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

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, win...

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Vydáno v:Electronics (Basel) Ročník 12; číslo 4; s. 1062
Hlavní autoři: Nallolla, Chinna Alluraiah, P, Vijayapriya, Chittathuru, Dhanamjayulu, Padmanaban, Sanjeevikumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.02.2023
Témata:
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
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Shrnutí: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.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12041062