Integrated Energy System Considering Demand Response Uncertainty Research on Optimal Scheduling

doi:10.11985/2023.01.017

Abstract

Abstract:

In order to reduce the uncertainty of demand side response in system scheduling and improve the flexibility of load side response during the integrated energy system operation, a comprehensive energy system optimal scheduling study considering demand response uncertainty and load forecasting error is established. Through the electrical joint demand response, the output of each unit in each period shall be reasonably arranged on the premise of ensuring the stable operation of the IES system. The objective function is transformed into an opportunity function to maximize the probability of event establishment in an uncertain environment by using relevant opportunity programming, so as to solve the model. Through the priority algorithm in multi-objective programming, the multi-objective programming is transformed into single objective programming, PSO algorithm is used to solve this model. The feasibility and economy of the model are verified by the simulation analysis of a practical example.

Key words: Integrated energy system, demand response, power balance equation, relevant opportunity planning, uncertainty

CLC Number:

TM73

LI Ning, SUN Wenhui, BI Yunfan, ZHANG Zhisheng. Integrated Energy System Considering Demand Response Uncertainty Research on Optimal Scheduling[J]. Journal of Electrical Engineering, 2023, 18(1): 153-159.

Figures/Tables 8

References 18

[1]	AHMARINEJAD A. A multi-objective optimization framework for dynamic planning of energy hub considering integrated demand response program[J]. Sustainable Cities and Society, 2021, 74:103136. doi: 10.1016/j.scs.2021.103136
[2]	孙建军, 张世泽, 曾梦迪, 等. 考虑分时电价的主动配电网柔性负荷多目标优化控制[J]. 电工技术学报, 2018, 33(2):401-412.
	SUN Jianjun, ZHANG Shize, ZENG Mengdi, et al. Multi-objective optimal control for flexible load in active distribution network considering time-of-use tariff[J]. Transactions of China Electrotechnical Society, 2018, 33(2):401-412.
[3]	孙宇军, 李扬, 王蓓蓓, 等. 计及不确定性需求响应的日前调度计划模型[J]. 电网技术, 2014, 38(10):2708-2714.
	SUN Yujun, LI Yang, WANG Beibei, et al. A day-ahead scheduling model considering demand response and its uncertainty[J]. Power System Technology, 2014, 38(10):2708-2714.
[4]	林俐, 张玉. 激励型需求响应参与主动配电网优化调度的不确定性分析[J]. 华北电力大学学报, 2020, 47(5):10-20.
	LIN Li, ZHANG Yu. Uncertainty analysis of incentive demand response participating in optimal scheduling of active distribution networ[J]. Journal of North China Electric Power University, 2020, 47(5):10-20.
[5]	李政洁, 于强, 龚文杰, 等. 计及模糊机会约束的电-气-热能源枢纽多目标优化调度[J]. 电力系统及其自动化学报, 2021, 33(9):49-56.
	LI Zhengjie, YU Qiang, GONG Wenjie, et al. Multi-objective optimal operation of electricity-gas-heat energy hub considering fuzzy chance constraint[J]. Proceedings of the CSU-EPSA, 2021, 33(9):49-56.
[6]	王子驰, 雷炳银, 杨灵艺, 等. 考虑电热联合需求响应的区域综合能源系统多目标双层优化调度[J]. 电力系统及其自动化学报, 2021, 33(7):120-127.
	WANG Zichi, LEI Bingyin, YANG Lingyi, et al. Multi-objective double-step optimal dispatching of regional integrated energy system considering combined electric and heat demand response[J]. Proceedings of the CSU-EPSA, 2021, 33(7):120-127.
