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华南理工大学电力学院,广东省广州市510640
陆展辉(1995—),男,硕士研究生,主要研究方向:电力市场、节点电价。E-mail:epzhlu@mail.scut.edu.cn
荆朝霞(1975—),女,通信作者,教授,博士生导师,主要研究方向:电力市场、电动汽车、电力系统运行与控制、综合能源系统优化。E-mail:zxjing@mail.scut.edu.cn
陈柏柯(1993—),男,硕士研究生,主要研究方向:电力市场。E-mail:epchenbaike@mail.scut.edu.cn
收稿日期:2020-06-30,
修回日期:2021-01-28,
网络出版日期:2021-04-23,
纸质出版日期:2021-06-25
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陆展辉,荆朝霞,陈柏柯.电力现货市场前瞻优化的周期选择[J].电力系统自动化,2021,45(12):84-91.
LU Zhanhui,JING Zhaoxia,CHEN Baike.Period Selection of Look-ahead Optimization for Electricity Spot Market[J].Automation of Electric Power Systems,2021,45(12):84-91.
陆展辉,荆朝霞,陈柏柯.电力现货市场前瞻优化的周期选择[J].电力系统自动化,2021,45(12):84-91. DOI: 10.7500/AEPS20200630020.
LU Zhanhui,JING Zhaoxia,CHEN Baike.Period Selection of Look-ahead Optimization for Electricity Spot Market[J].Automation of Electric Power Systems,2021,45(12):84-91. DOI: 10.7500/AEPS20200630020.
前瞻优化对于适应可再生能源的发展需求,确保电力现货市场良好运作,发挥着重要作用。而合理的前瞻周期选择能够有效地提高电力系统全局优化的可靠性与经济性。因此,迫切需要对前瞻周期的选择问题进行研究。为此,文中首先通过建立电力现货市场前瞻优化模型,推导了优化模型下的节点边际电价公式,分析了跨时段约束对节点边际电价的影响。然后
构建了前瞻周期的偏差量模型,设计了电力现货市场前瞻周期的选择流程。最后
通过不同的算例,揭示了不同前瞻周期下跨时段约束对节点边际电价的影响,验证了流程所选最佳前瞻周期的有效性。
Look-ahead optimization plays an important role in meeting development needs of renewable energy and ensuring good operation of electricity spot markets. Reasonable selection of look-ahead periods can effectively improve the reliability and economy of global optimization in power systems. Therefore
there is an urgent need to conduct research on the selection of the look-ahead period. To solve this problem
the article firstly establishes a look-ahead optimization model for electricity spot markets
derives the formula of the locational marginal price in the optimization model
and analyzes the impact of the intertemporal constraint on the locational marginal prices. Next
the deviation model of the look-ahead period is constructed and the selection procedure of the look-ahead period in electricity spot markets is designed. Finally
based on different examples
the impact of intertemporal constraints on the locational marginal price with different look-ahead periods is revealed
and the effectiveness of the best look-ahead period selected through the optimization procedure is verified.
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