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Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries

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  • Javadi, Mohammad Sadegh
  • Gough, Matthew
  • Lotfi, Mohamed
  • Esmaeel Nezhad, Ali
  • Santos, Sérgio F.
  • Catalão, João P.S.

Abstract

Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

Suggested Citation

  • Javadi, Mohammad Sadegh & Gough, Matthew & Lotfi, Mohamed & Esmaeel Nezhad, Ali & Santos, Sérgio F. & Catalão, João P.S., 2020. "Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries," Energy, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:energy:v:210:y:2020:i:c:s0360544220316765
    DOI: 10.1016/j.energy.2020.118568
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    15. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Jurado, Francisco, 2022. "A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems," Energy, Elsevier, vol. 253(C).
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    18. Wang, Guotao & Zhou, Yifan & Lin, Zhenjia & Zhu, Shibo & Qiu, Rui & Chen, Yuntian & Yan, Jinyue, 2024. "Robust energy management through aggregation of flexible resources in multi-home micro energy hub," Applied Energy, Elsevier, vol. 357(C).
    19. Cai, Qiran & Qing, Jing & Xu, Qingyang & Shi, Gang & Liang, Qiao-Mei, 2024. "Techno-economic impact of electricity price mechanism and demand response on residential rooftop photovoltaic integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
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    21. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
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