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Home Energy Management System Embedded with a Multi-Objective Demand Response Optimization Model to Benefit Customers and Operators

Author

Listed:
  • Aya Amer

    (Electrical Engineering Department, Qatar University, Doha 2713, Qatar)

  • Khaled Shaban

    (Computer Science and Engineering Department, Qatar University, Doha 2713, Qatar)

  • Ahmed Gaouda

    (Computer Science and Engineering Department, Qatar University, Doha 2713, Qatar)

  • Ahmed Massoud

    (Electrical Engineering Department, Qatar University, Doha 2713, Qatar)

Abstract

This paper proposes a Home Energy Management System (HEMS) that optimizes the load demand and distributed energy resources. The optimal demand/generation profile is presented while considering utility price signal, customer satisfaction, and distribution transformer condition. The electricity home demand considers electric vehicles (EVs), Battery Energy Storage Systems (BESSs), and all types of non-shiftable, shiftable, and controllable appliances. Furthermore, PV-based renewable energy resources, EVs, and BESSs are utilized as sources of generated power during specific time intervals. In this model, customers can only perform Demand Response (DR) actions with contracts with utility operators. A multi-objective demand/generation response is proposed to optimize the scheduling of various loads/supplies based on the pricing schemes. The customers’ behavior comfort level and a degradation cost that reflects the distribution transformer Loss-of-Life (LoL) are integrated into the multi-objective optimization problem. Simulation results demonstrate the mutual benefits that the proposed HEMS provides to customers and utility operators by minimizing electricity costs while meeting customer comfort needs and minimizing transformer LoL to enhance operators’ assets. The results show that the electricity operation cost and demand peak are reduced by 31% and 18%, respectively, along with transformer LoL % which is reduced by 28% compared with the case when no DR was applied.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:257-:d:475275
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    References listed on IDEAS

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    1. Yeongenn Kwon & Taeyoung Kim & Keon Baek & Jinho Kim, 2020. "Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment," Energies, MDPI, vol. 13(11), pages 1-16, June.
    2. Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Effect of Loads and Other Key Factors on Oil-Transformer Ageing: Sustainability Benefits and Challenges," Energies, MDPI, vol. 8(10), pages 1-40, October.
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    Cited by:

    1. 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.
    2. 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.
    3. Álvaro Gutiérrez, 2022. "Optimization Trends in Demand-Side Management," Energies, MDPI, vol. 15(16), pages 1-3, August.
    4. Mostafa Shibl & Loay Ismail & Ahmed Massoud, 2021. "Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation," Energies, MDPI, vol. 14(19), pages 1-22, September.
    5. Aya Amer & Khaled Shaban & Ahmed Massoud, 2022. "Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes," Energies, MDPI, vol. 15(21), pages 1-20, November.
    6. Tehseen Mazhar & Rizwana Naz Asif & Muhammad Amir Malik & Muhammad Asgher Nadeem & Inayatul Haq & Muhammad Iqbal & Muhammad Kamran & Shahzad Ashraf, 2023. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods," Sustainability, MDPI, vol. 15(3), pages 1-26, February.
    7. Emad M. Ahmed & Rajarajeswari Rathinam & Suchitra Dayalan & George S. Fernandez & Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar, 2021. "A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    8. 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.

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