Optimization of multiobjective hybrid meta heuristic algorithms on a smart microgrid

The development of technology, population growth, widespread use of electric vehicles, and the reduction of fossil resources have increased energy demand worldwide. Renewable Energy Resources are seen as alternative energy sources to non-renewable energy sources because of their friendliness, abundant nature in the country, reduction of soil erosion, lack of fuel cost. Microgrids are small, powerful energy networks that are connected to the network and independently from the network, consisting of renewable and non-renewable energy sources. Optimization is the most appropriate solution for possible design solutions. But all optimization techniques cannot find a suitable solution for the problem. However, the most suitable optimization technique can find the optimal solution to the problem. In this study, the optimization of a micro grid consisting of wind turbine, solar panel, diesel generator, battery and loads has been investigated. Three different meta heuristic optimizations have been used to optimize the reliability, environmental factor and sizing of the microgrid. These methods are Particle Swarm Optimization, Swallow Swarm Optimization and Hybrid Particle Swarm Optimization. Parameter analysis has also performed on these meta heuristic algorithms. The most suitable problems parameters have been obtained. These meta heuristic optimization techniques have been applied to solve the problem and the simulation results have been compared. Simulation results showed improvements for all three optimizations. However, Swallow Swarm Optimization has optimized the energy resources in the best way.