Optimal Scheduling Strategy for Integrated Energy System Considering Mixed Uncertainties of Integrated Demand Response

doi:10.12204/j.issn.1000-7229.2022.09.007

Abstract

Abstract:

Integrated demand response (IDR) is the main means to exploit the load-side regulation potential of the integrated energy system (IES). However, IDR users’ energy consumption behaviors are characterized by random-fuzzy mixed uncertainties, which will not only bring challenges to the formulation of scheduling strategy, but also affect the reliability of the system, and may even lead to load outage. To solve the above problem, firstly, a model based on the improved PMV-PPD index to evaluate user participation willingness is proposed. Secondly, according to the cloud model theory, a price IDR model considering mixed uncertainties is established. On the basis of these, the IES optimal scheduling strategy considering the uncertainties of various energy response boundaries and price elasticity coefficients is proposed. The results of examples show that the proposed IES optimal scheduling strategy can better deal with the load fluctuation caused by the uncertainties of IDR and improve system reliability effectively.

Key words: integrated energy system(IES), integrated demand response(IDR), optimal scheduling, mixed uncertainties, reliability, cloud model

CLC Number:

TM732

YANG Zhihao, LIU Hang, WEN Ming, ZHAO Haipeng, LIAO Jing, MIAO Shihong. Optimal Scheduling Strategy for Integrated Energy System Considering Mixed Uncertainties of Integrated Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(9): 66-76.

Figures/Tables 15

Fig.1 Basic framework of IES

Fig.2 Cloud droplet diagram with the coefficient of elastic uncertainty

Fig.3 The implementation process of IES optimal scheduling strategy

Table 1

Fig.4 The comparison of load curves before and after scheduling in S1

Fig.5 The fitness curves of PSO

Fig.6 Fluctuation range of the loads in S2, S3 and S4

Fig.7 Available range of loads in S3 and S4 strategies

Table 2 Reliability comparison of scheduling strategies in S3 and S4

Table A1 Parameters of productive equipment in IES

Table A2 Parameters of productive equipment in IES

Fig.A1 The relationship between energy prices and demand response

Fig.A2 Variation range of demand response

Fig.A3 Cloud droplet diagram of user willingness to participate IDR

Fig.A4 Forecasting curves of power, heating, cooling loads, and the maximum output of wind power

