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Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid

Author

Listed:
  • Kalim Ullah

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

  • Taimoor Ahmad Khan

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

  • Ghulam Hafeez

    (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)

  • Sadia Murawwat

    (Department of Electrical Engineering, Lahore College for Women University, Lahore 54000, Pakistan)

  • Basem Alamri

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Faheem Ali

    (Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Sajjad Ali

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

  • Sheraz Khan

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

Abstract

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6900-:d:920538
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    References listed on IDEAS

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    Cited by:

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