A Sequential Probabilistic Production Simulation Method for Coupled Systems Considering External Game and Internal Coordination
Ren Zhouyang1, Cheng Huan1, Zhou Guiping2, Zhao Yuanzhu2, Wang Lei2
1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Electrical Engineering Chongqing University Chongqing 400044 China; 2. State Grid Liaoning Electric Power Company Ltd Shenyang 110006 China
Abstract：The coupled systems consisting of thermal power plants and renewable energy plants are regarded as an important way to promote renewable energy accommodation. However, the existing probabilistic production simulation methods of multi-energy systems cannot adapt to the coupled systems since the relationship between the external game and internal coordination of the coupled systems in the market environment cannot be accurately simulated. This paper proposes a sequential probabilistic production simulation method for the coupled systems considering external game and internal coordination. The impacts of the internal and external cooperative competition mechanism on the operation of the coupled systems are considered in the probabilistic production simulation. The reliability and economic indices of the coupled systems considering the market trading strategies can also be obtained. Based on the master-slave game theory, the electricity-price competition relationship between the coupled system and other power suppliers and power grid companies is firstly modeled. A cooperative game model is established for the coupled systems to simulate the collaborative competition relationship between thermal power plants and renewable energy plants in a coupled system. A sequential probabilistic production simulation method for the coupled systems considering external game and internal coordination is developed. By simulating the operating state of the coupled system hour by hour, the reliability evaluation indices and the economic indices of the coupled systems can be obtained by simulating the hourly operation states of the coupled systems with the consideration of the market transaction strategies. An economic benefit allocation method is then proposed for the internal plants of the coupling systems. The comprehensive contribution of each power plant in a coupled system is quantified and considered. A local power grid in Northeastern China is used to test the proposed method. The simulation results indicate that ignoring the coordinating relationship between the internal interests and external interests of the coupled system will lead to underestimation of the competitiveness of the coupled system in the electricity market. The economic profits of the coupled system and renewable energy accommodation level are significantly increased by considering the external game and internal coordination of the coupled system. The electricity sale is increased by 5.20%, and the electricity sale revenue is increased by 12.01%. The revenues of the internal thermal power plant, wind power plant and photovoltaic power plant are increased by 12.17%, 10.12%, 4.14%, respectively. The reliability of the coupled system is also improved. The probability of losing contract load is reduced by 31.34%. The expected value of contract power shortage is reduced from 14.97 GW to 11.35 GW, which is decreased by 24.18%. Furthermore, the simulation results under different ratios of renewable energy capacity and thermal power capacity indicates that when the penetration rate of renewable energy exceeds 40%, the thermal power units are frequently adjusted to smooth the power outputs of the coupled system and the operation cost of the thermal power units and the renewable energy curtailment are significantly increased. The following conclusions can be drawn from the simulation analyses in the paper. (1) By considering the external game and internal coordination of the coupled system, the economic profit, the renewable energy accommodation and the reliability level of the coupled system are all greatly improved. (2) Increasing the proportion of renewable energy installed capacity in the coupled system can effectively reduce the unit power generation cost and improve the economic profit and competitiveness in the electricity market, while the reliability level of the coupled system will be decreased. If the proportion of renewable energy in a coupled system is too high, the renewable energy accommodation level will be decreased due to insufficient flexible adjustment capacity of thermal power. Therefore, it is of significant importance to reasonably configure the capacity ratios of thermal power and renewable energy installed capacity in a coupled system.
任洲洋, 程欢, 周桂平, 赵苑竹, 王磊. 考虑外部博弈和内部协同的耦合系统时序随机生产模拟[J]. 电工技术学报, 2023, 38(22): 6204-6217.
Ren Zhouyang1, Cheng Huan1, Zhou Guiping2, Zhao Yuanzhu2, Wang Lei2. A Sequential Probabilistic Production Simulation Method for Coupled Systems Considering External Game and Internal Coordination. Transactions of China Electrotechnical Society, 2023, 38(22): 6204-6217.
