风水储联合运行双层滚动优化调度方法

doi:10.11930/j.issn.1004-9649.202504088

摘要/Abstract

摘要：

随着风电等新能源的渗透率逐步提高，电网运行的不确定性也随之加剧，如何安全、高效地发挥水电的调节作用对电网的稳定运行具有重要意义。为此，提出一种风水储联合运行的双层滚动优化调度方法。首先，构建风水储联合运行的双层滚动优化控制模型，上层优化基于长时间尺度，以最小化系统运行成本为目标，下层优化基于短时间尺度，以最小化系统出力偏差为目标；其次，采用模型预测控制方法对优化模型进行求解，通过下层的实时滚动优化对上层调度计划进行反馈校正，降低了不确定性对系统的影响。最后，以中国南方某梯级水电系统为算例对象进行仿真分析，验证了所提方法在提升能源利用效率、降低运行成本和处理不确定性方面的有效性。

关键词: 可再生能源, 不确定性, 梯级水电站, 模型预测控制, 双层滚动

Abstract:

With the gradual increase in the penetration rate of wind power and other renewable energy sources, the uncertainty of power grids operation has intensified accordingly. How to safely and efficiently exert the regulating effect of hydropower is of great significance to the stable operation of power grids. To this end, a bi-level rolling optimal scheduling method for the joint operation of wind-hydro-storage systems is proposed. First, a bi-level rolling optimal control model for wind-hydro-storage joint operation is constructed. The upper-level optimization is based on a long time scale, with the goal of minimizing the system operation cost; the lower-level optimization is based on a short time scale, aiming to minimize the system output deviation. Second, the model predictive control (MPC) method is adopted to solve the optimization model. The real-time rolling optimization of the lower level is used to feedback and correct the upper-level scheduling plan, thereby reducing the impact of uncertainty on the system. Finally, a case study is carried out on a cascade hydropower system in South China. The simulation results verify the effectiveness of the proposed method in improving energy utilization efficiency, reducing operation costs and addressing system uncertainty.

Key words: renewable energy, uncertainty, cascade hydropower stations, model predictive control, two-layer rolling

阮宏华, 邓子琦, 陈飞雄, 林俊杰. 风水储联合运行双层滚动优化调度方法[J]. 中国电力, 2026, 59(4): 1-11.

RUAN Honghua, DENG Ziqi, CHEN Feixiong, LIN Junjie. Double layer rolling based optimization model for joint operation of wind-hydro- storage system[J]. Electric Power, 2026, 59(4): 1-11.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202504088

https://www.electricpower.com.cn/CN/Y2026/V59/I4/1

图/表 14

图 1 双层滚动优化模型结构

Fig.1 Structure diagram of double-layer rolling optimization model

图 2 双层滚动优化框架

Fig.2 Two-layer rolling optimization framework

图 3 风水储联合运行的双层滚动优化方法流程

Fig.3 Flowchart of the two-layer rolling optimization method for joint operation of wind-hydro-storage

图 4 梯级水电拓扑结构

Fig.4 Topology of cascaded hydropower

表 1 电站基本参数

Table 1 Basic parameters of the power station

电站	调节性能	装机容量/MW	正常水位/m	死水位/m	最大发电流量/(m³·s^–1)	最大出库流量/(m³·s^–1)
1号电站	多年	210	1140	1076	490.5	3886
2号电站	年	243	970	950	632.2	11142
3号电站	日	210	837	822	994.5	15956
4号电站	年	437	760	720	1250.0	18360

图 5 24 h预测曲线信息

Fig.5 24 h forecast information

图 6 系统调度运行方案

Fig.6 Scheduling operation program for system

图 7 1号电站、3号电站库容变化情况

Fig.7 Changes in reservoir capacity of power stations No. 1 and No. 3

表 2 储能接入前后成本对比

Table 2 Cost comparison before and after energy storage access 单位：万元

储能接入情况	储能运行成本	弃水成本	弃风成本	失负荷成本	运行维护成本	总成本
未接入	0	42.889	12.354	4.404	14.402	74.049
接入储能	12.524	27.320	5.906	3.535	14.695	63.980

