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文章摘要
基于改进人工蜂群算法的家庭储能容量优化配置
Optimal allocation of household energy storage capacity based on improved artificial bee colony algorithm
Received:October 08, 2020  Revised:December 21, 2022
DOI:10.19753/j.issn1001-1390.2023.10.004
中文关键词: 混合储能  家庭能源管理  容量配置  人工蜂群算法
英文关键词: hybrid energy storage, household energy management, capacity allocation, artificial bee colony algorithm
基金项目:国家自然科学基金资助项目(61401269,61572311);上海市科技创新行动计划地方院校能力建设项目(17020500900)
Author NameAffiliationE-mail
Jiang Wei School of Electrical Information and Engineering,Shanghai University of Electric Power shiepjw@shiep.edu.cn 
Chen Zhaoguang* School of Electrical Information and Engineering,Shanghai University of Electric Power 2314364003@qq.com 
Yan Hao School of Electrical Information and Engineering,Shanghai University of Electric Power sdyanhao01@outlook.com 
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中文摘要:
      家庭能源系统中的储能设备初始投资成本高,限制其实际应用。针对此问题,文章对混合储能的容量配置进行了研究。分别构建了刚性负荷、柔性负荷和储能类设备负荷模型;在此基础上搭建以用户每天用电费用最低为目标的家庭能源管理系统模型;提出一种改进的人工蜂群算法对模型求解。实验结果表明,通过和单储能的系统相比,在满足用户用电需求的同时,配置混合储能的家庭能源系统能有效减小用户每天用电费用。对文中算法与人工蜂群算法和粒子群算法优化结果进行比对,证实所提算法优化时长短、收敛速度快且不易于陷入局部最优。
英文摘要:
      The high initial investment cost of energy storage equipment in the household energy system limits its practical application. Therefore, the capacity configuration of hybrid energy storage is studied to solve this problem. Firstly, the rigid load, flexible load and energy storage equipment load models are constructed respectively; on this basis, the household energy management system model aimed at the lowest daily electricity cost of users is built; finally, an improved artificial bee colony algorithm is proposed to solve the model. Compared with the single energy storage system, the experimental results show that the home energy system equipped with hybrid energy storage can effectively reduce the daily electricity cost of users while meeting the electricity demand of users. By comparing the optimization results of the proposed algorithm with the artificial bee colony algorithm and particle swarm algorithm it is proved that the proposed algorithm has short optimization time, fast convergence speed and is not easy to fall into local optimum.
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