Hierarchical Optimal Scheduling for Pumped Storage to Enhance System Flexibility under Supply-Demand Uncertainty
Lv Wanyu1,2, Zhao Hongsheng3, Han Yingsheng4, Hu De1,2, Peng Xiaotao1,2
1. Key Laboratory for Hubei Province Integrated Energy Power System Equipment and System Security Wuhan 430072 China;
2. School of Electrical Engineering and Automation Wuhan University Wuhan 430072 China;
3. State Grid HBEPC Economic& Technology Research Institute Wuhan 430077 China;
4. Hubei Central China Technology Development of Electric Power Co. Ltd Wuhan 430077 China
Considering the impact of new energy grid connection on the supply-demand balance of system peak regulation and ramping flexibility, this study employs two approaches: On one hand, the semi-invariant method and Gram-Charlier series expansion are used to model the probability density function of conventional power sources' peak regulation and ramping capacity considering forced outages. On the other hand, Gaussian mixture distribution and an expectation-maximization based weight assignment method are adopted to model the probability density function of net load considering prediction errors of new energy and load. Based on this, a method for quantifying system flexibility supply shortages using convolution probability weighting is proposed.
Next, taking pumped storage as a flexibility regulation resource and considering its response characteristics as an independent operation entity, a hierarchical optimal dispatching model for pumped storage to enhance the system's flexibility supply capacity is established. The upper and lower layer optimization objectives are respectively set as minimizing flexibility supply shortages and system operation costs, and maximizing the economic benefits of pumped storage's flexible regulation operations. For solving the model: the particle swarm optimization algorithm is adopted to optimize the time-of-use pricing for pumped storage participation in flexibility regulation in the upper-layer model; The Gurobi solver is used to optimize the power output of pumped storage in the lower-layer model. The two layers achieve collaborative optimization through interactive iteration of their respective optimal solutions.
Then, Finally, three aspects of simulation studies were conducted: Firstly, comparative analysis was performed on the quantification methods of system flexibility shortage capacity through convolution probability weighting and mathematical expectation, verifying the effectiveness of the proposed flexibility shortage capacity quantification method considering supply-demand uncertainties. Secondly, simulations were carried out based on the scenario of pumped storage enhancing system flexibility to validate the effectiveness of the hierarchical optimal dispatch strategy. By comparing the improvement effects of the pumped storage optimal dispatch strategies determined under different flexibility shortage quantification results on system flexibility in supply-demand uncertainty scenarios, the robust adaptability of the hierarchical optimal dispatch method to uncertainty impacts was demonstrated. Thirdly,using the proposed dispatch strategy, a comparative study was conducted on the operational benefit characteristics of fixed-speed and variable-speed pumped storage in improving system flexibility under high new energy penetration rates.
Finally, the following conclusions can be drawn from the study: (1) The proposed flexibility shortage quantification method can account for the impacts of supply-demand uncertainties. Its evaluation results help enhance the robustness of dispatch strategies under uncertain scenarios such as unit forced outages and net load prediction errors, enabling pumped storage to more effectively enhance the system's flexibility supply capacity. (2) The proposed hierarchical optimal dispatch model balances the interests of the power grid and pumped storage plants. It can not only improve the grid's new energy accommodation capacity through pumped storage but also reduce the impact of uncertain fluctuations in grid power purchase prices on the expected benefits of pumped storage participating in flexibility regulation. (3) The gap in power supply flexibility capacity expands with the increase in new energy penetration. Compared with fixed-speed pumped storage, using variable-speed pumped storage to enhance the system's flexibility supply capacity offers better operational benefits.
吕婉煜, 赵红生, 韩应生, 胡德, 彭晓涛. 供需不确定性下抽水蓄能提升系统灵活性的分层优化调度方法研究[J]. 电工技术学报, 0, (): 20241815-20241815.
Lv Wanyu, Zhao Hongsheng, Han Yingsheng, Hu De, Peng Xiaotao. Hierarchical Optimal Scheduling for Pumped Storage to Enhance System Flexibility under Supply-Demand Uncertainty. Transactions of China Electrotechnical Society, 0, (): 20241815-20241815.
[1] IEA. Empowering variable renewables-options for flexible electricity systems[R]. Paris, France: International Energy Agency, 2008.
[2] 韩丽, 陈硕, 王施琪, 等. 考虑风光消纳与电动汽车灵活性的调度策略[J]. 电工技术学报, 2024, 39(21): 6793-6803.
Han Li, Chen Shuo, Wang Shiqi, et al.Scheduling strategy considering wind and photovoltaic power consumption and the flexibility of electric vehicles[J]. Transactions of China Electrotechnical Society, 2024, 39(21): 6793-6803.
[3] 冯艺萱, 边晓燕, 陈雯, 等. 新型电力系统灵活性资源成本回收机制分析及挑战[J/OL]. 电工技术学报, 2025: 1-16. (2025-01-08). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.241899.