References 25

[1]	贾宏杰, 王丹, 徐宪东, 等. 区域综合能源系统若干问题研究[J]. 电力系统自动化, 2015, 39(7): 198-207.
	JIA Hongjie, WANG Dan, XU Xiandong, et al. Research on some key problems related to integrated energy systems[J]. Automation of Electric Power Systems, 2015, 39(7): 198-207.
[2]	潘尔生, 宋毅, 原凯, 等. 考虑可再生能源接入的综合能源系统规划评述与展望[J]. 电力建设, 2020, 41(12): 1-13.
	PAN Ersheng, SONG Yi, YUAN Kai, et al. Review and prospect of integrated energy system planning considering the integration of renewable energy full text replacement[J]. Electric Power Construction, 2020, 41(12): 1-13.
[3]	黎静华, 朱梦姝, 陆悦江, 等. 综合能源系统优化调度综述[J]. 电网技术, 2021, 45(6): 2256-2272.
	LI Jinghua, ZHU Mengshu, LU Yuejiang, et al. Review on optimal scheduling of integrated energy systems[J]. Power System Technology, 2021, 45(6): 2256-2272.
[4]	宋莉, 刘敦楠, 庞博, 等. 需求侧资源参与电力市场机制及典型案例实践综述[J]. 全球能源互联网, 2021, 4(4): 401-410.
	SONG Li, LIU Dunnan, PANG Bo, et al. Mechanism of demand-side resource participation in the electricity market and typical case practice review[J]. Journal of Global Energy Interconnection, 2021, 4(4): 401-410.
[5]	LUO Z, HONG S, DING Y M. A data mining-driven incentive-based demand response scheme for a virtual power plant[J]. Applied Energy, 2019, 239: 549-559. doi: 10.1016/j.apenergy.2019.01.142 URL
[6]	周明. 电力需求侧市场运营的若干问题研究[D]. 保定: 华北电力大学, 2006.
	ZHOU Ming. Research on several key problems of electricity demand side market operation[D]. Baoding: North China Electric Power University, 2006.
[7]	魏震波, 马新如, 郭毅, 等. 碳交易机制下考虑需求响应的综合能源系统优化运行[J]. 电力建设, 2022, 43(1): 1-9.
	WEI Zhenbo, MA Xinru, GUO Yi, et al. Optimized operation of integrated energy system considering demand response under carbon trading mechanism[J]. Electric Power Construction, 2022, 43(1): 1-9.
[8]	李政洁, 撖奥洋, 周生奇, 等. 计及综合需求响应的综合能源系统优化调度[J]. 电力系统保护与控制, 2021, 49(21): 36-42.
	LI Zhengjie, HAN Aoyang, ZHOU Shengqi, et al. Optimization of an integrated energy system considering integrated demand response[J]. Power System Protection and Control, 2021, 49(21): 36-42.
[9]	DORAHAKI S, RASHIDINEJAD M, MOLLAHASSANI-POUR M, et al. An efficient hybrid structure to solve economic-environmental energy scheduling integrated with demand side management programs[J]. Electrical Engineering, 2019, 101(4): 1249-1260. doi: 10.1007/s00202-019-00866-x URL
[10]	徐业琰, 廖清芬, 刘涤尘, 等. 基于综合需求响应和博弈的区域综合能源系统多主体日内联合优化调度[J]. 电网技术, 2019, 43(7): 2506-2518.
	XU Yeyan, LIAO Qingfen, LIU Dichen, et al. Multi-player intraday optimal dispatch of integrated energy system based on integrated demand response and games[J]. Power System Technology, 2019, 43(7): 2506-2518.
[11]	王瑞, 程杉, 汪业乔, 等. 基于多主体主从博弈的区域综合能源系统低碳经济优化调度[J]. 电力系统保护与控制, 2022, 50(5): 12-21.
	WANG Rui, CHEN Shan, WANG Yeqiao, et al. Low-carbon and economic optimization of a regional integrated energy system based on a master-slave game with multiple stakeholders[J]. Power System Protection and Control, 2022, 50(5): 12-21.
[12]	彭春华, 张金克, 陈露, 等. 计及差异化需求响应的微电网源荷储协调优化调度[J]. 电力自动化设备, 2020, 40(3): 1-7.
	PENG Chunhua, ZHANG Jinke, CHEN Lu, et al. Source-load-storage coordinated optimal scheduling of microgrid considering differential demand response[J]. Electric Power Automation Equipment, 2020, 40(3): 1-7.
[13]	王燕, 杨秀媛, 陈麒宇, 等. 融合分时电价的居民可控负荷优先级控制策略[J]. 现代电力, 2021, 38(4): 422-433.
	WANG Yan, YANG Xiuyuan, CHEN Qiyu, et al. Priority control strategy of residential controllable load based on time-of-use price[J]. Modern Electric Power, 2021, 38(4): 422-433.
[14]	高佳, 肖迎群, 马蕊, 等. 考虑不确定性价格型需求响应的多源微网运行优化[J]. 现代电力, 2020, 37(4): 425-433.
	GAO Jia, XIAO Yingqun, MA Rui, et al. Multi-source micro-grid operation optimization considering uncertain electricity price demand response[J]. Modern Electric Power, 2020, 37(4): 425-433.
[15]	郑若楠, 李志浩, 唐雅洁, 等. 考虑居民用户参与度不确定性的激励型需求响应模型与评估[J]. 电力系统自动化, 2022, 46(8): 154-162.
	ZHENG Ruonan, LI Zhihao, TANG Yajie, et al. Incentive demand response model and evaluation considering uncertainty of residential customer participation degree[J]. Automation of Electric Power Systems, 2022, 46(8): 154-162.
[16]	林文智, 杨苹, 陈芯羽, 等. 计及需求响应不确定性的园区综合能源系统日前经济优化调度[J]. 电力建设, 2021, 42(12): 9-20.
	LIN Wenzhi, YANG Ping, CHEN Xinyu, et al. Day-ahead optimal economic dispatch of park integrated energy system considering uncertainty of demand response[J]. Electric Power Construction, 2021, 42(12): 9-20.
[17]	董晓颖, 李拓, 苏娟, 等. 配电网与蓄热式电采暖负荷的优化匹配策略[J]. 电力需求侧管理, 2021, 23(5): 24-28.
	DONG Xiaoying, LI Tuo, SU Juan, et al. The optimal matching strategy of distribution network and regenerative electric heating load[J]. Power Demand Side Management, 2021, 23(5): 24-28.
[18]	WANG X, JIANG C W, LI B S. RETRACTED: Active robust optimization for wind integrated power system economic dispatch considering hourly demand response[J]. Renewable Energy, 2016, 97: 798-808. doi: 10.1016/j.renene.2016.06.035 URL
[19]	刘文霞, 李征洲, 杨粤, 等. 计及需求响应不确定性的综合能源系统协同优化配置[J]. 电力系统自动化, 2020, 44(10): 41-49.
	LIU Wenxia, LI Zhengzhou, YANG Yue, et al. Collaborative optimal configuration for integrated energy system considering uncertainties of demand response[J]. Automation of Electric Power Systems, 2020, 44(10): 41-49.
[20]	欧聪颖, 刘何清, 张强, 等. 预计平均热感觉指数PMV的简化计算法[J]. 建筑节能, 2018, 46(7): 13-16.
	OU Congying, LIU Heqing, ZHANG Qiang, et al. Simplified calculation method of the predicted mean thermal sensation index PMV[J]. Building Energy Efficiency, 2018, 46(7): 13-16.
[21]	ZHANG D, QU B Y, LIU P F, et al. Comprehensive evaluation and optimization of rural space heating modes in cold areas based on PMV-PPD[J]. Energy and Buildings, 2021, 246: 111120. doi: 10.1016/j.enbuild.2021.111120 URL
[22]	肖先勇. 电力系统不确定性理论与测度[M]. 北京: 中国电力出版社, 2018.
[23]	叶琼, 李绍稳, 张友华, 等. 云模型及应用综述[J]. 计算机工程与设计, 2011, 32(12): 4198-4201.
	YE Qiong, LI Shaowen, ZHANG Youhua, et al. Cloud model and application overview[J]. Computer Engineering and Design, 2011, 32(12): 4198-4201.
[24]	董进军. 考虑风光出力和负荷不确定性的综合能源系统柔性调度研究[D]. 长沙: 长沙理工大学, 2017.
	DONG Jinjun. Research on flexible scheduling of integrated energy system considering wind and solar power output and load uncertainty[D]. Changsha: Changsha University of Science & Technology, 2017.
[25]	闫敏, 加鹤萍, 刘敦楠, 等. 考虑用户舒适度的虚拟电厂电/热综合需求响应[J]. 华北电力大学学报(自然科学版), 2022, 49(2): 106-117.
	YAN Min, JIA Heping, LIU Dunnan, et al. Electric/thermal integrated demand response of virtual power plant considering user comfort[J]. Journal of North China Electric Power University (Natural Science Edition), 2022, 49(2): 106-117.