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A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

Author

Listed:
  • Emad M. Ahmed

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Rajarajeswari Rathinam

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India)

  • Suchitra Dayalan

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India)

  • George S. Fernandez

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India)

  • 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)

  • Shady H. E. Abdel Aleem

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

  • Ahmed I. Omar

    (Electrical Power and Machines Engineering, The Higher Institute of Engineering at El-Shorouk City, El-Shorouk City 11837, Egypt)

Abstract

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2338-:d:639693
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    References listed on IDEAS

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    Cited by:

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    2. Junpei Nan & Jieran Feng & Xu Deng & Chao Wang & Ke Sun & Hao Zhou, 2022. "Hierarchical Low-Carbon Economic Dispatch with Source-Load Bilateral Carbon-Trading Based on Aumann–Shapley Method," Energies, MDPI, vol. 15(15), pages 1-17, July.
    3. Ćalasan, Martin & Micev, Mihailo & Hasanien, Hany M. & Abdel Aleem, Shady H.E., 2024. "PEM fuel cells: Two novel approaches for mathematical modeling and parameter estimation," Energy, Elsevier, vol. 290(C).
    4. Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar & Bahaa Saad Mahmoud, 2022. "Economical-Environmental-Technical Operation of Power Networks with High Penetration of Renewable Energy Systems Using Multi-Objective Coronavirus Herd Immunity Algorithm," Mathematics, MDPI, vol. 10(7), pages 1-43, April.

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