Source-Load Collaborative Optimization Method of Integrated Energy System Based on Service Provider Guidance

doi:10.12204/j.issn.1000-7229.2022.01.005

Abstract

Abstract:

As the energy market gradually opens up, multiple market factors have been involved in the traditional centralized optimal dispatching approach. In this context, the paper takes the optimal operation of community integrated energy system (CIES) as an application scenario and proposes a source-load collaborative optimization model under the guidance of an integrated energy service provider (IESP). On the demand-side, an integrated demand response (IDR) strategy is established according to the transferable electricity load and gas load as well as the virtual heat storage characteristics of the enclosed structures in the buildings. On the supply-side, an integrated energy service provider is introduced to replace the energy networks and lead the collaborative and flexible trade of multiple energies. Besides, it is able to comprehensively evaluate the community’s energy demand, respond feedback and the situations of interactive power of major grid and tie lines in the community so as to optimize the electricity-gas combined price signals. The bi-level optimization algorithm of particle swarm optimization combined with mixed integer linear programming is used to optimize the price signal of the upper service provider and the demand response and economic dispatching of the lower community. Considering the mutual influence in the process of bilateral interaction between supply-side and demand-side, the interactive strategy of all parties in pursuit of interest goals is solved by cycle iteration. Numerical simulation shows that the model can be used to exploit response potential in the participation satisfaction of the community and energy economy, realize“peak-shaving and valley filling”in the community and the main network, optimize the configuration of energy resources, and increase the overall economy of the system.

Key words: integrated energy service provider (IESP), integrated demand response (IDR), price mechanism, bi-level optimization, peak-shaving and valley filling

CLC Number:

TM734

LIAO Zongyi, WAN Wenlue, CHEN Xi, CHEN Lan. Source-Load Collaborative Optimization Method of Integrated Energy System Based on Service Provider Guidance[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(1): 38-48.

