An application of heuristic optimization algorithm for demand response in smart grids with renewable energy

Mohammed Jalalah; Lyu-Guang Hua; Ghulam Hafeez; Safeer Ullah; Hisham Alghamdi; Salem Belhaj; Mohammed Jalalah; Lyu-Guang Hua; Ghulam Hafeez; Safeer Ullah; Hisham Alghamdi; Salem Belhaj

doi:10.3934/math.2024688

AIMS Mathematics

2024, Volume 9, Issue 6: 14158-14185. doi: 10.3934/math.2024688

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An application of heuristic optimization algorithm for demand response in smart grids with renewable energy

1.
Department of Electrical Engineering, College of Engineering, Najran University, Najran 11001, Saudi Arabia
2.
Power China Hua Dong Engineering Corporation Limited, Hangzhou 311122, China
3.
Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
4.
Department of Electrical Engineering, Quaid-e-Azam College of Engineering & Technology, Sahiwal, 57000, Pakistan
5.
Computer Science Department, College of Science, Northern Border University, Arar 73222, Saudi Arabia

Received: 14 January 2024 Revised: 10 March 2024 Accepted: 22 March 2024 Published: 18 April 2024
MSC : 68T20

This work presented power usage scheduling by engaging consumers in demand response program (DRP) with and without using renewable energy generation (REG). This power usage scheduling problem was modeled as an optimization problem, which was solved using an energy scheduler (ES) based on the crossover mutated enhanced wind-driven optimization (CMEWDO) algorithm. The CMEWDO was an enhanced wind-driven optimization (WDO) algorithm, where the optimal solution returned from WDO was fed to crossover and mutation operations to further achieve the global optimal solution. The developed CMEWDO algorithm was verified by comparing it with other algorithms like the whale optimization algorithm (WOA), enhanced differential evolution algorithm (EDE), and the WDO algorithm in aspects of the electricity bill and peak to average demand ratio (PADR) minimization without compromising consumers' comfort. Also, the developed CMEWDO algorithm has a lower computational time (measured in seconds) and a faster convergence rate (measured in number of iterations) than the standard WDO algorithm and other comparative algorithms.
- Demand response,
- solar photovoltaic,
- smart grid,
- energy management,
- heuristic optimization algorithms
Citation: Mohammed Jalalah, Lyu-Guang Hua, Ghulam Hafeez, Safeer Ullah, Hisham Alghamdi, Salem Belhaj. An application of heuristic optimization algorithm for demand response in smart grids with renewable energy[J]. AIMS Mathematics, 2024, 9(6): 14158-14185. doi: 10.3934/math.2024688

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Abstract

This work presented power usage scheduling by engaging consumers in demand response program (DRP) with and without using renewable energy generation (REG). This power usage scheduling problem was modeled as an optimization problem, which was solved using an energy scheduler (ES) based on the crossover mutated enhanced wind-driven optimization (CMEWDO) algorithm. The CMEWDO was an enhanced wind-driven optimization (WDO) algorithm, where the optimal solution returned from WDO was fed to crossover and mutation operations to further achieve the global optimal solution. The developed CMEWDO algorithm was verified by comparing it with other algorithms like the whale optimization algorithm (WOA), enhanced differential evolution algorithm (EDE), and the WDO algorithm in aspects of the electricity bill and peak to average demand ratio (PADR) minimization without compromising consumers' comfort. Also, the developed CMEWDO algorithm has a lower computational time (measured in seconds) and a faster convergence rate (measured in number of iterations) than the standard WDO algorithm and other comparative algorithms.

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