Development of Energy Management System for Microgrid System Considering Demand Response Program for Clean and Economic Operation

Micro-grids are small-scale localized energy systems operating independently or in conjunction with the main power grid. A micro-grid system (MGS) is made up of various energy sources like solar panels, wind turbines, fuel cells, along with storage units, control mechanisms, and a network of radial load feeders, all designed to enhance the economic efficiency of the micro-grid system. A fundamental microgrid system experiences hourly fluctuations in load demand. To manage this variability, utilities implement time-of-use (TOU) pricing, which establishes different electricity prices at various times based on the demand curve. This pricing structure incentivizes consumers to adjust their energy usage according to price signals, promoting efficiency and better alignment with supply conditions throughout the day. The primary goal of this work is to develop a demand response (DR) model aimed at maximizing benefits for microgrid customers. This model assesses the utility and elasticity of different customer segments, considering their varied behaviors during peak and off-peak periods. A hybrid of the Moth flame optimization and JAYA (MFOJAYA) algorithm is applied to minimize the overall costs of the microgrid system, comparing outcomes with and without the DR program. The analysis incorporates various cost factors, including fuel costs, pollution fines, operating and maintenance expenses, and depreciation costs, ensuring a thorough evaluation of the system's economic viability. The implementation of a DR-based energy management microgrid system led to a notable reduction in overall generation costs, decreasing from $880 to $868, along with a reduction in pollutants, as supported by numerical data. Additionally, the peak demand was reduced by 8.3%, dropping from 180 kW to 165 kW. The proposed optimization method is highlighted as superior through central tendency analysis, further demonstrating its effectiveness in enhancing the system’s performance and efficiency compared to existing approaches in the literature.