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This research aims to enhance energy management systems (EMS) within a microgrid by emphasizing the significance of accurate renewable energy predictions and their strong correlation with load curtailment. The analysis of disturbance prediction accuracy reveals that forecasting one hour in advance is more effective than making immediate predictions or those several hours ahead.
Furthermore, the study explores scheduling load curtailment to manage peak power from renewable energy sources by comparing two distinct strategies: Case 1, which implements curtailments in both the morning and afternoon, and Case 2, which focuses solely on midday curtailment. The findings demonstrate that Case 1 effectively aligns load management with the peak output of photovoltaic (PV) energy, thereby reducing reliance on grid power and enhancing energy efficiency. In contrast, Case 2’s emphasis on midday curtailment leads to increased energy purchases from the grid, missing the opportunity to utilize abundant solar energy.
A critical finding of this research indicates that applying Case 1 for curtailment, alongside accurate forecasting, improves battery coordination and reduces stress on the supercapacitor, resulting in lower energy purchases from the grid. This interdependent relationship between precise forecasting and effective load management not only enhances the efficiency of the hybrid energy system (comprising battery and supercapacitor) but also increases reliance on renewable energy sources and storage systems. Consequently, this approach lowers overall energy costs, contributing to a more reliable and effective energy management system.
Keywords: Demand Response Program; Energy Management System; Hybrid Energy Storage; Model Predictive Control.
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Original article by NenPower, If reposted, please credit the source: https://nenpower.com/blog/simulation-of-energy-management-systems-in-ac-dc-microgrids-using-model-predictive-control/
