A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems. (27th December 2020)

• Record Type: Journal Article
• Authors: Arfeen, Zeeshan Ahmad; Sheikh, Usman Ullah; Azam, Mehreen Kausar; Hassan, Rabia; Faisal Shehzad, Hafiz Muhammad; Ashraf, Shahzad; Abdullah, Md Pauzi; Aziz, Lubna
• Is Part Of: Concurrency and computation. Volume 33:Number 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-27
• Subjects: artificial intelligence -- evolution‐based optimization -- hybrid optimization -- microgrid -- swarm‐based optimization; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6165
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.