图 8 功率缺额对比

Fig.8 Comparison of power deficit

表 3 算法效果对比

Table 3 Comparison of algorithm effects

方法	机组出力平均相对误差/%	储能电量平均相对误差/%	上层调度周期用时/s	下层控制平均用时/s
1	5.31	27.02	694.530593	7.234694
2	4.05	18.30	844.943545	8.801495

图 9 水电机组总出力与理想运行结果相对误差对比

Fig.9 Comparison of the relative error between the total output of the unit and the ideal operation result

图 10 储能电量与理想运行结果相对误差对比

Fig.10 Comparison of the relative error between the storage power and the ideal operation result

图 11 不同预测误差下成本对比

Fig.11 Comparison of costs with different prediction errors

参考文献 31

1	BIRD L, LEW D, MILLIGAN M, et al. Wind and solar energy curtailment: a review of international experience[J]. Renewable and Sustainable Energy Reviews, 2016, 65, 577- 586.
2	康重庆, 杜尔顺, 郭鸿业, 等. 新型电力系统的六要素分析[J]. 电网技术, 2023, 47 (5): 1741- 1750.
	KANG Chongqing, DU Ershun, GUO Hongye, et al. Primary exploration of six essential factors in new power system[J]. Power System Technology, 2023, 47 (5): 1741- 1750.
3	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42 (8): 2806- 2818.
	ZHANG Zhigang, KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42 (8): 2806- 2818.
4	胡晓静, 李慧, 崔晖, 等. 考虑灵活爬坡辅助服务和弃风惩罚的现货电能量市场出清模型[J]. 电力系统保护与控制, 2024, 52 (4): 133- 143.
	HU Xiaojing, LI Hui, CUI Hui, et al. Cleaning model of a spot electric energy market considering flexible ramping auxiliaryservices and wind curtailment penalty[J]. Power System Protection and Control, 2024, 52 (4): 133- 143.
5	陈铭宏天, 耿江海, 赵雨泽, 等. 基于两阶段随机优化的电氢耦合微电网周运行策略[J]. 中国电力, 2025, 58 (5): 82- 90.
	CHEN Minghongtian, GENG Jianghai, ZHAO Yuze, et al. Two-stage stochastic optimization based weekly operation strategy for electric-hydrogen coupled microgrid[J]. Electric Power, 2025, 58 (5): 82- 90.
6	齐郑, 吉苏朋. 水电机组调相运行与新能源发电协同优化技术研究[J]. 电力系统保护与控制, 2025, 53 (3): 108- 119.
	QI Zheng, JI Supeng. Synergistic optimization of phase modulation operation of hydropower units andpower generation of new energy units[J]. Power System Protection and Control, 2025, 53 (3): 108- 119.
7	郝国文, 徐青, 杨烨, 等. 基于网络水印技术的水电智能终端安全通信方法研究[J]. 电力信息与通信技术, 2024, 22 (6): 52- 58.
	HAO Guowen, XU Qing, YANG Ye, et al. Research on secure communication for intelligent terminals in hydropower plant aided by network watermark[J]. Electric Power Information and Communication Technology, 2024, 22 (6): 52- 58.
8	杨钰琪, 莫莉, 周建中, 等. 负荷频繁波动情景下梯级水电站实时调度策略[J]. 电力自动化设备, 2022, 42 (7): 205- 211,260.
	YANG Yuqi, MO Li, ZHOU Jianzhong, et al. Real-time dispatching strategy of cascaded hydropower stations under frequent load fluctuation[J]. Electric Power Automation Equipment, 2022, 42 (7): 205- 211,260.
9	WANG J, ZHAO Z P, ZHOU J L, et al. Developing operating rules for a hydro–wind–solar hybrid system considering peak-shaving demands[J]. Applied Energy, 2024, 360, 122762.
10	赵志鹏, 于志辉, 程春田, 等. 水风光综合基地多风险量化及长期多目标协调优化调度方法[J]. 电力系统自动化, 2024, 48 (22): 118- 130.
