新型电力系统与综合能源

含高比例气象敏感可再生能源电网日前调度时间颗粒度优化

展开
  • 1.上海交通大学 电子信息与电气工程学院, 上海 200240
    2.华南理工大学 电力学院, 广州 510640
叶志亮(1998-),硕士生,从事电力系统调度研究.

收稿日期: 2022-07-15

  修回日期: 2022-09-07

  录用日期: 2022-09-22

  网络出版日期: 2022-12-06

基金资助

国家自然科学基金(51977062)

Optimization of Day-Ahead Dispatch Time Resolution in Power System with a High Proportion of Climate-Sensitive Renewable Energy Sources

Expand
  • 1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai 200240, China
    2. School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China

Received date: 2022-07-15

  Revised date: 2022-09-07

  Accepted date: 2022-09-22

  Online published: 2022-12-06

摘要

调度计划的时间颗粒度指调度计划中每个时段长度.随着气象敏感可再生能源占比的提高,调度时段内电网净负荷的波动性显著增强,造成系统爬坡能力不足、频率异常等风险.因此,不同可再生能源渗透率下时间颗粒度的设置成为当前迫切需要解决的问题.提出基于全局灵敏度的日前调度时间颗粒度优化方法,采用Sobol'方法和多项式混沌展开的全局灵敏度方法量化不同时间颗粒度下净负荷波动性、不确定性对优化调度影响,在精细化程度和负荷预测准确率之间取得一种平衡,选择合适的时间颗粒度使优化调度效果最优.分析和仿真结果表明:时间颗粒度的选择主要由净负荷波动率决定,依据净负荷波动率选择合适时间颗粒度,使得不平衡功率最小化,可达到提升优化调度效果和降低调度成本的目的.

本文引用格式

叶志亮, 黎灿兵, 张勇军, 李立浧, 肖银璟, 吴雨杭, 邰能灵 . 含高比例气象敏感可再生能源电网日前调度时间颗粒度优化[J]. 上海交通大学学报, 2023 , 57(7) : 781 -790 . DOI: 10.16183/j.cnki.jsjtu.2022.277

Abstract

The time resolution of scheduling plan refers to the length of each time interval in the scheduling plan. With the increasing proportion of climate-sensitive renewable energy sources (RES), the volatility of net load during the dispatch interval is significantly enhanced, resulting in an insufficient system climbing capacity and abnormal frequency. Therefore, the setting of time resolution at different renewable energy penetration rates becomes an urgent problem to be solved at present. A global sensitivity-based day-ahead dispatch time resolution selection optimization method is proposed to select the time resolution for different volatility grids. To achieve a balance between the degree of refinement and the accuracy of load prediction, the global sensitivity analysis method based on Sobol' method and sparse chaos expansion is used to evaluate the impact of net load volatility and uncertainty on dispatch at different time resolutions, and an appropriate time resolution is chosen to optimize the scheduling effect. The analysis and simulation results show that the choice of time resolution is mainly determined by the net load fluctuation rate. The appropriate time granularity is chosen according to the net load fluctuation rate, which minimizes the unbalanced power, and the purpose of improving the optimal scheduling effect and reducing the dispatching cost is achieved.

