Two-Stage Robust Optimization of CCHP Microgrid with Consideration of Wind Power and PV Uncertainty andStorage System

doi:10.12204/j.issn.1000-7229.2020.11.012

Abstract

Abstract:

Microgrid with combined cooling, heating and power (CCHP) can realize the cascade utilization of energy, improve the efficiency of energy utilization and promote the consumption of renewable energy. In this paper, considering the particularity of CCHP microgrid operating in the power market and the uncertainty of wind and solar power output, a two-stage robust optimization model is established, which considers the integrated energy storage system of electricity and heat storage. The simulation results show that the robust optimization strategy considering uncertainty can effectively reduce the output deviation of renewable energy and increase the risk of CCHP microgrid operation cost, and the larger the output deviation of renewable energy, the more obvious the effect.

Key words: uncertainty, energy storage system, combined cooling, heating and power (CCHP) microgrid, electricity market, two-stage robust optimization

CLC Number:

TM743

ZHAO Huiru, LU Hao, LI Zijin, WANG Xuejie, LI Bingkang. Two-Stage Robust Optimization of CCHP Microgrid with Consideration of Wind Power and PV Uncertainty andStorage System[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(11): 116-125.

Figures/Tables 15

Fig.1 The structure of CCHP microgrid

Fig.2 Flow chart of model solution

Fig.3 Uncertain range of wind power and PV output

Fig.4 Time-of-use and real-time prices in electricity market

Fig.5 Electric power balance of CCHP microgrid under real-time price

Fig.6 Thermal power balance of CCHP microgrid under real-time price

Fig.7 Cooling power balance of CCHP microgrid under real-time price

Fig.8 Electric power balance of CCHP microgrid under time-of-use price

Fig.9 Thermal power balance of CCHP microgrid under time-of-use price

Fig.10 Cooling power balance of CCHP microgrid under time-of-use price

Table 1 Deviation operation cost of CCHP microgrid under different deviation degrees

