采用改进共生生物搜索算法的大规模电动汽车接入微电网协调优化研究

doi:10.3969/j.issn.1008- 0198.2023.05.21

湖南电力 ›› 2023, Vol. 43 ›› Issue (5): 144-150.doi: 10.3969/j.issn.1008- 0198.2023.05.21

采用改进共生生物搜索算法的大规模电动汽车接入微电网协调优化研究

康童^1,2, 朱吉然^1,2, 唐海国³, 周恒逸^1,2, 周可慧^1,2

1.国网湖南省电力有限公司电力科学研究院,湖南长沙 410208;
2.国网公司配电网智能化应用技术实验室,湖南长沙 410208;
3.上海交通大学电力传输与功率变换控制教育部重点实验室,上海 200240

收稿日期:2023-04-23 修回日期:2023-08-17 出版日期:2023-10-25 发布日期:2023-11-03
作者简介:康童(1987),男,高级工程师,通信作者,从事车网互动技术、新型配电系统及群智能优化算法相关研究。
基金资助:
国家重点研发计划项目(2020YFB2104500)

Research on Coordination and Optimization of Large Scale Electric Vehicles Connected to Microgrid Using Improved Symbiotic Organisms Search Algorithm

KANG Tong^1,2, ZHU Jiran^1,2, TANG Haiguo³, ZHOU Hengyi^1,2, ZHOU Kehui^1,2

1. State Grid Hunan Electric Power Company Limited Research Institute, Changsha 410208, China;
2. State Grid Corporation Laboratory of Intelligent Application Technology for Distribution Network, Changsha 410208, China;
3. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2023-04-23 Revised:2023-08-17 Online:2023-10-25 Published:2023-11-03

摘要/Abstract

摘要： 针对大规模电动汽车接入微电网运行问题,首先分析包含电动汽车的微电网架构、运行模式、输出特性和数学模型等,并利用蒙特卡罗方法建立电动汽车无序充电负荷模型,建立以微电网综合成本和峰谷差最小的目标函数。其次,为了求解该复杂、高维、非线性模型,且为提高标准共生生物搜索算法的寻优性能,提出改进型共生生物的搜索算法。该算法在标准SOS算法的生物种群初始化阶段采用准反射学习机制;在互利共生搜索阶段采用改进受益因子策略;在偏利共生搜索阶段采用收缩随机数产生因子区间策略。最后,通过电动汽车无序充电和协同优化运行场景实验,对微电网综合运行成本和峰谷差进行对比验证,结果表明,本文所提方法的科学性和有效性。

关键词: 电动汽车, 微电网, 分布式电源, 共生生物搜索算法, 准反射学习机制

Abstract: Aiming at the optimization and coordination operation of electric vehicles connected to microgrid, the architecture, operation mode, output characteristics and mathematical model of microgrid including electric vehicles are analyzed , and the disorderly charging load model of electric vehicles is established by using Monte Carlo method. An objective function is established to minimize the comprehensive cost and peak-valley difference of microgrid. To solve the complex, high-dimensional and model and enhance the performance of original symbiotic organisms search algorithm(SOS), a novel improved symbiotic organisms search algorithm algorithm(ImSOS)is proposed. In ImSOS, a quasi-reflection-based learning scheme is employed in the population initialization step of original SOS. Moreover, the strategy of the modifications of benefit factors is used in the mutualism phase of SOS. A strategy of narrowing the search range of randomly generated coefficients is adopted in the commensalism phase of SOS. Finally, by setting up scenarios of disordered charging and coordinated optimized operation of electric vehicles, the output comprehensive operating cost and peak-valley difference in microgrid are compared. The result presents the scientificity and effectiveness of the method proposed in this paper.

Key words: electric vehicles, microgrid, distributed generations, symbiotic organisms search algorithm, quasi-reflection-based learning mechanism

中图分类号:

TM714

康童, 朱吉然, 唐海国, 周恒逸, 周可慧. 采用改进共生生物搜索算法的大规模电动汽车接入微电网协调优化研究[J]. 湖南电力, 2023, 43(5): 144-150.

KANG Tong, ZHU Jiran, TANG Haiguo, ZHOU Hengyi, ZHOU Kehui. Research on Coordination and Optimization of Large Scale Electric Vehicles Connected to Microgrid Using Improved Symbiotic Organisms Search Algorithm[J]. Hunan Electric Power, 2023, 43(5): 144-150.

