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Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization

Author

Listed:
  • Diandian Hu

    (School of Mathmatics and Physics, North China Electric Power University, Baoding 071003, China)

  • Tao Wang

    (School of Mathmatics and Physics, North China Electric Power University, Baoding 071003, China
    Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Baoding 071003, China)

Abstract

In the pilot provinces of China’s electricity spot market, power generation companies usually adopt the separate bidding mode, which leads to a low willingness of demand-side response and poor flexibility in the interaction mechanism between supply and demand. Based on the analysis of the demand response mechanism of the power day-ahead market with the participation of power sales companies, this paper abstracted the game process of the “power grid-sales company-users” tripartite competition in the electricity market environment into a two-layer (purchase layer/sales layer) game model and proposed a master–slave game equilibrium optimization strategy for the day-ahead power market under the two-layer game. The multi-objective multi-universe optimization algorithm was used to find the Pareto optimal solution of the game model, a comprehensive evaluation was constructed, and the optimal strategy of the demand response was determined considering the peak cutting and valley filling quantity of the power grid, the profit of the electricity retailers, the cost of the consumers, and the comfort degree. Examples are given to simulate the day-ahead electricity market participated in by the electricity retailers, analyze and compare the benefits of each market entity participating in the demand response, and verify the effectiveness of the proposed model.

Suggested Citation

  • Diandian Hu & Tao Wang, 2023. "Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization," Energies, MDPI, vol. 16(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4009-:d:1143501
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    References listed on IDEAS

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    1. Motalleb, Mahdi & Siano, Pierluigi & Ghorbani, Reza, 2019. "Networked Stackelberg Competition in a Demand Response Market," Applied Energy, Elsevier, vol. 239(C), pages 680-691.
    2. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    3. Wanlei Xue & Xin Zhao & Yan Li & Ying Mu & Haisheng Tan & Yixin Jia & Xuejie Wang & Huiru Zhao & Yihang Zhao, 2023. "Research on the Optimal Design of Seasonal Time-of-Use Tariff Based on the Price Elasticity of Electricity Demand," Energies, MDPI, vol. 16(4), pages 1-17, February.
    4. Dai, Yeming & Sun, Xilian & Qi, Yao & Leng, Mingming, 2021. "A real-time, personalized consumption-based pricing scheme for the consumptions of traditional and renewable energies," Renewable Energy, Elsevier, vol. 180(C), pages 452-466.
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    Cited by:

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    2. Bo Yang & Jinhang Duan & Zhijian Liu & Lin Jiang, 2024. "Exploring Sustainable Development of New Power Systems under Dual Carbon Goals: Control, Optimization, and Forecasting," Energies, MDPI, vol. 17(16), pages 1-4, August.

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