IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p8739-d1158433.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/8739/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/11/8739/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    2. Ali Aillane & Karim Dahech & Larbi Chrifi-Alaoui & Aissa Chouder & Tarak Damak & Abdelhak Hadjkaddour & Pascal Bussy, 2023. "The Design and Processor-In-The-Loop Implementation of a Super-Twisting Control Algorithm Based on a Luenberger Observer for a Seamless Transition between Grid-Connected and Stand-Alone Modes in Micro," Energies, MDPI, vol. 16(9), pages 1-22, May.
    3. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    4. Abdelfettah Kerboua & Fouad Boukli-Hacene & Khaldoon A Mourad, 2020. "Particle Swarm Optimization for Micro-Grid Power Management and Load Scheduling," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 71-80.
    5. Kaiye Gao & Tianshi Wang & Chenjing Han & Jinhao Xie & Ye Ma & Rui Peng, 2021. "A Review of Optimization of Microgrid Operation," Energies, MDPI, vol. 14(10), pages 1-39, May.
    6. Mohammed Ali Khan & Ahteshamul Haque & Frede Blaabjerg & Varaha Satya Bharath Kurukuru & Huai Wang, 2021. "Intelligent Transition Control between Grid-Connected and Standalone Modes of Three-Phase Grid-Integrated Distributed Generation Systems," Energies, MDPI, vol. 14(13), pages 1-21, July.
    7. Ren, Kezheng & Liu, Jun & Wu, Zeyang & Liu, Xinglei & Nie, Yongxin & Xu, Haitao, 2024. "A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters," Applied Energy, Elsevier, vol. 355(C).
    8. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    9. Iraj Faraji Davoudkhani & Farhad Zishan & Saeedeh Mansouri & Farzad Abdollahpour & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya, 2023. "Allocation of Renewable Energy Resources in Distribution Systems While Considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm," Energies, MDPI, vol. 16(1), pages 1-17, January.
    10. Auza, Anna & Asadi, Ehsan & Chenari, Behrang & Gameiro da Silva, Manuel, 2024. "Review of cost objective functions in multi-objective optimisation analysis of buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    11. Luis Fernando Grisales-Noreña & Bonie Johana Restrepo-Cuestas & Brandon Cortés-Caicedo & Jhon Montano & Andrés Alfonso Rosales-Muñoz & Marco Rivera, 2022. "Optimal Location and Sizing of Distributed Generators and Energy Storage Systems in Microgrids: A Review," Energies, MDPI, vol. 16(1), pages 1-30, December.
    12. Felix Heider & Amra Jahic & Maik Plenz & Detlef Schulz, 2022. "Extended Residential Power Management Interface for Flexibility Communication and Uncertainty Reduction for Flexibility System Operators," Energies, MDPI, vol. 15(4), pages 1-23, February.
    13. Jing Wang & Kaitlyn Garifi & Kyri Baker & Wangda Zuo & Yingchen Zhang & Sen Huang & Draguna Vrabie, 2020. "Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities," Energies, MDPI, vol. 13(21), pages 1-29, October.
    14. Muhammad Majid Hussain & Rizwan Akram & Zulfiqar Ali Memon & Mian Hammad Nazir & Waqas Javed & Muhammad Siddique, 2021. "Demand Side Management Techniques for Home Energy Management Systems for Smart Cities," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    15. Muhammad Irfan & Sara Deilami & Shujuan Huang & Binesh Puthen Veettil, 2023. "Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review," Energies, MDPI, vol. 16(21), pages 1-29, October.
    16. Steffen Limmer & Nils Einecke, 2022. "An Efficient Approach for Peak-Load-Aware Scheduling of Energy-Intensive Tasks in the Context of a Public IEEE Challenge," Energies, MDPI, vol. 15(10), pages 1-23, May.
    17. Álvaro Gutiérrez, 2022. "Optimization Trends in Demand-Side Management," Energies, MDPI, vol. 15(16), pages 1-3, August.
    18. Daniel Akinyele & Juri Belikov & Yoash Levron, 2017. "Battery Storage Technologies for Electrical Applications: Impact in Stand-Alone Photovoltaic Systems," Energies, MDPI, vol. 10(11), pages 1-39, November.
    19. Tuomela, Sanna & de Castro Tomé, Mauricio & Iivari, Netta & Svento, Rauli, 2021. "Impacts of home energy management systems on electricity consumption," Applied Energy, Elsevier, vol. 299(C).
    20. Makhadmeh, Sharif Naser & Khader, Ahamad Tajudin & Al-Betar, Mohammed Azmi & Naim, Syibrah & Abasi, Ammar Kamal & Alyasseri, Zaid Abdi Alkareem, 2019. "Optimization methods for power scheduling problems in smart home: Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8739-:d:1158433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.