Design And Optimization Of Renewable Energy (Re)- Based Microgrid Using Genetic Algorithm

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Department of Electrical and Electronic Engineering


Renewable energy systems are proving to be efficient and environmentally sustainable ways of producing electricity. Day by day, using RE-based microgrid systems have been causing more to notice give power to disengaged or energy-lacking locales. To set up a microgrid system, optimization is required for cost and reliability analysis. Genetic algorithm is one of the most trending optimization techniques. In most off-grid areas, as an alternative to electricity provided by diesel generators, they are not environment friendly and very costly. This thesis presents a RE-based microgrid system and reduce diesel generator uses and capacity to satisfy the electrical load demand of a rural area in Saint Martin, Bangladesh. For optimizations, a recently proposed genetic algorithm is applied in this thesis. We use local weather data for calculating. The wind generation, photovoltaic generation, and load are modeled by historical hourly wind speed, solar irradiance, and temperature data. System optimization would be based on the sizing of the modules and the operating strategy. Programming of genetic algorithms is used to test all conditions to minimize the total net present expense for optimal configuration. To verify the proposed technique's strength, the results are compared with the results obtained from the particle swarm optimization (PSO) algorithm. The GA optimization technique provides efficient results. It is noticeable from the findings that the proposed system can maintain a steady power flow with the same optimum configuration also reduce the emission of CO2. The annualized system cost (ASC) from the proposed system's GA algorithm is a total of $34576.7(USD). It's seen that the PSO-based system emission of CO2 6.76 ton and GA-based system emission of CO2 4.11 ton. A comparison reveals that our proposed system reduces the cost 72.95 times and reduces the emission of CO2 2348.14 times by only using DG-based system.


presented by Md. Mofij Uddin, having Matric ID. ET 161063 and Md. Faisal Hossen Bablo, having Matric ID. ET 161058 of session Spring 2020