Multi-Time-Scale Frequency Regulation Market Clearing and Dispatch Strategy Considering Wind Power Uncertainty
Chen Chunyu1, Huang Chenkai2, Wang Jianxiao3, Dai Xuemei4, Wang Ying5
1. School of Electrical Engineering China University of Mining and Technology University Xuzhou 221116 China; 2. School of Electrical Engineering Chongqing University Chongqing 400044 China; 3. National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 100871 China; 4. College of Automation Engineering Shanghai University of Electrical Power Shanghai 200090 China; 5. School of Automation Southeast University Nanjing 210096 China
Abstract:To meet the requirements of the modern power system construction, the National Energy Administration introduces guidance and management requirements for new resources such as renewable energy and energy storage systems. In western regions with abundant wind energy resources, such as Xinjiang Province, wind farms meeting certain installed capacity requirements can participate in the frequency regulation ancillary services market. The dispatching agency optimizes clearing based on frequency demands. Wind farms provide frequency services and obtain settlement fees according to the clearing results. Unlike conventional units, declared capacity of wind farms is subject to uncertainty. In this situation, despite the desire of wind farms to occupy market share and increase revenue by utilizing the full capacity quota, the uncertainty in declared capacity may lead to actual capacity being lower than the reported value, posing a risk to the frequency regulation market. Therefore, this paper proposes a multi-time scale clearing and scheduling method for the frequency regulation market considering wind power uncertainty. Firstly, the impact of wind power uncertainty on the frequency regulation market clearing and scheduling process is analyzed through the analysis of the bid capacity default rate. By designing a risk cost based on conditional value-at-risk, the paper quantitatively evaluates the impact of wind power uncertainty on the frequency regulation market clearing. Secondly, by analyzing the frequency demand characteristics at different timescales, a multi-timescale clearing model and strategy considering frequency risk cost are proposed, providing generation plan boundary conditions for the next level of clearing and ensuring market safety and reliability. Finally, the paper quantitatively analyzes the impact of wind power uncertainty on frequency accuracy indicators and proposes a scheduling strategy considering the optimal comprehensive frequency performance indicators. The proposed scheduling strategy adopts a rolling mode, where the dispatching agency updates frequency performance indicators based on the previous round of scheduling performance, improving the participation and quality of high-quality regulation units in frequency regulation. Simulation analysis is conducted using a 3-wind-farm-and-1-thermal power system to reproduce the market clearing and scheduling process. The simulation results indicate that despite offering low prices of wind farms, if their default rate is too high, they may not clear priority due to risk costs. Thermal power units have the lowest default rate and lowest frequency regulation risk cost. To increase the clearing capacity of uncertain wind power, wind storage systems can be used to reduce the bid default rate. Through simulated rolling scheduling processes, it is observed that the performance of units in one round of scheduling affects their frequency performance indicators in the next round, thereby altering their regulation quantity. Through simulation analysis, the following conclusions can be drawn: (1)Wind farms with low quotes but high default rates may not be cleared due to the associated risk costs. To enhance the priority of wind power clearing and reduce uncertainty risk costs, it is possible to lower the default levels of wind power resources (e.g., by using a wind and storage joint system to replace standalone wind power). (2)The rolling optimization scheduling based on model predictive control (MPC) can dynamically adjust the dispatched output of bid resources in real-time using feedback-corrected frequency performance indicators, achieving optimal comprehensive frequency regulation performance. Future research should consider uncertainties from other resources like photovoltaics and controllable loads, as well as develop clearing strategies for their participation in the frequency regulation market. Additionally, approaches considering game theory or multi-objective optimization could be explored to optimize the trade-off between storage costs and uncertainty resource risk costs, thereby enhancing the economic efficiency and security of frequency regulation.
陈春宇, 黄宸恺, 王剑晓, 戴雪梅, 王颖. 考虑风电不确定性的调频辅助服务市场多时间尺度出清调度策略[J]. 电工技术学报, 2024, 39(21): 6804-6818.
Chen Chunyu, Huang Chenkai, Wang Jianxiao, Dai Xuemei, Wang Ying. Multi-Time-Scale Frequency Regulation Market Clearing and Dispatch Strategy Considering Wind Power Uncertainty. Transactions of China Electrotechnical Society, 2024, 39(21): 6804-6818.