	ZHAO Zhipeng, YU Zhihui, CHENG Chuntian, et al. Multi-risk quantification and long-term multi-objective coordinative optimal dispatch method for hydro-wind-solar integrated energy base[J]. Automation of Electric Power Systems, 2024, 48 (22): 118- 130.
11	FENG Z K, HUANG Q Q, NIU W J, et al. Peak operation optimization of cascade hydropower reservoirs and solar power plants considering output forecasting uncertainty[J]. Applied Energy, 2024, 358, 122533.
12	徐晓庆, 柴旭峥, 崔杨, 等. 含混合式抽水蓄能的梯级水电系统多时间尺度调度策略[J]. 电力系统保护与控制, 2025, 53 (19): 25- 38.
	XU Xiaoqing, CHAI Xuzheng, CUI Yang, et al. Multi-time scale scheduling strategy for cascade hydropower systems with hybrid pumped storage[J]. Power System Protection and Control, 2025, 53 (19): 25- 38.
13	杜倩昀, 周升, 李祖鑫, 等. 考虑天然来水量预报的小水电站富集型系统汛期优化调度策略[J]. 电力科学与技术学报, 2024, 39 (6): 33- 42.
	DU Qianyun, ZHOU Sheng, LI Zuxin, et al. Optimal scheduling strategy for small hydropower enrichment system duringflood season based on natural runoff forecast[J]. Journal of Electric Power Science and Technology, 2024, 39 (6): 33- 42.
14	陈飞雄, 郭奕鑫, 邵振国, 等. 计及新能源出力相关性的多能源微网仿射实时优化调度[J]. 电网技术, 2024, 48 (8): 3248- 3257.
	CHEN Feixiong, GUO Yixin, SHAO Zhenguo, et al. Affine real-time optimal dispatching for multi-energy microgrid with renewable energy output correlation[J]. Power System Technology, 2024, 48 (8): 3248- 3257.
15	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.
16	高聪哲, 黄文焘, 余墨多, 等. 基于智能软开关的主动配电网电压模型预测控制优化方法[J]. 电工技术学报, 2022, 37 (13): 3263- 3274.
	GAO Congzhe, HUANG Wentao, YU Moduo, et al. A model predictive control method to optimize voltages for active distribution networks with soft open point[J]. Transactions of China Electrotechnical Society, 2022, 37 (13): 3263- 3274.
17	陈宇星, 梁芙蓉, 尤炜, 等. 含分布式光伏的配电网模型预测控制优化方法[J]. 电力工程技术, 2023, 42 (6): 100- 109.
	CHEN Yuxing, LIANG Furong, YOU Wei, et al. Model predictive control optimization method for distribution network containing distributed photovoltaics[J]. Electric Power Engineering Technology, 2023, 42 (6): 100- 109.
18	杨宗铭, 朱红杰, 陈冠宇, 等. 基于模型预测控制和机会约束的主动配电网实时调度优化策略[J]. 电力需求侧管理, 2023, 25 (2): 23- 29.
	YANG Zongming, ZHU Hongjie, CHEN Guanyu, et al. Real-time dispatch optimization strategy for active distribution network based on model predictive control and opportunity constraints[J]. Power Demand Side Management, 2023, 25 (2): 23- 29.
19	罗政杰, 任惠, 辛国雨, 等. 基于模型预测控制的高比例可再生能源电力系统多时间尺度动态可靠优化调度[J]. 太阳能学报, 2024, 45 (6): 150- 160.
	LUO Zhengjie, REN Hui, XIN Guoyu, et al. Multi-time scale dynamic reliable optimal scheduling of power system with high proportion renewable energy based on model predictive control[J]. Acta Energiae Solaris Sinica, 2024, 45 (6): 150- 160.
20	路朋, 叶林, 汤涌, 等. 基于模型预测控制的风电集群多时间尺度有功功率优化调度策略研究[J]. 中国电机工程学报, 2019, 39 (22): 6572- 6583.
	LU Peng, YE Lin, TANG Yong, et al. Multi-time scale active power optimal dispatch in wind power cluster based on model predictive control[J]. Proceedings of the CSEE, 2019, 39 (22): 6572- 6583.
