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

Kalim Ullah, Taimoor Ahmad Khan, Ghulam Hafeez (), Imran Khan (), Sadia Murawwat, Basem Alamri, Faheem Ali, Sajjad Ali and Sheraz Khan
Additional contact information
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

Energies, 2022, vol. 15, issue 19, 1-14

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.

Keywords: hybrid demand response programs; smart grid; renewable energy sources; multi-objective genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/19/6900/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/19/6900/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:19:p:6900-:d:920538

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().