参考文献

[1] 国家发展和改革委员会能源研究所. 中国2050高比例可再生能源发展情景暨路径研究[R]. 北京: 国家发展和改革委员会能源研究所, 2015.
[1] Energy Research Institute of National Development and Reform Commission. China 2050 high percentage renewable energy development scenario and pathway study[R]. Beijing: Energy Research Institute of the National Development and Reform Commission, 2015.
[2] PAND?I? H, DVORKIN Y, WANG Y S, et al. Effect of time resolution on unit commitment decisions in systems with high wind penetration[C]// 2014 IEEE PES General Meeting | Conference & Exposition. National Harbor, MD, USA: IEEE, 2014: 1-5.
[3] 艾小猛, 韩杏宁, 文劲宇, 等. 考虑风电爬坡事件的鲁棒机组组合[J]. 电工技术学报, 2015, 30(24): 188-195.
[3] AI Xiaomeng, HAN Xingning, WEN Jinyu, et al. Robust unit commitment considering wind power ramp events[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 188-195.
[4] MILLIGAN M, KIRBY B, et al. Combining balancing areas’ variability: Impacts on wind integration in the western interconnection. (2010-05-23)[2022-06-26]. https://www.osti.gov/biblio/986254.
[5] GUY J D. Security constrained unit commitment[J]. IEEE Transactions on Power Apparatus & Systems, 1971, 90(3): 1385-1390.
[6] CONTAXIS G C, KABOURIS J. Short term scheduling in a wind/diesel autonomous energy system[J]. IEEE Transactions on Power Systems, 1991, 6(3): 1161-1167.
[7] 陈之栩, 谢开, 张晶, 等. 电网安全节能发电日前调度优化模型及算法[J]. 电力系统自动化, 2009, 33(1): 10-13.
[7] CHEN Zhixu, XIE Kai, ZHANG Jing, et al. Optimal model and algorithm for day-ahead generation scheduling of transmission grid security constrained convention dispatch[J]. Automation of Electric Power Systems, 2009, 33(1): 10-13.
[8] GANGAMMANAVAR H, SEN S, ZAVALA V M. Stochastic optimization of sub-hourly economic dispatch with wind energy[J]. IEEE Transactions on Power Systems, 2016, 31(2): 949-959.
[9] CHE L, LIU X, ZHU X, et al. Intra-interval security assessment in power systems with high wind penetration[J]. IEEE Transactions on Sustainable Energy, 2019, 10(4): 1890-1903.
[10] CHE L, LIU X, ZHU X, et al. Intra-interval security based dispatch for power systems with high wind penetration[J]. IEEE Transactions on Power Systems, 2019, 34(2): 1243-1255.
[11] SIOSHANSI R, O’NEILL R, OREN S S. Economic consequences of alternative solution methods for centralized unit commitment in day-ahead electricity markets[J]. IEEE Transactions on Power Systems, 2008, 23(2): 344-352.
[12] MORALES-ESPA?A G, RAMíREZ-ELIZONDO L, HOBBS B F. Hidden power system inflexibilities imposed by traditional unit commitment formulations[J]. Applied Energy, 2017, 191: 223-238.
[13] BAKIRTZIS E A, BISKAS P N, LABRIDIS D P, et al. Multiple time resolution unit commitment for short-term operations scheduling under high renewable penetration[J]. IEEE Transactions on Power Systems, 2014, 29(1): 149-159.
[14] BAKIRTZIS E A, SIMOGLOU C K, BISKAS P N, et al. Comparison of advanced power system operations models for large-scale renewable integration[J]. Electric Power Systems Research, 2015, 128: 90-99.
[15] BAKIRTZIS E A, BISKAS P N. Multiple time resolution stochastic scheduling for systems with high renewable penetration[J]. IEEE Transactions on Power Systems, 2017, 32(2): 1030-1040.
[16] ELA E, O’MALLEY M. Studying the variability and uncertainty impacts of variable generation at multiple timescales[J]. IEEE Transactions on Power Systems, 2012, 27(3): 1324-1333.
[17] WAN Y H. Analysis of wind power ramping behavior in ERCOT[R]. USA: Office of Scientific and Technical Information, 2011.
[18] 梁双, 胡学浩, 张东霞, 等. 光伏发电置信容量的研究现状与发展趋势[J]. 电力系统自动化, 2011, 35(19): 101-107.
[18] LIANG Shuang, HU Xuehao, ZHANG Dongxia, et al. Current status and development trend on capacity credit of photovoltaic generation[J]. Automation of Electric Power Systems, 2011, 35(19): 101-107.
[19] 邬超, 朱桂萍, 钱敏慧. 基于信息熵的历史数据选取对超短期风电功率预测精度影响研究[J]. 电网技术, 2021, 45(5): 1767-1772.
[19] WU Chao, ZHU Guiping, QIAN Minhui. Impact of historical data selection on accuracy of ultra-short-term wind power prediction based on prediction information entropy[J]. Power System Technology, 2021, 45(5): 1767-1772.
[20] PINEDA S, FERNáNDEZ-BLANCO R, MORALES J M. Time-adaptive unit commitment[J]. IEEE Transactions on Power Systems, 2019, 34(5): 3869-3878.
[21] 金国彬, 潘狄, 陈庆, 等. 考虑自适应实时调度的多电压等级直流配电网能量优化方法[J]. 电网技术, 2021, 45(10): 3906-3917.
[21] JIN Guobin, PAN Di, CHEN Qing, et al. Energy optimization method of multi-voltage-level DC distribution network considering adaptive real-time scheduling[J]. Power System Technology, 2021, 45(10): 3906-3917.
[22] AGENCY I E. Empowering variable renewables-options for flexible electricity systems[M]. Paris: OECD Publishing, 2009: 13-14.
[23] 楚成博. 含可调控负荷系统的调度灵活性研究[D]. 济南: 山东大学, 2013.
[23] CHU Chengbo. Studies on flexibility of power system dispatch with controllable loads[D]. Jinan: Shandong University, 2013.
[24] NI F, NIJHUIS M, NGUYEN P H, et al. Variance-based global sensitivity analysis for power systems[J]. IEEE Transactions on Power Systems, 2018, 33(2): 1670-1682.
[25] 孙鑫, 王博, 陈金富, 等. 基于稀疏多项式混沌展开的可用输电能力不确定性量化分析[J]. 中国电机工程学报, 2019, 39(10): 2904-2914.
[25] SUN Xin, WANG Bo, CHEN Jinfu, et al. Sparse polynomial chaos expansion based uncertainty quantification for available transfer capability[J]. Proceedings of the CSEE, 2019, 39(10): 2904-2914.
[26] KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proceedings of ICNN’95-International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
[27] 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.
[27] 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.
文章导航

/