计及条件风险价值的含储热光热电站与风电电力系统经济调度
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摘要
含储热的光热电站是一种可调度的可再生能源发电技术,其接入系统为实现高比例可再生能源消纳提供了新的技术手段。利用含储热光热电站可控的出力特性,以削减接入大规模风电后调峰问题带来的影响。考虑到风电和光照功率的不确定性会带来调度运行风险的增加,借鉴金融领域风险管理的概念,引入条件风险价值(CVaR)来度量不确定性因素给调度运行带来的风险损失,并以系统总调度成本和风险成本最低为目标,建立计及CVaR的含储热光热电站和风电的电力系统经济调度模型。采用IEEE 39系统对模型进行仿真分析,并探讨了不同风险系数、储热容量对优化调度结果的影响。
The concentrated solar power plant with thermal energy storage is a schedulable renewable energy power generation technology, and its integration provides a new technical means for achieving high proportion of renewable energy consumption. In this paper, the controllable characteristic of concentrated solar power plant with thermal storage is used to reduce the impact of peak shaving after large-scale wind power integration. Considering that the uncertainty of wind power and illumination power will increase the risk of dispatching operation, and referring to the concept of risk management in financial field, this paper introduces conditional value-at-risk(CVaR) to measure the risk loss caused by uncertainties to power generation dispatching. With the aim of minimizing the total dispatch cost and risk cost of the system, this paper establishes an economic dispatch model of power system with concentrated solar power plant and wind power system. The proposed model is simulated by using IEEE 39-node system, and then the effects of different risk factors, heat storage capacity and wind power access capacity on the optimal scheduling results are discussed.
The concentrated solar power plant with thermal energy storage is a schedulable renewable energy power generation technology, and its integration provides a new technical means for achieving high proportion of renewable energy consumption. In this paper, the controllable characteristic of concentrated solar power plant with thermal storage is used to reduce the impact of peak shaving after large-scale wind power integration. Considering that the uncertainty of wind power and illumination power will increase the risk of dispatching operation, and referring to the concept of risk management in financial field, this paper introduces conditional value-at-risk(CVaR) to measure the risk loss caused by uncertainties to power generation dispatching. With the aim of minimizing the total dispatch cost and risk cost of the system, this paper establishes an economic dispatch model of power system with concentrated solar power plant and wind power system. The proposed model is simulated by using IEEE 39-node system, and then the effects of different risk factors, heat storage capacity and wind power access capacity on the optimal scheduling results are discussed.
引文
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[22]麻秀范,李颖,王皓,等.基于电动汽车出行随机模拟的充电桩需求研究[J].电工技术学报,2017,32(增刊2):190-202.Ma Xiufan,Li Ying,Wang Hao,et al.Research on the demand of charging piles based on stochastic simulation of electric vehicle travel[J].Transactions of China Electrotechnical Society,2017,32(S2):190-202.
[2]中华人民共和国国家能源局.电力发展“十三五”计划[R].北京:国家能源局,2016.
[3]陈润泽,孙宏斌,李正烁,等.含储热光热电站的电网调度模型与并网效益分析[J].电力系统自动化,2014,38(19):1-7.Chen Runze,Sun Hongbin,Li Zhengshuo,et al.Power grid scheduling model and grid-connected benefit analysis of thermal storage thermal power station[J].Automation of Electric Power Systems,2014,38(19):1-7.
[4]Pousinho H M I,Silva H,Mendes V M F,et al.Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach[J].Energy,2014,78:524-534.
[5]Xu Ti,Zhang Ning.Coordinated operation of concentrated solar power and wind resources for the provision of energy and reserve services[J].IEEETransactions on Power Systems,2017,32(2):1260-1271.
[6]任博强.含风电场的电力系统短期调度及其经济性风险管理[D].上海:上海交通大学,2010.
[7]Zhao Z,Tong X J.Economic dispatch of wind integrated power systems with a conditional risk method[C]//IET International Conference on Advances in Power System Control,Hong Kong,China,2013,DOI:10.1049/cp.2012.2130.
[8]刘怀东,冯志强,王锦桥,等.计及条件风险价值的综合能源系统经济调度[J].电网技术,2018,42(5):1385-1392.Liu Huaidong,Feng Zhiqiang,Wang Jinqiao,et al.Economic dispatch of integrated energy system considering conditional value-at risk[J].Power System Technology,2018,42(5):1385-1392.
