Price-sensitive home energy management method based on Pareto optimisation

被引：3
|
作者：
Lyu, Jie [1 ]
Ye, Tairan [1 ]
Xu, Mengtian [1 ]
Ma, Gang [1 ]
Wang, Ying [1 ]
Li, Mengyue [1 ]
机构：
[1] Nanjing Normal Univ, Sch Elect & Automat Engn, Nanjing, Peoples R China
来源：
INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING | 2021年 / 14卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Smart home; price-sensitive; home energy management systems (HEMS); mixed integer linear programming(MILP); customer comfort; energy optimisation; SMART HOMES; WIND;
D O I：
10.1080/19397038.2020.1822948
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
With the emergence of smart home, residents can achieve demand response through home energy management system (HEMS), so as to improve energy utilisation. However, on the one hand, the existing household energy management method lacks the balance consideration of user economy and comfort, on the other hand, it lacks the response of adjustment scale to real-time electricity price. Aprice sensitive response mechanism considering the comfort of HVAC is added to the objective function of household energy management, and aprice sensitive household energy control method based on Pareto optimisation is proposed, which is solved dynamically by mixed integer linear programming (MILP). The simulation results show that PSHEMS can reduce household daily expenditure by 53%, and the PAPR value of home nano grid can be reduced by 70%.
引用
收藏
页码：433 / 441
页数：9
相关论文
共 50 条
  • [31] Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances
    Lee, Sangyoon
    Choi, Dae-Hyun
    SENSORS, 2019, 19 (18)
  • [32] Optimal Energy Scheduling Method under Load Shaping Demand Response Program in a Home Energy Management System
    Kakran, Sandeep
    Chanana, Saurabh
    INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS, 2019, 20 (02):
  • [33] Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty
    Issa, Haala Haj
    Abedini, Moein
    Hamzeh, Mohsen
    Anvari-Moghaddam, Amjad
    ENERGIES, 2024, 17 (21)
  • [34] A ZigBee-based Smart Home System: An Energy-saving Method
    Zang, Xuan
    Ying, Sun
    Li, Kejun
    MANUFACTURING PROCESSES AND SYSTEMS, PTS 1-2, 2011, 148-149 : 1528 - 1531
  • [35] An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages
    Pinthurat, Watcharakorn
    Surinkaew, Tossaporn
    Hredzak, Branislav
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2024, 202
  • [36] Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management
    Alfaverh, Fayiz
    Denai, M.
    Sun, Yichuang
    IEEE ACCESS, 2020, 8 (08): : 39310 - 39321
  • [37] Deep reinforcement learning based home energy management system with devices operational dependencies
    Si, Caomingzhe
    Tao, Yuechuan
    Qiu, Jing
    Lai, Shuying
    Zhao, Junhua
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (06) : 1687 - 1703
  • [38] Deep reinforcement learning based home energy management system with devices operational dependencies
    Caomingzhe Si
    Yuechuan Tao
    Jing Qiu
    Shuying Lai
    Junhua Zhao
    International Journal of Machine Learning and Cybernetics, 2021, 12 : 1687 - 1703
  • [39] Integrated Agent-Based Home Energy Management System for Smart Grids Applications
    Asare-Bediako, B.
    Kling, W. L.
    Ribeiro, P. F.
    2013 4TH IEEE/PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT EUROPE), 2013,
  • [40] An Agent-Based Model for Energy Management of Smart Home: Residences' Satisfaction Approach
    Ebrahimi, Mahoor
    Hajizade, Amin
    DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 2019, 801 : 378 - 384
12345