参考文献

[1] 刘潇潇,漆文辉,黎曦,等. 电动汽车及其发展趋势分析[J]. 湖南电力,2010,30(5):59-62.
[2] HAN S, HAN S, SEZAKI K. Development of an optimal vehicle-to-grid aggregator for frequency regulation[J]. IEEE Transactions on Smart Grid, 2010, 1(1): 65-72
[3] LU Z G,QI J T,ZHANG J F, et al. Modeling dynamic demand response for plug-in hybrid electric vehicles based on real-time charging pricing[J]. IET Generation.Transmission & Distribution, 2017, 11(1): 228-235.
[4] 禹威威,刘世林,陈其工,等. 考虑电动汽车充电和需求侧响应的光伏微电网多目标优化调度[J].电力系统及其自动化学报, 2018,30(1):88-97.
[5] CHENG S, WEI Z B, ZHAO Z K. Decentralized Scheduling optimization for charging-storage station considering patial-temporal transfer factors of electric vehicles[J]. International Journal of Energy Research, 2021, 45(5): 6800-6815.
[6] MURTY V V S N, KUMAR A.Retraction note:multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems[J]. Protection and Control of Modern Power Systems, 2020,5(4):304-310.
[7] FARZIN H, FOTUHI-FIRUZABAD M, MOEINI-AGHTAIE M. A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids[J]. IEEE Transactions on Smart Grid, 2017,8(1):117-127.
[8] CHEN W S,LI T. Distributed economic dispatch for energy internet based on multiagent consensus control[J]. IEEE Transactions on Automatic Control, 2021,66(1):137-152.
[9] ZHANG J, CHE L,WANG L,et al. Game-theory based V2G coordination strategy for providing ramping flexibility in power systems[J].Energies,2020, 13(19):5008.
[10] XU H L, MENG Z Y, WANG Y S,et al. Economic dispatching of microgrid considering renewable energy uncertainty and demand side response-science direct[J].Energy Reports, 2020, 6:196-204.
[11] YOUNESI A, SHAYEGHI H, SIANO P,et al. Enhancing the resilience of operational microgrids through a two-stage scheduling strategy considering the impact of uncertainties[J].IEEE Access.2021,9:18454-18464.
[12] LI Y, YANG Z, ZHAO D B,et al. Incorporating energy storage and user experience in isolated microgrid dispatch using a multi-objective model[J]. IET Renewable Power Generation, 2019, 13(6): 973-981.
[13] CHENG M Y, PRAYOGO D. Symbiotic organisms search: a new metaheuristic optimization algorithm [J]. Computers & Structures, 2014, 139: 98-112.
[14] CAO Y, HUANG L, LI Y Q, et al. Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique[J].International Journal of Electrical Power & Energy Systems,2020,117:105628.
[15] 李含怡.考虑电动汽车的微电网经济调度策略研究[D].南京:南京师范大学,2017.
[16] VALLEM V V S N M,KUMAR A.Optimal energy dispatch in microgrids with renewable energy sources and demand response[J]. International Transactions on Electrical Energy Systems, 2020,30(5):e12328.
[17] VALENTA D, LANGER M. On numerical 2D P colonies modelling the grey wolf optimization algorithm[J]. Processes,2021,9(2):330.
[18] LU X H,ZHOU K L,YANG S L, et al.Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles[J]. Journal of Cleaner Production, 2018, 195: 187-199.
[19] 程杉,汪业乔,廖玮霖,等. 含电动汽车的新能源微电网多目标分层优化调度[J]. 电力系统保护与控制, 2022, 50(12):63-71.

采用改进共生生物搜索算法的大规模电动汽车接入微电网协调优化研究

Research on Coordination and Optimization of Large Scale Electric Vehicles Connected to Microgrid Using Improved Symbiotic Organisms Search Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	石曼伶, 罗珍珍, 郑鑫龙, 粟梅. 基于小波相对滑窗能量的直流微电网故障诊断方法[J]. 湖南电力, 2023, 43(5): 49-54.
[2]	李奕杰, 宋恒, 叶晨晖, 申庆祥. 基于融合模型驱动和数据驱动的电动汽车充电负荷预测[J]. 湖南电力, 2023, 43(3): 9-15.
[3]	黄太昱, 韩伟, 马伟东, 刘超, 党一奇. 基于双输入式改进型二阶广义积分器的频率检测优化方法研究[J]. 湖南电力, 2023, 43(3): 16-20.
[4]	谢鹰, 郑众, 刘剑峰, 高希, 李大祥. 基于遗传算法计及充电行驶距离的电动汽车充电网络规划[J]. 湖南电力, 2023, 43(3): 29-36.
[5]	杨瑾, 宋春, 杨楠. 基于粒子群优化算法的分布式电源最优配置研究[J]. 湖南电力, 2023, 43(3): 64-69.
[6]	张尚德, 张莲, 赵梦琪, 贾浩, 谢文龙, 季鸿宇, 黄伟. 基于改进鲸鱼算法的配电网层级模型故障区段定位[J]. 湖南电力, 2023, 43(1): 1-7.
[7]	白鹏辉, 秦斌, 王欣. 基于改进虚拟直流电机的混合储能控制研究[J]. 湖南电力, 2023, 43(1): 17-22.
[8]	邓元望, 尹会春, 鄂加强, 刘帅, 朱浩. 电动汽车电池组绝缘监测方法研究[J]. 湖南电力, 2023, 43(1): 46-49.
[9]	杨楚原, 姚俊伟, 项川, 谢琼瑶, 陈兆乐, 王海亮, 谭柳叶, 李希喆, 杨楠. 面向增量配电网的协同规划方法研究[J]. 湖南电力, 2022, 42(6): 69-75.
[10]	息梦蕊, 蔡昌春, 王斌. 基于改进极限学习机的交直流微电网并网等效建模[J]. 湖南电力, 2022, 42(6): 83-89.