Optimal Cost and Component Configuration Analysis of Micro-grid Using GWO Algorithm

Abstract

Economic analysis is used to assess the ideal size of a micro-grid and its efficiency. In order to maintain and grow a micro-grid economically, optimization is essential. A variety of equality and inequality requirements may be met to reduce the entire production cost, which includes subsidies for things like capital, operations, pollution, and renewable energy. Grey wolf optimization (GWO) is a powerful and adaptable cost-cutting strategy. GWO is used in tandem with other AI-based optimization methods in particular situations. Here, we provide a model for evaluating the viability, expense, and societal and environmental effects of energy systems that operate independently from the grid. Harmonization of micro-grids. It's possible that the micro-mathematical grid's role is to recycle power output hour by hour in accordance with available resources and to store any excess energy in a battery. In this work, we simulate and optimize a PV-Wind-WtE-battery hybrid system in the halishahar thana of Chattogram, Bangladesh. Design concerns include renewable energy sources including solar panels, wind turbines, batteries, and diesel engines. By our estimates, the thana uses around 107,150 MWh of power annually. We use a Grey wolf optimization approach to find the optimal design parameters to minimize the overall yearly cost. This micro-grid can easily provide 1,40,423.8 MWh, more than enough to power Halishahar for a whole year. A low levelized cost of energy (LCOE) of 0.221 $kWh is achieved with this setup. It reduces carbon dioxide emissions by a larger margin than traditional power.