Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study. (July 2016) - Parisio, Alessandra; Rikos, Evangelos; Glielmo, Luigi
Abstract : Highlights: A multi-objective stochastic optimization model for microgrids is developed. The optimization model includes both thermal and electrical energy demand. A stochastic MPC controller for microgrids minimizing the imbalances is designed. Uncertainty due to demand and supply is incorporated in the controller. The proposed method is applied to an experimental microgrid located in Greece. Abstract: Microgrids are subsystems of the distribution grid which comprises generation capacities, storage devices and flexible loads, operating as a single controllable system either connected or isolated from the utility grid. In this work, microgrid management system is developed in a stochastic framework. It is seen as a constraint-based system that employs forecasts and stochastic techniques to manage microgrid operations. Uncertainties due to fluctuating demand and generation from renewable energy sources are taken into account and a two-stage stochastic programming approach is applied to efficiently optimize microgrid operations while satisfying a time-varying request and operation constraints. At the first stage, before the realizations of the random variables are known, a decision on the microgrid operations has to be made. At the second stage, after random variables outcomes become known, correction actions must be taken, which have a cost. The proposed approach aims at minimizing the expected cost of correction actions. Mathematically, the stochastic optimization problem is stated as a mixed-integer linear programming problem, which is solved in an efficient way by using commercial solvers. The stochastic problem is incorporated in a model predictive control scheme to further compensate the uncertainty through the feedback mechanism. A case study of a microgrid is employed to assess the performance of the on-line optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid: experimental results show the feasibility and the effectiveness of the proposed approach.