计及混合式抽水蓄能改造的清洁微网分布鲁棒容量优化配置

doi:10.19595/j.cnki.1000-6753.tces.240571

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Abstract To address the reliability issues of off-grid power systems in remote areas, retrofitting existing cascade hydropower with pumped storage has been regarded as a novel effective solution. The variable-speed pumped storage, due to its adjustable pumping capacity, is found to be more advantageous compared to fixed-speed systems. Consequently, the formation of a regional 100% clean energy off-grid system through the integration of variable-speed pumped storage with wind and photovoltaic re-sources is enabled. In this context, a data-driven capacity optimization model for off-grid cascaded hydro-wind-photovoltaic complementary system considering pumped-storage retrofitting is developed in this paper. Firstly, with the minimum comprehensive cost on an equivalent year as the objective, the optimal capacity configuration is achieved by considering the operation of the pumped-hydro-wind-photovoltaic system, power supply reliability, and various constraints. Secondly, using the McCormick linearization method, the original mixed-integer nonlinear model is transformed into a mixed-integer linear optimization model. Moreover, to handle uncertainties of load, wind, photo-voltaic, and inflow, a data-driven two-stage distributionally robust optimization configuration model for capacity optimization is presented based on both 1-norm and ∞-norm. The column and constraint generation (C&CG) algorithm is used to solve the proposed model. Finally, the feasibility is verified.
The following conclusions can be drawn from the simulation analysis: (1) With the scale-up of wind power and photovoltaic units, in order to better accommodate the curtailed electricity generated during system operation, the use of data-driven distributed robust optimization methods compared to traditional stochastic optimization methods can better integrate water, wind, and solar resources, reduce curtailment rates, and lower system operating costs. (2) The capacity optimization configuration model established in this paper has good operational performance in terms of accuracy and solution efficiency. It can make investment decisions by considering load demand curves and historical scenario data of wind and photovoltaic output, resulting in a slightly higher equivalent annual cost compared to traditional stochastic optimization methods but with lower conservatism. It can better consider the impact of extreme adverse scenarios on system power supply reliability, thus improving the reliability of off-grid systems. Additionally, considering the stochastic nature of wind and photovoltaic output scenarios, using a mixed norm instead of a single norm can improve accuracy. Moreover, using a parallelizable column and constraint generation algorithm for solving avoids dual transformation and bilinear term computations, leading to higher computational efficiency. (3) Due to the limited reservoir capacity and inadequate regulation capacity of existing cascade hydropower stations, a model for off-grid hybrid variable-speed pumped storage retrofit has been established in this paper. Compared to only adding wind and photovoltaic resources on top of cascade hydropower stations with limited regulation capacity, the retrofit with optimal capacity variable-speed pumped storage units significantly reduces curtailment rates during operation. This reflects a substantial improvement in the flexible regulation capacity of hybrid cascade hydropower after the variable-speed pumped storage retrofit, enabling better integration of wind and photovoltaic resources. (4) Furthermore, the optimization model constructed by retrofitting variable-speed pumped storage units into cascade hydropower stations used in this paper results in lower equivalent annual costs and more curtailed electricity absorption compared to using traditional fixed-speed pumped storage units, demonstrating stronger regulation effects.

Key words： Hybrid pumped storage retrofitting hydro-wind-photovoltaic-storage power supply reliability distributinally robust column and constraint generation

Received: 11 April 2024

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TM732

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	Wang Hailun
	Ding Yifan
	Li Yang
	Wu Feng
	Wang Zizhao

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Wang Hailun,Ding Yifan,Li Yang等. Distributionally Robust Capacity Optimization for Clean Energy Microgrid Considering Pumped-Storage Retrofitting[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2112-2126.

