Baseline-improved Economic Model Predictive Control for Optimal Microgrid Dispatch

Abstract

Economic Model Predictive Control (EMPC) optimizes economic performance over a prediction horizon rather than stabilizing to a reference, making it attractive for microgrid (MG) dispatch. However, since load and generation forecasts are known only 24-48 h ahead, economically optimal steady states or periodic trajectories are unavailable, and EMPC works relying on such signals are inadequate. Moreover, demand charges, based on the maximum monthly grid import, cannot be easily cast as an additive cost, which prevents a naive application of the principle of optimality. We propose to close this mismatch between the EMPC prediction horizon and monthly timescales via an appropriately generated baseline reference trajectory. We first propose an EMPC formulation for a generic deterministic discrete nonlinear time-varying system subject to hard state and input constraints. We then show that, under appropriate terminal ingredients -- sequential control invariance of the terminal region and a terminal control law causing a Lyapunov-like decrease of the terminal cost -- the asymptotic average economic cost of the proposed method is no worse than a baseline given by any arbitrary reference trajectory known only online. This yields a practical, finite-time upper bound on the average economic cost difference with the baseline that decreases linearly to zero as time goes to infinity. We then show how the framework solves optimal MG dispatch problems, introducing costs and constraints that conform to the required assumptions. Using data from the Port of San Diego MG, realistic simulations demonstrate that the proposed method reduces monthly electricity costs in closed loop relative to reference trajectories generated either by optimizing the electricity cost over the prediction horizon or by tracking an ideal grid import curve.