Research on optimization algorithm of industrial park microgrid configuration based on Pareto solution set

doi:10.3969/j.issn.2097-0706.2024.01.006

Abstract

Abstract:

In industrial parks， the coupling between traditional energy is weak，and systems of different energy operate independently，which leads to a low energy utilization rate. To improve the comprehensive efficiency of energy and utilization rate of renewable energy， different energy should be integrated in a system and complement each other. A scheme of an integrated energy microgrid for industrial parks is proposed. And the optimization model for the multi-energy microgrid is constructed， with the minimal total cost and pollutant treatment cost， as well as power grid stability and complementarity between wind and photovoltaic power as the model's objective functions and constraints. A multi-objective difference algorithm is designed based on Pareto optimal solution set， and the weight of each evaluation index is determined by entropy weight method， which turns a multi-objective function into single-objective functions. According to the simulation results， the utilization rate of renewable energy can be improved， the consumption of traditional energy， carbon emissions and economic costs of the microgrid can be reduced by the proposed algorithm.

Key words: industrial park, integrated energy, energy microgrid, multi-energy complementation, configuration optimization, Pareto optimization, multi-objective difference, entropy weight method, carbon emissions, renewable energy

CLC Number:

TK019：TP391.9

FANG Gang, WANG Jing, ZHANG Bobo, WANG Junzhe. Research on optimization algorithm of industrial park microgrid configuration based on Pareto solution set[J]. Integrated Intelligent Energy, 2024, 46(1): 49-55.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Fig.4

