基于粒子群优化算法的智慧微电网风光储容量优化配置

doi:10.3969/j.issn.2097-0706.2022.06.006

摘要/Abstract

摘要：

随着“双碳”目标的提出,国内多地鼓励建筑屋顶建设清洁能源发电系统,由风光储组成微电网,通过新能源发电有效降低区域生产活动的碳排放,并提高生活办公区的供电可靠性。针对微电网投资的经济性,其风光储容量配比一直属于研究热点。依托海南省特有的地理环境,根据当地的光照、风速条件,利用粒子群优化算法,以最大经济效益为寻优目标,选取最优风光储装机容量配比,并利用节能办公住宅区智能微电网建设示范项目所采集的数据进行经济性分析,可以为智能微电网节能改造提供理论和数据基础。

关键词: 智慧微电网, 粒子群优化算法, 碳中和, 容量配比, 多目标优化, 风电, 光伏发电, 储能, 节能减排, 电能替代

Abstract:

With the proposal of the goals of carbon peaking and carbon neutrality,constructing clean energy generators on roof tops is widely recommended in different regions of China.The microgrid consisting of wind power,photovoltaic power and energy storage devices,can reduce carbon emissions from regional production activities through new energy generation,and improve the power supply reliability of living and office areas.To optimize the return of the investment on microgrid,the ratio of wind power,photovoltaic power and energy storage capacity of a hybrid system has been the focus of researches.According to the light condition,wind speed and other unique geographical features in Hainan Province,the optimal allocation of a wind?PV?battery hybrid system in smart microgrid is calculated by particle swarm optimization algorithm.Data collected from the living and office areas of the smart microgrid demonstrative project are used in an economic analysis,which provide theoretical basis and data resources for the further energy efficiency retrofit of smart microgrids.

Key words: smart microgrid, particle swarm optimization algorithm, carbon neutrality, capacity ratio, multi-objective optimization, wind power, photovoltaic power generation, energy storage, energy conservation and emission reduction, electric energy substitution

中图分类号:

TK01：TM731

王鑫, 陈祖翠, 卞在平, 王业耀, 吴育苗. 基于粒子群优化算法的智慧微电网风光储容量优化配置[J]. 综合智慧能源, 2022, 44(6): 52-58.

WANG Xin, CHEN Zucui, BIAN Zaiping, WANG Yeyao, WU Yumiao. Optimal allocation of a wind‒PV‒battery hybrid system in smart microgrid based on particle swarm optimization algorithm[J]. Integrated Intelligent Energy, 2022, 44(6): 52-58.