21	颜湘武, 徐韵, 李若瑾, 等. 基于模型预测控制含可再生分布式电源参与调控的配电网多时间尺度无功动态优化[J]. 电工技术学报, 2019, 34 (10): 2022- 2037.
	YAN Xiangwu, XU Yun, LI Ruojin, et al. Multi-time scale reactive power optimization of distribution grid based on model predictive control and including RDG regulation[J]. Transactions of China Electrotechnical Society, 2019, 34 (10): 2022- 2037.
22	齐国民, 李天野, 于洪, 等. 基于MPC的户用光-储系统容量配置及运行优化模型[J]. 中国电力, 2025, 58 (1): 185- 195.
	QI Guomin, LI Tianye, YU Hong, et al. Capacity allocation and operation optimization model of household photovoltaic-storage system based on MPC[J]. Electric Power, 2025, 58 (1): 185- 195.
23	刘旭东, 黄虎军, 冷国华, 等. 基于模型预测控制的水电站系统运行控制策略[J]. 电子设计工程, 2024, 32 (9): 51- 55.
	LIU Xudong, HUANG Hujun, LENG Guohua, et al. Operation control strategy of hydropower station system based on model predictive control[J]. Electronic Design Engineering, 2024, 32 (9): 51- 55.
24	姚建国, 段家华. 基于月年混合径流量的预测控制模型[J]. 小水电, 2014, (6): 12- 15.
25	李捷, 余涛, 潘振宁. 基于强化学习的增量配电网实时随机调度方法[J]. 电网技术, 2020, 44 (9): 3321- 3330.
	LI Jie, YU Tao, PAN Zhenning. Real-time stochastic dispatch method for incremental distribution network based on reinforcement learning[J]. Power System Technology, 2020, 44 (9): 3321- 3330.
26	郑之杰, 黄静思, 黄元生. 基于模型预测控制的水电制氢系统优化调度研究[J]. 电力科学与工程, 2022, 38 (7): 25- 33.
	ZHENG Zhijie, HUANG Jingsi, HUANG Yuansheng. Optimal scheduling of hydro-electricity hydrogen production system based on model predictive control[J]. Electric Power Science and Engineering, 2022, 38 (7): 25- 33.
27	程雄, 程春田, 刘冀, 等. 快速响应负荷需求的大规模水电站群超短期调度模型[J]. 中国电机工程学报, 2018, 38 (4): 1016- 1025, 6.
	CHENG Xiong, CHENG Chuntian, LIU Ji, et al. Rapidly response load demand of large-scale hydropower plants ultra-short term scheduling model[J]. Proceedings of the CSEE, 2018, 38 (4): 1016- 1025, 6.
28	李咸善, 丁胜彪, 李飞, 等. 考虑水电调节费用补偿的风光水联盟优化调度策略[J]. 中国电力, 2024, 57 (5): 26- 38.
	LI Xianshan, DING Shengbiao, LI Fei, et al. Optimal scheduling strategy for wind-solar-hydro alliance considering compensation of regulation by hydropower[J]. Electric Power, 2024, 57 (5): 26- 38.
29	武震, 霍彦达, 张翼, 等. 考虑新能源发电量季节性分布的省区级季节性储能需求测算方法[J]. 中国电力, 2023, 56 (8): 40- 47.
	WU Zhen, HUO Yanda, ZHANG Yi, et al. Seasonal energy storage capacity planning for provincial region considering unbalanced seasonal renewable energy generation[J]. Electric Power, 2023, 56 (8): 40- 47.
30	王皓, 艾芊, 甘霖, 等. 基于多场景随机规划和MPC的冷热电联合系统协同优化[J]. 电力系统自动化, 2018, 42 (13): 51- 58.
	WANG Hao, AI Qian, GAN Lin, et al. Collaborative optimization of combined cooling heating and power system based on multi-scenario stochastic programming and model predictive control[J]. Automation of Electric Power Systems, 2018, 42 (13): 51- 58.
31	陈飞雄, 林炜晖, 邵振国. 含电转气和混合储能的微能网双层滚动优化控制方法[J]. 电力自动化设备, 2022, 42 (5): 23- 31.
	CHEN Feixiong, LIN Weihui, SHAO Zhenguo. Two-layer receding horizon optimal control method for multi-energy microgrid with power-to-gas and hybrid energy storage[J]. Electric Power Automation Equipment, 2022, 42 (5): 23- 31.

