基于Wasserstein距离分布鲁棒的电解铝负荷协同火储深度调峰方法研究Deep peak shaving method of electrolytic aluminum load cooperating with thermal power and energy storage system based on Wasserstein distance distribution robust
刘昕明,王海云
摘要(Abstract):
大规模的风电并网致使现有系统调峰资源难以为继,风电消纳受阻。为此,文中综合考虑风电出力与电价的不确定性,提出一种基于Wasserstein距离的电解铝负荷协同火储深度调峰分布鲁棒优化方法。首先,结合电解铝的负荷特性,计及储能辅助火电机组优化深度调峰容量,建立了电解铝负荷协同火储深度调峰的电力系统优化框架;其次,借鉴Wasserstein距离的分布鲁棒模型的思想,构建了上级电网购售电价与可再生能源出力的Wasserstein模糊集约束,设计了电解铝负荷协同火储深度调峰的分布鲁棒优化模型;最后,通过仿真分析验证了所提方法可有效降低系统运行成本,改善系统调峰压力,促进风电消纳,通过对比分析验证了其方法的经济性和鲁棒性。
关键词(KeyWords): Wasserstein距离;电解铝负荷;深度调峰;分布鲁棒;不确定性
基金项目(Foundation): 新疆维吾尔自治区重点研发计划(2022B01020-3)~~
作者(Author): 刘昕明,王海云
DOI: 10.19666/j.rlfd.202403083
参考文献(References):
- [1]黎博,陈民铀,钟海旺,等.高比例可再生能源新型电力系统长期规划综述[J].中国电机工程学报, 2023,43(2):555-581.LI Bo, CHEN Minyou, ZHONG Haiwang, et al. A review of long-term planning of new power systems with large share of renewable energy[J]. Proceedings of the CSEE, 2023, 43(2):555-581.
- [2]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):2806-2819.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):2806-2819.
- [3]马汀山,王妍,吕凯,等.“双碳”目标下火电机组耦合储能的灵活性改造技术研究进展[J].中国电机工程学报, 2022, 42(增刊1):136-148.MA Tingshan, WANG Yan, LYU Kai, et al. Research progress on flexibility transformation technology of coupled energy storage for thermal power units under the“dual-carbon” goal[J]. Proceedings of the CSEE, 2022,42(Suppl.1):136-148.
- [4]聂世豪,陈磊,闵勇,等.工业负荷参与一次调频潜力与特性分析[J].电网技术, 2023, 47(10):3994-4005.NIE Shihao, CHEN Lei, MIN Yong, et al. Potential and characteristic analysis on participation of industrial load in primary frequency regulation[J]. Power System Technology, 2023, 47(10):3994-4005.
- [5]赵冬梅,宋原,王云龙,等.考虑柔性负荷响应不确定性的多时间尺度协调调度模型[J].电力系统自动化,2019, 43(22):21-30.ZHAO Dongmei, SONG Yuan, WANG Yunlong, et al.Multi-time scale coordinated scheduling model considering response uncertainty of flexible load[J].Automation of Electric Power Systems, 2019, 43(22):21-30.
- [6]胡志勇,郭雪丽,王爽,等.考虑响应意愿的电动汽车群-空调集群需求响应策略研究[J].电力系统保护与控制, 2023, 51(15):109-119.HU Zhiyong, GUO Xueli, WANG Shuang, et al.Research on demand response strategy of electric vehicle group-air-conditioning cluster considering response intention[J]. Power System Protection and Control, 2023,51(15):109-119.
- [7]师景佳,袁铁江, KHAN S A,等.计及电动汽车可调度能力的风/车协同参与机组组合策略[J].高电压技术, 2018, 44(10):3433-3440.SHI Jingjia, YUAN Tiejiang, KHAN S A, et al. Unit commitment strategy considering cooperated dispatch of electric vehicles based on scheduling capacity and wind power generation[J]. High Voltage Technology, 2018,44(10):3433-3440.
- [8]晋宏杨,孙宏斌,郭庆来,等.基于能源互联网用户核心理念的高载能-风电协调调度策略[J].电网技术,2016, 40(1):139-145.JIN Hongyang, SUN Hongbin, GUO Qinglai, et al.Dispatch strategy based on energy internet customer-centered concept for energy intensive enterprise and renewable generation to improve renewable integration[J]. Power System Technology,2016, 40(1):139-145.
- [9] KONG X, SUN B, ZHANG J, et al. Power retailer air-conditioning load aggregation operation control method and demand response[J]. IEEE Access, 2020, 8:112041-112056.
- [10]王海林,袁中琛,郭凌旭,等.面向区域能源供应商的综合能源系统日前优化调度[J].电力系统及其自动化学报, 2019, 31(12):55-63.WANG Hailin, YUAN Zhongchen, GUO Lingxu, et al.Day-ahead optimal dispatching of integrated energy system for district-level energy suppliers[J]. Journal of Electric Power System and Automation, 2019, 31(12):55-63.
- [11] LI Y, HAN M, SHAHIDEHPOUR M, et al. Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response[J].Applied Energy, 2023, 335:120749.
- [12] YOLANDA M, FELIPE F. A two-stage stochastic Stackelberg model for microgrid operation with chance constraints for renewable energy generation uncertainty[J]. Applied Energy, 2021, 303:117608.
- [13] Y W WANG, SONG M H, JIA M Y, et al.Multi-objective distributionally robust optimization for hydrogen-involved total renewable energy CCHP planning under source-load uncertainties[J]. Applied Energy, 2023, 342:121212.
