Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Abstract

Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.

Keywords

• Resiliency
• Microgrid
• Two-stage robust optimization
• Demand response
• Storage
• Electric vehicle