Table B1 Operating parameters of the CCHP microgrid

Table B2 Parameters of energy storage system

Table B3 Other basic parameters

Fig. B1 The cooling, thermal and electric load of CCHP microgrid

References 20

[1]	李泓泽, 陆昊, 李兵抗 . 基于随机生产模拟的风电优先调度对发电企业的外部性量化研究[J]. 电网技术, 2017,41(12):3803-3808.
	LI Hongze, LU Hao, LI Bingkang . Quantitative research on externalities brought by wind power priority scheduling to power generation enterprises based on probabilistic production simulation[J]. Power System Technology, 2017,41(12):3803-3808.
[2]	周晓倩, 艾芊, 林琳 , 等. 多能互补微网集群分布式优化调度[J]. 电网技术, 2019,43(10):3678-3686.
	ZHOU Xiaoqian, AI Qian, LIN Lin , et al. Distributed optimal dispatch of interconnected CCHP microgrids[J]. Power System Technology, 2019,43(10):3678-3686.
[3]	李美成, 梅文明, 张凌康 , 等. 基于可再生能源不确定性的多能源微网调度优化模型研究[J]. 电网技术, 2019,43(4):1260-1270.
	LI Meicheng, MEI Wenming, ZHANG Lingkang , et al. Research on multi-energy microgrid scheduling optimization model based on renewable energy uncertainty[J]. Power System Technology, 2019,43(4):1260-1270.
[4]	王守相, 吴志佳, 庄剑 . 考虑微网间功率交互和微源出力协调的冷热电联供型区域多微网优化调度模型[J]. 中国电机工程学报, 2017,37(24):7185-7194.
	WANG Shouxiang, WU Zhijia, ZHUANG Jian . Optimal dispatching model of CCHP type regional multi-microgrids considering interactive power exchange among microgrids and output coordination among micro-sources[J]. Proceedings of the CSEE, 2017,37(24):7185-7194.
[5]	杨智斌, 林舜江, 王雅平 , 等. 冷热电联供微网能量流的解耦计算方法[J]. 电网技术, 2017,41(12):3876-3883.
	YANG Zhibin, LIN Shunjiang, WANG Yaping , et al. A decoupling method for energy flow calculation of microgrids with combined cooling, heating and power[J]. Power System Technology, 2017,41(12):3876-3883.
[6]	林顺富, 刘持涛, 李东东 , 等. 考虑电能交互的冷热电区域多微网系统双层多场景协同优化配置[J]. 中国电机工程学报, 2020,40(5):1409-1420.
	LIN Shunfu, LIU Chitao, LI Dongdong , et al. Bi-level multiple scenarios collaborative optimization configuration of CCHP regional multi-microgrid system considering power interaction among microgrids[J]. Proceedings of the CSEE, 2020,40(5):1409-1420.
[7]	严毅, 张承慧, 李珂 , 等. 含压缩空气储能的冷热电联供微网优化运行策略[J]. 中国电机工程学报, 2018,38(23):6924-6936.
	YAN Yi, ZHANG Chenghui, LI Ke , et al. The optimal operation strategy for hybrid combined cooling, heating and power microgrid with compressed air energy storage[J]. Proceeding of the CSEE, 2018,38(23):6924-6936.
[8]	吴盛军, 刘建坤, 周前 , 等. 考虑储能电站服务的冷热电多微网系统优化经济调度[J]. 电力系统自动化, 2019,43(10):10-23.
	WU Shengjun, LIU Jiankun, ZHOU Qian , et al. Optimal economic scheduling for multi-microgrid system with combined cooling, heating and power considering service of energy storage station[J]. Automation of Electric Power Systems, 2019,43(10):10-23.
[9]	杨志鹏, 张峰, 梁军 , 等. 含热泵和储能的冷热电联供型微网经济运行[J]. 电网技术, 2018,42(6):1735-1742.
	YANG Zhipeng, ZHANG Feng, LIANG Jun , et al. Economic generation scheduling of CCHP microgrid with heat pump and energy storage[J]. Power System Technology, 2018,42(6):1735-1742.
[10]	陈恒安, 管霖, 卢操 , 等. 新能源发电为主电源的独立微网多目标优化调度模型和算法[J]. 电网技术, 2020,44(2):664-672.
	CHEN Heng’an, GUAN Lin, LU Cao , et al. Multi-objective optimal dispatch model and its algorithm in isolated microgrid with renewable energy generation as main power supply[J]. Power System Technology, 2020,44(2):664-672.
[11]	李艳, 胡志豪, 随权 , 等. 考虑风光热不确定性和相变储能系统的城市建筑微网电-热联合调度[J]. 电网技术, 2019,43(10):3687-3697.
	LI Yan, HU Zhihao, SUI Quan , et al. Microgrid joint electro-thermal scheduling for phase change energy storage system of urban buildings considering uncertainty of wind, light and heat[J]. Power System Technology, 2019,43(10):3687-3697.
[12]	曹知奥, 汪晋宽, 韩英华 , 等. 基于交叉-变异人工蜂群算法的微网优化调度[J]. 控制与决策, 2020,35(9):2059-2069.
	CAO Zhiao, WANG Jinkuan, HAN Yinghua , et al. Optimal scheduling of microgrid based on crossover mutation artificial bee colony algorithm[J]. Control and Decision Making, 2020,35(9):2059-2069.
[13]	吴倩雯, 张会林, 高宁 . 基于MOFA算法的微网系统优化调度[J]. 电子测量技术, 2019,42(1):40-44.
	WU Qianwen, ZHANG Huilin, GAO Ning . Optimal dispatch of micro-grid system based on multi-objective firefly algorithm[J]. Electronic Measurement Technology, 2019,42(1):40-44.
[14]	于丹文, 杨明, 翟鹤峰 , 等. 鲁棒优化在电力系统调度决策中的应用研究综述[J]. 电力系统自动化, 2016,40(7):134-143, 148.
	YU Danwen, YANG Ming, ZHAI Hefeng , et al. An overview of robust optimization used for power system dispatch and decision-making[J]. Automation of Electric Power Systems, 2016,40(7):134-143, 148.
[15]	XIE L, GU Y Z, ZHU X X , et al. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch[J]. IEEE Transactions on Smart Grid, 2014,5(1):511-520.
[16]	方绍凤, 周任军, 许福鹿 , 等. 考虑电热多种负荷综合需求响应的园区微网综合能源系统优化运行[J]. 电力系统及其自动化学报, 2020,32(1):50-57.
	FANG Shaofeng, ZHOU Renjun, XU Fulu , et al. Optimal operation of integrated energy system for park micro-grid considering comprehensive demand response of power and thermal loads[J]. Proceedings of the CSU-EPSA, 2020,32(1):50-57.
[17]	LORCA A, SUN X A . Adaptive robust optimization with dynamic uncertainty sets for multi-period economic dispatch under significant wind[J]. IEEE Transactions on Power Systems, 2015,30(4):1702-1713.
[18]	YAO F, DONG Z Y, MENG K , et al. Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia[J]. IEEE Transactions on Industrial Informatics, 2012,8(4):880-888.
[19]	ZHAO H R, WANG Y W, ZHAO M R , et al. Day-ahead market modeling for strategic wind power producers under robust market clearing[J]. Energies, 2017,10(7):924.
[20]	赵会茹, 王玉玮, 张超 , 等. 阶梯电价下居民峰谷分时电价测算优化模型[J]. 电力建设, 2016,37(3):17-23.
	ZHAO Huiru, WANG Yuwei, ZHANG Chao , et al. Optimization model of residential time-of-use tariff calculation under multi-step electricity price[J]. Electric Power Construction, 2016,37(3):17-23.