Multi-Objective Optimization of Microgrids Based on Recent Metaheuristic Methods

As the technology develops in the modern world, the need for electrical energy has increased. Renewable energy sources have emerged as an alternative energy source to fossil energy sources. Micro grids are the hybrid energy sources for both renewable and non-renewable energy sources. The choice of the microgrid depends on meeting the supply and low cost requirements while avoiding environmental pollution. Therefore, emission, reliability and sizing of a micro grid have been investigated in the present study. In addition, Swallow Swarm Optimization (SSO) and Hybrid Particle Swallow Swarm Optimization (HPSSO) algorithms were not found in micro grid related optimization studies. Performance of SSO and HPSSO algorithms was also evaluated. Particle Swarm Optimization (PSO), SSO, and HPSSO were adjusted in this study as multi-objective optimization method for increasing the reliability, decreasing emission and sizing energy resources of a microgrid feeding a 10 MW residence. A microgrid consisting of 8 MW solar panel, 4,5 MW wind turbine, 15 MW diesel generator, and 4 MW battery has been taken into consideration. The efficiencies of these algorithms were compared for different iterations and populations. In this study, the best results were obtained with the SSO algorithm. Loss of power supply probability (LPSP) = 0, Renewable factor (RF) = 1, with this algorithm our micro-grid has achieved a safe energy and minimum emission to feed the residence. In addition, a system that connects and disconnects the energy resources in varying load conditions was actualized with the SSO algorithm. With this algorithm LPSP = 0, RF = 1, Psize = 0,001. Maximum reliability, zero emission and minimum sizing of the energy sources in our microgrid were achieved with loads of up to 50%. Moreover, LPSP = 0.39, RF = 0.086, Psize = 0,21 values were obtained for loads 50% and above and good results were obtained for reliability, emission and sizing of energy sources