Reliable Cost-Aware Multi-Source Microgrid Configuration for Demand-Supply Balance

Microgrids are increasingly integrating renewable sources like solar and wind to improve sustainability and to reduce reliance on fossil fuels. However, the variable nature of the load demand and the renewable power generation create challenges in maintaining stable and efficient operation of the microgrid. To address these challenges, a distributionally robust chance-constrained model integrated with the grey wolf optimizer is proposed for reliable scheduling of renewable generation and load. This study focuses on a multi-objective energy management model which analyzes eight grid-connected microgrid configurations. In addition, eight diverse optimization algorithms are utilized to compare configurations of these microgrids based on total cost, emissions, and reliability. The investigated results show that the optimal configuration reduces total costs by 47.06% and greenhouse gas emission cost by 78.92% in comparison to the base case, in which the grid alone meets the load. The investigation also confirms that the optimal configuration requires an investment return of 315.75% and a reduction in social welfare cost of 47.063%. In addition, the reliability enhancement is shown by the low values of Expected Energy Not Served, Loss of Load Expectation, Loss of Load Probability, System Average Interruption Duration Index, and Annual Expenditure on Load Interruptions. The optimal configuration is validated on the standard IEEE-33 and IEEE-69 bus systems, and application results are presented to show the benefits and practicality of this work.