考虑可平移负荷的综合能源系统动态优化调度策略

doi:10.16180/j.cnki.issn1007-7820.2023.04.011

Abstract

Abstract:

The integrated energy system has attracted wide attention from all walks of life because of its multi-energy complementation, coordination and optimization and other characteristics. However, when the thermal power unit in the system is running, its peak shaving ability has certain limitations. In order to reduce the energy cost of the integrated energy system, increase the energy efficiency of the system and improve its peak shaving capacity, this study proposes a dynamic optimal dispatch strategy for the integrated energy system considering the shiftable load. With the aim of minimizing the overall operation and maintenance cost of the system, a simulation model is built by combining the translational load and related examples, and the adaptive chaotic particle swarm optimization algorithm is used to solve the problem. The results show that when the shiftable load is introduced, the multi-energy microgrid can better achieve the purpose of peak shaving and valley filling, and reduce the overall operating cost of the system, and achieve the effect of energy saving and emission reduction. At the same time, this study compares the traditional particle swarm algorithm with the adaptive chaotic particle swarm algorithm and verifies that the adaptive chaotic particle swarm algorithm is superior to the traditional particle swarm algorithm in terms of accuracy and efficiency.

Key words: comprehensive energy, adaptive chaotic particle swarm optimization, shiftable load, cogeneration, multi-energy complementation, coordination and optimization, demand response, dynamic optimal scheduling

CLC Number:

TP202+.1

LIU Jinzhi,ZHANG Huilin,MA Lixin,WANG Hao,TANG Zheng. Dynamic Optimal Scheduling Strategy for Integrated Energy Systems Considering Shiftable Loads[J].Electronic Science and Technology, 2023, 36(4): 78-83.

Figures/Tables 11

Table 1.

Table 2.

Correlation coefficient of energy storage equipment"

设备名称	参数	数值
GB	η_GB	0.70
CHP	γ η_CHP	2.30 0.30
BA	ε_ch ε_dis SO $C B A m i n$ SO $C B A m a x$	0.90 0.90 0.20 0.80
QBA	β_ch β_dis SO $C Q B A m i n$ SO $C Q B A m a x$	0.95 0.95 0.20 0.80

Table 2.

Table 3.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Table 4.

Figure 7.

