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<p>As the proportion of renewable energy sources continues to rise, their randomness and volatility pose severe challenges to the supply-demand balance of the power grid. Exploring the flexible regulation resources on the load side and actively guiding the adjustment of electricity consumption behavior through incentive measures will effectively enhance the level of renewable energy consumption, achieving mutual benefit between the grid side and the load side. However, current research often focuses only on the regulation of a single type of load while neglecting the coordinated cooperation among multiple types of loads. Meanwhile, the nonlinear characteristics of the load model further increase the computational burden for real-time scheduling. To address these issues, this paper proposes an economic dispatch strategy for power systems that considers the priority of multi-type load demand responses. Firstly, this paper classifies controllable loads within the regional power grid, establishing mathematical models that include high-energy-consuming loads, electric vehicle (EV) loads, and commercial loads. These are divided into flexible regulation resources of different time scales based on their electricity consumption behavior characteristics. Under the premise of satisfying user needs, a flexibility evaluation model is then established to obtain the flexibility regulation range of different types of loads. Finally, the improved IEEE-57 node test system demonstrates that the participation of multi-type dispatchable loads can fully explore the dispatch potential on the user side, effectively improving the level of renewable energy consumption in the region. Through analysis, the scheduling strategy proposed in this paper can reduce the average wind curtailment rate by up to 2.84% compared to single-type load scheduling. On the other hand, compared to traditional nonlinear models, the proposed method can improve computational efficiency by up to 83.8% in large-scale test systems, meeting the requirements for real-time online calculation.</p>

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Economic Dispatch Strategy for Microgrids Based on the Priority of Multitype Load Demand Responses

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Fuchang Yue,
  • Jian Du,
  • Xiaolong Xiao,
  • Yang Gu,
  • Guangxi Li

摘要

As the proportion of renewable energy sources continues to rise, their randomness and volatility pose severe challenges to the supply-demand balance of the power grid. Exploring the flexible regulation resources on the load side and actively guiding the adjustment of electricity consumption behavior through incentive measures will effectively enhance the level of renewable energy consumption, achieving mutual benefit between the grid side and the load side. However, current research often focuses only on the regulation of a single type of load while neglecting the coordinated cooperation among multiple types of loads. Meanwhile, the nonlinear characteristics of the load model further increase the computational burden for real-time scheduling. To address these issues, this paper proposes an economic dispatch strategy for power systems that considers the priority of multi-type load demand responses. Firstly, this paper classifies controllable loads within the regional power grid, establishing mathematical models that include high-energy-consuming loads, electric vehicle (EV) loads, and commercial loads. These are divided into flexible regulation resources of different time scales based on their electricity consumption behavior characteristics. Under the premise of satisfying user needs, a flexibility evaluation model is then established to obtain the flexibility regulation range of different types of loads. Finally, the improved IEEE-57 node test system demonstrates that the participation of multi-type dispatchable loads can fully explore the dispatch potential on the user side, effectively improving the level of renewable energy consumption in the region. Through analysis, the scheduling strategy proposed in this paper can reduce the average wind curtailment rate by up to 2.84% compared to single-type load scheduling. On the other hand, compared to traditional nonlinear models, the proposed method can improve computational efficiency by up to 83.8% in large-scale test systems, meeting the requirements for real-time online calculation.