[1] 伍梦尧. 构建新型电力系统为实现“3060”目标提供重要支撑: 摘自国家电网有限公司总经理张智刚在2021年国际能源变革对话上的主旨演讲[N]. 中国电力报, 2021-06-30(2). [2] 毛庆汉. 储能联合火电机组参与调频辅助服务市场的工程应用[J]. 电气技术, 2021, 22(7): 103-108. Mao Qinghan.Engineering application of battery energy storage system coordinated with thermal power unit in regulating ancillary service market[J]. Electrical Engineering, 2021, 22(7): 103-108. [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] 国家能源局. 关于公开征求对《国家发展改革委国家能源局关于开展“风光水火储一体化”“源网荷储一体化”的指导意见(征求意见稿)》意见的公告[EB/OL]. [2020-08-27]. http://www.nea.gov.cn/ 2020-08/27/c_139321964.htm. [5] 程光, 徐飞, 胡博, 等. 可再生能源与火电集成耦合的协同性能及组合设计研究[J]. 电网技术, 2021, 45(6): 2178-2191. Cheng Guang, Xu Fei, Hu Bo, et al.Synergistic performance and combination design of coupling of renewable energy and thermal power[J]. Power System Technology, 2021, 45(6): 2178-2191. [6] 谈天夫, 高山, 李海峰, 等. 基于等效间隔-频率分布的含风电场电力系统随机生产模拟[J]. 电工技术学报, 2014, 29(12): 148-157. Tan Tianfu, Gao Shan, Li Haifeng, et al.Power system probabilistic production simulation based on equivalent interval frequency distribution including wind farms[J]. Transactions of China Electrotechnical Society, 2014, 29(12): 148-157. [7] 丁明, 楚明娟, 毕锐, 等. 基于序贯蒙特卡洛随机生产模拟的风电接纳能力评价方法及应用[J]. 电力自动化设备, 2016, 36(9): 67-73. Ding Ming, Chu Mingjuan, Bi Rui, et al.Wind power accommodation capability evaluation based on sequential Monte Carlo probabilistic production simulation and its application[J]. Electric Power Automation Equipment, 2016, 36(9): 67-73. [8] 李林川, 王锡凡, 王秀丽. 基于等效电量函数法的互联电力系统随机生产模拟[J]. 中国电机工程学报, 1996, 16(3): 180-184. Li Linchuan, Wang Xifan, Wang Xiuli.Probabilistic modeling for interconnected power systems based on the equivalent energy function approach[J]. Chinese Society for Electrical Engineering, 1996, 16(3): 180-184. [9] 姚力, 黄镔, 王秀丽, 等. 考虑风火联合外送的互联系统随机生产模拟[J]. 电网技术, 2015, 39(5): 1219-1225. Yao Li, Huang Bin, Wang Xiuli, et al.Probabilistic production simulation of interconnected system by considering the joint delivery of wind power and thermal power generation[J]. Power System Technology, 2015, 39(5): 1219-1225. [10] 孙伟卿, 李恒, 杜习周, 等. 基于有效容量分布的互联系统风电消纳能力评估[J]. 电网技术, 2019, 43(9): 3321-3327. Sun Weiqing, Li Heng, Du Xizhou, et al.Assessment of wind power accommodation ability based on available capacity distribution in interconnected system[J]. Power System Technology, 2019, 43(9): 3321-3327. [11] 丁明, 林玉娟, 潘浩. 考虑负荷与新能源时序特性的随机生产模拟[J]. 中国电机工程学报, 2016, 36(23): 6305-6314, 6595. Ding Ming, Lin Yujuan, Pan Hao.Probabilistic production simulation considering time sequence characteristics of load and new energy[J]. Proceedings of the CSEE, 2016, 36(23): 6305-6314, 6595. [12] 朱睿, 胡博, 谢开贵, 等. 含风电-光伏-光热-水电-火电-储能的多能源电力系统时序随机生产模拟[J]. 