Multi-Objective Optimization Algorithms for a Hybrid AC/DC Microgrid Using RES: A Comprehensive Review

• Published in: Electronics (Basel) Vol. 12; no. 4; p. 1062
• Main Authors: Nallolla, Chinna Alluraiah, P, Vijayapriya, Chittathuru, Dhanamjayulu, Padmanaban, Sanjeevikumar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Algorithms; Alternative energy sources; Biomass; Costs; Diesel generators; Distributed generation; Distributed generation (Electric power); Electric vehicles; Energy costs; Energy resources; Energy storage; Evolutionary algorithms; Fossil fuels; Genetic algorithms; Hybrid systems; Industrial plant emissions; Mathematical optimization; Minimum cost; Multiple objective analysis; Operating costs; Optimization; Particle swarm optimization; Renewable energy sources; Renewable resources; Wind power; Wind turbines; India
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12041062

Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, wind turbine, battery storage, converter, and diesel generator. There is a steady increase in the utilization of hybrid renewable energy sources with hybrid AC/DC microgrids; consequently, it is necessary to solve optimization techniques. Therefore, the present study proposed utilizing multi-objective optimization methods using evolutionary algorithms. In this context, a few papers were reviewed regarding multi-objective optimization to determine the capacity and optimal design of a hybrid AC/DC microgrid with RESs. Here, the optimal system consisted of the minimum cost of energy, minimum net present cost, low operating cost, low carbon emissions and a high renewable fraction. These were determined by using multi-objective optimization (MOO) algorithms. The sizing optimization of the hybrid AC/DC microgrid was based on the multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO). Similarly, multi-objective optimization with different evolutionary algorithms (MOGA, MOGOA etc.) reduces energy cost and net present cost, and increases the reliability of islanded hybrid microgrid systems.