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参考文献 23

[1]	詹天津, 谢玉荣. 国内分布式光伏发展形势分析及思考[J]. 华电技术, 2021, 43(12):60-65.
	ZHAN Tianjin, XIE Yurong. Development status analysis and consideration on domestic distributed PV energy[J]. Huadian Technology, 2021, 43(12):60-65.
[2]	卞在平, 王鑫, 王小明. 区域微电网离网转并网改造方案设计[J]. 华电技术, 2018, 40(10):19-21,76-77.
	BIAN Zaiping, WANG Xin, WANG Xiaoming. The design of reginal microgrid switched from island to grid-connected[J]. Huadian Technology, 2018, 40(10):19-21,76-77.
[3]	朱燕梅, 陈仕军, 黄炜斌, 等. 一定弃风光率下的水光风互补发电系统容量优化配置研究[J]. 水电能源科学, 2018, 36(7):215-218.
	ZHU Yanmei, CHEN Shijun, HUANG Weibin, et al. Optimal capacity configuration of hydro-photovoltaic-wind complementary power generation system under wind and photovoltaic curtailment[J]. Water Resources and Power, 2018, 36(7):215-218.
[4]	周磊, 董学育, 朱建忠, 等. 基于改进蜂群算法的微电网优化调度[J]. 电气自动化, 2020, 42(5):45-47.
	ZHOU Lei, DONG Xueyu, ZHU Jianzhong, et al. Optimal micro-grid scheduling based on the improved bee colony algorithm[J]. Electrical Automation, 2020, 42(5):45-47.
[5]	张程翔, 汪科, 陆莹, 等. 微电网中储能运行模式自发切换的对策研究[J]. 电气传动, 2021, 51(23):70-75.
	ZHANG Chengxiang, WANG Ke, LU Ying, et al. Research on countermeasures for spontaneously mode switching of energy storage system in microgrid[J]. Electric Drive, 2021, 51(23):70-75.
[6]	吴倩, 王洋, 王琳媛, 等. 计及波动平抑与经济性的风光储系统中混合储能容量优化配置[J]. 电测与仪表, 2020, 59(4):112-119.
	WU Qian, WANG Yang, WANG Linyuan, et al. Optimal capacity allocation of hybrid energy storage system in wind-solar-battery system considering fluctuation smoothing and economy[J]. Electrical Measurement & Instrumentation, 2020, 59(4):112-119.
[7]	文旭, 陈鑫, 张爱枫, 等. 含可调节负荷的风光互补微电网随机调度市场风险管控方法[J]. 电网技术, 2021, 45(11):4308-4318.
	WEN Xu, CHEN Xin, ZHANG Aifeng, et al. Stochastic dispatching market risk control for wind and photovoltaic microgrid integrated with adjustable load[J]. Power System Technology, 2021, 45(11):4308-4318.
[8]	李彦哲, 郭小嘉, 董海鹰, 等. 风/光/储微电网混合储能系统容量优化配置[J]. 电力系统及其自动化学报, 2020, 32(6):123-128.
	LI Yanzhe, GUO Xiaojia, DONG Haiying, et al. Optimal capacity configuration of wind/PV/storage hybrid energy storage system in microgrid[J]. Proceedings of the CSU-EPSA, 2020, 32(6):123-128.
[9]	李延和, 杨立滨, 郝丽丽, 等. 基于改进样板机法的风光互补新能源电站容量配比优化[J]. 电力工程技术, 2022, 41(2):224-233.
	LI Yanhe, YANG Libin, HAO Lili, et al. Capacity ratio optimization of wind-solar hybrid new energy power station based on improved model-generator method[J]. Electric Power Engineering Technology, 2022, 41(2):224-233.
[10]	李湃, 方保民, 祁太元, 等. 基于源-荷匹配的区域电网风/光/储容量配比优化方法[J]. 中国电力, 2022, 55(1):46-54.
	LI Pai, FANG Baomin, QI Taiyuan, et al. Capacity proportion optimization of wind,solar power and battery energy storage system for regional power grid based on source-load matching[J]. Electric Power, 2022, 55(1):46-54.
[11]	胡林静, 刘彤, 侯梦梦. 基于免疫粒子群算法的风/光/柴/储容量优化配置[J]. 科学技术与工程, 2020, 20(36):14967-14973.
	HU Linjing, LIU Tong, HOU Mengmeng. Optimal allocation of wind/photovoltaic/diesel/storage capacity based on immune particle swarm optimization[J]. Science Technology and Engineering, 2020, 20(36):14967-14973.
[12]	杨颖, 刘友波, 黄媛, 等. 电能替代下考虑需求响应的综合园区风光储配置方法[J]. 电力建设, 2021, 42(10):9-18.
	YANG Ying, LIU Youbo, HUANG Yuan, et al. Allocation of wind-solar-battery under electric energy substitution in integrated park considering demand response[J]. Electric Power Construction, 2021, 42(10):9-18.
[13]	罗星岩, 马少华. 考虑经济性的大规模风光储系统容量优化配置[J]. 东北电力技术, 2021, 42(9):13-17,25.
	LUO Xingyan, MA Shaohua. Optimal configuration of large-scale wind and solar storage system capacity considering economy[J]. Northeast Electric Power Technology, 2021, 42(9):13-17,25.
[14]	陈志峰, 梁海锋, 张紫凡, 等. 楼宇光储直流微电网结构及控制[J]. 电气自动化, 2022, 44(2):41-44.
	CHEN Zhifeng, LIANG Haifeng, ZHANG Zifan, et al. Structure and control of DC microgrid for building optical storage[J]. Electrical Automation, 2022, 44(2):41-44.
[15]	吴小涛, 谭超, 王国博. 基于自适应PI控制器的直流微电网电压控制策略研究[J]. 华电技术, 2021, 43(9):14-22.
	WU Xiaotao, TAN Chao, WANG Guobo. Voltage control strategy for DC microgrid based on adaptive PI controller[J]. Huadian Technology, 2021, 43(9):14-22.
[16]	ZHANG S S, YANG W J, LI X D, et al. Economic evaluation of wind-PV-pumpedstorage hybrid system considering carbon emissions[J]. Energy Reports, 2022, 8(S5):1249-1258.
[17]	郝晓弘, 王锐, 裴婷婷, 等. 基于改进鲸鱼算法的微电网系统容量优化研究[J]. 自动化与仪表, 2022, 37(3):11-16.
	HAO Xiaohong, WANG Rui, PEI Tingting, et al. Research on microgrid system capacity optimization based on improved whale algorithm[J]. Automation & Instrumentation, 2022, 37(3):11-16.
[18]	李彬, 杜亚彬, 曹望璋, 等. 考虑风光储互补与工作负载分配的数据中心优化调度[J]. 现代电力, 2022, 39(3):356-363.
	LI Bin, DU Yabin, CAO Wangzhang, et al. Optimal scheduling of data center considering wind-solar-storage complementary and workload distribution[J]. Modern Electric Power, 2022, 39(3):356-363.
[19]	程韧俐, 梁顺, 傅强, 等. 基于虚拟储能的微电网风光储容量优化配置方法研究[J]. 可再生能源, 2021, 39(3):372-379.
	CHENG Renli, LIANG Shun, FU Qiang, et al. Research on optimal configuration method of microgrid storage capacity based on virtual energy storage[J]. Renewable Energy Resources, 2021, 39(3):372-379.
[20]	汪希玥, 徐箭, 廖思阳, 等. 考虑虚拟储能特性的柔性负荷调控裕度评估方法[J]. 华电技术, 2021, 43(9):37-45.
	WANG Xiyue, XU Jian, LIAO Siyang, et al. DFlexible load regulation margin evaluation method considering virtual energy storage characteristics[J]. Huadian Technology, 2021, 43(9):37-45.
[21]	郝晓弘, 郝雪君, 周强, 等. 风光气储联合发电最优容量配比问题研究[J]. 自动化与仪表, 2020, 35(2):90-94.
	HAO Xiaohong, HAO Xuejun, ZHOU Qiang, et al. Research on optimal capacity ratio of wind,solar and natural gas storage combined generation[J]. Automation & Instrumentation, 2020, 35(2):90-94.
[22]	肖白, 刘健康, 张博, 等. 考虑阶梯碳交易和需求响应的含氢储能的并网型微电网优化配置[J/OL]. 电力自动化设备, 2022.(2022-04-25) [2022-05-11]. https://doi.org/10.16081/j.epae.202204074.
	XIAO Bai, LIU Jiankang, ZHANG Bo, et al. Optimal configuration ofgrid-connected microgrid with hydrogen energy storage considering ladder-type carbon trading and demand response[J/OL]. Electric Power Automation Equipment, 2022.(2022-04-25) [2022-05-11].DOI: 10.16081/j.epae.202204074. doi: 10.16081/j.epae.202204074
[23]	王少林, 王刚, 王晓磊, 等. 基于改进蜂群算法的电动汽车微电网优化调度[J]. 电气应用, 2022, 41(4):63-70,13-14.
	WANG Shaolin, WANG Gang, WANG Xiaolei, et al. Optimal scheduling of electric vehicle microgrid based on improved bee colony algorithm[J]. Electrotechnical Application, 2022, 41(4):63-70,13-14.