电网技术, 2020, 44(9): 3246-3253. Zhu Rui, Hu Bo, Xie Kaigui, et al.Sequential probabilistic production simulation of multi-energy power system with wind power, photovoltaics, concentrated solar power, cascading hydro power, thermal power and battery energy storage[J]. Power System Technology, 2020, 44(9): 3246-3253. [13] 廖庆龙, 谢开贵, 胡博. 含风电和储能电力系统的时序随机生产模拟[J]. 电网技术, 2017, 41(9): 2769-2776. Liao Qinglong, Xie Kaigui, Hu Bo.Sequential probabilistic production simulation of power systems with wind power and energy storage[J]. Power System Technology, 2017, 41(9): 2769-2776. [14] 赵书强, 索璕, 许朝阳, 等. 考虑断面约束的多能源电力系统时序性生产模拟[J]. 电力自动化设备, 2021, 41(7): 1-6. Zhao Shuqiang, Suo Xun, Xu Zhaoyang, et al.Time series production simulation of multi-energy power system considering section constraints[J]. Electric Power Automation Equipment, 2021, 41(7): 1-6. [15] 周明, 李琰, 李庚银. 基于随机生产模拟的日前发电-备用双层决策模型[J]. 电网技术, 2019, 43(5): 1606-1613. Zhou Ming, Li Yan, Li Gengyin.A day-ahead power generation-reserve Bi-level decision-making model for power system based on probabilistic production simulation[J]. Power System Technology, 2019, 43(5): 1606-1613. [16] 谭忠富, 宋艺航, 张会娟, 等. 大规模风电与火电联合外送体系及其利润分配模型[J]. 电力系统自动化, 2013, 37(23): 63-70. Tan Zhongfu, Song Yihang, Zhang Huijuan, et al.Joint delivery system of large-scale wind power and thermal power generation and its profit distribution model[J]. Automation of Electric Power Systems, 2013, 37(23): 63-70. [17] 刘思源, 艾芊, 郑建平, 等. 多时间尺度的多虚拟电厂双层协调机制与运行策略[J]. 中国电机工程学报, 2018, 38(3): 753-761. Liu Siyuan, Ai Qian, Zheng Jianping, et al.Bi-level coordination mechanism and operation strategy of multi-time scale multiple virtual power plants[J]. Proceedings of the CSEE, 2018, 38(3): 753-761. [18] 赵文会, 闫豪楠, 何威. 基于风火网非合作博弈的电力市场均衡模型[J]. 电网技术, 2018, 42(1): 103-111. Zhao Wenhui, Yan Haonan, He Wei.Equilibrium model of electricity market based on non-cooperative game of wind farms, thermal power plants and power grid company[J]. Power System Technology, 2018, 42(1): 103-111. [19] 杨龙杰, 周念成, 胡博, 等. 计及火电阶梯式爬坡率的耦合系统优化调度方法[J]. 中国电机工程学报, 2022, 42(1): 153-164. Yang Longjie, Zhou Niancheng, Hu Bo, et al.Optimal scheduling method for coupled system based on ladder-type ramp rate of thermal power units[J]. Proceedings of the CSEE, 2022, 42(1): 153-164. [20] 陈岑, 武传涛, 林湘宁, 等. 计及上下游市场的园区综合能源商购售能策略[J]. 电工技术学报, 2022, 37(1): 220-231. Chen Cen, Wu Chuantao, Lin Xiangning, et al.Purchase and sale strategies of park integrated energy suppliers in wholesale and retail markets[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 220-231. [21] 熊宇峰, 司杨, 郑天文, 等. 基于主从博弈的工业园区综合能源系统氢储能优化配置[J]. 电工技术学报, 2021, 36(3): 507-516. Xiong Yufeng, Si Yang, Zheng Tianwen, et al.Optimal configuration of hydrogen storage in industrial park integrated energy system based on stackelberg game[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 507-516. [22] 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. [23] 李军祥, 王宇倩, 何建佳, 等. 