Feng Yixuan, Bian Xiaoyan, Chen Wen, et al. Analysis and challenges of new power system flexibility resource cost recovery mechanisms[J/OL]. Transactions of China Electrotechnical Society, 2025: 1-16. (2025-01-08). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.241899.
[4] Lannoye E, Flynn D, O’Malley M. Evaluation of power system flexibility[J]. IEEE Transactions on Power Systems, 2012, 27(2): 922-931.
[5] 詹勋淞, 管霖, 卓映君, 等. 基于形态学分解的大规模风光并网电力系统多时间尺度灵活性评估[J]. 电网技术, 2019, 43(11): 3890-3901.
Zhan Xunsong, Guan Lin, Zhuo Yingjun, et al.Multi-scale flexibility evaluation of large-scale hybrid wind and solar grid-connected power system based on multi-scale morphology[J]. Power System Technology, 2019, 43(11): 3890-3901.
[6] 孟秋, 廖凯, 郑舜玮, 等. 考虑灵活性区域互济的电力系统源-网-储协同规划[J]. 电网技术, 2024, 48(8): 3165-3174.
Meng Qiu, Liao Kai, Zheng Shunwei, et al.Source-grid-storage coordinated planning for power system considering flexibility mutual aid among regions[J]. Power System Technology, 2024, 48(8): 3165-3174.
[7] 孙伟卿, 宋赫, 秦艳辉, 等. 考虑灵活性供需不确定性的储能优化配置[J]. 电网技术, 2020, 44(12): 4486-4497.
Sun Weiqing, Song He, Qin Yanhui, et al.Energy storage system optimal allocation considering flexibility supply and demand uncertainty[J]. Power System Technology, 2020, 44(12): 4486-4497.
[8] 杨策, 孙伟卿, 韩冬. 考虑新能源消纳能力的电力系统灵活性评估方法[J]. 电网技术, 2023, 47(1): 338-349.
Yang Ce, Sun Weiqing, Han Dong.Power system flexibility evaluation considering renewable energy accommodation[J]. Power System Technology, 2023, 47(1): 338-349.
[9] 鲁宗相, 李海波, 乔颖. 高比例可再生能源并网的电力系统灵活性评价与平衡机理[J]. 中国电机工程学报, 2017, 37(1): 9-20.
Lu Zongxiang, Li Haibo, Qiao Ying.Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity[J]. Proceedings of the CSEE, 2017, 37(1): 9-20.
[10] 倪晋兵, 张云飞, 施浩波, 等. 基于时序生产模拟的抽水蓄能促进新能源消纳作用量化研究[J]. 电网技术, 2023, 47(7): 2799-2809.
Ni Jinbing, Zhang Yunfei, Shi Haobo, et al.Pumped storage quantification in promoting new energy consumption based on time series production simulation[J]. Power System Technology, 2023, 47(7): 2799-2809.
[11] 邹金, 赖旭, 汪宁渤. 以减少电网弃风为目标的风电与抽水蓄能协调运行[J]. 电网技术, 2015, 39(9): 2472-2477.
Zou Jin, Lai Xu, Wang Ningbo.Mitigation of wind curtailment by coordinating with pumped storage[J]. Power System Technology, 2015, 39(9): 2472-2477.
[12] 夏沛, 邓长虹, 龙志君, 等. 含抽水蓄能机组的风电消纳鲁棒机组组合[J]. 电力系统自动化, 2018, 42(19): 41-49.
Xia Pei, Deng Changhong, Long Zhijun, et al.Robust unit commitment with pumped storage units for wind power accommodation[J]. Automation of Electric Power Systems, 2018, 42(19): 41-49.
[13] 林俐, 岳晓宇, 许冰倩, 等. 计及抽水蓄能和火电深度调峰效益的抽蓄-火电联合调峰调用顺序及策略[J]. 电网技术, 2021, 45(1): 20-32.
Lin Li, Yue Xiaoyu, Xu Bingqian, et al.Sequence and strategy of pumped storage-thermal combined peak shaving considering benefits of pumped storage and deep regulation of thermal power[J]. Power System Technology, 2021, 45(1): 20-32.
[14] 黄炜栋, 李杨, 李璟延, 等. 考虑可再生能源不确定性的风-光-储-蓄多时间尺度联合优化调度[J]. 电力自动化设备, 2023, 43(4): 91-98.
Huang Weidong, Li Yang, Li Jingyan, et al.Multi-time scale joint optimal scheduling for wind-photovoltaic-electrochemical energy storage-pumped storage considering renewable energy uncertainty[J]. Electric Power Automation Equipment, 2023, 43(4): 91-98.
[15] 王思远, 吴文传. 灵活性资源聚合参考模型与量化指标体系[J]. 电力系统自动化, 2024, 48(3): 1-9.
Wang Siyuan, Wu Wenchuan.Aggregation reference model and quantitative metric system of flexible energy resources[J]. Automation of Electric Power Systems, 2024, 48(3): 1-9.
