A novel stochastic model for flexible unit commitment of off-grid microgrids. (1st February 2023) - Polimeni, Simone; Moretti, Luca; Martelli, Emanuele; Leva, Sonia; Manzolini, Giampaolo
Highlights: Stochastic optimization approach with integer recourse for flexible Unit Commitment of generators within a Microgrid. Piecewise affine linear decision rules for adapting the Unit Commitment to the on-field load measurements, that consider the non-anticipative behavior of the stationary storage systems. Out-of-sample comparison with state-of-the-art benchmarks for day-ahead and rolling horizon operations. Laboratory implementation and experimental validation of numerical simulations. Abstract: This work proposes a novel two stage stochastic optimization approach for the Energy Management System (EMS) of MicroGrids (MG). It combines the scenario-based uncertainty characterization with the use of piecewise affine correction rules for the recourse decision. These rules are used by the EMS to set the commitment status of the programmable generators and to correct the storage management as a function of the realization of the uncertainty, measured in real-time. The novel stochastic model is integrated in a hierarchical EMS, based on two control layers: the first one employs the proposed stochastic approach to determine the daily strategic scheduling of the MG, and the second layer optimizes the real-time dispatch.. The proposed EMS is applied to a real-world case study of a rural MG. Results indicate that the proposed EMS outperforms state-of-the-art optimization approaches in terms ofservice reliability (99.8%) and fuel efficiency. Moreover, rolling horizon simulations of the proposed stochastic model showed 100% reliability, and 30% of fuel cost savings with respects to state-of-the-art methods. The novel EMS is then deployed on a laboratory-scale microgrid (Multi-Goods MicroGrid Laboratory, MG 2 lab), demonstrating secure and economic operations. A comparative analysis is made with respect to deterministic and standard stochastic two-stage approaches; the proposed solution outperforms the other models during real operations, with savings of fuel cost up to almost 10%.