Two-Stage Optimal Configuration of Microgrid Based on Fuzzy Scene Clustering

doi:10.16183/j.cnki.jsjtu.2022.090

Abstract

Abstract:

Aimed at the impact of extreme weather on the stable operation of microgrid, an optimal configuration strategy of microgrid based on fuzzy scene clustering is proposed. Using historical weather data, a fuzzy scene clustering method is used to deal with the problem of new energy output fluctuations caused by random weather on the source side, and a robust optimization model is established on the load side to deal with load fluctuations within a certain range. Using scenes of 8 760 hours in a year, typical scenes and extreme scenes are obtained by distinguishing the unique membership characteristics of fuzzy scene clusters. Considering the impact of extreme scenarios on the optimal configuration of the microgrid, a two-stage robust model with the smallest comprehensive cost is established, which is decomposed by the column and constraint method, and is finally solved iteratively by Cplex solver. The effectiveness and feasibility of the proposed optimal configuration strategy are verified by simulation analysis.

Key words: microgrid, fuzzy scenario clustering, robust optimization, optimal configuration

CLC Number:

TM712

MI Yang, LI Haipeng, CHEN Boyang, PENG Jianwei, WEI Wei, YAO Yan. Two-Stage Optimal Configuration of Microgrid Based on Fuzzy Scene Clustering[J]. Journal of Shanghai Jiao Tong University, 2023, 57(9): 1137-1145.

