Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid
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...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Online Access: | https://www.mdpi.com/1996-1073/15/19/6900 |
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| author | Kalim Ullah Taimoor Ahmad Khan Ghulam Hafeez Imran Khan Sadia Murawwat Basem Alamri Faheem Ali Sajjad Ali Sheraz Khan |
| author_facet | Kalim Ullah Taimoor Ahmad Khan Ghulam Hafeez Imran Khan Sadia Murawwat Basem Alamri Faheem Ali Sajjad Ali Sheraz Khan |
| author_sort | Kalim Ullah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:49:48Z |
| format | Article |
| id | doaj.art-5cd9e1b04f3046cb9c697cd121fddc0e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:49:48Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5cd9e1b04f3046cb9c697cd121fddc0e2023-11-23T20:09:48ZengMDPI AGEnergies1996-10732022-09-011519690010.3390/en15196900Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart GridKalim Ullah0Taimoor Ahmad Khan1Ghulam Hafeez2Imran Khan3Sadia Murawwat4Basem Alamri5Faheem Ali6Sajjad Ali7Sheraz Khan8Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Mardan 23200, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Mardan 23200, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Mardan 23200, PakistanDepartment of Electrical Engineering, Lahore College for Women University, Lahore 54000, PakistanDepartment of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, PakistanDepartment of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Mardan 23200, PakistanDistributed 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.https://www.mdpi.com/1996-1073/15/19/6900hybrid demand response programssmart gridrenewable energy sourcesmulti-objective genetic algorithm |
| spellingShingle | Kalim Ullah Taimoor Ahmad Khan Ghulam Hafeez Imran Khan Sadia Murawwat Basem Alamri Faheem Ali Sajjad Ali Sheraz Khan Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid Energies hybrid demand response programs smart grid renewable energy sources multi-objective genetic algorithm |
| title | Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid |
| title_full | Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid |
| title_fullStr | Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid |
| title_full_unstemmed | Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid |
| title_short | Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid |
| title_sort | demand side management strategy for multi objective day ahead scheduling considering wind energy in smart grid |
| topic | hybrid demand response programs smart grid renewable energy sources multi-objective genetic algorithm |
| url | https://www.mdpi.com/1996-1073/15/19/6900 |
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