Figures/Tables 20

Fig.1 Architecture of CIES

Fig.2 IESP guidance of source-load interaction

Fig.3 Solution flow of PSO-MILP bi-level optimization

Table 1 Equipment output and operation parameters

Table 2 Operation and maintenance cost per unit output of equipment

Table 3 Structural parameters of building maintenance

Table 4 Scene information

Fig.4 Convergence of PSO-MILP feasible solution

Table 5 Comparison of the results of different guidance modes

Fig.5 IESP price signal in scenario D

Fig.6 IESP guidance effect in scenario D

Fig.7 Balance of power supply and demand

Fig.8 Balance of natural gas supply and demand

Fig.9 Heat energy supply and demand balance

Fig.10 Cooling energy supply and demand balance

Fig.11 Indoor and outdoor temperature in CIES

Fig.A1 IESP price signal in scenario A

Fig.A2 IESP price signal in scenario B

Fig.A3 IESP price signal in scenario C

Fig.A4 IESP guidance effect in scenario A, B and C

References 22

[1]	刘天琪, 张琪, 何川. 考虑气电联合需求响应的气电综合能源配网系统协调优化运行[J]. 中国电机工程学报, 2021, 41(5): 1664-1677.
	LIU Tianqi, ZHANG Qi, HE Chuan. Coordinated optimal operation of electricity and natural gas distribution system considering integrated electricity-gas demand response[J]. Proceedings of the CSEE, 2021, 41(5): 1664-1677.
[2]	杜炜, 张筱辰, 杨冬梅, 等. 面向综合能源的信息物理系统体系架构及应用[J]. 电力建设, 2020, 41(4): 90-99.
	DU Wei, ZHANG Xiaochen, YANG Dongmei, et al. Architecture and application of cyber physical system for integrated energy system[J]. Electric Power Construction, 2020, 41(4): 90-99.
[3]	陈启鑫, 刘敦楠, 林今, 等. 能源互联网的商业模式与市场机制(一)[J]. 电网技术, 2015, 39(11): 3050-3056.
	CHEN Qixin, LIU Dunnan, LIN Jin, et al. Business models and market mechanisms of energy Internet(1)[J]. Power System Technology, 2015, 39(11): 3050-3056.
[4]	杨海柱, 李梦龙, 江昭阳, 等. 考虑需求侧电热气负荷响应的区域综合能源系统优化运行[J]. 电力系统保护与控制, 2020, 48(10): 30-37.
	YANG Haizhu, LI Menglong, JIANG Zhaoyang, et al. Optimal operation of regional integrated energy system considering demand side electricity heat and natural-gas loads response[J]. Power System Protection and Control, 2020, 48(10): 30-37.
[5]	GUO Z H, ZHANG R, WANG L, et al. Optimal operation of regional integrated energy system considering demand response[J]. Applied Thermal Engineering, 2021, 191: 116860.
[6]	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.
	LI Peng, YANG Shenbo, LI Huixuan, et al. Optimization scheduling model of park integrated energy system considering multiple energies and multiple demand responses[J]. Electric Power Construction, 2020, 41(5): 45-57.
[7]	张涛, 郭玥彤, 李逸鸿, 等. 计及电气热综合需求响应的区域综合能源系统优化调度[J]. 电力系统保护与控制, 2021, 49(1): 52-61.
	ZHANG Tao, GUO Yuetong, LI Yihong, et al. Optimization scheduling of regional integrated energy systems based on electric-thermal-gas integrated demand response[J]. Power System Protection and Control, 2021, 49(1): 52-61.
[8]	王昀, 谢海鹏, 孙啸天, 等. 计及激励型综合需求响应的电-热综合能源系统日前经济调度[J]. 电工技术学报, 2021, 36(9): 1926-1934.
	WANG Yun, XIE Haipeng, SUN Xiaotian, et al. Day-ahead economic dispatch for electricity-heating integrated energy system considering incentive integrated demand response[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1926-1934.
[9]	王海洋, 李珂, 张承慧, 等. 基于主从博弈的社区综合能源系统分布式协同优化运行策略[J]. 中国电机工程学报, 2020, 40(17): 5435-5445.
	WANG Haiyang, LI Ke, ZHANG Chenghui, et al. Distributed coordinative optimal operation of community integrated energy system based on stackelberg game[J]. Proceedings of the CSEE, 2020, 40(17): 5435-5445.
[10]	李东东, 周冠廷, 李宽宏, 等. 基于主从博弈的区域综合能源服务商运行策略[J]. 电力建设, 2021, 42(1): 10-19.
	LI Dongdong, ZHOU Guanting, LI Kuanhong, et al. Operation strategy of regional integrated energy service providers applying master-slave game[J]. Electric Power Construction, 2021, 42(1): 10-19.
[11]	向恩民, 高红均, 刘畅, 等. 基于供需双侧博弈互动的园区多能运营商能源交易优化决策[J]. 中国电机工程学报, 2021, 41(8): 2744-2757.
	XIANG Enmin, GAO Hongjun, LIU Chang, et al. Optimal decision of energy trading for community multi-energy operator based on game interaction with supply and demand sides[J]. Proceedings of the CSEE, 2021, 41(8): 2744-2757.
[12]	徐成司, 董树锋, 张舒鹏, 等. 面向工业园区的集中-分布式综合需求响应方法[J]. 电网技术, 2021, 45(2): 489-500.
	XU Chengsi, DONG Shufeng, ZHANG Shupeng, et al. Centralized-distributed integrated demand response method for industrial park[J]. Power System Technology, 2021, 45(2): 489-500.
[13]	张伊宁, 何宇斌, 晏鸣宇, 等. 计及需求响应与动态气潮流的电-气综合能源系统优化调度[J]. 电力系统自动化, 2018, 42(20): 1-8.
	ZHANG Yining, HE Yubin, YAN Mingyu, et al. Optimal dispatch of integrated electricity-natural gas system considering demand response and dynamic natural gas flow[J]. Automation of Electric Power Systems, 2018, 42(20): 1-8.
[14]	陈永龙, 朱金大, 杨冬梅, 等. 基于多方利益博弈的园区级综合能源系统经济优化运行技术研究[J]. 高电压技术, 2021, 47(1): 102-112.
	CHEN Yonglong, ZHU Jinda, YANG Dongmei, et al. Research on economic optimization operation technology of park-level integrated energy system based on multi-party interest game[J]. High Voltage Engineering, 2021, 47(1): 102-112.
[15]	靳小龙, 穆云飞, 贾宏杰, 等. 融合需求侧虚拟储能系统的冷热电联供楼宇微网优化调度方法[J]. 中国电机工程学报, 2017, 37(2): 581-591.
	JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Optimal scheduling method for a combined cooling, heating and power building microgrid considering virtual storage system at demand side[J]. Proceedings of the CSEE, 2017, 37(2): 581-591.
[16]	CRISTOFARI C, NORVAIŠIENE R, CANALETTI J L, et al. Innovative alternative solar thermal solutions for housing in conservation-area sites listed as national heritage assets[J]. Energy and Buildings, 2015, 89: 123-131.
[17]	BUONOMANO A, PALOMBO A. Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies[J]. Applied Energy, 2014, 113: 788-807. doi: 10.1016/j.apenergy.2013.08.004 URL
[18]	封京梅, 刘三阳. 基于惯性权重指数递减的粒子群优化算法求解绝对值方程[J]. 吉林大学学报(理学版), 2016, 54(6): 1265-1269.
	FENG Jingmei, LIU Sanyang. Particle swarm optimization algorithm based on inertia weight exponentially decreasing for solving absolute value equations[J]. Journal of Jilin University (Science Edition), 2016, 54(6): 1265-1269.
[19]	唐可心, 梁晓磊, 周文峰, 等. 具有重组学习和混合变异的动态多种群粒子群优化算法[J]. 控制与决策: 1-10. (2020-11-02)[2021-07-16]. https://doi.org/10.13195/j.kzyjc.2020.0898.
	TANG Kexin, LIANG Xiaolei, ZHOU Wenfeng, et al. Dynamic multi-population particle swarm optimization algorithm with recombined learning and hybrid mutation[J]. Control and Decision: 1-10. (2020-11-02)[2021-07-16]. https://doi.org/10.13195/j.kzyjc.2020.0898.
[20]	王磊, 姜涛, 宋丹, 等. 基于灵活热电比的区域综合能源系统多目标优化调度[J]. 电力系统保护与控制, 2021, 49(8): 151-159.
	WANG Lei, JIANG Tao, SONG Dan, et al. Multi-objective optimal dispatch of a regional integrated energy system based on a flexible heat-to-electric ratio[J]. Power System Protection and Control, 2021, 49(8): 151-159.
[21]	李春燕, 许中, 马智远. 计及用户需求响应的分时电价优化模型[J]. 电力系统及其自动化学报, 2015, 27(3): 11-16.
	LI Chunyan, XU Zhong, MA Zhiyuan. Optimal time-of-use electricity price model considering customer demand response[J]. Proceedings of the CSU-EPSA, 2015, 27(3): 11-16.
[22]	马立荣, 王中瑞, 夏雨航, 等. 综合能源系统电价制定策略研究[J]. 华北电力大学学报(自然科学版), 2021, 48(5): 72-80.
	MA Lirong, WANG Zhongrui, XIA Yuhang, et al. Research on electricity price formulation strategy of integrated energy system[J]. Journal of North China Electric Power University (Natural Science Edition), 2021, 48(5): 72-80.