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An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs

Author

Listed:
  • Kalim Ullah

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Sajjad Ali

    (Department of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Taimoor Ahmad Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Imran Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Sadaqat Jan

    (Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Ibrar Ali Shah

    (Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Ghulam Hafeez

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
    Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad Campus, Islamabad 44000, Pakistan)

Abstract

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.

Suggested Citation

  • Kalim Ullah & Sajjad Ali & Taimoor Ahmad Khan & Imran Khan & Sadaqat Jan & Ibrar Ali Shah & Ghulam Hafeez, 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs," Energies, MDPI, vol. 13(21), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5718-:d:438715
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    References listed on IDEAS

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    Cited by:

    1. Fatima Zahra Zahraoui & Mehdi Et-taoussi & Houssam Eddine Chakir & Hamid Ouadi & Brahim Elbhiri, 2023. "Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids," Energies, MDPI, vol. 16(19), pages 1-26, September.
    2. Vasileios M. Laitsos & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2021. "An Incentive-Based Implementation of Demand Side Management in Power Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    3. Herodotos Herodotou, 2021. "Introduction to the Special Issue on Data-Intensive Computing in Smart Microgrids," Energies, MDPI, vol. 14(9), pages 1-3, May.
    4. Ahmad Alzahrani & Ghulam Hafeez & Sajjad Ali & Sadia Murawwat & Muhammad Iftikhar Khan & Khalid Rehman & Azher M. Abed, 2023. "Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    5. Taimoor Ahmad Khan & Amjad Ullah & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Faheem Ali & Sajjad Ali & Sheraz Khan & Khalid Rehman, 2022. "A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach," Energies, MDPI, vol. 15(23), pages 1-14, November.
    6. Kalim Ullah & Taimoor Ahmad Khan & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Basem Alamri & Faheem Ali & Sajjad Ali & Sheraz Khan, 2022. "Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid," Energies, MDPI, vol. 15(19), pages 1-14, September.
    7. Fahad R. Albogamy, 2022. "Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid," Energies, MDPI, vol. 15(21), pages 1-31, October.

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