何洋,黄龙,陈皓勇,尚金成,李鹏,刘雨梦
HE Yang,HUANG Long,CHEN Haoyong,SHANG Jincheng,LI Peng,LIU Yumeng

• 1:河南电力交易中心有限公司
• 2:华南理工大学电力学院
• 3:国网河南省电力公司经济技术研究院

摘要(Abstract)：

仿真实验是验证市场规则的有效手段,从国内电力市场的月度集中竞价规则出发,基于协同进化算法,对相应的统一出清和报价撮合出清进行对比分析。首先,提出一种基于策略集形式的协同进化机制,用于模拟真实市场下的购售电双方博弈竞争情况。其次,研究月度集中竞价相关规则,基于购电商和发电商各自利益最大化,重点对市场环境下统一出清模型和撮合出清模型进行对比分析,定量分析了市场中购售双方的博弈过程,从而求解市场仿真均衡。仿真结果表明,统一出清较撮合出清波动性更大,市场的出清情况与发电商和购电商的成本和效益函数有关。
The simulation experiment is an effective means to verify the market rules. Based on the monthly centralized bidding rules in the domestic electricity market and the co-evolutionary algorithm, the corresponding market clearing price(MCP) and high-low matching clearing price are compared and analyzed.Firstly, a cooperative evolutionary mechanism based on strategy set is proposed to simulate the game competition between buyers and sellers in the real market. Secondly, relevant rules of monthly concentrated bidding are studied. The comparative analysis of the MCP model and the high-low matching clearing price model is compared based on the maximization of the respective interests of electricity purchasers and power producers.Quantitative analysis of the game process between buyers and sellers in the market is made to solve the equilibrium of market simulation. The simulation analysis shows that the MCP is more volatile than the high-low matching clearing price, and the clearing of the market is related to the cost and benefit function of electricity producer and purchaser.

关键词(KeyWords)： 策略集;协同进化机制;统一出清;撮合出清;仿真均衡
strategy set;co-evolution mechanism;MCP;high-low matching clearing price;simulation equalization

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划项目(2016YFB0900102);; 中央高校基本科研业务费项目(2018py11)

作者(Author): 何洋,黄龙,陈皓勇,尚金成,李鹏,刘雨梦
HE Yang,HUANG Long,CHEN Haoyong,SHANG Jincheng,LI Peng,LIU Yumeng

DOI: 10.19585/j.zjdl.201907002

参考文献(References)：

• [1]陈皓勇,付超.统一价格和PAB竞价的实验分析[J].电力系统自动化,2007,31(4):12-17.
• [2]陈皓勇,谭科,荆朝霞,等.电力市场综合模拟系统---实验方法与系统架构研究[J].电力自动化设备,2010,30(3):25-29.
• [3]陈皓勇,陈玮,朱刚毅,等.“电力市场设计学”的基本概念及方法论[J].电力大数据,2018,21(6):1-8.
• [4]张静华,韩璞.多算法多种群协同优化差分进化算法[J].系统仿真学报,2018,30(5):1690-1699.
• [5]WANG L,SHEN J N,WANG S Y,et al.Advances in coevolutionary algorithms[J].Control&Decision,2015,30(2):193-202.DOI:10.13195/j.kzyjc.2014.0624.
• [6]DREZEWSKI R,DOROZ K.An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization[J].Symmetry,2017,9(9):168.DOI:10.3390/sym9090168
• [7]王凌,沈婧楠,王圣尧,等.协同进化算法研究进展[J].控制与决策,2015,30(2):193-202.
• [8]刘振,鲁华杰,刘文彪.自适应协同进化蝙蝠算法[J/OL].控制与决策:1-10[2019-07-02].https://doi.org/10.13195/j.kzyjc.2018.0029.
• [9]刘科研,盛万兴,贾东梨,等.基于协同进化蚁群算法的含光伏发电的配电网重构[J].可再生能源,2017,35(5):702-708.
• [10]张宏立,李远梅.基于滤子混合协同进化算法的无功优化[J].控制与决策,2017,32(9):1701-1706.
• [11]杨彦,陈皓勇,张尧,等.基于协同进化算法求解寡头电力市场均衡[J].电力系统自动化,2009,33(18):42-46.
• [12]王建学,王锡凡,陈皓勇,等.基于协同进化法的电力系统无功优化[J].中国电机工程学报,2004,24(9):124-129.
• [13]陈皓勇,付超.统一价格和PAB竞价的实验分析[J].电力系统自动化,2007,31(4):12-17.
• [14]李俊,刘俊勇,张力,等.基于电力市场PAB竞价的最优概率随机匹配机制[J].四川大学学报(工程科学版),2011,43(3):150-154.
• [15]施展武,杨莉,甘德强.PAB和MCP电价机制下考虑不同容量水平的市场均衡分析[J].电力系统自动化,2005,29(19):10-13.
• [16]谭海云,甘德强,杨莉,等.电力市场PAB机制下混合策略的均衡分析[J].电力系统自动化,2009,33(14):41-45.
• [17]王睿琛,张茂林,黄宋波,等.云南电力市场集中竞价交易规则设计[J].云南电力技术,2017,45(6):115-119.
• [18]王林,李晨,刘嘉佳,等.基于复式竞价撮合的电力市场交易模式设计与实践[J].电力系统自动化,2018,42(24):188-197.
• [19]丁军策,石可,陈皓勇,等.基于实验经济学的广东月度竞价分析[J].广东电力,2018,31(6):19-24.
• [20]荆朝霞,朱继松.月度电量集中竞价市场规则的仿真实验分析[J].电力系统自动化,2017,41(24):42-48.