基于自适应粒子群算法的智能家居管理系统负荷优化模型

doi:10.6040/j.issn.1672-3961.0.2017.533

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 57-62.doi: 10.6040/j.issn.1672-3961.0.2017.533

基于自适应粒子群算法的智能家居管理系统负荷优化模型

马汉杰¹,林霞²,胥晓晖²,张健²,张智晟^1*

1. 青岛大学自动化与电气工程学院, 山东青岛 266071;2. 国网枣庄供电公司, 山东枣庄 277100

收稿日期:2017-09-21 出版日期:2017-12-20 发布日期:2017-09-21
通讯作者: 张智晟(1975— ),男,山东青岛人,教授,博士后,主要研究方向为电力系统负荷预测和经济负荷分配. E-mail:slnzzs@126.com E-mail:972762966@qq.com
作者简介:马汉杰(1994— ),男,山东菏泽人,工学硕士,主要研究方向为电力系统智能需求侧响应. E-mail:972762966@qq.com
基金资助:
国家自然科学基金资助项目(51477078);山东电力科技计划资助项目(2017A-88)

Load optimization model of smart home management system based on adaptive particle swarm optimization

MA Hanjie¹, LIN Xia², XU Xiaohui², ZHANG Jian², ZHANG Zhisheng^1*

1. College of Automation and Electrical Engineering, Qingdao University, Qingdao 266071, Shandong, China;
2. State Grid Zaozhuang Power Supply Company, Zaozhuang 277100, Shandong, China

Received:2017-09-21 Online:2017-12-20 Published:2017-09-21

摘要/Abstract

摘要： 在能源互联网发展的背景下,针对电网需求侧响应的策略及用户节约用电成本的要求,设计智能家居管理系统(smart home management system, SHMS)的基本结构,构建智能家居管理系统负荷优化模型,并采用引入衰减因子的自适应粒子群算法对模型进行求解,可得到满足用户要求的家庭负荷运行方案。仿真算例采用了实际的分时电价、室外温度、负荷参数等信息,与优化前相比,用户负荷曲线得到改善,用电成本及用电量明显下降,验证了算法的有效性。

关键词: 负荷优化, 能源互联网, 自适应粒子群算法, 智能家居管理系统

Abstract: In the situation of the development of Energy Internet, the basic structure of smart home management system(SHMS)was designed, the intelligent house management system load optimization model was constructed, which met the demand response strategy for power grid and the user's requirement to save the electricity cost. Particle swarm optimization(PSO)algorithm for introducing attenuation factors was used to solve the model, which got the program to meet the needs of users. The simulation results were based on the actual time-sharing price, outdoor temperature and load parameters. After the simulation, the user load curve was improved and the electricity consumption and energy used were obviously reduced, which proved the algorithm was effective.

Key words: load optimization, smart home management system, adaptive PSO, Energy Internet

中图分类号:

TM73

马汉杰,林霞,胥晓晖,张健,张智晟. 基于自适应粒子群算法的智能家居管理系统负荷优化模型[J]. 山东大学学报(工学版), 2017, 47(6): 57-62.

MA Hanjie, LIN Xia, XU Xiaohui, ZHANG Jian, ZHANG Zhisheng. Load optimization model of smart home management system based on adaptive particle swarm optimization[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(6): 57-62.

