考虑风光发电不确定性的风光水火多能互补区域电网中期优化调度方法

doi:10.11930/j.issn.1004-9649.202506064

摘要/Abstract

摘要：

新能源高渗透率下，电网连续多日的安全稳定运行受大规模风光日间出力的波动性及不确定性影响日益显著，保供难度加剧。提出考虑风光发电不确定性的风光水火多能互补区域电网中期优化调度方法，采用协方差矩阵表征风光发电预测误差的时变特性，融合混合高斯抽样生成风光出力场景集，利用条件抽样策略保留预测误差统计特性并合理缩减场景；以全网缺电与弃电平方和最小、火电运行成本最小为目标，构建了风光水火互补运行多目标优化模型，提出分层权重优化方法兼顾目标优先级和各目标相对重要性，实现了高效求解。依托华东电网实际数据仿真，结果表明：与常规方法相比，所提方法可使系统整体缺电降低33.3%以上，同时保证了较高的网调计划可靠性，日最大缺电、弃电仅占负荷的1.3%、1.7%，为新能源高渗透率下电网连续多日的调度计划安排提供了有效途径。

关键词: 时变特性, 区域电网, 中期调度, 分层权重优化法

Abstract:

Under the high penetration of new energy sources, the stable operation of the power grids over multiple consecutive days is increasingly affected by the volatility and uncertainty of large-scale wind and solar power output during the daytime, which exacerbates the difficulty of power supply guarantee. This paper proposes a medium-term optimal dispatching method for wind-solar-hydro-thermal multi-energy complementary regional power grids considering the uncertainty of wind and solar power generation. The covariance matrix is adopted to characterize the time-varying characteristics of the wind and solar power generation prediction errors, and the hybrid Gaussian sampling is integrated to generate the scenario set of wind and solar power output. Meanwhile, a conditional sampling strategy is utilized to retain the statistical characteristics of the prediction errors while reasonably reducing the number of scenarios. With the objectives of minimizing the sum of squares of grid-wide power shortage and curtailment as well as minimizing the thermal power operating cost, a multi-objective optimization model for wind-PV-hydro-thermal complementary operation is established. A hierarchical weight optimization method is proposed to balance the target priorities and the relative importance of each objective, thus achieving efficient solution. Simulation scheduling based on the actual data of East China Power Grid shows that, compared with conventional methods, the proposed method can reduce the overall power shortage of the system by more than 33.3%, while ensuring high reliability of grid dispatching plans. The daily maximum power shortage/curtailment only accounts for 1.3% and 1.7% of the load, respectively, which provides an effective approach for the formulation of multi-day consecutive dispatching plans of power grids under high renewable energy penetration.

Key words: time-varying characteristics, regional power grid, medium-term dispatching, hierarchical weight optimization method

陆建宇, 张凯旋, 李建华, 王月, 周毅, 申建建. 考虑风光发电不确定性的风光水火多能互补区域电网中期优化调度方法[J]. 中国电力, 2026, 59(4): 24-34.

LU Jianyu, ZHANG Kaixuan, LI Jianhua, WANG Yue, ZHOU Yi, SHEN Jianjian. Medium-term optimal dispatching method for wind–solar–hydro–thermal multi-energy complementary regional power system considering wind and solar power uncertainties[J]. Electric Power, 2026, 59(4): 24-34.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202506064

https://www.electricpower.com.cn/CN/Y2026/V59/I4/24

图/表 11

表 1 电站基本参数

Table 1 Basic parameters of power plants

类型	电站名称	装机容量/MW	单机组合/(台·MW)
水电	XAJ	850	9×95
水电	FCJ	360	6×60
火电	LH	1260	2×630
	PX	3280	2×640+2×1000
	SZ	1260	2×630
	YZ	2660	2×630+2×700
	FT	2580	2×630+2×660
	LZH	1260	2×630
	ZJ	1320	2×660

表 2 华东地区各省（市）电源装机情况

Table 2 Installed capacities of provinces in East China

省（市）	风电/MW	光伏/MW	火电/MW	水电/MW
上海	1066	4114	25421	0
江苏	23214	61647	109522	3100
浙江	6491	47274	71646	14980
安徽	8991	43112	63134	6190
福建	8030	12583	38606	17200

图 1 新能源实际出力、预测出力和生成的出力场景

Fig.1 Actual output, predicted output, and output scenarios of renewable energy

表 3 2种调度模式下各地区弃缺情况（1月1日—1月10日）

Table 3 Curtailment and shortage status of various regions under two dispatching modes (January 1 st to January 10 th) 单位：MW

