diez & Enrique Rosales-Asensio & Ana I. Palmero-Marrero & Emin Acikkalp, 2022. "Optimization of CSP Plants with Thermal Energy Storage for Electricity Price Stability in Spot Markets," Energies, MDPI, vol. 15(5), pages 1-25, February.
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    Cited by:

    1. Cui, Jia & Zhang, Ximing & Liu, Wei & Yan, Xinyue & Hu, Zhen & Li, Chaoran & Huang, Jingbo, 2024. "A novel trading optimization strategy of source-load bilateral thermoelectric spot based on industrial parks interior," Energy, Elsevier, vol. 302(C).

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