IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6852-d1249350.html
   My bibliography  Save this article

Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm

Author

Listed:
  • Christodoulos Spagkakas

    (Department of Electrical and Computer Engineering, School of Engineering, University of Western Macedonia, 50100 Kozani, Greece)

  • Dimitrios Stimoniaris

    (Department of Electrical and Computer Engineering, School of Engineering, University of Western Macedonia, 50100 Kozani, Greece)

  • Dimitrios Tsiamitros

    (Department of Electrical and Computer Engineering, School of Engineering, University of Western Macedonia, 50100 Kozani, Greece)

Abstract

Given its adaptable and efficient energy consuming devices during peak hours, the residential building sector is urged to take part in demand response (DR) initiatives with the use of a building energy management system (BMS). The residents of buildings with BMS enjoy secure, pleasant, and fully managed lifestyles. Although the BMS helps the building consume less energy and encourages occupant engagement in energy-saving initiatives, unwelcome interruptions and harsh instructions from the system are inconvenient for the inhabitants, which further discourages their participation in DR initiatives. Building automation control is a crucial factor for improving buildings’ energy efficiency and management, as well as improving the electricity grid’s reliability indices. Smart houses that use the right sizing procedure and energy-management techniques can help lower the demand on the entire grid and potentially sell clean energy to the utility. Recently, smart houses have been presented as an alternative to traditional power-system issues including thermal plant emissions and the risk of blackouts brought on by malfunctioning bulk plants or transmission lines. This paper describes the necessary technology requirements and presents the methodology and the decentralized building automation novel algorithm for efficient demand side management in a building management system. Human comfort aspects including thermal comfort and visual comfort were taken into consideration when selecting heating and lighting controls. The suggested BMS relies primarily on a load-shifting technique, which moves controllable loads to low-cost periods to avoid high loading during peak hours. The model aims to minimize the individual household electricity consumption cost while considering customers’ comfort and lifestyle. All these are applied in an experimental university microgrid, and the results are presented in terms of energy saving in kWh, money in €, and working hours. The results demonstrated that the proposed approach might successfully lower energy use during the DR period and enhance occupant comfort.

Suggested Citation

  • Christodoulos Spagkakas & Dimitrios Stimoniaris & Dimitrios Tsiamitros, 2023. "Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm," Energies, MDPI, vol. 16(19), pages 1-17, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6852-:d:1249350
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6852/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/19/6852/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Eissa, M.M., 2019. "Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol," Applied Energy, Elsevier, vol. 236(C), pages 273-292.
    3. Nadeem Javaid & Adnan Ahmed & Sohail Iqbal & Mahmood Ashraf, 2018. "Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles," Energies, MDPI, vol. 11(6), pages 1-28, June.
    4. Omaji Samuel & Sakeena Javaid & Nadeem Javaid & Syed Hassan Ahmed & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(11), pages 1-27, November.
    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. Herie Park, 2020. "Human Comfort-Based-Home Energy Management for Demand Response Participation," Energies, MDPI, vol. 13(10), pages 1-15, May.
    2. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    3. Waqas Ahmad & Nasir Ayub & Tariq Ali & Muhammad Irfan & Muhammad Awais & Muhammad Shiraz & Adam Glowacz, 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine," Energies, MDPI, vol. 13(11), pages 1-17, June.
    4. Sharif Naser Makhadmeh & Mohammed Azmi Al-Betar & Mohammed A. Awadallah & Ammar Kamal Abasi & Zaid Abdi Alkareem Alyasseri & Iyad Abu Doush & Osama Ahmad Alomari & Robertas Damaševičius & Audrius Zaja, 2022. "A Modified Coronavirus Herd Immunity Optimizer for the Power Scheduling Problem," Mathematics, MDPI, vol. 10(3), pages 1-29, January.
    5. Muhammad Kashif Rafique & Zunaib Maqsood Haider & Khawaja Khalid Mehmood & Muhammad Saeed Uz Zaman & Muhammad Irfan & Saad Ullah Khan & Chul-Hwan Kim, 2018. "Optimal Scheduling of Hybrid Energy Resources for a Smart Home," Energies, MDPI, vol. 11(11), pages 1-19, November.
    6. Lynch, Muireann Á. & Nolan, Sheila & Devine, Mel T. & O’Malley, Mark, 2019. "The impacts of demand response participation in capacity markets," Applied Energy, Elsevier, vol. 250(C), pages 444-451.
    7. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    8. Héctor Migallón & Akram Belazi & José-Luis Sánchez-Romero & Héctor Rico & Antonio Jimeno-Morenilla, 2020. "Settings-Free Hybrid Metaheuristic General Optimization Methods," Mathematics, MDPI, vol. 8(7), pages 1-25, July.
    9. Ghayour, Sepideh Saravani & Barforoushi, Taghi, 2022. "Optimal scheduling of electrical and thermal resources and appliances in a smart home under uncertainty," Energy, Elsevier, vol. 261(PA).
    10. Schellenberg, C. & Lohan, J. & Dimache, L., 2020. "Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    11. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    12. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    13. 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.
    14. Xiang, Yue & Cai, Hanhu & Gu, Chenghong & Shen, Xiaodong, 2020. "Cost-benefit analysis of integrated energy system planning considering demand response," Energy, Elsevier, vol. 192(C).
    15. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    16. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    17. Sooyoung Jung & Yong Tae Yoon & Jun-Ho Huh, 2020. "An Efficient Micro Grid Optimization Theory," Mathematics, MDPI, vol. 8(4), pages 1-21, April.
    18. Sławomir Zator & Waldemar Skomudek, 2020. "Impact of DSM on Energy Management in a Single-Family House with a Heat Pump and Photovoltaic Installation," Energies, MDPI, vol. 13(20), pages 1-20, October.
    19. Roksana Yasmin & B. M. Ruhul Amin & Rakibuzzaman Shah & Andrew Barton, 2024. "A Survey of Commercial and Industrial Demand Response Flexibility with Energy Storage Systems and Renewable Energy," Sustainability, MDPI, vol. 16(2), pages 1-41, January.
    20. Zheng, Junjie & Lai, Chun Sing & Yuan, Haoliang & Dong, Zhao Yang & Meng, Ke & Lai, Loi Lei, 2020. "Electricity plan recommender system with electrical instruction-based recovery," Energy, Elsevier, vol. 203(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:jeners:v:16:y:2023:i:19:p:6852-:d:1249350. 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.