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Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

Author

Listed:
  • Suchitra Dayalan

    (Department of EEE, SRMIST, Kattankulathur 603203, India)

  • Sheikh Suhaib Gul

    (Department of EEE, SRMIST, Kattankulathur 603203, India)

  • Rajarajeswari Rathinam

    (Department of EEE, SRMIST, Kattankulathur 603203, India)

  • George Fernandez Savari

    (Department of EEE, SRMIST, Kattankulathur 603203, India)

  • Shady H. E. Abdel Aleem

    (Department of Electrical Engineering, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalyubia 44971, Egypt)

  • Mohamed A. Mohamed

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt)

  • Ziad M. Ali

    (Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
    Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters.

Suggested Citation

  • Suchitra Dayalan & Sheikh Suhaib Gul & Rajarajeswari Rathinam & George Fernandez Savari & Shady H. E. Abdel Aleem & Mohamed A. Mohamed & Ziad M. Ali, 2022. "Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization," Sustainability, MDPI, vol. 14(17), pages 1-25, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10985-:d:905434
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    References listed on IDEAS

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    5. Abdulaziz Almalaq & Khalid Alqunun & Mohamed M. Refaat & Anouar Farah & Fares Benabdallah & Ziad M. Ali & Shady H. E. Abdel Aleem, 2022. "Towards Increasing Hosting Capacity of Modern Power Systems through Generation and Transmission Expansion Planning," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    6. Fotouhi Ghazvini, Mohammad Ali & Soares, João & Horta, Nuno & Neves, Rui & Castro, Rui & Vale, Zita, 2015. "A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers," Applied Energy, Elsevier, vol. 151(C), pages 102-118.
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    1. 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.

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