[7]	祁江浩, 李凤婷, 张高航. 需求响应分段参与的多时间尺度源荷协调调度策略[J]. 电力系统保护与控制, 2021, 49(11):61-69.
	QI Jianghao, LI Fengting, ZHANG Gaohang. Multi-time scale scheduling strategy for source-load coordination considering demand response block participation[J]. Power System Protection and Control, 2021, 49(11):61-69.
[8]	SINGH G, SINGH A. Extension of particle swarm optimization algorithm for solving transportation problem in fuzzy environment[J]. Applied Soft Computing, 2021, 110:107619. doi: 10.1016/j.asoc.2021.107619
[9]	牛文娟, 李扬, 王蓓蓓. 考虑不确定性的需求响应虚拟电厂建模[J]. 中国电机工程学报, 2014, 34(22):3630-3637.
	NIU Wenjuan, LI Yang, WANG Beibei. Demand response based virtual power plant modeling considering uncertainty[J]. Proceedings of the CSEE, 2014, 34(22):3630-3637.
[10]	VESA A V, CIOARA T, ANGHEL I, et al. Energy flexibility prediction for data center engagement in demand response programs[J]. Sustainability, 2020, 12(4):1417. doi: 10.3390/su12041417
[11]	HEYDARI R, NIKOUKAR J, GANDOMKAR M. Optimal operation of virtual power plant with considering the demand response and electric vehicles[J]. Journal of Electrical Engineering & Technology, 2021, 16(5):2407-2419.
[12]	HUO Da, GU Chenghong, GREENWOOD D, et al. Chance-constrained optimization for integrated local energy systems operation considering correlated wind generation[J]. International Journal of Electrical Power & Energy Systems, 2021, 132:107153. doi: 10.1016/j.ijepes.2021.107153
[13]	LI Zhiwei, JIN Tianran, ZHAO Shuqiang, et al. Power system day-ahead unit commitment based on chance-constrained dependent chance goal programming[J]. Energies, 2018, 11(7):1718. doi: 10.3390/en11071718
[14]	王健鹏. 相关机会规划的解法及其应用[D]. 成都: 西南交通大学, 2004.
	WANG Jianpeng. The solving method and application of dependent chancce programming[D]. Chengdu: Southwest Jiaotong University, 2004.
[15]	朱兰, 田泽清, 唐陇军, 等. 计及细节层次直接负荷控制的区域综合能源系统多时间尺度优化调度[J]. 电网技术, 2021, 45(7):2763-2774.
	ZHU Lan, TIAN Zeqing, TANG Longjun, et al. Multi-time-scale optimal dispatch of regional integrated energy system considering level of detail direct load control[J]. Power System Technology, 2021, 45(7):2763-2774.
[16]	PAPAKONSTANTINOU P A. Hierarchies for classes of priority algorithms for job scheduling[J]. Theoretical Computer Science, 2006, 352(1-3):181-189. doi: 10.1016/j.tcs.2005.10.045
[17]	李鹏, 杨莘博, 李慧璇, 等. 计及多能源多需求响应手段的园区综合能源系统优化调度模型[J]. 电力建设, 2020, 41(5):45-57. doi: 10.12204/j.issn.1000-7229.2020.05.006
	LI Peng, YANG Xinbo, LI Huixuan, et al. Optimal scheduling model of park integrated energy system considering multi energy and multi demand response means[J]. Electric Power Construction, 2020, 41(5):45-57.
[18]	周旭, 包海龙, 徐凡, 等. 计及负荷预测误差的可中断负荷优化管理[J]. 电力需求侧管理, 2011, 13(2):16-19,31.
	ZHOU Xu, BAO Hailong, XU Fan, et al. The interruptible load optimization management considering the load forecast error[J]. Power Demand Side Management, 2011, 13(2):16-19,31.

能源类别	时段		电价/[元/(kW·h)]
电	峰时	10:00—16:00	1.120
	峰时	18:00—20:00	1.120
	平时	7:00—10:00	0.667
		16:00—18:00
		20:00—23:00
	谷时	23:00—7:00	0.332
气	峰时	7:00—8:00	0.428
		11:00—12:00
		17:00—19:00
	平时	8:00—11:00	0.302
		12:00—17:00
		19:00—23:00
	谷时	23:00—7:00	0.162

模型	机会函数成立概率水平(%)	成本/万元
1	—	5.247 2
2	95	5.210 2
3	≥95	5.057 3