Game Optimization Scheduling of High Proportion Wind Power and Multiple Flexible Resources Considering Flexibility Compensation
Pan Zhengnan1, Deng Changhong2, Xu Huihui3, Liang Ning1, He Xiyu1
1. Faculty of Electric Power Engineering Kunming University of Science and Technology Kunming 650000 China; 2. School of Electrical Engineering and Automation Wuhan University Wuhan 430072 China; 3. Institute of Economic Technology State Grid Gansu Electric Power Company Lanzhou 730000 China
Abstract：The large scale grid connection of new energy sources, mainly wind power, has led to increasing uncertainty and anti-peak characteristics of the system, and the system is facing a huge challenge in terms of power balance. At present, scholars have explored the flexibility regulation potential of the system from multiple levels of "source-load-storage", which has improved the flexibility of system operation to a certain extent, but there are problems such as the lack of incentive mechanisms for flexibility regulation, the lack of reasonable compensation for the flexibility value of flexibility resources, and the lack of enthusiasm of flexibility resources to participate in regulation. This paper proposed an optimal dispatching method taking into account the game between a high proportion of wind power and multiple flexibility resources. On the basis of the individual profit-seeking properties of wind power and each flexibility resource subject, it used the idea of Stackelberg game to establish a multi-object game optimal dispatching model for supply and demand of flexibility regulation services, so as to achieve the optimization of supply and demand of flexibility resources and reasonable compensation of flexibility value. Firstly, considering the spatial and temporal coupling characteristics of wind power volatility and load volatility, the evaluation index of wind power volatility was defined, and based on this, a method for quantifying the demand for wind power flexibility regulation was proposed. Secondly, considering the differences and tendency of each flexibility resource's decisions, a method of equilibrium analysis of flexibility supply and demand based on Stackelberg game was proposed, and an optimization model of flexibility resource supply and demand game was established with the objective of maximizing the interests of each subject. Finally, the proposed method was verified through simulation cases that the proposed method can effectively improve the enthusiasm of multiple flexibility resources to participate in regulation and promote the online consumption of a high proportion of wind power. The simulation results of the new power system with high percentage of wind power show that the proposed model effectively improves the depth of thermal power peaking and the enthusiasm of energy storage and demand response to participate in flexibility regulation during the high time of wind power at night. And it achieves the full consumption of wind power. Meanwhile, in terms of economics, the proposed model improves the net benefits of thermal power, energy storage, and demand response, so that the individual value of each flexibility resource subject converges with the system's flexible operation goal. The experimental results under different flexibility resource adequacy show that the higher the flexibility resource adequacy of the system, the lower the flexibility regulation cost of wind power, and the more economical and efficient the consumption of wind power. The sensitivity analysis results of the volatility assessment index parameters show that the larger the R is, the gradually less volatility balancing responsibility the wind power operator needs to bear, and the volatility of the residual load gradually rises, and setting a reasonable R value can realize a reasonable division of the volatility balancing responsibility and effectively protect the interests of each subject. The following conclusions can be drawn from the simulation analysis: (1) The flexibility compensation mechanism can effectively increase the enthusiasm of flexibility resources to participate in regulation, fully exploit the flexibility regulation potential of source-load-storage, and promote the online consumption of a high proportion of wind power. (2) After adopting the Stackelberg game strategy proposed in this paper to optimize the supply and demand of flexibility resources, it effectively reflects the supply and demand of flexibility resources, and the flexibility values of thermal power operators, energy storage operators and demand response aggregators are reasonably compensated. (3) The volatility assessment index can realize the management of wind power feed-in volatility and effectively reduce the difficulties caused by high percentage of wind power volatility on power balance.
潘郑楠, 邓长虹, 徐慧慧, 梁宁, 何熙宇. 考虑灵活性补偿的高比例风电与多元灵活性资源博弈优化调度[J]. 电工技术学报, 2023, 38(zk1): 56-69.
