Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid

Fahad R. Albogamy, Sajjad Ali Khan, Ghulam Hafeez, Sadia Murawwat, Sheraz Khan, Syed Irtaza Haider, Abdul Basit and Klaus-Dieter Thoben
Additional contact information
Fahad R. Albogamy: Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Sajjad Ali Khan: US Pakistan Center for Advance Studies in Energy, University of Engineering and Technology, Peshawar 25000, Pakistan
Ghulam Hafeez: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Sadia Murawwat: Department of Electrical Engineering, Lahore College for Women University, Lahore 51000, Pakistan
Sheraz Khan: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Syed Irtaza Haider: College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Abdul Basit: US Pakistan Center for Advance Studies in Energy, University of Engineering and Technology, Peshawar 25000, Pakistan
Klaus-Dieter Thoben: Faculty of Production Engineering, University of Bremen, 28359 Bremen, Germany

Sustainability, 2022, vol. 14, issue 3, 1-28

Abstract: With the smart grid development, the modern electricity market is reformatted, where residential consumers can actively participate in the demand response (DR) program to balance demand with generation. However, lack of user knowledge is a challenging issue in responding to DR incentive signals. Thus, an Energy Management Controller (EMC) emerged that automatically respond to DR signal and solve energy management problem. On this note, in this work, a hybrid algorithm of Enhanced Differential Evolution (EDE) and Genetic Algorithm (GA) is developed, namely EDGE. The EMC is programmed based with EDGE algorithm to automatically respond to DR signals to solve energy management problems via scheduling three types of household load: interruptible, non-interruptible, and hybrid. The EDGE algorithm has critical features of both algorithms (GA and EDE), enabling the EMC to generate an optimal schedule of household load to reduce energy expense, carbon emission, Peak to Average Ratio (PAR), and user discomfort. To validate the proposed EDGE algorithm, simulations are conducted compared to the existing algorithms like Binary Particle Swarm Optimization (BPSO), GA, Wind Driven Optimization (WDO), and EDE. Results illustrate that the proposed EDGE algorithm outperforms benchmark algorithms in energy expense minimization, carbon emission minimization, PAR alleviation, and user discomfort maximization.

Keywords: scheduling; batteries; electric vehicles; demand response; renewable energy sources; smart grid (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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