Day-Ahead Robust Operation Planning of Microgrids Under Uncertainties Considering DERs and Demand Response

Abstract

With the steady development and growth of distributed power energy resources (DERs), traditional distribution networks are being transformed in active distribution networks (ADNs), characterized by the presence of microgrids. Therefore, optimization of microgrid's resources has gained importance. The main challenges on optimization of microgrids are the high penetration of renewables such as PV and wind power, since these sources have an intermittent behavior that increases uncertainties, and managing DERs and energy transactions with the distribution system. This work proposes a framework for the day-ahead optimal operation planning of a microgrid containing batteries, controllable loads, PV generation, and thermal generation that accounts for the uncertainties using a robust optimization (RO) approach. The microgrid network feeders were also represented, aiming to ensure compliance with the operational constraints such as bus voltages and feeders' capacity limits. The system was modeled as a mixed-integer linear programming problem (MILP) and solved using a two-stage decomposition via a column and constraint generation algorithm (C&CG). The results indicate that the proposed framework can solve the microgrid optimization problem within reasonable computational time (< 5s) and demonstrate the importance of considering uncertainties, since a mere 15% uncertainty in load and PV generation forecast caused a 29,8% increase in daily operational costs.