参考文献

[1] 汤奕, 鲁针针, 宁佳,等. 基于电力需求响应的智能家电管理控制方案[J]. 电力系统自动化, 2014, 38(9):93-99. TANG Yi, LU Zhenzhen, NING Jia, et al. Management and control scheme for intelligent home appliances based on electricity demand response[J]. Automation of Electric Power Systems, 2014, 38(9):93-99.
[2] 黄焘, 马溪原, 雷金勇,等. 考虑分时电价和需求响应的家庭型用户侧微电网优化运行[J]. 南方电网技术, 2015, 9(4):47-53. HUANG Tao, MA Xiyuan, LEI Jinyong, et al. Optimal operation of household user-side microgrid considering time-of-use price and demand response[J]. Southern Power System Technology, 2015, 9(4):47-53.
[3] 盛万兴, 张波, 邸宏宇,等. 一种基于动态抗体记忆库的免疫优化算法在自动需求响应中的应用[J]. 中国电机工程学报, 2014, 34(25):4199-4206. SHENG Wanxing, ZHANG Bo, DI Hongyu, et al. A flexible antibody memory based immune optimization algorithm with application to automatic demand response[J]. Proceedings of the CSEE, 2014, 34(25):4199-4206.
[4] SHAO S, PIPATTANASOMPORN M, RAHMAN S. Development of physical-based demand response-enabled residential load models[J]. IEEE Transactions on Power Systems, 2013, 28(2):607-614.
[5] POURMOUSAVI S A, PATRICK S N, NEHRIR M H. Real-time demand response through aggregate electric water heaters for load shifting and balancing wind generation[J]. IEEE Transactions on Smart Grid, 2014, 5(2):769-778.
[6] GAO H, KWONG S, YANG J, et al. Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation[J]. Information Sciences, 2013, 250(11):82-112.
[7] ALRASHIDI M R, El-NAGGAR K M. Long term electric load forecasting based on particle swarm optimization[J]. Applied Energy, 2010, 87(1):320-326.
[8] CUI X, HARDIN C T, RAGADE R K, et al. Tracking non-stationary optimal solution by particle swarm optimizer[C] //International conference on software engineering, artificial intelligence, Networking and parallel/distributed computing and first acis international workshop on self-assembling wireless networks.[S.l.] : IEEE Computer Society, 2005:133-138.
[9] 刘秀梅, 动态系统中粒子群优化算法综述[J]. 软件导刊, 2016, 15(10):43-46. LIU Xiumei, The summarization of particle swarm optimization algorithm in dynamic system[J]. Software Guide, 2016, 15(10):43-46.
[10] 何可人, 滕欢, 郭宁. 考虑峰谷差因素的用户侧负荷优化运行[J]. 科学技术与工程, 2016, 16(27):177-182. HE Keren, TENG Huan, GUO Ning. Optimal operation of user side load considering peak-valley difference[J]. Science Technology and Engineering, 2016, 16(27):177-182.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于自适应粒子群算法的智能家居管理系统负荷优化模型

Load optimization model of smart home management system based on adaptive particle swarm optimization

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

多维度评价

本文评价

推荐阅读 0

[1]	谢国辉,樊昊. 太阳能光热发电技术成熟度预测模型[J]. 山东大学学报(工学版), 2017, 47(6): 83-88.
[2]	石访,张恒旭,张磊. 全球能源互联网宏观运行特性仿真框架[J]. 山东大学学报(工学版), 2017, 47(6): 151-156.
[3]	刘晓明,许乃媛,杨斌,魏鑫,张丽娜,曹永吉. 全球能源互联网受端特高压网架双阶段优化[J]. 山东大学学报(工学版), 2017, 47(6): 1-6.
[4]	张恒旭,韩林晓,石访. 基于最小偏差法的全球能源优化配置方法[J]. 山东大学学报(工学版), 2017, 47(6): 128-133.
[5]	李海石, 徐向艺, 张磊. “一带一路”背景下全球能源互联网运行机制构建[J]. 山东大学学报(工学版), 2017, 47(6): 134-142.
[6]	张希华,卢姗姗,苏建军. 全球能源互联网关键技术专利发展现状与对策[J]. 山东大学学报(工学版), 2017, 47(6): 143-150.
[7]	张恒旭,施啸寒,刘玉田,杨冬. 我国西北地区可再生能源基地对全球能源互联网构建的支撑作用[J]. 山东大学学报(工学版), 2016, 46(4): 96-102.
[8]	褚晓东,张荣祥,黄昊怡,唐茂森. 全球能源互联网物理-信息系统协同仿真平台[J]. 山东大学学报(工学版), 2016, 46(4): 103-110.
[9]	赵康,王春义,杨冬,刘玉田. 考虑单相短路电流控制的特高压受端电网限流优化[J]. 山东大学学报(工学版), 2016, 46(4): 117-124.