[1] Sadeghi-Mobarakeh A, Mohsenian-Rad H.Optimal bidding in performance-based regulation markets: an MPEC analysis with system dynamics[J]. IEEE Transactions on Power Systems, 2017, 32(2): 1282-1292. [2] 徐湘楚, 米增强, 詹泽伟, 等. 考虑多重不确定性的电动汽车聚合商参与能量-调频市场的鲁棒优化模型[J]. 电工技术学报, 2023, 38(3): 793-805. Xu Xiangchu, Mi Zengqiang, Zhan Zewei, et al.A robust optimization model for electric vehicle aggregator participation in energy and frequency regulation markets considering multiple uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(3): 793-805. [3] 张谦, 邓小松, 岳焕展, 等. 计及电池寿命损耗的电动汽车参与能量-调频市场协同优化策略[J]. 电工技术学报, 2022, 37(1): 72-81. Zhang Qian, Deng Xiaosong, Yue Huanzhan, et al.Coordinated optimization strategy of electric vehicle cluster participating in energy and frequency regulation markets considering battery lifetime degradation[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 72-81. [4] 叶晨, 王蓓蓓, 薛必克, 等. 考虑超售的共享分布式光储混合运营模式协同策略研究[J]. 电工技术学报, 2022, 37(7): 1836-1846. Ye Chen, Wang Beibei, Xue Bike, et al.Study on the coordination strategy of sharing distributed photovoltaic energy storage hybrid operation mode considering overselling[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1836-1846. [5] 关舒丰, 王旭, 蒋传文, 等. 基于可控负荷响应性能差异的虚拟电厂分类聚合方法及辅助服务市场投标策略研究[J]. 电网技术, 2022, 46(3): 933-944. Guan Shufeng, Wang Xu, Jiang Chuanwen, et al.Classification and aggregation of controllable loads based on different responses and optimal bidding strategy of VPP in ancillary market[J]. Power System Technology, 2022, 46(3): 933-944. [6] 王浩浩, 陈嘉俊, 朱涛, 等. 计及储能寿命与调频性能的风储联合投标模型及算法[J]. 电网技术, 2021, 45(1): 208-217. Wang Haohao, Chen Jiajun, Zhu Tao, et al.Joint bidding model and algorithm of wind-storage system considering energy storage life and frequency regulation performance[J]. Power System Technology, 2021, 45(1): 208-217. [7] Shiltz D J, Cvetković M, Annaswamy A M.An integrated dynamic market mechanism for real-time markets and frequency regulation[J]. IEEE Transactions on Sustainable Energy, 2016, 7(2): 875-885. [8] 王霞, 应黎明, 卢少平. 考虑动态频率约束的一次调频和二次调频联合优化模型[J]. 电网技术, 2020, 44(8): 2858-2867. Wang Xia, Ying Liming, Lu Shaoping.Joint optimization model for primary and secondary frequency regulation considering dynamic frequency constraint[J]. Power System Technology, 2020, 44(8): 2858-2867. [9] 肖云鹏, 张兰, 张轩, 等. 包含独立储能的现货电能量与调频辅助服务市场出清协调机制[J]. 中国电机工程学报, 2020, 40(增刊1): 167-180. Xiao Yunpeng, Zhang Lan, Zhang Xuan, et al.Coordination mechanism of spot electric energy with independent energy storage and market clearing of FM auxiliary service[J]. Proceedings of the CSEE, 2020, 40(S1): 167-180. [10] 李军徽, 侯涛, 穆钢, 等. 电力市场环境下考虑风电调度和调频极限的储能优化控制[J]. 电工技术学报, 2021, 36(9): 1791-1804. Li Junhui, Hou Tao, Mu Gang, et al.Optimal control strategy for energy storage considering wind farm scheduling plan and modulation frequency limitation under electricity market environment[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1791-1804. [11] 郭钰锋, 潘梦琪. 基于风功率三参数幂律模型预测日前调频需求的调频辅助服务市场研究[J]. 中国电机工程学报, 2021, 41(20): 6941-6949. Guo Yufeng, Pan Mengqi.Research on frequency modulation ancillary service market based on three parameter power law model of wind power to predict the day-ahead frequency modulation demand[J]. Proceedings of the CSEE, 2021, 41(20): 6941-6949. [12] 崔达, 史沛然, 陈启鑫, 等. 风电参与能量-调频联合市场的优化策略[J]. 电力系统自动化, 2016, 40(13): 5-12. Cui Da, Shi Peiran, Chen Qixin, et al.Optimal strategy for wind power bidding in energy and frequency regulation markets[J]. Automation of Electric Power Systems, 2016, 40(13): 5-12. [13] 杨家琪, 喻洁, 田宏杰, 等. 考虑新能源性能风险的调频辅助服务市场出清与调度策略[J]. 