日期	缺电、弃电（缺电为正、弃电为负）
	传统调度方法					本文所提方法
	上海	江苏	浙江	安徽	福建	上海	江苏	浙江	安徽	福建
2024-01-01	–75.1	–667.4	–300.6	–421.7	379.3	–342.4	–469.8	–149.7	–482.7	238.6
2024-01-02	–18.7	1607.1	–960.6	283.6	302.4	–317.7	1761.3	–902.6	567.4	–64.5
2024-01-03	–6.2	841.0	249.5	279.2	81.7	–305.2	1372.3	–129.3	332.0	–158.5
2024-01-04	139.9	2835.1	–132.6	–816.2	–281.9	–285.4	1492.1	–364.7	–1038.7	–404.5
2024-01-05	64.1	6967.2	86.5	–801.8	150.9	–389.0	4914.6	376.5	–938.7	113.4
2024-01-06	72.3	6525.2	–950.2	331.4	379.0	–368.0	4411.5	–779.0	181.9	137.6
2024-01-07	–145.5	8103.3	–1094.6	–191.0	23.7	–350.5	3855.5	–815.1	68.0	50.9
2024-01-08	–839.0	10111.7	–1279.0	–59.9	–526.8	–454.7	6712.3	–506.8	744.7	–324.5
2024-01-09	–1067.1	8119.0	–95.3	393.0	–351.9	–481.1	4333.6	308.7	1408.2	191.9
2024-01-10	–111.5	5464.1	568.5	454.2	257.4	–692.4	791.0	–1115.1	–111.3	–63.9

表 4 2种调度模式下各地区弃缺表现更优数量

Table 4 Number of regions with superior curtailment & shortage performance under the two dispatching modes

地区	传统方法		本文方法
地区	弃电更优/个	缺电更优/个	弃电更优/个	缺电更优/个
上海	34	8	2	28
江苏	21	14	15	22
浙江	22	15	14	21
安徽	27	11	9	25
福建	29	12	7	24

图 2 火电调节范围（传统方法）

Fig.2 Thermal power regulation range (traditional method)

图 3 火电出力可调下全网弃缺情况（传统方法）

Fig.3 Grid-wide curtailment and shortage status under adjustable thermal power output (traditional method)

图 4 火电调节范围（本文方法）

Fig.4 Thermal power regulation range (proposed method in this paper)

图 5 火电出力可调下全网弃缺情况（本文方法）

Fig.5 Grid-wide curtailment and shortage status under adjustable thermal power output (proposed method in this paper)

图 6 全网累积缺电最大时段各类电源发电过程

Fig.6 Power generation process of various power sources during the peak period of grid-wide cumulative power shortage

图 7 全网累积弃电最大时段各类电源发电过程

Fig.7 Power generation process of various power sources during the peak period of grid-wide cumulative power curtailment

