新能源基地直流外送系统多时间尺度鲁棒协同优化调度

doi:10.3969/j.issn.2097-0706.2026.04.006

摘要/Abstract

摘要：

针对风电、光伏出力不稳定给直流外送系统带来的调度问题，提出一种融合多时间尺度优化及自适应鲁棒优化的风光火储直协同优化调度方法。以经济效益、环境效益、系统效益及灵活性等综合效益最大化为目标，以功率平衡、储能荷电状态等为约束，建立风光火储直的两阶段协同优化调度模型。在日前计划阶段，建立目标函数及约束条件，采用非支配排序遗传算法Ⅱ求解该多目标问题。在日内滚动优化阶段，通过构建描述风光出力不确定性的偏差集合，采用min-max鲁棒优化及列与约束生成算法进行求解，实现极端情况下外送功率能够满足中长期直流外送计划。仿真表明，所提策略在场景4下综合成本最低，较场景1降低约7%；中长期外送功率波动幅度降低30%以上；日内滚动优化后，外送功率跟踪误差在2%以内，鲁棒优化模型在最坏情景下仍能获得满足硬性约束的全局最优解。策略在新能源基地直流外送系统中可以实现综合效益最大化的目标，确保实时外送功率能够紧密跟踪中长期外送计划，且所述鲁棒优化模型在考虑最坏情景的不确定性场景时，仍能获得满足硬性约束的全局最优解。

关键词: 新能源, 直流外送系统, 多时间尺度, 综合效益最大化, 非支配排序遗传算法Ⅱ, 自适应鲁棒优化, 列与约束生成算法

Abstract:

To address the scheduling issues caused by the instability of wind and photovoltaic outputs in DC transmission systems， a coordinated optimal scheduling method for wind-photovoltaic-thermal-storage DC systems integrating multi-time-scale optimization and adaptive robust optimization was proposed. A two-stage coordinated optimal scheduling model for wind-photovoltaic-thermal-storage DC systems was established with the objective of maximizing comprehensive benefits， including economic， environmental，system and flexibility benefits， subject to constraints such as power balance and energy storage state of charge （SOC）. In the day-ahead planning stage， the objective function and constraints were established， and the multi-objective problem was solved using the non-dominated sorting genetic algorithm Ⅱ. In the intraday rolling optimization stage， a deviation set describing the uncertainty of wind and photovoltaic outputs was constructed， and a min-max robust optimization method was employed. The model was solved using the column-and-constraint generation algorithm to ensure that transmission power met medium- and long-term DC transmission plans under extreme conditions. The simulation results demonstrated that the proposed strategy achieved the lowest comprehensive cost under scenario 4， which was approximately 7% lower than that under scenario 1. The fluctuation amplitude of medium- and long-term transmission power reduced by more than 30%. After intraday rolling optimization， the tracking error of the transmission power was controlled within 2%， and the robust optimization model still obtained a globally optimal solution satisfying hard constraints under the worst-case scenario. The proposed scheduling strategy can maximize comprehensive benefits in the DC transmission systems of new energy bases， ensuring that real-time transmission power closely tracks medium and long-term transmission plans. Furthermore， the robust optimization model can still obtain a globally optimal solution satisfying hard constraints even when considering uncertainty scenarios under the worst-case scenario.

Key words: new energy, DC transmission system, multi-time scale, comprehensive benefit maximization, non-dominated sorting genetic algorithm Ⅱ, adaptive robust optimization, column-and-constraint generation algorithm

中图分类号:

TK01：TM732

王启玺, 韩自奋, 刘克权, 董海鹰. 新能源基地直流外送系统多时间尺度鲁棒协同优化调度[J]. 综合智慧能源, 2026, 48(4): 47-59.

WANG Qixi, HAN Zifen, LIU Kequan, DONG Haiying. Multi-time-scale robust coordinated optimal scheduling of DC transmission systems in new energy bases[J]. Integrated Intelligent Energy, 2026, 48(4): 47-59.