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[1] 王朋朋, 杜璇, 张桐博, 等. 基于小水电的某乡镇10 kV微电网规划研究[J]. 电力与能源, 2023, 44(4): 385-390.
Wang Pengpeng, Du Xuan, Zhang Tongbo, et al.Research on 10 kV microgrid planning in a township based on small hydropower[J]. Power & Energy, 2023, 44(4): 385-390.
[2] Li Jie, Liu Yikui, Wu Lei.Optimal operation for community-based multi-party microgrid in grid-connected and islanded modes[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 756-765.
[3] Xu Xiao, Hu Weihao, Cao Di, et al.Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system[J]. Renewable Energy, 2020, 147: 1418-1431.
[4] 顾雪平, 白岩松, 李少岩, 等. 考虑风电不确定性的电力系统恢复全过程两阶段鲁棒优化方法[J]. 电工技术学报, 2022, 37(21): 5462-5477.
Gu Xueping, Bai Yansong, Li Shaoyan, et al.Two stage robust optimization method for the whole-process power system restoration considering wind power uncertainty[J]. Transactions of China Electro-technical Society, 2022, 37(21): 5462-5477.
[5] 韩晓言, 丁理杰, 陈刚, 等. 梯级水光蓄互补联合发电关键技术与研究展望[J]. 电工技术学报, 2020, 35(13): 2711-2722.
Han Xiaoyan, Ding Lijie, Chen Gang, et al.Key technologies and research prospects for cascaded hydro-photovoltaic-pumped storage hybrid power generation system[J]. Transactions of China Electro-technical Society, 2020, 35(13): 2711-2722.
[6] 邵志芳, 赵强, 张玉琼. 独立型微电网源荷协调配置优化[J]. 电网技术, 2021, 45(10): 3935-3946.
Shao Zhifang, Zhao Qiang, Zhang Yuqiong.Source side and load side coordinated configuration optimization for stand-alone micro-grid[J]. Power System Technology, 2021, 45(10): 3935-3946.
[7] 张安安, 张红, 吴建中, 等. 离岸微型综合能源系统多目标随机规划[J]. 电力系统自动化, 2019, 43(7): 129-135, 173.
Zhang An’an, Zhang Hong, Wu Jianzhong, et al.Multi-objective stochastic planning for offshore micro integrated energy systems[J]. Automation of Electric Power Systems, 2019, 43(7): 129-135, 173.
[8] Liu Huiwen, Wang Shengtie, Liu Guangchen, et al.SARAP algorithm of multi-objective optimal capacity configuration for WT-PV-DE-BES stand-alone micro-grid[J]. IEEE Access, 2020, 8: 126825-126838.
[9] Xing Yongchen, Duan Qing, Zhang Guobao, et al.Differential evolution algorithm based on entropy weight method to determine the weight to optimize the configuration of wind, solar, and diesel microgrid[J]. Journal of Physics: Conference Series, 2021, 1871(1): 012034.
[10] 国家能源局. 抽水蓄能中长期发展规划(2021-2035年)[R]. 北京: 国家能源局, 2021.
[11] 郭怿, 明波, 黄强, 等. 考虑输电功率平稳性的水-风-光-储多能互补日前鲁棒优化调度[J]. 电工技术学报, 2023, 38(9): 2350-2363.
Guo Yi, Ming Bo, Huang Qiang, et al.Day-ahead robust optimal scheduling of hydro-wind-PV-storage complementary system considering the steadiness of power delivery[J]. Transactions of China Electro-technical Society, 2023, 38(9): 2350-2363.
[12] 康俊杰, 赵春阳, 周国鹏, 等. 风光水火储多能互补示范项目发展现状及实施路径研究[J]. 发电技术, 2023, 44(3): 407-416.
Kang Junjie, Zhao Chunyang, Zhou Guopeng, et al.Research on development status and implementation path of wind-solar-water-thermal-energy storage multi-energy complementary demonstration project[J]. Power Generation Technology, 2023, 44(3): 407-416.
