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Open AccessArticle

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

1
Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
2
Department of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
3
Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
4
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad Campus, Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Energies 2020, 13(21), 5718; https://doi.org/10.3390/en13215718
Received: 28 July 2020 / Revised: 23 September 2020 / Accepted: 25 September 2020 / Published: 2 November 2020
(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids)
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. View Full-Text
Keywords: multi-objective energy optimization; smart grid; renewable energy sources; wind; photovoltaic; demand response programs multi-objective energy optimization; smart grid; renewable energy sources; wind; photovoltaic; demand response programs
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MDPI and ACS Style

Ullah, K.; Ali, S.; Khan, T.A.; Khan, I.; Jan, S.; Shah, I.A.; Hafeez, G. An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs. Energies 2020, 13, 5718. https://doi.org/10.3390/en13215718

AMA Style

Ullah K, Ali S, Khan TA, Khan I, Jan S, Shah IA, Hafeez G. An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs. Energies. 2020; 13(21):5718. https://doi.org/10.3390/en13215718

Chicago/Turabian Style

Ullah, Kalim; Ali, Sajjad; Khan, Taimoor A.; Khan, Imran; Jan, Sadaqat; Shah, Ibrar A.; Hafeez, Ghulam. 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs" Energies 13, no. 21: 5718. https://doi.org/10.3390/en13215718

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