Stochastic Robust Economic Dispatch of Combined Heat and Power Microgrid Considering Renewable Energy Uncertainty

doi:10.12204/j.issn.1000-7229.2022.01.003

Abstract

Abstract:

In order to deal with the problem of low economy caused by the uncertainty of renewable energy output and the single energy supply form of traditional microgrid, this paper proposes a two-stage stochastic robust optimization model for multi-energy microgrid. The model considers the grid structure of the power grid and the heating network. The objective function is to minimize the two-stage microgrid cost in the worst wind power output scenario, which includes the start and stop costs of the first stage and the operating costs of the second stage. Because the decision-making and optimization results of the first stage and the second stage influence each other, the two-stage optimization problem is difficult to solve directly. This paper uses a stochastic robust optimization framework for linear decision-making to solve the model. Firstly, the related theories of linear decision-making methods are applied to transform the second stage. Secondly, the cone-shaped fuzzy set is used to describe the uncertainty of renewable energy output. Finally, the“sup-min”problem in the second stage is derived as the“min”problem of cone optimization, and then combined with the“min”problem in the first stage to obtain the single-layer cone optimization problem which can be solved directly, and the optimal solution is obtained by using the solver. The simulation results verify the effectiveness of the proposed model and method.

Key words: renewable energy, regional heat and power grid, stochastic robust optimization, conical fuzzy set

CLC Number:

TM73

OUYANG Han, LÜ Lin, LIU Junyong, GAO Hongjun. Stochastic Robust Economic Dispatch of Combined Heat and Power Microgrid Considering Renewable Energy Uncertainty[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(1): 19-28.

Figures/Tables 15

Fig.1 Schematic diagram of energy flow in a combined heat and power microgrid

Fig.2 System architecture of multi-energy microgrid

Fig.3 Forecast of wind power and photovoltaic output

Fig.4 Load demand

Table 1 Basic parameters of the example

Table 2 EES basic parameters

Table 3 Pipeline flow rate

Table 4 Basic parameters of units

Table 5 Electricity purchase price by period

Fig.5 Day-ahead schedule

Fig.6 Comparison of CHP and EB output of model with or without heat network

Table 6 Comparison of operating costs with or without the consideration of heat network