[13] 鲜于虎成, 黄显峰, 张艳青, 等. 考虑短期波动与弃电风险的水光互补系统中长期双层嵌套优化调度[J]. 电工技术学报, 2023, 38(21): 5781-5793.
Xianyu Hucheng, Huang Xianfeng, Zhang Yanqing, et al.Medium-and long-term double-layer nested optimal scheduling of hydro-PV complementary system considering short-term power fluctuation and curtailment risk[J]. Transactions of China Electro-technical Society, 2023, 38(21): 5781-5793.
[14] Feng Chen, Zheng Yuan, Li Chaoshun, et al.Cost advantage of adjustable-speed pumped storage unit for daily operation in distributed hybrid system[J]. Renewable Energy, 2021, 176: 1-10.
[15] Zhang Yusheng, Lian Jijian, Ma Chao, et al.Optimal sizing of the grid-connected hybrid system integrating hydropower, photovoltaic, and wind considering cascade reservoir connection and photovoltaic-wind complementarity[J]. Journal of Cleaner Production, 2020, 274: 123100.
[16] 李进, 蔡泽祥, 岑伯维, 等. 基于功率能量特性的含小水电微电网储能容量配置方法[J]. 电力建设, 2024, 45(4): 123-133.
Li Jin, Cai Zexiang, Cen Bowei, et al.Energy storage capacity configuration method for microgrids con-taining small hydropower based on power-energy characteristics[J]. Electric Power Construction, 2024, 45(4): 123-133.
[17] Gao Yang, Liu Bin, He Jinyu, et al.Capacity configuration optimization and analysis for multi-energy complementary microgrid in hydropower station considering the renewable energy accommodation[J]. Journal of Physics: Conference Series, 2021, 2108(1): 012064.
[18] 谭晶, 何川, 陈保瑞, 等. 考虑水光蓄互补和直流外送的电力系统分布鲁棒优化调度方法[J]. 中国电机工程学报, 2024, 44(15): 5947-5960.
Tan Jing, He Chuan, Chen Baorui, et al.Distr-ibutionally robust optimal scheduling method of power system considering hydropower-photovoltaic-pumped storage complementarity and DC transmission[J]. Proceedings of the CSEE, 2024, 44(15): 5947-5960.
[19] Zhang Shuai, Yang Jingxian, Gao Hongjun, et al.Probabilistic chronological production simulation-based coordinated dispatching for cascaded hydro-PV-PSH combined power generation system[J]. Mathematical Problems in Engineering, 2020, 2020: 6677301.
[20] 左逢源, 张玉琼, 赵强, 等. 计及源荷不确定性的综合能源生产单元运行调度与容量配置两阶段随机优化[J]. 中国电机工程学报, 2022, 42(22): 8205-8215.
Zuo Fengyuan, Zhang Yuqiong, Zhao Qiang, et al.Two-stage stochastic optimization for operation scheduling and capacity allocation of integrated energy production unit considering supply and demand uncertainty[J]. Proceedings of the CSEE, 2022, 42(22): 8205-8215.
[21] 吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485.
Wu Mengxue, Fang Fang.Distributionally robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485.
[22] 郑晓东, 陈皓勇, 段声志, 等. 基于场景概率驱动的输电网和储能分布鲁棒规划[J]. 电力自动化设备, 2022, 42(6): 170-178.
Zheng Xiaodong, Chen Haoyong, Duan Shengzhi, et al.Distributionally robust planning of transmission network and energy storage based on scenario probability-driven[J]. Electric Power Automation Equipment, 2022, 42(6): 170-178.
[23] 孙旭, 邱晓燕, 张志荣, 等. 基于数据驱动的交直流配电网分布鲁棒优化调度[J]. 电网技术, 2021, 45(12): 4768-4778.
Sun Xu, Qiu Xiaoyan, Zhang Zhirong, et al.Dis-tributed robust optimal dispatching of AC/DC distribution network based on data driven mode[J]. Power System Technology, 2021, 45(12): 4768-4778.