[9]王英瑞.基于条件风险价值的电-热-气综合能源系统经济调度[D].北京:华北电力大学,2017.
[10]Hemmati R,Saboori H,Saboori S.Stochastic risk-averse coordinated scheduling of grid integrated energy storage units in transmission constrained wind-thermal systems within a conditional valueat-risk framework[J].Energy,2016,113:762-775.
[11]晋宏杨,孙宏斌,郭庆来,等.含大规模储热的光热电站-风电联合系统多日自调度方法[J].电力系统自动化,2016,40(11):17-23.Jin Hongyang,Sun Hongbin,Guo Qinglai,et al.Multi-day self-scheduling method for solar thermal power station with large-scale heat storage-wind power combined system[J].Automation of Electric Power Systems,2016,40(11):17-23.
[12]Price H,Lupfert E,Kearney D,et al.Advances in parabolic trough solar power technology[J].Journal of Solar Energy Engineering,2002,124(2):109-125.
[13]卫志农,陈妤,黄文进,等.考虑条件风险价值的虚拟电厂多电源容量优化配置模型[J].电力系统自动化,2018,42(4):39-46.Wei Zhinong,Chen Wei,Huang Wenjin,et al.Opitimal allocation model for multi-energy capacity of virtual power plant considering conditional value-at-risk[J].Automation of Electric Power Sytems,2018,42(4):39-46.
[14]刘兴宇,温步瀛,江岳文.基于条件风险价值的含风电电力系统旋转备用效益研究[J].电工技术学报,2017,32(9):169-178.Liu Xingyu,Wen Buying,Jiang Yuewen.Study on rotary standby benefit of wind power system with conditional risk value[J].Transactions of China Electrotechnical Society,2017,32(9):169-178.
[15]吴俊利.计及充裕性风险的含大规模风电电力系统经济调度问题研究[D].武汉:华中科技大学,2015.
[16]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120,159.Bie Chaohong,Hu Guowei,Xie Haipeng,et al.Optimal dispatch of wind power systems with demand response[J].Power System Automation,2014,38(13):115-120,159.
[17]崔杨,张汇泉,仲悟之,等.基于分时能量互补的风电-光热联合外送容量优化配置方法[J].电网技术,2018,DOI:10.13335/j.1000-3673.pst.2018.2184.Cui Yang,Zhang Huiquan,Zhong Wuzhi,et al.Optimized configuration method of wind powerphotothermal combined with external capacity based on time-sharing energy complementation[J].Power Grid Technology,2018,DOI:10.13335/j.1000-3673.pst.2018.2184.
[18]林少伯,韩民晓,赵国鹏,等.基于随机预测误差的分布式光伏配网储能系统容量配置方法[J].中国电机工程学报,2013,33(4):25-33,5.Lin Shaobo,Han Minxiao,Zhao Guopeng,et al.Capacity allocation method of distributed photovoltaic distribution network energy storage system based on random prediction error[J].Chinese Journal of Electrical Engineering,2013,33(4):25-33,5.
[19]王雅平,林舜江,杨智斌,等.微电网多目标随机动态优化调度算法[J].电工技术学报,2018,33(10):2196-2207.Wang Yaping,Lin Shunjiang,Yang Zhibin,et al.Multi-objective stochastic dynamic optimization scheduling algorithm for microgrid[J].Transactions of China Electrotechnical Society,2018,33(10):2196-2207.
[20]于东,孙欣,高丙团,等.考虑风电不确定出力的风电并网协调优化模型[J].电工技术学报,2016,31(9):34-41.Yu Dong,Sun Xin,Gao Bingtuan,et al.A wind power grid-connected optimization model considering wind power uncertainty output[J].Transactions of China Electrotechnical Society,2016,31(9):34-41.
[21]肖浩,裴玮,孔力.含大规模电动汽车接入的主动配电网多目标优化调度方法[J].电工技术学报,2017,32(增刊2):179-189.Xiao Hao,Pei Wei,Kong Li.Multi-objective optimal dispatching method for active distribution network with large-scale electric vehicle access[J].Transactions of China Electrotechnical Society,201732(S2):179-189.
[22]麻秀范,李颖,王皓,等.基于电动汽车出行随机模拟的充电桩需求研究[J].电工技术学报,2017,32(增刊2):190-202.Ma Xiufan,Li Ying,Wang Hao,et al.Research on the demand of charging piles based on stochastic simulation of electric vehicle travel[J].Transactions of China Electrotechnical Society,2017,32(S2):190-202.