Baseline-improved Economic Model Predictive Control for Optimal Microgrid Dispatch

As opposed to stabilizing to a reference trajectory or state, Economic Model Predictive Control (EMPC) optimizes economic performance over a prediction horizon, making it particularly attractive for economic microgrid (MG) dispatch. However, as load and generation forecasts are only known 24-48 h in advance, economically optimal steady states or periodic trajectories are not available and the EMPC-based works that rely on these signals are inadequate. In addition, demand charges, based on maximum monthly grid import power of the MG, cannot be easily casted as an additive cost, which prevents the application of the principle of optimality if introduced naively. In this work, we propose to close this mismatch between the EMPC prediction horizon and existing monthly timescales by means of an appropriately generated baseline reference trajectory. To do this, we first propose an EMPC formulation for a generic deterministic discrete non-linear time-varying system subject to hard state and input constraints. We then show that, under mild assumptions on the terminal cost and region, the asymptotic average economic cost of the proposed method is no worse than a baseline given by any arbitrary reference trajectory that is only known online. In particular, this results into 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 proposed EMPC framework can be used to solve optimal MG dispatch problems, introducing various costs and constraints that conform to the required assumptions. By means of this framework, we conduct realistic simulations with data from the Port of San Diego MG, which demonstrate that the proposed method can reduce monthly electricity costs in closed-loop with respect to established baseline reference trajectories.