Optimal household energy management based on smart residential energy hub considering uncertain behaviors

被引:69
作者
Lu, Qing [1 ]
Lu, Shuaikang [1 ,2 ]
Leng, Yajun [1 ]
Zhang, Zhixin [1 ]
机构
[1] Shanghai Univ Elect Power, Coll Econ & Management, Shanghai 200090, Peoples R China
[2] State Grid Shaoxing Elect Power Supply Co, Shaoxing 321000, Peoples R China
基金
中国国家自然科学基金;
关键词
Home energy management system (HEMS); Smart residential energy hub (SREH); Optimal scheduling; Uncertain behaviors; Comfort deviation; DEMAND RESPONSE; LOAD MANAGEMENT; ELECTRIC VEHICLES; OPTIMAL OPERATION; SYSTEM; STRATEGY; OPTIMIZATION; CONSUMPTION; AGGREGATOR; STORAGE;
D O I
10.1016/j.energy.2020.117052
中图分类号
O414.1 [热力学];
学科分类号
摘要
Nowadays, confronting with the emerging energy crisis and environmental pressure, multi energy integrating technologies are considered as effective patterns to augment the renewable energy consumption and improve energy efficiency in the context of energy transformation and reform. In this paper, household energy management based on smart residential energy hub (SREH) whose inputs include electricity and natural gas is designed for modern households. Relevant energy-using equipment models as well as control strategies are proposed through the physical characteristics and household users' preferences, respectively. A multi-objective optimization problem is formulated to allocate energy supply in the SREH, and provide scheduling schemes for energy-using equipment beside the classified ordinary appliances. Six kinds of uncertain behaviors are modelled in comfort deviation as sub-objective. The overall objective of the problem is to minimize both the energy consumption expense and comfort deviation. Then, four cases studies are presented to verify the effectiveness of the proposed model, where both of the sub-objective value improves as a result. Finally, the robustness of the model are illustrated with actual behaviors of household users. The sensibility analysis of departure time distribution, weighing factors and number of uncertain scenarios are carried out to optimize the decision configuration. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:19
相关论文
共 45 条
  • [1] True real time pricing and combined power scheduling of electric appliances in residential energy management system
    Anees, Amir
    Chen, Yi-Ping Phoebe
    [J]. APPLIED ENERGY, 2016, 165 : 592 - 600
  • [2] Distributed energy storage system scheduling considering tariff structure, energy arbitrage and solar PV penetration
    Babacan, Oytun
    Ratnam, Elizabeth L.
    Disfani, Vahid R.
    Kleissl, Jan
    [J]. APPLIED ENERGY, 2017, 205 : 1384 - 1393
  • [3] A general model for energy hub economic dispatch
    Beigvand, Soheil Derafshi
    Abdi, Hamdi
    La Scala, Massimo
    [J]. APPLIED ENERGY, 2017, 190 : 1090 - 1111
  • [4] Optimal Operation of Residential Energy Hubs in Smart Grids
    Bozchalui, Mohammad Chehreghani
    AhsanHashmi, Syed
    Hassen, Hussin
    Canizares, Claudio A.
    Bhattacharya, Kankar
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (04) : 1755 - 1766
  • [5] BP, 2019, BP STAT REV WORLD EN, DOI DOI 10.1016/J.ENPOL.2018.06.002
  • [6] Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system
    Brahman, Faeze
    Honarmand, Masoud
    Jadid, Shahram
    [J]. ENERGY AND BUILDINGS, 2015, 90 : 65 - 75
  • [7] Stochastic operation of home energy management systems including battery cycling
    Correa-Florez, Carlos Adrian
    Gerossier, Alexis
    Michiorri, Andrea
    Kariniotakis, Georges
    [J]. APPLIED ENERGY, 2018, 225 : 1205 - 1218
  • [8] Appliance Commitment for Household Load Scheduling
    Du, Pengwei
    Lu, Ning
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2011, 2 (02) : 411 - 419
  • [9] Optimizing the management of smart home energy resources under different power cost scenarios
    Goncalves, Ivo
    Gomes, Alvaro
    Antunes, Carlos Henggeler
    [J]. APPLIED ENERGY, 2019, 242 : 351 - 363
  • [10] Good Nicholas, 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM), DOI 10.1109/PESGM.2016.7741618