参考文献 30

1	程春田. 碳中和下的水电角色重塑及其关键问题[J]. 电力系统自动化, 2021, 45 (16): 29- 36.
	CHENG Chuntian. Function remolding of hydropower systems for carbon neutral and its key problems[J]. Automation of Electric Power Systems, 2021, 45 (16): 29- 36.
2	明波, 李研, 刘攀, 等. 嵌套短期弃电风险的水光互补中长期优化调度研究[J]. 水利学报, 2021, 52 (6): 712- 722.
	MING Bo, LI Yan, LIU Pan, et al. Long-term optimal operation of hydro-solar hybrid energy systems nested with short-term energy curtailment risk[J]. Journal of Hydraulic Engineering, 2021, 52 (6): 712- 722.
3	李惠玲, 王曦, 高剑, 等. 新型电力系统背景下西部送端直流电网方案构建[J]. 中国电力, 2023, 56 (5): 12- 21.
	LI Huiling, WANG Xi, GAO Jian, et al. Scheme construction for sending end DC grids in western China under the background of new power system[J]. Electric Power, 2023, 56 (5): 12- 21.
4	刘纯, 李湃, 张金平, 等. 储能在新型电力系统电力电量平衡中的作用研究[J]. 电网技术, 2025, 49 (8): 3136- 3152.
	LIU Chun, LI Pai, ZHANG Jinping, et al. Research on the role of energy storage system in power and energy balance of the new type power system[J]. Power System Technology, 2025, 49 (8): 3136- 3152.
5	HUO Z S, ZHANG J T, CHENG C T, et al. A synergistic model framework for identifying variable renewable energy integration capacity and deployment sites for hydro-wind-PV integrated energy bases[J]. Energy, 2025, 314, 134326.
6	LIN M K, SHEN J J, GUO X H, et al. Comparison of pumping station and electrochemical energy storage enhancement mode for hydro-wind-photovoltaic hybrid systems[J]. Energy, 2025, 315, 134362.
7	陈仕军, 许唯临, 陈作强, 等. 面向航运的金沙江下游水库群中长期多目标协同调度研究[J/OL]. 工程科学与技术, 1–11[2026-02-28].
	CHEN Shijun, XU Weilin, CHEN Zuoqiang, et al. Study on medium-and long term multi objective collaborative scheduling of the reservoirs group in the lower reaches of the Jinsha River for navigation[J/OL]. Advanced Engineering Sciences, 1–11[2026-02-28].
8	NIU Y B, WANG J Z, ZHANG Z Y, et al. De-Trend First, Attend Next: a Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure[J]. Applied Energy, 2024, 353, 122169.
9	BERGMEIR C, DE NIJS F, GENOV E, et al. Predict+Optimize problem in renewable energy scheduling[J]. IEEE Access, 2025, 13, 60064- 60087.
10	KAZMI H, TAO Z M. How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead[J]. Applied Energy, 2022, 323, 119565.
11	陈仕军, 于倩楠, 赵夏, 等. 小水电改造混合式抽水蓄能机组对水光互补系统的发电影响研究[J]. 水电与抽水蓄能, 2024, 10 (6): 45- 53, 86.
	CHEN Shijun, YU Qiannan, ZHAO Xia, et al. Power generation impact of hybrid pumped storage units in small hydropower transformation on hydro-photovoltaic complementary systems[J]. Hydropower and Pumped Storage, 2024, 10 (6): 45- 53, 86.
12	王月, 申建建, 程春田, 等. 考虑电站集群与发电时变特性的风光出力描述方法[J]. 电网技术, 2023, 47 (4): 1558- 1572.
	WANG Yue, SHEN Jianjian, CHENG Chuntian, et al. Description of wind and solar power generation considering power plant clusters and time-varying power characteristics[J]. Power System Technology, 2023, 47 (4): 1558- 1572.
13	王莉丽, 戚永志, 黄越辉, 等. 电力系统弃风指标和弃风序列建模研究[J]. 中国电力, 2019, 52 (4): 178- 184.
	WANG Lili, QI Yongzhi, HUANG Yuehui, et al. Wind power curtailment index and sequence model for power systems[J]. Electric Power, 2019, 52 (4): 178- 184.
14	李湃, 黄越辉, 张金平, 等. 多能互补发电系统电/热/氢储能容量协调优化配置[J]. 中国电机工程学报, 2024, 44 (13): 5158- 5169.
	LI Pai, HUANG Yuehui, ZHANG Jinping, et al. Capacity coordinated optimization of battery, thermal and hydrogen storage system for multi-energy complementary power system[J]. Proceedings of the CSEE, 2024, 44 (13): 5158- 5169.
15	周健, 冯楠, 季怡萍, 等. 考虑源网协同支撑作用的含新能源电力系统网架重构决策优化方法[J]. 中国电力, 2024, 57 (10): 150- 157.
	ZHOU Jian, FENG Nan, JI Yiping, et al. A decision-making optimization method for network reconfiguration of power system with new energy considering the synergistic support of source-grid[J]. Electric Power, 2024, 57 (10): 150- 157.
16	赵唯嘉, 张宁, 康重庆, 等. 光伏发电出力的条件预测误差概率分布估计方法[J]. 电力系统自动化, 2015, 39 (16): 8- 15.
	ZHAO Weijia, ZHANG Ning, KANG Chongqing, et al. A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation[J]. Automation of Electric Power Systems, 2015, 39 (16): 8- 15.
