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A Novel Multiobjective Optimization Algorithm for Home Energy Management System in Smart Grid

机译：智能电网中家庭能源管理系统的多目标优化算法

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摘要

Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the reliability of power system. In smart grid, implementing DR through home energy management system (HEMS) in residential sector has a great significance. However, an algorithm that only optimally controls parts of HEMS rather than the overall system cannot obtain the best results. In addition, single objective optimization algorithm that minimizes electricity cost cannot quantify user's comfort level and cannot take a tradeoff between electricity cost and comfort level conveniently. To tackle these problems, this paper proposes a framework of HEMS that consists of grid, load, renewable resource (i.e., solar resource), and battery. In this framework, a user has the ability to sell electricity to utility grid for revenue. Different comfort level indicators are proposed for different home appliances according to their characteristics and user preferences. Based on these comfort level indicators, this paper proposes a multiobjective optimization algorithm for HEMS that minimizes electricity cost and maximizes user's comfort level simultaneously. Simulation results indicate that the algorithm can reduce user's electricity cost significantly, ensure user's comfort level, and take a tradeoff between the cost and comfort level conveniently.
机译：需求响应（DR）是降低需求峰均比，促进可再生资源（例如风能和太阳能）与插电式混合动力汽车整合以及增强电力系统可靠性的有效方法。在智能电网中，通过住宅领域的家庭能源管理系统（HEMS）实施灾难恢复具有重要意义。但是，仅最佳控制HEMS的部分而不是整个系统的算法无法获得最佳结果。另外，使电费最小化的单目标优化算法不能量化用户的舒适度，并且不能方便地在电费和舒适度之间进行取舍。为了解决这些问题，本文提出了一个HEMS框架，该框架由电网，负荷，可再生资源（即太阳能）和电池组成。在此框架中，用户可以将电力出售给公用电网以获取收入。针对不同家用电器的特性和用户偏好，提出了不同的舒适度指标。基于这些舒适度指标，本文提出了一种用于HEMS的多目标优化算法，该算法可最小化电费并同时最大化用户的舒适度。仿真结果表明，该算法可以显着降低用户的用电成本，确保用户的舒适度，并在成本和舒适度之间进行权衡。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第7期|807527.1-807527.19|共19页
  • 作者

    Zhang Yanyu; Zeng Peng; Li Shuhui; Zang Chuanzhi; Li Hepeng;

  • 作者单位

    Univ Chinese Acad Sci, Beijing 100049, Peoples R China.;

    Chinese Acad Sci, Shenyang Inst Automat, Key Lab Networked Control Syst, Shenyang 110016, Peoples R China.;

    Univ Alabama, Dept Elect & Comp Engn, Tuscaloosa, AL 35487 USA.;

    Chinese Acad Sci, Shenyang Inst Automat, Key Lab Networked Control Syst, Shenyang 110016, Peoples R China.;

    Chinese Acad Sci, Shenyang Inst Automat, Key Lab Networked Control Syst, Shenyang 110016, Peoples R China.;

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