Toward Optimal Day-Ahead Scheduling and Operation Control of Microgrids Under Uncertainty

Abstract

In this paper, we present optimal day-ahead scheduling and control models for microgrids under uncertainty. The traditional deterministic mixed integer programming used in wholesale market fails to produce adequate results, especially when variations and risks are high. We develop stochastic programming optimization models with risk neutral and risk-averse options. We also characterize microgrids with their mean design capacity configuration and operational volatility. A dissimilarity measure determines the relative volatility between two microgrids. We demonstrate that risk-based decision-making in microgrids can significantly change the day-ahead scheduling and control outcomes, and capacity configuration and volatility measures are determining factors for microgrid risks and savings.