17	赵书强, 张婷婷, 李志伟, 等. 基于数值特性聚类的日前光伏出力预测误差分布模型[J]. 电力系统自动化, 2019, 43 (13): 36- 45.
	ZHAO Shuqiang, ZHANG Tingting, LI Zhiwei, et al. Distribution model of day-ahead photovoltaic power forecasting error based on numerical characteristic clustering[J]. Automation of Electric Power Systems, 2019, 43 (13): 36- 45.
18	卫志农, 马智刚, 陈胜, 等. 考虑光伏随机性的交直流混合配电网鲁棒机会约束安全域模型[J]. 中国电机工程学报, 2024, 44 (6): 2208- 2219, I0010.
	WEI Zhinong, MA Zhigang, CHEN Sheng, et al. Robust chance-constrained security region model of AC/DC hybrid distribution network considering the uncertainty of photovoltaic generation[J]. Proceedings of the CSEE, 2024, 44 (6): 2208- 2219, I0010.
19	朱兰, 李孝均, 唐陇军, 等. 考虑相变储能与建筑蓄能特性的微网分布鲁棒优化调度[J]. 电网技术, 2021, 45 (6): 2308- 2319.
	ZHU Lan, LI Xiaojun, TANG Longjun, et al. Distributionally robust optimal operation for microgrid considering phase change storage and building storage[J]. Power System Technology, 2021, 45 (6): 2308- 2319.
20	王月, 申建建, 郝婷洁, 等. 耦合多受端日内负荷特征的流域水风光系统中期调度方法[J]. 电力系统自动化, 2025, 49 (19): 174- 184.
	WANG Yue, SHEN Jianjian, HAO Tingjie, et al. Mid-term scheduling method for basin hydro-wind-solar system coupled with intraday load characteristics of multiple receiving ends[J]. Automation of Electric Power Systems, 2025, 49 (19): 174- 184.
21	SHEN J J, WANG Y, LIN M K, et al. Quantifying the impact of extreme weather on China's hydropower–wind–solar renewable energy system[J]. Nature Water, 2025, 3 (4): 415- 429.
22	HOSEINI N, SHEIKHOLESLAMI A, BARFOROUSHI T, et al. Preventive maintenance mid-term scheduling of resources in multi-carrier energy systems[J]. Energy, 2020, 197, 117164.
23	汤明润, 李若旸, 刘慕然, 等. 电力系统稳态下可再生能源大规模接入量预测[J]. 中国电力, 2025, 58 (2): 126- 132.
	TANG Mingrun, LI Ruoyang, LIU Muran, et al. Prediction of large-scale renewable energy access under steady state of electric power system[J]. Electric Power, 2025, 58 (2): 126- 132.
24	许竞, 赵铁军, 高小刚, 等. 高比例新能源电力系统调节资源灵活性不足风险分析[J]. 中国电力, 2024, 57 (11): 129- 138.
	XU Jing, ZHAO Tiejun, GAO Xiaogang, et al. Risk analysis of insufficient flexibility from regulation resources in high proportion renewable energy power systems[J]. Electric Power, 2024, 57 (11): 129- 138.
25	李湃, 范越, 黄越辉, 等. 基于电源聚合-分解模型的新能源电力系统月度发电计划优化方法[J]. 电网技术, 2020, 44 (9): 3281- 3293.
	LI Pai, FAN Yue, HUANG Yuehui, et al. Monthly generation scheduling method of renewable energy power system based on power plant aggregation & decomposition models[J]. Power System Technology, 2020, 44 (9): 3281- 3293.
26	冯松杰, 魏韡. 考虑新能源消纳和电力保供的电力系统多时间尺度平衡方法[J]. 电力系统自动化, 2025, 49 (4): 128- 140.
	FENG Songjie, WEI Wei. Multi-timescale balancing method for power systems considering renewable energy accommodation and power supply support[J]. Automation of Electric Power Systems, 2025, 49 (4): 128- 140.
27	张磊, 马晓伟, 王满亮, 等. 互联新能源电力系统区内AGC机组分布式协同控制策略[J]. 中国电力, 2025, 58 (3): 8- 19.
	ZHANG Lei, MA Xiaowei, WANG Manliang, et al. Distributed collaborative control strategy for intra-regional AGC units in interconnected power system with renewable energy[J]. Electric Power, 2025, 58 (3): 8- 19.
28	王麒翔, 韩震焘, 梁毅. 基于博弈论均衡的风险防控经济调度分析[J]. 中国电力, 2024, 57 (10): 69- 77.
	WANG Qixiang, HAN Zhentao, LIANG Yi. Economic dispatch analysis of risk prevention and control based on game theory equilibrium[J]. Electric Power, 2024, 57 (10): 69- 77.
29	苏承国, 张俊, 申建建, 等. 考虑多级断面约束的月度火电机组组合方法[J]. 中国电机工程学报, 2018, 38 (S1): 158- 167.
	SU Chengguo, ZHANG Jun, SHEN Jianjian, et al. Method for monthly thermal unit commitment with multi-level transmission constraints[J]. Proceedings of the CSEE, 2018, 38 (S1): 158- 167.
30	林伟, 杨知方, 余娟, 等. 考虑机组启停的省间电量交易可行域确定方法[J]. 中国电机工程学报, 2021, 41 (15): 5119- 5127.
	LIN Wei, YANG Zhifang, YU Juan, et al. Determination on energy trading region among regional networks considering unit commitment[J]. Proceedings of the CSEE, 2021, 41 (15): 5119- 5127.

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