Two-Stage Robust Optimization for Multiple Microgrids with Varying Loads under Uncertainty Conditions

Abstract

The volatility in electricity consumption caused by the growing integration of Electric Vehicles (EV) might significantly impact the reliability of microgrids. Hence, this study aims to decrease the operational expenses of multi-microgrids and enhance the efficiency of solution algorithms for their enhanced electric power distribution. This is achieved by introducing an innovative two-stage robust optimization dispatch system that considers the uncertainties related to loads, renewable energy sources, and electric vehicle consumption.  The multi-microgrid layer adjusts the limitations based on the number of EVsadded in real-time and manages various energy units to achieve the minimum operational expenses in the most unfavorable situations. The EVAggregator (EVA) layer ensures minimal power outages and optimal charging operations by regulating charging power while preventing safety breaches. The enhanced uncertainty set is derived from a neural network that undergoes training using a substantial amount of past information, thereby eliminating unrealistic worst-case situations. The suggested method effectively captures the system's features, allowing for the substitution of a considerable amount of past information with system attributeswhen creating the uncertainty set. This approach ensures both high dependability and a substantial decrease in convergence time.