[24] Bo Yaolong, Xia Yanghong, Wei Wei, et al.Hyperfine optimal dispatch for integrated energy microgrid considering uncertainty[J]. Applied Energy, 2023, 334: 120637.
[25] Castro M P.Tightening piecewise McCormick relaxations for bilinear problems[J]. Computers and Chemical Engineering, 2015, 72: 300-311.
[26] 徐小圣, 徐昌睿, 李梦诗, 等. 考虑风电消纳的电热联合系统优化调度模型[J]. 南方电网技术, 2024, 18(11): 106-118.
Xu Xiaosheng, Xu Changrui, Li Mengshi, et al.Optimization dispatching model of the combined heat and power system considering wind power consumption[J]. Southern Power System Technology, 2024, 18(11): 106-118.
[27] 井浩然, 赵红生, 姚伟, 等. 含分布式变速抽水蓄能的新能源发电系统灵活性资源规划[J]. 电力自动化设备, 2023, 43(11): 117-123, 173.
Jing Haoran, Zhao Hongsheng, Yao Wei, et al.Flexible resource planning of renewable generation systems with distributed variable speed pumped storages[J]. Electric Power Automation Equipment, 2023, 43(11): 117-123, 173.
[28] Castillo P A, Castro P M, Mahalec V.Global optimization of MIQCPs with dynamic piecewise relaxations[J]. Journal of Global Optimization, 2018, 71(4): 691-716.
[29] 张帅, 汪子涵, 张蜀程, 等. 基于数据驱动分布鲁棒优化的梯级水光蓄联合优化调度[J]. 工程科学与技术, 2023, 55(2): 128-140.
Zhang Shuai, Wang Zihan, Zhang Shucheng, et al.Data-driven distributionally robust optimization based coordinated dispatching for cascaded hydro-PV-PSH combined system[J]. Advanced Engineering Sciences, 2023, 55(2): 128-140.
[30] 王波, 王蔚, 马恒瑞, 等. 基于Wasserstein两阶段分布鲁棒的多主体多能微网合作博弈优化调度[J/OL]. 电工技术学报, 2024: 1-18[2024-11-02]. http://kns.cnki.net/kcms/detail/11.2188.tm.20241025.1338.002.html.
Wang Bo, Wang Wei, Ma Hengrui, et al. Multi-Agent multi-energy microgrid cooperative game scheduling based on wasserstein two stage robust optimization [J/OL]. Transactions of China Electro-technical Society, 2024: 1-18[2024-11-02]. http://kns.cnki.net/kcms/detail/11.2188.tm.20241025.1338.002.html.
[31] Ding Tao, Yang Qingrun, Liu Xiyuan, et al.Duality-free decomposition based data-driven stochastic security-constrained unit commitment[J]. IEEE Transactions on Sustainable Energy, 2019, 10(1): 82-93.
[32] 江一航, 赵书强, 王慧, 等. 计及风电、调相机支撑特性的频率安全约束分布鲁棒机组组合调度方法[J]. 电工技术学报, 2025: 40(1): 80-95.
Jiang Yihang, Zhao Shuqiang, Wang Hui, et al.Distributionally robust frequency constrained unit commitment with frequency support of wind power and synchronous condenser[J]. Transactions of China Electrotechnical Society, 2025: 40(1): 80-95
[33] 李运鸿, 徐潇源, 严正. 基于生成对抗网络的独立微电网光-储容量分布鲁棒优化配置[J]. 电力系统自动化, 2023, 47(7): 51-62.
Li Yunhong, Xu Xiaoyuan, Yan Zheng.Distributionally robust optimal allocation for capacity distribution of photovoltaic and energy storage units in standalone microgrid based on generative adversarial network[J]. Automation of Electric Power Systems, 2023, 47(7): 51-62.
[34] Zeng Bo, Zhao Long.Solving two-stage robust optimization problems using a column-and-constraint generation method[J]. Operations Research Letters, 2013, 41(5): 457-461.