Pan Zhengnan, Deng Changhong, Xu Huihui, Liang Ning, He Xiyu. Game Optimization Scheduling of High Proportion Wind Power and Multiple Flexible Resources Considering Flexibility Compensation. Transactions of China Electrotechnical Society, 2023, 38(zk1): 56-69.
[1] 亢丽君, 王蓓蓓, 薛必克, 等. 计及爬坡场景覆盖的高比例新能源电网平衡策略研究[J]. 电工技术学报, 2022, 37(13): 3275-3288. Kang Lijun, Wang Beibei, Xue Bike, et al.Research on the balance strategy for power grid with high proportion renewable energy considering the ramping scenario coverage[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3275-3288. [2] 王雪纯, 陈红坤, 陈磊. 提升区域综合能源系统运行灵活性的多主体互动决策模型[J]. 电工技术学报, 2021, 36(11): 2207-2219. Wang Xuechun, Chen Hongkun, Chen Lei.Multi-player interactive decision-making model for operational flexibility improvement of regional integrated energy system[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2207-2219. [3] 姜云鹏, 任洲洋, 李秋燕, 等. 考虑多灵活性资源协调调度的配电网新能源消纳策略[J]. 电工技术学报, 2022, 37(7): 1820-1835. Jiang Yunpeng, Ren Zhouyang, Li Qiuyan, et al.An accommodation strategy for renewable energy in distribution network considering coordinated dispatching of multi-flexible resources[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1820-1835. [4] 菅学辉, 张利, 杨立滨, 等. 高比例风电并网下基于卡尔多改进的深度调峰机制[J]. 电力系统自动化, 2018, 42(8): 110-118. Jian Xuehui, Zhang Li, Yang Libin, et al.Deep-peak regulation mechanism based on kaldor improvement under high-penetration wind power[J]. Automation of Electric Power Systems, 2018, 42(8): 110-118. [5] 李军徽, 张嘉辉, 穆钢, 等. 储能辅助火电机组深度调峰的分层优化调度[J]. 电网技术, 2019, 43(11): 3961-3970. Li Junhui, Zhang Jiahui, Mu Gang, et al.Hierarchical optimization scheduling of deep peak shaving for energy-storage auxiliary thermal power generating units[J]. Power System Technology, 2019, 43(11): 3961-3970. [6] 李军徽, 张嘉辉, 穆钢, 等. 计及负荷峰谷特性的储能调峰日前优化调度策略[J]. 电力自动化设备, 2020, 40(7): 128-133, 140, 134. Li Junhui, Zhang Jiahui, Mu Gang, et al. Day-ahead optimal scheduling strategy of peak regulation for energy storage considering peak and valley characteristics of load[J]. Electric Power Automation Equipment, 2020, 40(7): 128-133, 140, 134. [7] 韩笑, 周明, 李庚银. 计及储能和空调负荷的主动配电网多目标优化调度[J]. 电力系统保护与控制, 2018, 46(7): 14-23. Han Xiao, Zhou Ming, Li Gengyin.Multi-objective optimal dispatching of active distribution networks considering energy storage systems and air-conditioning loads[J]. Power System Protection and Control, 2018, 46(7): 14-23. [8] 徐成司, 董树锋, 华一波, 等. 基于改进一致性算法的工业园区分布式综合需求响应[J]. 电工技术学报, 2022, 37(20): 5175-5187. Xu Chengsi, Dong Shufeng, Hua Yibo, et al.Distributed comprehensive demand response of industrial parks based on improved consistency algorithm[J]. Transactions of China Electrotechnical Society, 2022, 37(20): 5175-5187. [9] 袁晓冬, 费骏韬, 胡波, 等. 资源聚合商模式下的分布式电源、储能与柔性负荷联合调度模型[J]. 