电力系统自动化, 2020, 44(8): 66-73. Yang Jiaqi, Yu Jie, Tian Hongjie, et al.Clearing and scheduling strategy of frequency regulation ancillary service market considering performance risk of renewable energy[J]. Automation of Electric Power Systems, 2020, 44(8): 66-73. [14] 韩丽, 王冲, 于晓娇, 等. 考虑风电爬坡灵活调节的碳捕集电厂低碳经济调度[J]. 电工技术学报, 2024, 39(7): 2033-2045. Han Li, Wang Chong, Yu Xiaojiao, et al.Low-carbon and economic dispatch considering the carbon capture power plants with flexible adjustment of wind power ramp[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 2033-2045. [15] 吴含欣, 董树锋, 张祥龙, 等. 考虑碳交易机制的含风电电力系统日前优化调度[J]. 电网技术, 2024, 48(1): 70-80. Wu Hanxin, Dong Shufeng, Zhang Xianglong, et al.Optimal dispatching of power system with wind power considering carbon trading mechanism[J]. Power System Technology, 2024, 48(1): 70-80. [16] 王彦红, 刘浩, 李洪伟. 碳交易机制下旁路补偿供热消纳风电的电热经济调度[J]. 南方电网技术, 2024, 18(4): 50-58. Wang Yanhong, Liu Hao, Li Hongwei.Economic dispatch of electric and thermal energy with wind power integration and bypass heat compensation under carbon trading mechanisms[J]. Southern Power System Technology, 2024, 18(4): 50-58. [17] 潘郑楠, 邓长虹, 徐慧慧, 等. 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(增刊1): 56-69. Pan Zhengnan, Deng Changhong, Xu Huihui, et al.Game optimization scheduling of high proportion wind power and multiple flexible resources considering flexibility compensation[J]. Transactions of China Electrotechnical Society, 2023, 38(S1): 56-69. [18] 黄弦超, 封钰, 丁肇豪. 多微网多时间尺度交易机制设计和交易策略优化[J]. 电力系统自动化, 2020, 44(24): 77-88. Huang Xianchao, Feng Yu, Ding Zhaohao.Design of multi-time scale trading mechanism and trading strategy optimization for multiple microgrids[J]. Automation of Electric Power Systems, 2020, 44(24): 77-88. [19] 唐杰, 吕林, 叶勇, 等. 多时间尺度下主动配电网源-储-荷协调经济调度[J]. 电力系统保护与控制, 2021, 49(20): 53-64. Tang Jie, Lü Lin, Ye Yong, et al.Source-storage-load coordinated economic dispatch of an active distribution network under multiple time scales[J]. Power System Protection and Control, 2021, 49(20): 53-64. [20] 李翔宇, 赵冬梅. 基于模糊-概率策略实时反馈的虚拟电厂多时间尺度优化调度[J]. 电工技术学报, 2021, 36(7): 1446-1455. Li Xiangyu, Zhao Dongmei.Research on multi-time scale optimal scheduling of virtual power plant based on real-time feedback of fuzzy-probability strategy[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1446-1455. [21] 胡俊杰, 童宇轩, 刘雪涛, 等. 计及精细化氢能利用的综合能源系统多时间尺度鲁棒优化策略[J]. 电工技术学报, 2024, 39(5): 1419-1435. Hu Junjie, Tong Yuxuan, Liu Xuetao, et al.Multi-time-scale robust optimization strategy for integrated energy system considering the refinement of hydrogen energy use[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1419-1435. [22] 陈明昊, 孙毅, 谢志远. 基于双层深度强化学习的园区综合能源系统多时间尺度优化管理[J]. 电工技术学报, 2023, 38(7): 1864-1881. Chen Minghao, Sun Yi, Xie Zhiyuan.The multi-time-scale management optimization method for park integrated energy system based on the Bi-layer deep reinforcement learning[J]. Transactions of China Electrotechnical Society, 2023, 38(7): 1864-1881. [23] 楚帅, 葛维春, 滕云, 等. 海水淡化与电制热负荷联合消纳风电的多时间尺度调度策略[J]. 电力系统自动化, 2023, 47(8): 120-131. Chu Shuai, Ge Weichun, Teng Yun, et al.Multi-time-scale scheduling strategy for combined accommodation of wind power by seawater desalination and electric heating loads[J]. Automation of Electric Power Systems, 2023, 47(8): 120-131. [24] 胡俊杰, 赖信辉, 郭伟, 等. 考虑电动汽车灵活性与风电消纳的区域电网多时间尺度调度[J]. 电力系统自动化, 2022, 46(16): 52-60. Hu Junjie, Lai Xinhui, Guo Wei, et al.Multi-time-scale scheduling for regional power grid considering flexibility of electric vehicle and wind power accommodation[J]. Automation of Electric Power Systems, 2022, 46(16): 52-60.