A model predictive control approach for the optimization of polygeneration microgrids and demand response strategies

In this paper, the optimization of a microgrid operating in a 'urban district-like' environment is considered, combining both the optimal scheduling of the microgrid sources and demand response strategies, implemented in the district buildings. In particular, the overall system is optimized in order to schedule generation plants, storage systems, electrical vehicles, deferrable and variable loads with the minimum daily operating costs, taking also into account network constraints. Binary and auxiliary variables have been used to reduce the nonlinearity of the model. Moreover, a technique based on Model Predictive Control (MPC) is proposed to minimize uncertainties coming from renewable resources and to reduce the complexity of the overall decision problem solution. The proposed approach has been developed exploiting, as a reference and practical test-case, the Savona Campus of the Genoa University, where the research infrastructure Smart Polygeneration Microgrid (SPM) is in operation; day-ahead optimization results have been compared with those of the proposed MPC approach.