Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Ahmad Alzahrani, Ghulam Hafeez (), Sajjad Ali, Sadia Murawwat, Muhammad Iftikhar Khan, Khalid Rehman and Azher M. Abed
Additional contact information
Ahmad Alzahrani: Electrical Engineering Department, College of Engineering, Najran University, Najran 11001, Saudi Arabia
Ghulam Hafeez: 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
Sadia Murawwat: Department of Electrical Engineering, Lahore College for Women University, Lahore 51000, Pakistan
Muhammad Iftikhar Khan: Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Khalid Rehman: Department of Electrical Engineering, CECOS University of IT and Emerging Sciences, Peshawar 25100, Pakistan
Azher M. Abed: Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon 51001, Iraq

Sustainability, 2023, vol. 15, issue 13, 1-21

Abstract: Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

Keywords: demand response; multi-objective optimization; distributed generation; solar; wind; batteries; smart grid (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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