Optimal Cost and Component Configuration Analysis of Micro-grid using SSO Algorithm

The efficiency and optimal size of a micro-grid can be evaluated through economic analysis. Optimization is crucial for the sustainable development and upkeep of a micro-grid from a financial perspective. T he total cost of production can be lowered by taking advantage of available subsidies for things like capital, operations, pollution, and renewable energy, as well as satisfying a number of equality and inequality standards. The social spider optimization (SSO) method is an effective and versatile way to save money. In some cases, SSO is used in conjunction with other AI-based optimization techniques. In this work, we present a methodology for assessing the economic and environmental sustainability of grid-connected energy systems. The mathematical function of a micro-grid may consist of recycling the electricity generated each hour in relation to the resources available and storing the surplus in a super capacitor battery bank. Using a model developed for the Halishahar area of Chattogram, Bangladesh, we maximize the performance of a hybrid system consisting of photovoltaic cells, wind turbines, biomass, and a super capacitor battery. Solar panels, wind turbines, super capacitor batteries, and biomass are just some of the sustainable energy sources that the designers are thinking about. The area is estimated to consume roughly 107,150 MWh of electricity each year. We employ a social spider optimization strategy to determine which configuration settings w ill yield the lowest annual cost. More than a year's worth of electricity for Halishahar can be generated by this micro-grid. With this configuration, t he L COE for electricity i so nly 0. 127 $/kWh (dollar per kilowatt-hour). It's more effective at cutting carbon dioxide emissions than conventional power.