广东工业大学学报 ›› 2019, Vol. 36 ›› Issue (06): 66-73.doi: 10.12052/gdutxb.190034

• 综合研究 • 上一篇    下一篇

基于局部粒子群算法的家庭用电负荷优化控制策略

吴丹琦1, 赖俊升1,2, 杨俊华1, 李学聪1, 赖来利1, 熊锋俊1   

  1. 1. 广东工业大学 自动化学院, 广东 广州 510006;
    2. 英国利兹大学 工程学院, 西约克郡 利兹 LS2 9JT
  • 收稿日期:2019-03-11 出版日期:2019-11-28 发布日期:2019-11-01
  • 通信作者: 李学聪(1978-),男,讲师,博士,主要研究方向为储能技术、能源管理系统和区块链技术.E-mail:52549700@qq.com E-mail:52549700@qq.com
  • 作者简介:吴丹琦(1993-),女,硕士研究生,主要研究方向为智能电网、能源管理系统和电力市场.
  • 基金资助:
    中央财政支持地方高校发展专项资金项目（2016[202]）；广东普通高校创新团队项目（2016KCXTD022）

A Household Electricity Load Optimal Control Strategy Based on Local Particle Swarm Optimization

Wu Dan-qi1, Lai Chun Sing1,2, Yang Jun-hua1, Li Xue-cong1, Lai Loi Lei1, Xiong Feng-jun1   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Faculty of Engineering, The University of Leeds, Leeds LS2 9JT, UK
  • Received:2019-03-11 Online:2019-11-28 Published:2019-11-01

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摘要： 提出一种分时电价政策下电能总花费最低的家庭用电负荷优化控制策略.采用局部粒子群算法对家庭中4类常见用电负荷的花费进行优化，与无优化处理和传统粒子群算法进行对比分析，并在Python平台上搭建数学模型和开展仿真实验.结果表明，局部粒子群算法可大幅度减少家庭用电花费，具有全局搜索能力强、收敛速度快等优点，可推广应用到家庭能源管理领域相关研究.

关键词: 家庭能源管理, 分时电价, 仿真实验, 局部粒子群算法

Abstract: In order to optimize the household electricity load, an optimal control strategy is proposed for household electricity consumption to minimize the total electricity charges under the time-of-use electricity pricing policy. Local Particle Swarm Optimization (LPSO) algorithm is used to minimize the electricity charges of four kinds of household electricity loads, and then compared with no-optimization treatment and traditional Particle Swarm Optimization (PSO) respectively. Using Python software, the mathematical model is built and simulated tests are carried out. The simulation results show that LPSO can significantly reduce the total electricity charges and has the advantages of strong global search ability and fast convergence speed, which can be applied to relevant researches in the field of household energy management.

Key words: household energy management, time-of-use pricing, simulation experiment, local particle swarm optimization

中图分类号: 

  • TM73
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