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参考文献 25

[1]	ZHOU Y Z, SHAHIDEHPOUR M, WEI Z N, et al. Distributionally robust unit commitment in coordinated electricity and district heating networks[J]. IEEE Transactions on Power Systems, 2020, 35(3): 2155-2166. doi: 10.1109/TPWRS.59
[2]	张宁, 胡兆光, 周渝慧, 等. 考虑需求侧低碳资源的新型模糊双目标机组组合模型[J]. 电力系统自动化, 2014, 38(17): 25-30.
	ZHANG Ning, HU Zhaoguang, ZHOU Yuhui, et al. A novel fuzzy bi-objective unit commitment model considering demand side low carbon resources[J]. Automation of Electric Power Systems, 2014, 38(17):25-30
[3]	刘吉臻. 大规模新能源电力安全高效利用基础问题[J]. 中国电机工程学报, 2013, 33(16): 1-8, 25.
	LIU Jizhen. Basic issues of the utilization of large-scale renewable power with high security and efficiency[J]. Proceedings of the CSEE, 2013, 33(16): 1-8, 25.
[4]	石庆均, 耿光超, 江全元. 独立运行模式下的微网实时能量优化调度[J]. 中国电机工程学报, 2012, 32(16): 26-35.
	SHI Qingjun, GENG Guangchao, JIANG Quanyuan. Real-time optimal energy dispatch of standalone microgrid[J]. Proceedings of the CSEE, 2012, 32(16): 26-35.
[5]	AZIZIVAHED A, AREFI A, GHAVIDEL S, et al. Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage[J]. IEEE Transactions on Sustainable Energy, 2020, 11(2): 662-673. doi: 10.1109/TSTE.5165391
[6]	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): 70-78.
	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): 70-78.
[7]	马为民, 吴方劼, 杨一鸣, 等. 柔性直流输电技术的现状及应用前景分析[J]. 高电压技术, 2014, 40(8): 2429-2439.
	MA Weimin, WU Fangjie, YANG Yiming, et al. Flexible HVDC transmission technology's today and tomorrow[J]. High Voltage Engineering, 2014, 40(8): 2429-2439.
[8]	洪春雪, 肖海平, 谭甲群, 等. 考虑容量电价的风光火储能源基地多目标优化调度[J]. 综合智慧能源, 2025, 47(7): 1-11. doi: 10.3969/j.issn.2097-0706.2025.07.001
	HONG Chunxue, XIAO Haiping, TAN Jiaqun, et al. Multi-objective optimal schedule of a wind-photovoltaic-thermal-storage energy base considering capacity tariffs[J]. Integrated Intelligent Energy, 2025, 47(7): 1-11. doi: 10.3969/j.issn.2097-0706.2025.07.001
[9]	PILLAI J R, BAK-JENSEN B. Integration of vehicle-to-grid in the western Danish power system[J]. IEEE Transactions on Sustainable Energy, 2011, 2(1): 12-19.
[10]	徐伟航, 杨茂, 孙莉. 利用电化学储能追踪风电预测曲线的风储联合调度经济性分析[J]. 南方电网技术, 2023, 17(11): 87-96. doi: 10.13648/j.cnki.issn1674-0629.2023.11.009
	XU Weihang, YANG Mao, SUN Li. Economic analysis of wind storage joint scheduling using battery energy storage to track wind power prediction curve[J]. Southern Power System Technology, 2023, 17(11): 87-96. doi: 10.13648/j.cnki.issn1674-0629.2023.11.009
[11]	赵诣, 方陈, 冯冬涵, 等. 考虑电池储能寿命损耗的风光储微网随机日前调度策略[J]. 电气工程学报, 2022, 17(3): 210-218.
	ZHAO Yi, FANG Chen, FENG Donghan, et al. Stochastic day-ahead scheduling optimization for wind-PV-ES microgrid considering battery life loss[J]. Journal of Electrical Engineering, 2022, 17(3): 210-218.
[12]	王凯, 延肖何, 刘念. 基于投资组合理论的风光储场站参与多时间尺度电力现货市场的出力分配优化方法[J]. 工程科学与技术, 2023, 55(1): 101-109.
	WANG Kai, YAN Xiaohe, LIU Nian. Output allocation optimization method for the wind photovoltaic energy storage power station in the multi-time-scale electricity spot market under portfolio theory[J]. Advanced Engineering Sciences, 2023, 55(1): 101-109.
[13]	XING L T, MISHRA Y, TIAN Y C, et al. Dual-consensus-based distributed frequency control for multiple energy storage systems[J]. IEEE Transactions on Smart Grid, 2019, 10(6): 6396-6403. doi: 10.1109/TSG.5165411
[14]	谭甲群, 吕如轩, 鞠洪晋, 等. 基于改进秃鹰搜索算法的风光火储综合能源系统优化调度策略研究[J]. 综合智慧能源, 2025, 47(8): 68-76. doi: 10.3969/j.issn.2097-0706.2025.08.008
	TAN Jiaqun, LYU Ruxuan, JU Hongjin, et al. Research on optimal scheduling strategy of wind-photovoltaic-thermal-storage integrated energy system based on IBES[J]. Integrated Intelligent Energy, 2025, 47(8): 68-76. doi: 10.3969/j.issn.2097-0706.2025.08.008