基于区块链的微电网电力市场电价与电量动态博弈[J]. 电力系统自动化, 2021, 45(17): 11-19. Li Junxiang, Wang Yuqian, He Jianjia, et al.Blockchain-based dynamic game of electricity price and power for microgrid electricity market[J]. Automation of Electric Power Systems, 2021, 45(17): 11-19. [24] 黎灿兵, 胡亚杰, 赵弘俊, 等. 合约电量分解通用模型与算法[J]. 电力系统自动化, 2007, 31(11): 26-30. Li Canbing, Hu Yajie, Zhao Hongjun, et al.General model and algorithm for contract energy decomposition[J]. Automation of Electric Power Systems, 2007, 31(11): 26-30. [25] 赵书强, 胡利宁, 田捷夫, 等. 基于中长期风电光伏预测的多能源电力系统合约电量分解模型[J]. 电力自动化设备, 2019, 39(11): 13-19. Zhao Shuqiang, Hu Lining, Tian Jiefu, et al.Contract power decomposition model of multi-energy power system based on mid-long term wind power and photovoltaic electricity forecasting[J]. Electric Power Automation Equipment, 2019, 39(11): 13-19. [26] 黄永皓, 尚金成, 康重庆, 等. 电力中长期合约交易市场的运作机制及模型[J]. 电力系统自动化, 2003, 27(4): 24-28. Huang Yonghao, Shang Jincheng, Kang Chongqing, et al.An operation mechanism and model of long and middle term contract market[J]. Automation of Electric Power Systems, 2003, 27(4): 24-28. [27] 麻秀范, 余思雨, 朱思嘉, 等. 基于多因素改进Shapley的虚拟电厂利润分配[J]. 电工技术学报, 2020, 35(增刊2): 585-595. Ma Xiufan, Yu Siyu, Zhu Sijia, et al.Profit allocation to virtual power plant members based on improved multifactor shapley value method[J]. Transactions of China Electrotechnical Society, 2020, 35(S2): 585-595. [28] 伍栋文, 于艾清. 大规模多源联合外送协调调度中基于核仁理论的利润分配[J]. 电网技术, 2016, 40(10): 2975-2981. Wu Dongwen, Yu Aiqing.Research on nucleolus theory based profit distribution method for joint delivery system of large-scale hybrid power generation[J]. Power System Technology, 2016, 40(10): 2975-2981. [29] 李壮阔, 陈水鹏. 联盟收益不确定下合作博弈的多目标粒子群扩展算法求解[J]. 数学的实践与认识, 2020, 50(13): 25-37. Li Zhuangkuo, Chen Shuipeng.Multi-objective particle swarm optimization algorithm for cooperative game under uncertainty of alliance income[J]. Mathematics in Practice and Theory, 2020, 50(13): 25-37. [30] Wang Jianxue, Wei Jingdong, Zhu Yuchao, et al.The reliability and operational test system of a power grid with large-scale renewable integration[J]. CSEE Journal of Power and Energy Systems, 2019, 6(3): 704-711. [31] 黄珊. 风火打捆系统参与大用户直购交易双边定价博弈[D]. 北京: 华北电力大学, 2020. [32] Chen Yue, Wei Wei, Wang Han, et al.An energy sharing mechanism achieving the same flexibility as centralized dispatch[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3379-3389. [33] 俞建. 博弈论选讲[M]. 北京: 科学出版社, 2014. [34] Luo Zhiquan, Pang J S, Ralph D.Mathematical programs with equilibrium constraints[M]. Cambridge: Cambridge University Press, 1996 [35] 李光久. 博弈论基础教程[M]. 北京: 化学工业出版社, 2005.
2023, Vol. 38 