分布式电源	参数
分布式装机容量范围/kW	0~60
电网功率范围/kW	1~50
储能双向换流器功率范围/kW	-30~30
最大迭代次数	300
搜索空间维数	72
粒子个数	600

设备类型	功率/kW	数量/台	合计/kW
电脑	0.25	16	4.00
空调	2.00	5	10.00
照明	0.04	50	2.00
复印机	1.50	2	3.00
打印机	0.50	10	5.00
电热水器	2.00	1	2.00
电冰箱	0.05	1	0.05
机房设备	4.00	1	4.00
音控设备	1.00	1	1.00
会议设备	5.50	1	5.50
总计			36.55

月份	多年月平均总辐照值/(MJ∙m^-2)	风速/(m∙s^-1)
1	522.36	3.78
2	558.36	3.78
3	702.00	3.00
4	747.36	3.33
5	738.72	4.78
6	678.24	4.66
7	688.68	5.78
8	649.44	2.50
9	585.36	4.55
10	543.60	5.55
11	465.48	4.22
12	436.32	4.33

电源	装机成本/(万元∙kW^-1)	运行维护成本/[元∙(kW∙a)^-1]
PV	0.45	100
风电	1.00	130
储能	0.15	20

指标	数据
平均年发电量/(MW∙h)	23.3
平均年节约标准煤/t	7.048
平均年减少CO₂排放/t	19.385