[15]	王芊瑞, 阮景昕, 王跃社. 考虑风、光出力时空相关性的电-氢协同储能系统经济性优化调度研究[J]. 综合智慧能源, 2025, 47(12): 34-45. doi: 10.3969/j.issn.2097-0706.2025.12.004
	WANG Qianrui, RUAN Jingxin, WANG Yueshe. Economic optimal scheduling of electricity-hydrogen coordinated energy storage system considering spatiotemporal correlation of wind and photovoltaic power outputs[J]. Integrated Intelligent Energy, 2025, 47(12): 34-45. doi: 10.3969/j.issn.2097-0706.2025.12.004
[16]	邱文婷, 董家乐, 吴笛, 等. 基于NSGA-Ⅱ的含氢多能源系统经济与低碳协同优化调度[J]. 综合智慧能源, 2025, 47(10): 34-44. doi: 10.3969/j.issn.2097-0706.2025.10.004
	QIU Wenting, DONG Jia, WU Di, et al. Economic and low-carbon coordinated optimization scheduling of hydrogen-integrated multi-energy system based on NSGA-Ⅱ[J]. Integrated Intelligent Energy, 2025, 47(10): 34-44. doi: 10.3969/j.issn.2097-0706.2025.10.004
[17]	张磊, 杨洪明, 刘文洵. 基于矩不确定分布鲁棒的微网经济调度[J]. 电力科学与技术学报, 2015, 30(2): 35-40, 67.
	ZHANG Lei, YANG Hongming, LIU Wenxun. Microgrid economic dispatch based on distributionally robust of moment uncertainty[J]. Journal of Electric Power Science and Technology, 2015, 30(2): 35-40, 67.
[18]	GUPTA R A, GUPTA N K. A robust optimization based approach for microgrid operation in deregulated environment[J]. Energy Conversion and Management, 2015, 93: 121-131. doi: 10.1016/j.enconman.2015.01.008
[19]	ALAVI S A, AHMADIAN A, ALIAKBAR-GOLKAR M. Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method[J]. Energy Conversion and Management, 2015, 95: 314-325. doi: 10.1016/j.enconman.2015.02.042
[20]	曾鸣, 段金辉, 孙静惠. 基于改进和声搜索算法的微网电池储能系统规划模型[J]. 电力建设, 2015, 36(10): 27-33. doi: 10.3969/j.issn.1000-7229.2015.10.004
	ZENG Ming, DUAN Jinhui, SUN Jinghui. Battery energy storage system planning model of micro grid based on improved harmony search algorithm[J]. Electric Power Construction, 2015, 36(10): 27-33. doi: 10.3969/j.issn.1000-7229.2015.10.004
[21]	肖峻, 张泽群, 张磐, 等. 用于优化微网联络线功率的混合储能容量优化方法[J]. 电力系统自动化, 2014, 38(12): 19-26.
	XIAO Jun, ZHANG Zequn, ZHANG Pan, et al. A capacity optimization method of hybrid energy storage system for optimizing tie-line power in microgrids[J]. Automation of Electric Power Systems, 2014, 38(12): 19-26.
[22]	邵佳扬. 互联综合能源系统的多时间尺度优化调度[D]. 哈尔滨: 哈尔滨工业大学, 2024.
	SHAO Jiayang. Multi-time scale optimal scheduling of interconnected integrated energy systems[D]. Harbin: Harbin Institute of Technology, 2024.
[23]	廖扬. 考虑阶梯式碳交易的互联综合能源系统分散协调调度[D]. 哈尔滨: 哈尔滨工业大学, 2022.
	LIAO Yang. Distributed coordinated dispatch of interconnected integrated energy system considering ladder-type carbon trading mechanism[D]. Harbin: Harbin Institute of Technology, 2022.
[24]	张振宇, 王文倬, 王智伟, 等. 跨区直流外送模式对新能源消纳的影响分析及应用[J]. 电力系统自动化, 2019, 43(11): 174-180.
	ZHANG Zhenyu, WANG Wenzhuo, WANG Zhiwei, et al. Impact analysis and application of cross-region HVDC delivery mode in renewable energy accommodation[J]. Automation of Electric Power Systems, 2019, 43(11): 174-180.
[25]	孙建龙, 吴锁平, 陈燕超. 基于改进NSGA 2算法的配电网分布式电源优化配置[J]. 电力建设, 2014, 35(2): 86-90. doi: 10.3969/j.issn.1000-7229.2014.02.017
	SUN Jianlong, WU Suoping, CHEN Yanchao. Optimal configuration of distributed generation in distribution network based on improved NSGA2[J]. Electric Power Construction, 2014, 35(2): 86-90. doi: 10.3969/j.issn.1000-7229.2014.02.017

时段	购电电价	售电电价
00:00 — 07:00，22:00 —24:00	0.35	0.22
07:00 — 09:00，12:00 —17:00, 21:00 —22:00	0.68	0.42
09:00 —12:00,17:00 —21:00	1.09	0.65

场景	交易收益	运维成本	用电总成本	综合成本
1	-367.9	4 725	9 054.4	13 411.5
2	-418.3	4 585	8 799.1	12 966.1
3	-413.5	4 673	8 852.3	13 111.5
4	-467.8	4 613	8 324.9	12 470.1