Techno-economic optimization of a renewable micro grid using multi-objective particle swarm optimization algorithm

Global warming and energy security concerns have attracted research efforts toward renewable micro grids. Since micro grids possess multiple energy sources with different availability states and required costs, optimi-zation practices could be helpful in their sizing and scheduling. In this study, multi-objective particle swarm optimization algorithm was employed on three renewable micro grid configurations for Shiraz climate, Iran. The considered micro grid configurations were wind turbine (WT) and combined heat and power (CHP) system, photovoltaic (PV) and CHP and PV, WT and CHP. The micro grid was assumed to be connected to gas and electricity networks and excess produced electricity was considered to be sold to the grid. Loss of power supply probability and the cost of energy per unit were considered as the optimization objective functions. The acquired results revealed that only employing WT could significantly increase the reliance on the power grid due to the low availability of wind energy in the climate. Since the climate possesses high levels of solar insolation, adding a PV system to the configuration significantly decreased the electrical load supplied by the grid. The cost of energy per unit for each scenario was acquired to be 0.266 USD, 0.235 USD, and 0.247 USD. The calculated optimum loss of power supply values were 0.285, 0.3218, and 0.207 for the three scenarios, respectively. While the cost of each energy unit was higher for the third scenario as compared to the second scenario by 5%, the share of demand load supplied by renewable sources increased by 20%. Simultaneous utilization of wind and solar energy was shown to be more beneficial, especially if carbon tax policies or renewable energy incentives were to be considered for future applications.