Figures/Tables 11

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References 20

[1]	黄强, 郭怿, 江建华, 等. “双碳”目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509.
	HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[2]	米阳, 王鹏, 邓锦, 等. 孤岛交直流混合微电网群分层协调控制[J]. 电力系统保护与控制, 2021, 49(20): 1-8.
	MI Yang, WANG Peng, DENG Jin, et al. Hierarchical coordinated control of island ac/dc hybrid microgrids[J]. Power System Protection and Control, 2021, 49(20): 1-8.
[3]	李玲芳, 陈占鹏, 胡炎, 等. 基于灵活性和经济性的可再生能源电力系统扩展规划[J]. 上海交通大学学报, 2021, 55(7): 791-801.
	LI Lingfang, CHEN Zhanpeng, HU Yan, et al. Expansion planning of renewable energy power system considering flexibility and economy[J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 791-801.
[4]	苏永新, 聂伟棋, 谭貌. 考虑风电接入和气电转换的综合能源系统日前区间优化[J]. 电力系统自动化, 2019, 43(17): 63-71.
	SU Yongxin, NIE Weiqi, TAN Mao. Day-ahead interval optimization of integrated energy system considering wind power integration and gas-to-electricity transformation[J]. Automation of Electric Power Systems, 2019, 43(17): 63-71.
[5]	徐岩, 陈泽雄, 于博文, 等. 基于机会约束的电动汽车充光储一体化充电站容量优化方法[J]. 智慧电力, 2021, 49(12): 25-30.
	XU Yan, CHEN Zexiong, YU Bowen, et al. Capacity optimization method of charging-PV-storage integrated station for electric vehicles[J]. Smart Power, 2021, 49(12): 25-30.
[6]	LEI Y, WANG D, JIA H J, et al. Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy[J]. Applied Energy, 2020, 276: 115395. doi: 10.1016/j.apenergy.2020.115395 URL
[7]	张靠社, 冯培基, 张刚, 等. 考虑机会约束的多能源微电网双层优化配置[J]. 太阳能学报, 2021, 42(8): 41-48.
	ZHANG Kaoshe, FENG Peiji, ZHANG Gang, et al. Bi-level optimization configuration method for multienergy microgrid considering chance constraints[J]. Acta Energiae Solaris Sinica, 2021, 42(8): 41-48.
[8]	BILLIONNET A, COSTA M C, POIRION P L. Robust optimal sizing of a hybrid energy stand-alone system[J]. European Journal of Operational Research, 2016, 254(2): 565-575. doi: 10.1016/j.ejor.2016.03.013 URL
[9]	朱光远, 林济铿, 罗治强. 鲁棒优化在电力系统发电计划中的应用综述[J]. 中国电机工程学报, 2017, 37(20): 5881-5892.
	ZHU Guangyuan, LIN Jikeng, LUO Zhiqiang. Review of robust optimization for generation scheduling in power systems[J]. Proceedings of the CSEE, 2017, 37(20): 5881-5892.
[10]	周士超, 刘晓林, 熊展, 等. 考虑韧性提升的交直流配电网线路加固和储能配置策略[J]. 上海交通大学学报, 2021, 55(12): 1619-1630.
	ZHOU Shichao, LIU Xiaolin, XIONG Zhan, et al. Line hardening and energy storage system configuration strategies for resilience enhancement of a hybrid AC-DC distribution system[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1619-1630.
[11]	臧海祥, 马铭欣, 周亦洲, 等. 电力市场环境下风电-光热-生物质混合电站鲁棒优化调度模型[J]. 电力系统保护与控制, 2022, 50(5): 1-11.
	ZANG Haixiang, MA Mingxin, ZHOU Yizhou, et al. Robust optimal scheduling model for a “wind power-concentrating solar power-biomass” hybrid power plant in the electricity market[J]. Power System Protection & Control, 2022, 50(5): 1-11.
[12]	吴巍, 汪可友, 李国杰. 考虑风电时空相关性的仿射可调鲁棒机组组合[J]. 中国电机工程学报, 2017, 37(14): 4089-4097.
	WU Wei, WANG Keyou, LI Guojie. Affinely adjustable robust unit commitment considering the spatiotemporal correlation of wind power[J]. Proceedings of the CSEE, 2017, 37(14): 4089-4097.
[13]	陈庆攀, 刘洋, 许立雄. 含风电的电力系统鲁棒优化调度[J]. 电测与仪表, 2020, 57(8): 78-84.
	CHEN Qingpan, LIU Yang, XU Lixiong. Robust optimal dispatch of power system containing wind power[J]. Electrical Measurement & Instrumentation, 2020, 57(8): 78-84.
[14]	赵天阳, 陈奇芳, 张建华. 基于自适应鲁棒优化的互联微网能量/备用共享调度[J]. 电网技术, 2016, 40(12): 3783-3791.
	ZHAO Tianyang, CHEN Qifang, ZHANG Jianhua. Energy/reserve sharing dispatch for interconnected multiple microgrids based on adaptive robust optimization[J]. Power System Technology, 2016, 40(12): 3783-3791.
[15]	彭春华, 郑聪, 陈婧, 等. 基于置信间隙决策的综合能源系统鲁棒优化调度[J]. 中国电机工程学报, 2021, 41(16): 5593-5604.
	PENG Chunhua, ZHENG Cong, CHEN Jing, et al. Robust optimal scheduling of integrated energy systems based on confidence gap decision[J]. Proceedings of the CSEE, 2021, 41(16): 5593-5604.
[16]	袁洪涛, 韦钢, 张贺, 等. 计及充换储一体站的主动配电网鲁棒优化调度[J]. 中国电机工程学报, 2020, 40(8): 2453-2468.
	YUAN Hongtao, WEI Gang, ZHANG He, et al. Robust optimal scheduling of active distribution network considering with the charging-swapping-storage integrated station[J]. Proceedings of the CSEE, 2020, 40(8): 2453-2468.
[17]	桑博, 张涛, 刘亚杰, 等. 期望场景下的并网型微电网两阶段鲁棒优化调度[J]. 中国电机工程学报, 2020, 654(19): 6161-6173.
	SANG Bo, ZHANG Tao, LIU Yajie, et al. Two-stage robust optimal scheduling of grid-connected microgrid under expected scenarios[J]. Proceedings of the CSEE, 2020, 654(19): 6161-6173.
[18]	杨博, 俞磊, 王俊婷, 等. 基于自适应蝠鲼觅食优化算法的分布式电源选址定容[J]. 上海交通大学学报, 2021, 55(12): 1673-1688.
	YANG Bo, YU Lei, WANG Junting, et al. Optimal sizing and placement of distributed generation based on adaptive manta ray foraging optimization[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1673-1688.
[19]	宋福龙, 吴洲洋, 张艳, 等. 大规模风电下基于模糊场景聚类的网-储协调规划方法[J]. 电力自动化设备, 2018, 38(2): 74-80.
	SONG Fulong, WU Zhouyang, ZHANG Yan, et al. Fuzzy scene clustering based grid-energy storage coordinated planning method with large-scale wind power[J]. Electric Power Automation Equipment, 2018, 38(2): 74-80.
[20]	游广增, 汤翔鹰, 胡炎, 等. 基于典型运行场景聚类的电力系统灵活性评估方法[J]. 上海交通大学学报, 2021, 55(7): 802-813.
	YOU Guangzeng, TANG Xiangying, HU Yan, et al. Flexibility evaluation method for power system based on clustering of typical operating scenarios[J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 802-813.

类型	规格	投资费用× 10^-4/(元· 台^-1)	运维费用/ (元·kW^-1· a^-1)	折现年限/a
光伏组件	1 kW	0.8	85	15
风力发电机	100 kW	80	46	10
燃气轮机	100 kW	10	30	20
储能装置	2 V/1 000 A·h	0.16	10	15

场景	风力发电机/台	光伏/ 块	储能装置/块	燃气轮机/台	年投资成本/元	失负荷概率/%
A	10	1407	1208	8	226×10⁴	0.851
B	14	1502	1804	12	281×10⁴	0.146

不确定参数	燃气轮机/台	光伏/ 块	风力发电机/ 台	储能装置/ 块	年投资成本× 10^-4/元	总成本× 10^-4/ 元	失负荷概率/%
0	8	1407	10	1208	226	2198	0.851
0.15	9	1481	12	2713	275	2668	0.423
0.25	13	1567	14	3044	307	2990	0.165

方案	燃气轮机/ 台	光伏/ 块	风力发电机/ 台	储能装置/ 块	运行成本/ 元	失负荷概率/%	年投资成本× 10^-4/元
1	8	1407	10	1208	201755	0.851	226
2	12	1502	14	1804	264150	0.146	281
3	9	1481	12	2713	250345	0.426	275
4	14	1403	11	2835	268555	0.102	267