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1.河海大学能源与电气学院,江苏省南京市 211100
2.国网江苏省电力有限公司营销服务中心,江苏省南京市 210000
姜婷玉(1995—),女,博士,讲师,主要研究方向:需求响应、辅助服务。E-mail:jtingyuyuki@163.com
陶劲宇(2001—),男,硕士研究生,主要研究方向:电力系统负荷建模。E-mail:innotao@foxmail.com
王珂(1980—),女,通信作者,博士,教授,主要研究方向:源网荷储互动运行调度、柔性负荷调度。E-mail:20220021@hhu.edu.cn
收稿:2023-08-06,
修回:2023-12-18,
网络出版:2024-02-19,
纸质出版:2025-02-10
移动端阅览
姜婷玉,陶劲宇,王珂等.基于非合作博弈的高耗能工业负荷参与调峰策略[J].电力系统自动化,2025,49(3):13-21.
JIANG Tingyu,TAO Jinyu,WANG Ke,et al.Non-cooperative Game Based Strategy for Energy-Intensive Industrial Load Participating in Peak-regulation Demand[J].Automation of Electric Power Systems,2025,49(3):13-21.
姜婷玉,陶劲宇,王珂等.基于非合作博弈的高耗能工业负荷参与调峰策略[J].电力系统自动化,2025,49(3):13-21. DOI: 10.7500/AEPS20230806003.
JIANG Tingyu,TAO Jinyu,WANG Ke,et al.Non-cooperative Game Based Strategy for Energy-Intensive Industrial Load Participating in Peak-regulation Demand[J].Automation of Electric Power Systems,2025,49(3):13-21. DOI: 10.7500/AEPS20230806003.
高耗能工业负荷耗电量大、自动化水平高、可控性强,需求响应潜力可观,可作为应对新型电力系统峰谷差持续拉大的重要手段。为此,评估高耗能工业负荷的响应行为,
并制定相关策略有利于发挥其调峰潜能。首先,分析了高耗能工业负荷的响应特征,并针对性提出工业负荷参与调峰的阈值判断方法。其次,对典型工业负荷响应行为和调度控制中心决策行为分别建立模型。再次,考虑到调峰市场参与者行为理性特点,建立1-
N
的非合作博弈模型描述调峰市场参与者间的交互行为。进一步讨论了不同激励方式对调峰市场参与者的影响,并给出相应求解算法。最后,结合某工业园区算例进行仿真分析。结果表明,所提策略的调峰效果显著,且所有调峰市场参与者均能获取最优收益。
Energy-intensive industrial loads have high power consumption
high automation levels
and strong controllability
which show great demand response (DR) potentials and can be regarded as an important means to deal with the continuous expansion of the peak-valley difference in the new power system. Therefore
the estimation of the response behaviors of the industrial loads and the formulation of the relevant strategies are helpful to fulfill their DR potentials. Firstly
the response characteristics of the energy-intensive industrial loads are analyzed
and the specific threshold judgment method for industrial loads participating in peak regulation is proposed. Secondly
the models are established to describe the response behaviors of the typical industrial loads and the decision-making behavior of the dispatching and control center
respectively. Thirdly
considering the rationality of the behaviors for peak-regulation market participants
a 1-
N
non-cooperative game model is established to describe the interaction behaviors between the peak-regulation market participants. Then
the impact of the different incentive modes on peak-regulation market participants is further discussed
and the corresponding solution algorithm is given. Finally
the case simulation analyses are conducted with an industrial park
and the results verify that the proposed strategy has significant peak-regulation effects
and all the peak-regulation market participants can obtain the optimal benefits.
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