[16] 高元海, 王淳. 级数展开法拟合概率潮流分布函数的局限及改进[J]. 中国电机工程学报, 2021, 41(17): 5900-5911.
Gao Yuanhai, Wang Chun.Limitation analysis and improvement for series expansion methods to fit the distribution function of probabilistic power flow[J]. Proceedings of the CSEE, 2021, 41(17): 5900-5911.
[17] 易明月, 童晓阳. 考虑风荷预测误差不确定性的动态经济调度[J]. 电网技术, 2019, 43(11): 4050-4057.
Yi Mingyue, Tong Xiaoyang.Dynamic economic dispatch considering uncertainties of wind power and load forecast error[J]. Power System Technology, 2019, 43(11): 4050-4057.
[18] 曾林俊, 许加柱, 王家禹, 等. 考虑区间构造的改进极限学习机短期电力负荷区间预测[J]. 电网技术, 2022, 46(7): 2555-2563.
Zeng Linjun, Xu Jiazhu, Wang Jiayu, et al.Short-term electrical load interval forecasting based on improved extreme learning machine considering interval construction[J]. Power System Technology, 2022, 46(7): 2555-2563.
[19] 王进, 张粒子, 赵志芳, 等. 抽水蓄能电站市场化运行机制和日前市场出清模型[J]. 电力系统自动化, 2023, 47(12): 145-153.
Wang Jin, Zhang Lizi, Zhao Zhifang, et al.Marketization operation mechanism and clearing model of day-ahead market for pumped storage stations[J]. Automation of Electric Power Systems, 2023, 47(12): 145-153.
[20] 崔杨, 陈志, 严干贵, 等. 基于含储热热电联产机组与电锅炉的弃风消纳协调调度模型[J]. 中国电机工程学报, 2016, 36(15): 4072-4081.
Cui Yang, Chen Zhi, Yan Gangui, et al.Coordinated wind power accommodating dispatch model based on electric boiler and CHP with thermal energy storage[J]. Proceedings of the CSEE, 2016, 36(15): 4072-4081.
[21] 张占安, 蔡兴国. 考虑可变速抽水蓄能机组运行特性的低碳调度[J]. 中国电机工程学报, 2016, 36(增刊1): 51-60.
Zhang Zhan’an, Cai Xingguo.Low-carbon dispatch considering operating characteristics of variable speed pumped storage[J]. Proceedings of the CSEE, 2016, 36(S1): 51-60.
[22] Zhao Jingfeng, Oh U J, Park J C, et al.A review of world-wide advanced pumped storage hydropower technologies[J]. IFAC-PapersOnLine, 2022, 55(9): 170-174.
[23] 支晓晨, 李玉齐, 高熹, 等. 基于抽蓄电站自动电压控制系统的改进无功分配策略研究与应用[J]. 电气技术, 2023, 24(4): 74-80.
Zhi Xiaochen, Li Yuqi, Gao Xi, et al.Research and application of improved reactive power distribution strategy based on automatic voltage control system of pumped storage power station[J]. Electrical Engineering, 2023, 24(4): 74-80.
[24] 徐福强, 邹德旋, 李灿, 等. 引入Circle映射和正弦余弦因子的改进粒子群算法[J]. 计算机工程与应用, 2023, 59(17): 80-90.
Xu Fuqiang, Zou Dexuan, Li Can, et al.Improved particle swarm optimization algorithm with circle mapping and sine cosine factor[J]. Computer Engineering and Applications, 2023, 59(17): 80-90.
[25] Liang Jiaqi, Harley R G.Pumped storage hydro-plant models for system transient and long-term dynamic studies[C]//IEEE PES General Meeting, Minneapolis, MN, USA, 2010: 1-8.
[26] Elia. 电网风电数据[EB/OL].
[2024-03-02] . http://www.elia.be/en/grid-data/.
[27] 庄凯勋, 孙建军, 丁理杰, 等. 提升双馈变速抽水蓄能机组频率响应特性的控制策略[J]. 电工技术学报, 2023, 38(23): 6292-6304.
Chuang Kaihsun, Sun Jianjun, Ding Lijie, et al.A control strategy with improved frequency response characteristics of variable speed DFIM pumped storage[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6292-6304.
[28] 高本锋, 崔浩江, 杨鹏, 等. 抑制直流连续换相失败的可变速抽水蓄能机组协调控制策略[J]. 电工技术学报, 2025, 40(5): 1368-1381, 1454.
Gao Benfeng, Cui Haojiang, Yang Peng, et al.Coordinated control strategy of variable speed pumped storage unit for suppressing continuous commutation failure of HVDC[J]. Transactions of China Electrotechnical Society, 2025, 40(5): 1368-1381, 1454.
[29] 王海伦, 丁一凡, 李杨, 等. 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[J]. 电工技术学报, 2025, 40(7): 2112-2126.
Wang Hailun, Ding Yifan, Li Yang, et al.Distributionally robust capacity optimization for clean energy microgrid considering pumped-storage retrofitting[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2112-2126.