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- 2017
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
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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。
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
| [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] | 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. |
| [3] | 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. |
| [4] | 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. |
| [5] | 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. |
| [6] | 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. |
| [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] | 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. |
| [9] | 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. |
| [10] | 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. |