References 18

[1]	孙宏斌, 郭庆来, 吴文传, 等. 面向能源互联网的多能流综合能量管理系统:设计与应用[J]. 电力系统自动化, 2019, 43(12):122-128.
	Sun Hongbin, Guo Qinglai, Wu Wenchuan, et al. Integrated energy management system with multi-energy flow for energy internet:Design and application[J]. Automation of Electric Power Systems, 2019, 43(12):122-128.
[2]	汤伟成. 考虑需求侧响应和分布式发电的配电网优化配置研究[D]. 广州: 广东工业大学, 2019.
	Tang Weicheng. Research on optimal condiguration of distribution network considering demand side responseand distributed generation[D]. Guangzhou: Guangdong University of Technology, 2019.
[3]	刘慧. 基于需求侧响应的微网能量管理策略研究[D]. 无锡: 江南大学, 2019.
	Liu Hui. Research on microgrid energy management strategy based on demand-side response[D]. Wuxi: Jiangnan University, 2019.
[4]	钟少云. 考虑需求侧响应的风光燃储微网经济运行规划和研究[D]. 南昌: 南昌大学, 2020.
	Zhong Shaoyun. Planning and research on the economic operation of micro-grid wind photovoltaic gas-turbine and energy storage considering demand side response[D]. Nanchang: Nanchang University, 2020.
[5]	杨龙杰, 李华强, 余雪莹, 等. 计及灵活性的孤岛型微电网多目标日前优化调度方法[J]. 电网技术, 2018, 42(5):1432-1440.
	Yang Longjie, Li Huaqiang, Yu Xueying, et al. Multi-obj-ective day-ahead optimal scheduling of isolated microgrid considering flexibility[J]. Power System Technology, 2018, 42(5):1432-1440.
[6]	艾欣, 周树鹏, 陈政琦, 等. 多随机因素下含可中断负荷的电力系统优化调度模型与求解方法研究[J]. 中国电机工程学报, 2017, 37(8):2231-2242.
	Ai Xin, Zhou Shupeng, Chen Zhengqi, et al. Research on optimal scheduling model and solving method for power system with interruptible load considering multi stochastic factors[J]. Proceedings of the CSEE, 2017, 37(8):2231-2242.
[7]	Dong J, Nie S L, Huang H, et al. Research on economic operation strategy of CHP microgrid considering renewable energy sources and integrated energy demand response[J]. Sustainability, 2019, 11(18):1-22. doi: 10.3390/su11010001
[8]	Majidi M, Mohammadi-Ivatloo B, Anvari-Moghaddam A. Optimal robust operation of combined heat and power systems with demand response programs[J]. Applied Thermal Engineering, 2018, 149(24):1359-1369. doi: 10.1016/j.applthermaleng.2018.12.088
[9]	Chen S J, Yang Y B, Xu Q S, et al. Coordinated dispatch of multi-energy microgrids and distribution network with a flexible structure[J]. Applied Sciences, 2019, 9(24):978-986. doi: 10.3390/app9050978
[10]	何义. 智能楼宇可控负荷的用电特性及其需求响应潜力研究[D]. 湘潭: 湘潭大学, 2020.
	He Yi. Research on power consumption characteristics and demand response potential of controllable load in intelligent buildings[D]. Xiangtan: Xiangtan University, 2020.
[11]	伍惠铖, 王淳, 尹发根, 等. 考虑负荷转移和多重博弈的智能小区需求响应策略[J]. 电网技术, 2019, 43(12):4550-4557.
	Wu Huicheng, Wang Chun, Yin Fagen, et al. Demand re-sponse strategy for smart residential grid considering residential user’s load shifting and multi-level games[J]. Power System Technology, 2019, 43(12):4550-4557.
[12]	邵晴. 粒子群算法研究及其工程应用案例[D]. 长春: 吉林大学, 2017.
	Shao Qing. Particle swarm optimization and its application in engineering[D]. Changchun: Jilin University, 2017.
[13]	万千, 夏成军, 管霖, 等. 含高渗透率分布式电源的独立微网的稳定性研究综述[J]. 电网技术, 2019, 43(2):598-612.
	Wan Qian, Xia Chengjun, Guan Lin, et al. Review on stability of isolated microgrid with highly penetrated distributed generations[J]. Power System Technology, 2019, 43(2):598-612.
[14]	李云龙, 田书, 马亚光. 含多微网的主动配电网分层优化调度[J]. 电力系统及其自动化学报, 2020, 32(6):88-93.
	Li Yunlong, Tian Shu, Ma Yaguang. Hierarchical optimal dispatching of active distribution network with multi-microgrid[J]. Proceedings of the CSU-EPSA, 2020, 32(6):88-93.
[15]	康健, 靳斌, 段秀娟, 等. 基于贝叶斯-粒子群算法的微电网优化运行[J]. 电力系统保护与控制, 2018, 46(12):32-41.
	Kang Jian, Jin Bin, Duan Xiujuan, et al. Optimal operation of microgrid based on Bayesian-PSO algorithm[J]. Power System Protection and Control, 2018, 46(12):32-41.
[16]	梁龙基, 张靖, 何宇, 等. 基于博弈论的互联微电网多主体协同优化配置[J]. 电子科技, 2021, 34(7):43-49.
	Liang Longji, Zhang Jing, He Yu, et al. Optimal coordin-ation sizing analysis of different entities interconnected microgrids base on game theory[J]. Electronic Science and Technology, 2021, 34(7):43-49.
[17]	何明慧, 徐怡, 王冉, 等. 改进的粒子群算法优化神经网络及应用[J]. 计算机工程与应用, 2018, 54(19):107-113. doi: 10.3778/j.issn.1002-8331.1706-0206
	He Minghui, Xu Yi, Wang Ran, et al. Combination dynamic inertia weight particle swarm optimization algorithm to optimize neural network and application[J]. Computer Engineering and Applications, 2018, 54(19):107-113. doi: 10.3778/j.issn.1002-8331.1706-0206
[18]	杜美君, 张伟, 谢亚莲. 基于粒子群算法的PID控制器参数优化[J]. 电子科技, 2019, 32(6):7-11.
	Du Meijun, Zhang Wei, Xie Yalian. Particle swarm opti-mization based PID controller parameter optimization[J]. Electronic Science and Technology, 2019, 32(6):7-11.

供能设备	C_cap /元·kW^-1	C_EM /元·kW^-1	P_max /元·kW^-1	P_min /元·kW^-1
Wind	6 000	0.018 0	30	0
PV	7 000	0.020 0	30	0
CHP	6 000	0.010 0	65	15
GB	800	0.010 0	65	0
BA	600	0.001 5	-	-
QBA	300	0.002 0	-	-

时段	起止时间/h	购价/元·kWh^-1
峰	(10∶00~15∶00,18∶00~21∶00)	0.93
平	(07∶00~10∶00,15∶00~18∶00,21∶00~23∶00)	0.69
谷	(0∶00~07∶00,23∶00~24∶00)	0.18

Dynamic Optimal Scheduling Strategy for Integrated Energy Systems Considering Shiftable Loads

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