电力系统保护与控制, 2019, 47(22): 17-26. Yuan Xiaodong, Fei Juntao, Hu Bo, et al.Joint scheduling model of distributed generation, energy storage and flexible load under resource aggregator mode[J]. Power System Protection and Control, 2019, 47(22): 17-26. [10] 崔杨, 修志坚, 刘闯, 等. 计及需求响应与火-储深度调峰定价策略的电力系统双层优化调度[J]. 中国电机工程学报, 2021, 41(13): 4403-4415. Cui Yang, Xiu Zhijian, Liu Chuang, et al.Dual level optimal dispatch of power system considering demand response and pricing strategy on deep peak regulation[J]. Proceedings of the CSEE, 2021, 41(13): 4403-4415. [11] 常源, 刘宗歧, 黄珊, 等. 风火网混合博弈协调规划及利益分配方法[J]. 电网技术, 2019, 43(11): 3899-3907. Chang Yuan, Liu Zongqi, Huang Shan, et al.Coordinated planning and profit distribution of wind power, thermal power and grid based on mixed game theory[J]. Power System Technology, 2019, 43(11): 3899-3907. [12] 武昭原, 周明, 姚尚润, 等. 基于合作博弈论的风储联合参与现货市场优化运行策略[J]. 电网技术, 2019, 43(8): 2815-2824. Wu Zhaoyuan, Zhou Ming, Yao Shangrun, et al.Optimization operation strategy of wind-storage coalition in spot market based on cooperative game theory[J]. Power System Technology, 2019, 43(8): 2815-2824. [13] 陈启鑫, 刘学, 房曦晨, 等. 考虑可再生能源保障性消纳的电力市场出清机制[J]. 电力系统自动化, 2021, 45(6): 26-33. Chen Qixin, Liu Xue, Fang Xichen, et al.Electricity market clearing mechanism considering guaranteed accommodation of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(6): 26-33. [14] 钟佳宇, 陈皓勇, 陈武涛, 等. 含灵活性资源交易的电力市场实时出清[J]. 电网技术, 2021, 45(3): 1032-1041. Zhong Jiayu, Chen Haoyong, Chen Wutao, et al.Real-time clearing of electricity markets with flexible resource transactions[J]. Power System Technology, 2021, 45(3): 1032-1041. [15] 吴珊, 边晓燕, 张菁娴, 等. 面向新型电力系统灵活性提升的国内外辅助服务市场研究综述[J]. 电工技术学报, 2023, 38(6): 1662-1677. Wu Shan, Bian Xiaoyan, Zhang Jingxian, et al.A review of domestic and foreign ancillary services market for improving flexibility of new power system[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1662-1677. [16] 安麒, 王剑晓, 武昭原, 等. 高比例可再生能源渗透下的电力市场价值分配机制设计[J]. 电力系统自动化, 2022, 46(7): 13-22. An Qi, Wang Jianxiao, Wu Zhaoyuan, et al.Benefit allocation mechanism design of electricity markets with penetration of high proportion of renewable energy[J]. Automation of Electric Power Systems, 2022, 46(7): 13-22. [17] 李璐, 郑亚先, 陈长升, 等. 风电的波动成本计算及应用研究[J]. 中国电机工程学报, 2016, 36(19): 5155-5163, 5396. Li Lu, Zheng Yaxian, Chen Changsheng, et al.Calculation of wind power variation costs and its application research[J]. Proceedings of the CSEE, 2016, 36(19): 5155-5163, 5396. [18] 赵书强, 吴杨, 李志伟, 等. 考虑风光出力不确定性的电力系统调峰能力及经济性分析[J]. 电网技术, 2022, 46(5): 1752-1761. Zhao Shuqiang, Wu Yang, Li Zhiwei, et al.Analysis of power system peaking capacity and economy considering uncertainty of wind and solar output[J]. Power System Technology, 2022, 46(5): 1752-1761. [19] 胡佳. 融合多种策略的改进粒子群算法[J]. 计算机系统应用, 2021, 30(7): 172-177. Hu Jia.Improved particle swarm optimization algorithm combining multiple strategies[J]. Computer Systems & Applications, 2021, 30(7): 172-177.
2023, Vol. 38 
