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

Kalim Ullah, Sajjad Ali, Taimoor Ahmad Khan, Imran Khan, Sadaqat Jan, Ibrar Ali Shah and Ghulam Hafeez
Additional contact information
Kalim Ullah: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Sajjad Ali: Department of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Taimoor Ahmad Khan: 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
Sadaqat Jan: Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Ibrar Ali Shah: Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Ghulam Hafeez: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan

Energies, 2020, vol. 13, issue 21, 1-17

Abstract: 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.

Keywords: multi-objective energy optimization; smart grid; renewable energy sources; wind; photovoltaic; demand response programs (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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