Fig.7 Economic comparison of different schemes

Table 7 Economics of different schemes

Table 8 Comparison of different historical data

References 23

[1]	萨妮耶·麦合木提, 王海云, 王维庆, 等. 基于可再生能源的CCHP系统综合性能研究[J]. 太阳能学报, 2020, 41(8): 129-136.
	SANIYE·Maihemuti, WANG Haiyun, WANG Weiqing, et al. Comprehensive performance research of re-CCHP system[J]. Acta Energiae Solaris Sinica, 2020, 41(8): 129-136.
[2]	杨帆. 人类命运共同体视域下的全球生态保护与治理研究[D]. 长春: 吉林大学, 2020.
	YANG Fan. Study on the gblobal ecological protection and governance under the perspective of building a human community with a shared future[D]. Changchun: Jilin University, 2020.
[3]	刘辉, 刘强, 张立, 等. 考虑需求侧协同响应的热电联供微网多目标规划[J]. 电力系统保护与控制, 2019, 47(5): 43-51.
	LIU Hui, LIU Qiang, ZHANG Li, et al. Multi-objective planning for combined heat and power microgrid considering demand side cooperative response[J]. Power System Protection and Control, 2019, 47(5): 43-51.
[4]	张晓辉, 闫鹏达, 钟嘉庆, 等. 可再生能源激励制度下的低碳经济电源规划[J]. 电网技术, 2015, 39(3): 655-662.
	ZHANG Xiaohui, YAN Pengda, ZHONG Jiaqing, et al. Research on generation expansion planning in low-carbon economy environment under incentive mechanism of renewable energy sources[J]. Power System Technology, 2015, 39(3): 655-662.
[5]	孙文嘉, 高闯, 黄伟光. ZK2000太阳能燃气轮机的性能分析与优化[J]. 中国电机工程学报, 2021, 41(8): 2803-2815.
	SUN Wenjia, GAO Chuang, HUANG Weiguang. Performance analysis and optimization of ZK2000 solar gas turbine[J]. Proceedings of the CSEE, 2021, 41(8): 2803-2815.
[6]	刘畅, 卓建坤, 赵东明, 等. 利用储能系统实现可再生能源微电网灵活安全运行的研究综述[J]. 中国电机工程学报, 2020, 40(1): 1-18,369.
	LIU Chang, ZHUO Jiankun, ZHAO Dongming, et al. A review on the utilization of energy storage system for the flexible and safe operation of renewable energy microgrids[J]. Proceedings of the CSEE, 2020, 40(1): 1-18,369.
[7]	盛万兴, 吴鸣, 季宇, 等. 分布式可再生能源发电集群并网消纳关键技术及工程实践[J]. 中国电机工程学报, 2019, 39(8): 2175-2186.
	SHENG Wanxing, WU Ming, JI Yu, et al. Key techniques and engineering practice of distributed renewable generation clusters integration[J]. Proceedings of the CSEE, 2019, 39(8): 2175-2186.
[8]	赵昱宣, 韩畅, 林振智, 等. 含可再生能源的电力系统两阶段核心骨干网架优化策略[J]. 电网技术, 2019, 43(2): 371-386.
	ZHAO Yuxuan, HAN Chang, LIN Zhenzhi, et al. Two-stage core backbone network optimization strategy for power systems with renewable energy[J]. Power System Technology, 2019, 43(2): 371-386.
[9]	DING Y, SINGH C, GOEL L, et al. Short-term and medium-term reliability evaluation for power systems with high penetration of wind power[J]. IEEE Transactions on Sustainable Energy, 2014, 5(3): 896-906. doi: 10.1109/TSTE.2014.2313017 URL
[10]	杨新法, 苏剑, 吕志鹏, 等. 微电网技术综述[J]. 中国电机工程学报, 2014, 34(1): 57-70.
	YANG Xinfa, SU Jian, LÜ Zhipeng, et al. Overview on micro-grid technology[J]. Proceedings of the CSEE, 2014, 34(1): 57-70.
[11]	周京华, 翁志鹏, 宋晓通. 兼顾可靠性与经济性的孤岛型光储微电网容量配置方法[J]. 电力系统自动化, 2021, 45(8): 166-174.
	ZHOU Jinghua, WENG Zhipeng, SONG Xiaotong. Capacity configuration method of islanded microgrid with photovoltaic and energy storage system considering reliability and economy[J]. Automation of Electric Power Systems, 2021, 45(8): 166-174.
[12]	谢鹏, 蔡泽祥, 刘平, 等. 考虑多时间尺度不确定性耦合影响的风光储微电网系统储能容量协同优化[J]. 中国电机工程学报, 2019, 39(24): 7126-7136,7486.
	XIE Peng, CAI Zexiang, LIU Ping, et al. Cooperative optimization of energy storage capacity for renewable and storage involved microgrids considering multi time scale uncertainty coupling influence[J]. Proceedings of the CSEE, 2019, 39(24): 7126-7136,7486.
