Title: Smart grid resources allocation using smart genetic heuristic

Authors: Abderezak Touzene; Sultan Al Yahyai; Farid Melgani

Addresses: Computer Science Department, Sultan Qaboos University, Muscat, Oman ' Department of Information Technology, Mazoon Electricity Company, Al Hamriya, Muscat, Oman ' Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 14, I-38123, Trento, Italy

Abstract: In this paper, we propose a new smart genetic algorithm SGA-SG which allows Smart Grid Constituencies (SGC) such as Power Generators, Power Distributers, and Power Consumers to optimise their pay-offs. The proposed resource allocation algorithm connects real time power consumers to the best power distributers in terms of cost. SGA-SG algorithm is using the concept of genetic algorithm, smartly guided towards the solution by reducing the random walk effect of the classical genetic algorithm. Usually, smart grid systems are large scale systems (millions of customers). Hence, the design of the proposed SGA-SG algorithm takes into consideration the scale of the system in terms of memory and speed requirements to produce a good quality allocation within a reasonable time. SGA-SG algorithm is designed to quickly respond to any power failure on a real-time basis. Experimental results show that SGA-SG algorithm gives near optimal solution and reduces by 20% the overall cost of the smart grid constituencies compared to the traditional grid system.

Keywords: smart grid; resource allocation; optimisation; smart genetic algorithm.

DOI: 10.1504/IJCAT.2020.107918

International Journal of Computer Applications in Technology, 2020 Vol.63 No.1/2, pp.125 - 134

Received: 01 Jun 2019
Accepted: 27 Jan 2020
Published online: 30 Jun 2020 *

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