Low-complexity Risk-averse MPC for EMS

Abstract

A data-driven stochastic MPC strategy is presented as an EMS for the Skagerak Energilab microgrid. Uncertainties, introduced due to the intermittent nature of RES and load demands, are systematically incorporated into the MPC problem via adaptive chance-constraints. These chance-constraints promote admissible probabilistic operation of the microgrid within the stipulated SOC bounds of an ESS. For computational tractability, these chance-constraints are approximated by solving the inverse cumulative distribution function of a disturbance innovation sequence. This disturbance innovation sequence defines the difference between forecast and realized disturbances, and is sampled for a sliding window as disturbances are revealed over closed-loop operation. No a-prior assumptions are made on the distribution function of the disturbance innovation sequence; instead, solving the Maximum Spacings Estimation problem (off-line), we adapt some parametrized distribution function to fit this disturbance innovation sequence. The proposed strategy has computational complexity comparable to nominal deterministic MPC, promote the satisfaction of constraints in a probabilistic sense, and, decrease closed-loop operational costs by 26%.

Low-complexity Risk-averse MPC for EMS