Fig.5

Table 3

Fig.6

References 17

[1]	王昭. 多能互补系统中电价机制对电加热功率配置及运行的影响分析[J]. 智慧电力, 2018, 46(7):23-28.
	WANG Zhao. Study on the influence of electricity price mechanism on configuration and operation of electric heating power in multiple energy complementary system[J]. Smart Power, 2018, 46 (7): 23-28.
[2]	方磊, 牛玉刚, 王思明, 等. 基于日前调度与实时控制的微网储能系统容量配置[J]. 电力系统保护与控制, 2018, 46(23):102-110.
	FANG Lei, NIU Yugang, WANG Siming, et al. Optimal capacity determination method based on day-ahead scheduling and real-time control[J]. Power System Protection and Control, 2018, 46(23): 102-110.
[3]	MURTY V V S N, KUMAR A. Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimization[J]. IET Smart Grid, 2019, 2(2): 192-202. doi: 10.1049/stg2.v2.2
[4]	姚芳, 杨晓娜, 葛磊蛟, 等. 风-光-氢能源系统容量优化配置研究[J]. 综合智慧能源, 2022, 44(5):56-63. doi: 10.3969/j.issn.2097-0706.2022.05.006
	YAO Fang, YANG Xiaona, GE Leizhou, et al. Optimization of capacity allocation scheme for wind-solar-hydrogen energy system[J]. Integrated Intelligent Energy, 2022, 44(5):56-63. doi: 10.3969/j.issn.2097-0706.2022.05.006
[5]	钟鹏元, 杨晓宏, 寇建玉. 含储氢结构的园区综合能源系统优化配置研究[J]. 综合智慧能源, 2022, 44(9):11-19. doi: 10.3969/j.issn.2097-0706.2022.09.002
	ZHONG Pengyuan, YANG Xiaohong, KOU Jianyu. Research on the optimal configuration of integrated energy systems for parks with hydrogen storage devices[J]. Integrated Intelligent Energy, 2022, 44(9):11-19. doi: 10.3969/j.issn.2097-0706.2022.09.002
[6]	刘自发, 周翰泽. 计及多主体能源交易的综合能源系统规划方法研究[J]. 电网技术, 2022, 46(9):3524-3536.
	LIU Zifa, ZHOU Hanze. Research on comprehensive energy system planning method considering multi agent energy transaction[J]. Power System Technology, 2022, 46 (9):3524-3536.
[7]	李颖毅, 郑伟民, 孙可, 等. 考虑多种能源耦合载体的综合能源系统建模[J]. 浙江工业大学学报, 2021, 49(2):194-202.
	LI Yingyi, ZHENG Weimin, SUN Ke, et al. Integrated energy system modeling considering multiple energy coupling carriers[J]. Journal of Zhejiang University of Technology, 2021, 49(2):194-202.
[8]	李奇, 邹雪俐, 蒲雨辰, 等. 基于氢储能的热电联供型微电网优化调度方法[J/OL]. 西南交通大学学报, 2021:1-12 [2023-01-08]. https://kns.cnki.net/kcms/detail/51.1277.U.20211028.0902.002.html.
	LI Qi, ZOU Xueli, PU Yuchen, et al. Optimal schedule of combined heat-power microgrid based on hydrogen energy storage[J/OL]. Journal of Southwest Jiaotong University, 2021:1-12[2023-01-08]. https://kns.cnki.net/kcms/detail/51.1277.U.20211028.0902.002.html.
[9]	王春博. 基于改进粒子群算法冷热电联供型的微网优化运行研究[D]. 北京: 中国矿业大学, 2022.
	WANG Chunbo. Research on optimization operation of micro-grid based on improved particle swarm optimization algorithm[D]. Beijing: China University of Mining and Technology, 2022.
[10]	张会福. 多能互补系统经济运行方法研究[J]. 综合智慧能源, 2022, 44(11): 79-86. doi: 10.3969/j.issn.2097-0706.2022.11.011
	ZHANG Huifu. Research on economic operation method of multi energy complementary systems[J]. Integrated Intelligent Energy, 2022, 44(11): 79-86. doi: 10.3969/j.issn.2097-0706.2022.11.011
[11]	ZHENG L Q, LI Y Y, WEI C. A data-driven method for operation pattern analysis of the integrated energy microgrid[J]. Energy Conversion and Management:X, 2021, 11:100092. doi: 10.1016/j.ecmx.2021.100092
[12]	武志宏, 杨永标, 李熙, 等. 综合能源系统中多能源协同优化方法研究[J]. 电气传动, 2022, 52(2):67-73.
	WU Zhihong, YANG Yongbiao, LI Xi, et al. Research on multi-energy collaborative optimization method in integrated energy system[J]. Electric Drive, 2022, 52 (2): 67-73.
[13]	韩宇, 马立新, 徐文彬. 双种群模糊粒子动态优化多能互益微网配置[J]. 控制工程, 2022, 29(2):368-373.
	HAN Yu, MA Lixin, XU Wenbin. Dynamic optimization of dual-population fuzzy particles for multi-energy mutual benefit microgrid configuration[J]. Control Engineering of China, 2022, 29 (2): 368-373.
[14]	薛福亮, 曹洁. 基于Pareto最优和信任关系改进的协同过滤算法[J]. 信息系统工程, 2021 (1):149-152.
	XUE Fuliang, CAO Jie. Improved collaborative filtering algorithm based on Pareto optimization and trust relationship[J]. Information Systems Engineering, 2021 (1): 149-152.
[15]	邢永陈. 城市智慧能源系统概念设计的研究[D]. 南京: 东南大学, 2021.
	XING Yongchen. Research on conceptual design of urban smart energy system[D]. Nanjing: Southeast University, 2021.
[16]	刘隽楷, 袁旭峰, 赵靓玮, 等. 基于Pareto最优的电力系统环境经济调度[J]. 电力科学与工程, 2018, 34(8):6-11.
	LIU Junkai, YUAN Xufeng, ZHAO Liangwei, et al. Environmental and economic scheduling of power system based on Pareto optimal[J]. Electric Power Science and Engineering, 2018, 34 (8): 6-11.
[17]	李雪松, 滕欢, 郭宁, 等. 考虑外网效益的微网改进多目标优化调度[J]. 科学技术与工程, 2017, 17(32):125-131.
	LI Xuesong, TENG Huan, GUO Ning, et al. Multi-objective optimization scheduling of microgrid considering benefit of external power grid[J]. Science Technology and Engineering, 2017, 17 (32): 125-131.

微源设备	设备容量/kW	装机成本/ (万元·kW^-1)	运维成本/ [元·(kW·h)^-1]
风机	0.32	2.373	0.029 5
光伏	0.28	3.652	0.009 8
蓄电池	1.56	0.196	0.009 2
柴油发电机	5.25	1.624	0.088 6

污染物类型	污染物排放量/[g·(kW·h)^-1]	治污费用/（元·kg^-1）
CO₂	328.000	0.042
NO_x	6.152	13.423
CO	3.526	1.946
SO₂	0.338	5.423

算法	数量/台				ψ	L_ROPS
算法	风机	光伏	储能	柴油发电机	ψ	L_ROPS
1	301	502	10	26	0.018	0.182
2	391	312	6	16	0.062	0.093
3	322	315	4	19	0.008	0.088