IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2855-d555410.html
   My bibliography  Save this article

Reinforced Demand Side Management for Educational Institution with Incorporation of User’s Comfort

Author

Listed:
  • Karthick Tamilarasu

    (Department of EEE, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu, India)

  • Charles Raja Sathiasamuel

    (Department of EEE, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu, India)

  • Jeslin Drusila Nesamalar Joseph

    (Department of EEE, Kamaraj College of Engineering and Technology, Madurai 625015, Tamil Nadu, India)

  • Rajvikram Madurai Elavarasan

    (Clean and Resilient Energy Systems (CARES) Laboratory, Texas A&M University, Galveston, TX 77553, USA)

  • Lucian Mihet-Popa

    (Faculty of Electrical Engineering, Ostfold University College, 1757 Halden, Norway)

Abstract

Soaring energy demand and the establishment of various trends in the energy market have paved the way for developing demand-side management (DSM) from the consumer side. This paper proposes a reinforced DSM (RDSM) approach that uses an enhanced binary gray wolf optimization algorithm (EBGWO) that benefits the consumer premises with load scheduling, and peak demand reduction. To date, DSM research has been carried out for residential, commercial and industrial loads, whereas DSM approaches for educational loads have been less studied. The institution load also consumes much utility energy during peak hours, making institutional consumers pay a high amount of cost for energy consumption during peak hours. The proposed objective is to reduce the total electricity cost and to improve the operating efficiency of the entire load profile at an educational institution. The proposed architecture integrates the solar PV (SPV) generation that supplies the user-comfort loads during peak operating hours. User comfort is determined with a metric termed the user comfort index (UCI). The novelty of the proposed work is highlighted by modeling a separate class of loads for temperature-controlled air conditioners (AC), supplying the user comfort loads from SPV generation and determining user comfort with percentage UCI. The improved transfer function used in the proposed EBGWO algorithm performs faster in optimizing nonlinear objective problems. The electricity price in the peak hours is high compared to the off-peak hours. The proposed EBGWO algorithm shift and schedules the loads from the peak hours to off-peak hours, and incorporating SPV in satisfying the user comfort loads aids in reducing the power consumption from the utility during peak hours. Thus, the proposed EBGWO algorithm greatly helps the consumer side decrease the peak-to-average ratio (PAR), improve user comfort significantly, reduce the peak demand, and save the institution’s electricity cost by USD 653.046.

Suggested Citation

  • Karthick Tamilarasu & Charles Raja Sathiasamuel & Jeslin Drusila Nesamalar Joseph & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2021. "Reinforced Demand Side Management for Educational Institution with Incorporation of User’s Comfort," Energies, MDPI, vol. 14(10), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2855-:d:555410
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2855/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/10/2855/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    2. Chen, Yongbao & Xu, Peng & Chen, Zhe & Wang, Hongxin & Sha, Huajing & Ji, Ying & Zhang, Yongming & Dou, Qiang & Wang, Sheng, 2020. "Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage," Applied Energy, Elsevier, vol. 280(C).
    3. Elavarasan, Rajvikram Madurai & Leoponraj, S. & Vishnupriyan, J. & Dheeraj, A. & Gangaram Sundar, G., 2021. "Multi-Criteria Decision Analysis for user satisfaction-induced demand-side load management for an institutional building," Renewable Energy, Elsevier, vol. 170(C), pages 1396-1426.
    4. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    5. Mbungu, Nsilulu T. & Bansal, Ramesh C. & Naidoo, Raj M. & Bettayeb, Maamar & Siti, Mukwanga W. & Bipath, Minnesh, 2020. "A dynamic energy management system using smart metering," Applied Energy, Elsevier, vol. 280(C).
    6. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    7. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Belusko, Martin & Boer, Dieter & Cabeza, Luisa F., 2018. "Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV," Applied Energy, Elsevier, vol. 211(C), pages 604-616.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. S. Charles Raja & A. C. Vishnu Dharssini & J. Jeslin Drusila Nesmalar & T. Karthick, 2023. "Deployment of IoT-Based Smart Demand-Side Management System with an Enhanced Degree of User Comfort at an Educational Institution," Energies, MDPI, vol. 16(3), pages 1-24, January.

    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. Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
    2. Fahad R. Albogamy & Ghulam Hafeez & Imran Khan & Sheraz Khan & Hend I. Alkhammash & Faheem Ali & Gul Rukh, 2021. "Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    3. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    4. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
    5. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    6. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    7. Lei Li & Yude Wu & Yi Lu & Xiao Yang & Qiyang Wang & Xiaoai Wang & Yulin Wang, 2022. "Numerical Simulation on the Structural Design of a Multi-Pore Water Diffuser during the External Ice Melting Process of an Ice Storage System," Energies, MDPI, vol. 15(6), pages 1-17, March.
    8. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    9. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    10. Abdul K Hamid & Nsilulu T Mbungu & A. Elnady & Ramesh C Bansal & Ali A Ismail & Mohammad A AlShabi, 2023. "A systematic review of grid-connected photovoltaic and photovoltaic/thermal systems: Benefits, challenges and mitigation," Energy & Environment, , vol. 34(7), pages 2775-2814, November.
    11. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    12. Ikutegbe, Charles A. & Farid, Mohammed M., 2020. "Application of phase change material foam composites in the built environment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    13. Busiswe Skosana & Mukwanga W. Siti & Nsilulu T. Mbungu & Sonu Kumar & Willy Mulumba, 2023. "An Evaluation of Potential Strategies in Renewable Energy Systems and Their Importance for South Africa—A Review," Energies, MDPI, vol. 16(22), pages 1-27, November.
    14. Jayachandran, M. & Rao, K. Prasada & Gatla, Ranjith Kumar & Kalaivani, C. & Kalaiarasy, C. & Logasabarirajan, C., 2022. "Operational concerns and solutions in smart electricity distribution systems," Utilities Policy, Elsevier, vol. 74(C).
    15. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    16. Benavides (Dir. proy.), Juan & Cadena, Ximena & Delgado-Rojas, Martha Elena & García, Helena & García, María Claudia & Medellín, Juan Camilo, 2021. "Asistencia técnica para la implementación del Proyecto de Acción de los NDC en Colombia (UNEP): Entregable 2.2. Informe que contiene los procesos que permitirían la destinación y gestión efectiva de f," Informes de Investigación 21036, Fedesarrollo.
    17. Tong Lei & Zuoqin Qian & Jie Ren, 2023. "Performance Evaluation of LiBr-H 2 O and LiCl-H 2 O Working Pairs in Compression-Assisted Double-Effect Absorption Refrigeration Systems for Utilization of Low-Temperature Heat Sources," Energies, MDPI, vol. 16(16), pages 1-19, August.
    18. Constantinos Vassiliades & Christos Minterides & Olga-Eleni Astara & Giovanni Barone & Ioannis Vardopoulos, 2023. "Socio-Economic Barriers to Adopting Energy-Saving Bioclimatic Strategies in a Mediterranean Sustainable Real Estate Setting: A Quantitative Analysis of Resident Perspectives," Energies, MDPI, vol. 16(24), pages 1-18, December.
    19. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    20. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(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:14:y:2021:i:10:p:2855-:d:555410. 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.