[1]	崔一宸, 王鹤, 王莉丽, 银涛, 黄山松. 基于改进生成对抗网络场景生成的跨流域水风光互补中长期优化调度方法[J]. 中国电力, 2026, 59(4): 35-46.
[2]	李欣, 宋金金. 计及源荷双重不确定性的高速路域虚拟电厂运行优化策略[J]. 中国电力, 2026, 59(4): 79-93.
[3]	张啸林, 杜尔顺, 张光斗, 王佳旭, 宋亮, 刘昱良. 考虑发电与碳排放不确定性的园区综合能源系统分布鲁棒优化[J]. 中国电力, 2026, 59(2): 1-12.
[4]	林济铿, 石涛. 基于模型预测控制的区域电网频率控制方法[J]. 中国电力, 2025, 58(9): 124-137.
[5]	高芳杰, 孙玉杰, 李忆, 乐鹰, 张继广, 许传博, 刘敦楠. 计及海上风电制氢的海岛多能微网鲁棒优化调度[J]. 中国电力, 2025, 58(7): 68-79.
[6]	孔令国, 田杨进, 康建东, 方磊, 刘闯, 蔡国伟. 考虑多电解槽协同的海上独立能源岛不确定性双层优化配置[J]. 中国电力, 2025, 58(7): 80-90, 104.
[7]	张博航, 戚军, 谢路耀, 张有兵, 张博扬. 考虑需求侧响应的互联电力系统分布式模型预测频率控制[J]. 中国电力, 2025, 58(7): 105-114.
[8]	赵琳, 郭尚民, 商文颖, 董健, 王炜. 基于FDOA的可再生能源系统配置优化[J]. 中国电力, 2025, 58(7): 168-176.
[9]	周楷, 陶正顺, 潘庭龙, 许德智. 基于CCS-MPC的储能锂电池组均衡控制策略[J]. 中国电力, 2025, 58(7): 177-186.
[10]	翟哲, 陈梓煜, 刘起兴, 梁彦杰, 李智勇. 计及风险管理的分布式资源聚合商电力市场交易模型[J]. 中国电力, 2025, 58(6): 56-66, 155.
[11]	王彩霞, 吴思, 时智勇. 绿色电力消费认证国际实践与启示[J]. 中国电力, 2025, 58(5): 43-51.
[12]	檀勤良, 贺嘉明, 吕函谕, 丁毅宏. 考虑成本不确定性的发电企业低碳技术采纳决策优化研究[J]. 中国电力, 2025, 58(5): 62-73.
[13]	汪进锋, 李金鹏, 许银亮, 刘海涛, 何锦雄, 许建远. 考虑不确定性和绿证交易的虚拟电厂与配电网分布式优化[J]. 中国电力, 2025, 58(4): 21-30, 192.
[14]	王宣元, 张玮, 李长宇, 谢欢, 郭庆来, 王彬, 张宇谦. 考虑随机性的主动配电网有功无功可行域计算方法[J]. 中国电力, 2025, 58(4): 182-192.
[15]	戴道明, 赵莺. 考虑消纳责任权重的可再生能源电力供应链绿证监管演化博弈分析[J]. 中国电力, 2025, 58(4): 216-229.