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Improved Low-Cost Home Energy Management Considering User Preferences with Photovoltaic and Energy-Storage Systems

Author

Listed:
  • Nedim Tutkun

    (Department of Electrical & Electronics Engineering, İstanbul Ticaret University, 34840 İstanbul, Turkey)

  • Luigi Scarcello

    (ICAR-CNR, Via P. Bucci, 8/9 C, 87036 Rende, Italy)

  • Carlo Mastroianni

    (ICAR-CNR, Via P. Bucci, 8/9 C, 87036 Rende, Italy)

Abstract

With smart appliances, it has been possible to achieve low-cost electricity bills in smart-grid-tied homes including photovoltaic panels and an energy-storage system. Apparently, many factors are important in achieving this and the minimization problem formulated requires a solution depending on a certain number of constraints. It should also be emphasized that electricity tariffs and the appliance operation type and range play a major role in this cost reduction, in particular, with dynamic electricity pricing usually available in a smart-grid environment. A limited number of metaheuristic methods are used to solve such a minimization problem, in which the start time of a controllable smart home appliance is the variable. However, the datasets used in many studies are different from each other and it is mostly unclear which of the proposed methods is better in this regard. In this study, we aim to minimize the daily energy consumption cost in a typical smart home with an energy-storage system integrated into a photovoltaic system under dynamic electricity pricing. While minimizing the daily energy consumption cost only, the user’s discomfort and the peak-to-average ratio inevitably tend to increase, as expected. Therefore, a balance can be established among the objectives using multi-objective optimization. Solving this problem helps comparatively reduce the daily energy consumption cost, the peak-to-average ratio and the user’s discomfort. The results are meaningful and encouraging for the optimization problem under consideration.

Suggested Citation

  • Nedim Tutkun & Luigi Scarcello & Carlo Mastroianni, 2023. "Improved Low-Cost Home Energy Management Considering User Preferences with Photovoltaic and Energy-Storage Systems," Sustainability, MDPI, vol. 15(11), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8739-:d:1158433
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    References listed on IDEAS

    as
    1. Dinh, Huy Truong & Lee, Kyu-haeng & Kim, Daehee, 2022. "Supervised-learning-based hour-ahead demand response for a behavior-based home energy management system approximating MILP optimization," Applied Energy, Elsevier, vol. 321(C).
    2. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    3. Aya Amer & Khaled Shaban & Ahmed Gaouda & Ahmed Massoud, 2021. "Home Energy Management System Embedded with a Multi-Objective Demand Response Optimization Model to Benefit Customers and Operators," Energies, MDPI, vol. 14(2), pages 1-19, January.
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