- [14]白庆立,赵志鹏,靳晓雨,等.考虑风电出力和电价不确定性的水风联合现货市场竞价策略[J].电力系统自动化, 2024, 48(11):122-133.BAI Qingli, ZHAO Zhipeng, JIN Xiaoyu, et al.Hydro-wind power joint bidding strategies for electricity spot market considering uncertainties ofwind power output and electricity price[J]. Automation of Electric Power Systems, 2024, 48(11):122-133.
- [15]王继东,边翊楠,许秋铭,等.考虑风险和碳交易机制的微电网分布鲁棒优化调度[J/OL].高电压技术,1-12[2024-07-29]. https://doi.org/10.13336/j.1003-6520.hve.20230501.WANG Jidong, BIAN Yinan, XU Qiuming, et al.Distributionally robust optimal dispatching of microgrid considering risk and carbon trading mechanism[J/OL].High Voltage Engineering, 1-12[2024-07-29]. https://doi.org/10.13336/j.1003-6520.hve.20230501.
- [16]杜佳男,韩肖清,李廷钧,等.考虑电价不确定性和博弈欺诈行为的多微网电能合作运行优化策略[J].电网技术, 2022, 46(11):4217-4230.DU Jianan, HAN Xiaoqing, LI Tingjun, et al.Optimization strategy for multimicrogrid power cooperative operation considering electricity price uncertainty and game fraud[J]. Power System Technology, 2022, 46(11):4217-4230.
- [17]姜正庭,王建学,肖云鹏,等.基于两阶段随机优化的电能量与深度调峰融合市场出清模型及定价方法[J].电网技术, 2023, 47(9):3597-3613.JIANG Zhengting, WANG Jianxue, XIAO Yunpeng, et al. Market clearing model and pricing method based on two-stage stochastic optimization of electric energy and deep peak regulation[J]. Power System Technology,2023, 47(9):3597-3613.
- [18] FACCINI D, MAGGIONI F, POTRA F A. Robust and distributionally robust optimization models for linear support vector machine[J]. Computers&Operations Research, 2022, 147:105930.
- [19]文艺林,胡泽春,宁剑,等.基于分布鲁棒机会约束的充电运营商参与调峰市场投标策略[J].电力系统自动化, 2022, 46(7):23-32.WEN Yilin, HU Zechun, NING Jian, et al. Bidding strategy of charging operators participating in peak shaving market based on distributed robust opportunistic constraints[J]. Automation of Electric Power Systems,2022, 46(7):23-32.
- [20] ZHOU Y Z, LI X, HAN H T, et al. Resilience-oriented planning of integrated electricity and heat systems:a stochastic distributionally robust optimization approach[J]. Applied Energy, 2024, 353(Part.A):122053.
- [21]郑林烽,缪源诚,滕晓毕,等.考虑配储的火电机组灵活性改造模型与方法[J/OL].中国电机工程学报,1-14[2024-07-29]. https://doi.org/10.13334/j.0258-8013.pcsee.231566.ZHENG Linfeng, MIAO Yuancheng, TENG Xiaobi, et al. Model and method for flexible retrofit of thermal power units considering energy storage configuration[J/OL]. Proceedings of the CSEE,1-14[2024-07-29]. https://doi.org/10.13334/j.0258-8013.pcsee.231566.
- [22]张斌,司大军,李凡,等.计及电解铝负荷需求侧响应的风电并网调峰研究[J].电工电能新技术, 2023,42(7):31-38.ZHANG Bin, SI Dajun, LI Fan, et al. Research on grid-connected peak regulation of wind power considering the demand-side response of electrolytic aluminum load[J]. New Technology of Electrical Engineering and Energy, 2023, 42(7):31-38.
- [23]赵书强,吴杨,李志伟,等.考虑风光出力不确定性的电力系统调峰能力及经济性分析[J].电网技术, 2022,46(5):1752-1761.ZHAO Shuqiang, WU Yang, LI Zhiwei et al. Peak regulation capacity and economic analysis of power system considering the uncertainty of wind and solar output[J]. Power System Technology, 2022, 46(5):1752-1761.
- [24]李军徽,张嘉辉,穆钢,等.储能辅助火电机组深度调峰的分层优化调度[J].电网技术, 2019, 43(11):3961-3970.LI Junhui, ZHANG Jiahui, MU Gang, et al. Hierarchical optimization scheduling of deep peak shaving for energy-storage auxiliary thermal power generating units[J]. Power System Technology, 2019, 43(11):3961-3970.
- [25] ESFAHANI M P, KUHN D. Data-driven distributionally robust optimization using the Wasserstein metric:performance guarantees and tractable reformulations[J].Mathematical Programming, 2018, 171(1/2):115-166.
- [26] XIE W. On distributionally robust chance constrained programs with Wasserstein distance[J]. Mathematical Programming, 2019, 186(1/2):1-41..
- [27] NGUYEN H T, CHOI D H. Distributionally robust model predictive control for smart electric vehicle charging station with V2G/V2V capability[J]. IEEE Transactions on Smart Grid, 2023, 14(6):4621-4633.
- [28]葛少云,杜咏梅,郭玥,等.基于分布鲁棒优化的车-站-网日前能量管理与交易[J].电力系统自动化, 2024,48(5):11-20.GE Shaoyun, DU Yongmei, GUO Yue, et al.Vehicle-station-network day-ahead energy management and transaction based on distributed robust optimization[J]. Power System Automation, 2024, 48(5):11-20.