[13]	葛少云, 曹雨晨, 刘洪, 等. 考虑可靠性约束的综合能源微网供能能力评估[J]. 电力系统自动化, 2020, 44(7): 31-37.
	GE Shaoyun, CAO Yuchen, LIU Hong, et al. Evaluation of energy supply capability for multi-energy microgrid considering reliability constraint[J]. Automation of Electric Power Systems, 2020, 44(7): 31-37.
[14]	武梦景, 万灿, 宋永华, 等. 含多能微网群的区域电热综合能源系统分层自治优化调度[J]. 电力系统自动化, 2021, 45(12): 20-29.
	WU Mengjing, WAN Can, SONG Yonghua, et al. Hierarchical autonomous optimal dispatching of district integrated heating and power system with multi-energy microgrids[J]. Automation of Electric Power Systems, 2021, 45(12): 20-29.
[15]	潘华, 梁作放, 肖雨涵, 等. 多场景下区域综合能源系统的优化运行[J]. 太阳能学报, 2021, 42(1): 484-492.
	PAN Hua, LIANG Zuofang, XIAO Yuhan, et al. Optimal operation of regional integrated energy system under multiple scenes[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 484-492.
[16]	朱涛, 朱建全, 刘明波, 等. 含风电场电力系统的动态经济调度分散随机优化方法[J]. 电力系统自动化, 2017, 41(11): 48-54.
	ZHU Tao, ZHU Jianquan, LIU Mingbo, et al. Decentralized stochastic optimization method for dynamic economic dispatch of power system with wind farms[J]. Automation of Electric Power Systems, 2017, 41(11): 48-54.
[17]	朱嘉远, 刘洋, 许立雄, 等. 考虑风电消纳的热电联供型微网日前鲁棒经济调度[J]. 电力系统自动化, 2019, 43(4): 40-48.
	ZHU Jiayuan, LIU Yang, XU Lixiong, et al. Robust day-ahead economic dispatch of microgrid with combined heat and power system considering wind power accommodation[J]. Automation of Electric Power Systems, 2019, 43(4): 40-48.
[18]	税月, 刘俊勇, 高红均, 等. 考虑风电不确定性的电热综合系统分布鲁棒协调优化调度模型[J]. 中国电机工程学报, 2018, 38(24): 7235-7247,7450.
	SHUI Yue, LIU Junyong, GAO Hongjun, et al. A distributionally robust coordinated dispatch model for integrated electricity and heating systems considering uncertainty of wind power[J]. Proceedings of the CSEE, 2018, 38(24): 7235-7247,7450.
[19]	王婉璐, 杨莉, 王蕾, 等. 考虑供热网储热特性的电-热综合能源系统优化调度[J]. 电力系统自动化, 2018, 42(21): 45-52.
	WANG Wanlu, YANG Li, WANG Lei, et al. Optimal dispatch of integrated electricity-heat energy system considering heat storage characteristics of heating network[J]. Automation of Electric Power Systems, 2018, 42(21): 45-52.
[20]	孙鹏, 滕云, 回茜, 等. 考虑热惯性不确定性的多能源系统两阶段鲁棒优化调度模型[J/OL]. 中国电机工程学报, 2020. (2020-12-09) [2021-04-01]. https://doi.org/10.13334/j.0258-8013.pcsee.201297.
	SUN Peng, TENG Yun, HUI Qian, et al. Two-stage robust optimal scheduling model for multi-energy systems considering thermal inertia uncertainty[J/OL]. Proceedings of the CSEE, 2020. (2020-12-09) [2021-04-01]. https://doi.org/10.13334/j.0258-8013.pcsee.201297.
[21]	LI Z G, WU W C, WANG J H, et al. Transmission-constrained unit commitment considering combined electricity and district heating networks[J]. IEEE Transactions on Sustainable Energy, 2016, 7(2): 480-492. doi: 10.1109/TSTE.2015.2500571 URL
[22]	ANVARI-MOGHADDAM A, MONSEF H, RAHIMI-KIAN A. Optimal smart home energy management considering energy saving and a comfortable lifestyle[J]. IEEE Transactions on Smart Grid, 2017, 6(1):324-332. doi: 10.1109/TSG.2014.2349352 URL
[23]	LI B, QIAN X, SUN J, et al. A model of distributionally robust two-stage stochastic convex programming with linear recourse[J]. Applied Mathematical Modelling, 2018, 58: 86-97. doi: 10.1016/j.apm.2017.11.039 URL