Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

Article retrieval

文章检索

首页 >> 文章检索 >> 最新索引

差分进化鸟群算法的微电网多目标优化运行

来源:电工电气发布时间:2020-08-22 10:22 浏览次数:90
差分进化鸟群算法的微电网多目标优化运行
 
薛阳1,李蕊1,张宁1,王琳2
(1 上海电力大学 自动化工程学院,上海 200090;2 国网上海市电力公司,上海 200122)
 
    摘 要:为提高微电网在安全可靠前提下调度运行的经济性和环保性,提出了一种基于差分进化鸟群算法的微电网多目标优化运行策略。建立了考虑经济性、环保性及供电可靠性等因素的微电网多目标模型,并给出了满足微电网安全稳定运行所需的约束条件;将多目标函数转换为单目标函数,应用差分进化鸟群算法对其进行求解;将所得结果分别与各单目标下求解结果进行对比。实验结果表明,所提方法在经济性和环保性上较传统模型均有所提高,更充分利用可再生能源,降低系统运行成本,并且在负荷变动明显的情况下,系统波动性较小,一定程度上提高系统稳定性;同时该组合算法增加了种群的多样性,防止训练过程陷入局部最优解,具有效率高、鲁棒性好的优点。
    关键词:微电网;多目标;优化运行;差分进化算法;鸟群算法
    中图分类号:TM711     文献标识码:A     文章编号:1007-3175(2020)08-0001-06
 
Multi-Objective Optimal Operation of Micro-Grid Based on Differential Evolutionary Bird Swarm Algorithm
 
XUE Yang1, LI Rui1, ZHANG Ning 1, WANG Lin2
(1 College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2 State Grid Shanghai Electric Power Company, Shanghai 200122, China)
 
    Abstract: In order to improve the economics and environment friendly of micro-grid dispatching operation under the premise of safety and reliability, a multi-objective optimization operation strategy for micro-grid based on differential evolution bird swarm algorithm is proposed. In this paper, it established a micro-grid multi-objective model that considers factors such as economy, environment friendly, and power supply reliability, and gave the constraints required to meet the safe and stable operation of the micro-grid, the multi-objective function is converted into a single-objective function, and the differential evolution bird swarm algorithm is used to solve it; the obtained results are compared with the solution results under each single-objective. The experimental results show that the proposed method is improved in economy and environmental protection compared with the traditional model, making full use of renewable energy, reducing system operating costs, and under the condition of obvious load fluctuations, the system volatility is small, and to a certain extent, the stability of the system is improved. At the same time, the combined algorithm increases the diversity of the population and prevents the training process from falling into the local optimal solution. It has the advantages of high efficiency and good robustness.
    Key words: micro-grid; multi-objective; optimal operation; differential evolutionary algorithm; bird swarm algorithm
 
参考文献
[1] 吴雄,王秀丽,刘世民,等. 微电网能量管理系统研究综述[J]. 电力自动化设备,2014,34(10):7-14.
[2] 张翔宇, 李丹, 张予燮, 等. 计及储能系统的微电网优化调度模型[J]. 水电能源科学,2017,35(11):203-206.
[3] 郭志忠,叶瑞丽,刘瑞叶,等. 含抽水蓄能电站的可再生能源电网优化调度策略[ J ] . 电力自动化设备,2018,38(3):7-15.
[4] EVANGELOPOULOS V A, GEORGILAKIS P S.Optimal Distributed Generation Placement Under Uncertainties Based on Point Estimate Method Embedded Genetic Algorithm[J].IET Generation Transmission & Distribution,2014,8(3):389-400.
[5] GANIVADA P K, VENKAIAH C.Optimal Placement and Sizing of Multi Distributed Generators Using Teaching and Learning Based Optimization[C]//2014 International Conference on Smart Electric Grid (ISEG),2014:1-6.
[6] 王雅平,林舜江,杨智斌,等. 微电网多目标随机动态优化调度算法[J]. 电工技术学报,2018,33(10):2196-2207.
[7] 曾嶒,彭春华,王奎,等. 基于鸟群算法的微电网多目标运行优化[J]. 电力系统保护与控制,2016,44(13):117-122.
[8] 毛瑞龙,李春华. 基于petri网和粒子群算法的微电网优化运行[J]. 智慧电力,2018,46(10):96-102.
[9] 高佳,肖迎群. 风、光、燃、储多源互补微电网的日优化运行[J]. 科学技术与工程,2019,19(15):136-142.
[10] 钱超,周志华. 基于分解策略的多目标演化子集选择算法[J]. 中国科学:信息科学,2016,46(9):1276-1287.
[11] 杨毅,雷霞,叶涛,等. 考虑安全性与可靠性的微电网电能优化调度[J]. 中国电机工程学报,2014,34(19):3080-3088.
[12] 赵磊,曾芬钰,王霜,等. 基于经济性与环保性的微电网多目标优化调度研究[J]. 高压电器,2015,51(6):127-132.
[13] MENG X B, GAO X Z, LU L, et al.A New Bio-Inspired Optimization Algorithm:Bird Swarm Algorithm[J].Journal of Experimental & Theoretical Artificial Intelligence,2016,28(4):1-15.
[14] 郑建国,陈克明,蔡万刚. 基于种群自适应调整的多目标差分进化算法[J]. 运筹与管理,2017,26(6):29-34.

 

山西11选5走势图 红韵彩票注册开户投注平台 旺彩彩票平台 搜狐彩票注册开户投注平台 王者彩票开户 利新彩票开户注册投注 好运彩票注册开户投注平台 山东11选5开奖 玖玖网彩票注册开户投注平台 亚洲彩票