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

HTML

PDF (PC)

2757

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
[1] DU P, LU N. Appliance commitment for household load scheduling[J]. IEEE Transactions on Smart Grid, 2011, 2(2):411-419
[2] PIPATTANASOMPORN M, KUZLU M, RAHMAN S. An algorithm for intelligent home energy management and demand response analysis[J]. IEEE Transactions on Smart Grid, 2012, 3(4):2166-2173
[3] KISHORE S, SNYDER L V. Control mechanisms for residential electricity demand in smart grids[C]. First IEEE International Conference on Smart Grid Communications, 2010:443-448.
[4] THOMAS A G, JAHANGIRI P, WU D, et al. Intelligent residential air-conditioning system with smart-grid functionality[J]. IEEE Transactions on Smart Grid, 2012, 3(4):2240-2251
[5] 张延宇, 曾鹏, 李忠文, 等. 智能电网环境下空调系统多目标优化控制算法[J]. 电网技术, 2014, 38(7):1819-1826 ZHANG Y Y, ZENG P, LI Z W, et al. A multi-objective optimal control algorithm for air conditioning system in smart grid[J]. Power System Technology, 2014, 38(7):1819-1826
[6] GOTTWALT S, KETTER W, BLOCK C, et al. Demand side management-a simulation of household behavior under variable prices[J]. Energy Policy, 2011, 39(12):8163-8174
[7] EROL-KANTARCI M, MOUFTAH H T. Wireless sensor networks for cost-efficient residential energy management in the smart grid[J]. IEEE Transactions on Smart Grid, 2011, 2(2):314-325
[8] 刘振亚. 建设坚强智能电网支撑又好又快发展[J]. 电网与清洁能源, 2009, 25(9):1-3 LIU Z Y. Build strong smart grid as pillar of sound and rapid development[J]. Power System and Clean Energy, 2009, 25(9):1-3
[9] 王守相, 孙智卿, 刘喆. 面向智能用电的家庭能量协同调度策略[J]. 电力系统自动化, 2015(17):108-113 WANG S X, SUN Z Q, LIU Z. Co-scheduling strategy of home energy for smart power utilization[J]. Automation of Eclectic Power Systems, 2015(17):108-113
[10] 孙超. 独立运行的光伏系统的研究[D]. 南京:南京航空航天大学, 2009.
[11] 牟黎. 电网友好型智能负荷中心的研究[D]. 北京:北京交通大学, 2013. MOU L. Research on grid friendly smart load center[D]. Beijing:Beijing Jiaotong University, 2013.
[12] 回海滨, 孔祥鹤. 低碳经济下的智能用电技术与环境安全[J]. 中国环境管理干部学院学报, 2011, 2(21):37-38 HUI H B, KONG X H. Intelligent power consumption technology and environmental safety in low-carbon economy[J]. Journal of EMCC, 2011, 2(21):37-38
[13] 姜力, 刘宏, 蔡鹤皋. 多维力/力矩传感器静态解耦的研究[J]. 仪器仪表学报, 2004, 25(3):284-287 JIANG L, LIU H, CAI H G. Nonlinear static decoupling of multi-axis force/torque sensor[J]. Chinese Journal of Scientific instrument, 2004, 25(3):284-287
[14] 吴伟坡. 基于实时电价的智能家庭能源优化控制[D]. 上海:上海交通大学, 2013.
[15] LU N. An evaluation of the load potential for providing load balancing service[J]. IEEE Transactions on Smart Grid, 2012, 3(3):1263-1270
[16] HUBERT T, GRIJALVA S. Modeling for residential electricity optimization in dynamic pricing environments[J]. IEEE Transactions on Smart Grid, 2012, 3(3):2224-2231
[17] 王继东, 杨羽昊, 周越, 等. 家庭智能用电系统建模及优化策略分析[J]. 电力系统及其自动化学报, 2014, 26(11):63-71 WANG J D, YANG Y H, ZHOU Y, et al. Model and optimization strategy for smart home power consumption system[J]. Proceedings of the CSU-EPSA, 2014, 26(11):63-71
[18] 尹忠东, 林挚, 李德智, 等. 基于混合型粒子群算法的智能家庭能量管理策略[J]. 华北电力大学学报, 2018, 45(4):25-33 YIN Z D, LIN Z, LI D Z, et al. Home energy management based on hybrid particle swarm optimization[J]. Journal of North China Electric Power University, 2018, 45(4):25-33
[1] 符政鑫, 刘霜, 段意强, 朱伟东. 一种电网需求响应资源参与天然气市场的协调框架[J]. 广东工业大学学报, 2023, 40(02): 64-73.
[2] 陈友鹏, 陈璟华. 基于鲸鱼优化参数的最小二乘支持向量机短期负荷预测方法[J]. 广东工业大学学报, 2020, 37(03): 75-81.
[3] 唐俊杰, 陈璟华, 邱明晋. 基于动态模糊混沌粒子群算法的含电动汽车微电网多目标优化调度研究[J]. 广东工业大学学报, 2018, 35(03): 100-106.
[4] 马留洋, 孟安波, 殷豪. 基于混合纵横交叉算法的机组组合优化[J]. 广东工业大学学报, 2017, 34(06): 68-72,77.
[5] 陈璟华, 丁林军, 郭壮志, 粱丽丽, 李锦焙. 计及电动汽车规模化入网的电力系统机组组合优化[J]. 广东工业大学学报, 2017, 34(02): 63-68.
[6] 陈璟华, 梁丽丽, 丁林军, 周俊, 刘国祥. 计及风电场并网的机会约束规划的机组组合优化[J]. 广东工业大学学报, 2017, 34(01): 50-54,64.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发