Distributed Multistage Optimization of Large-Scale Microgrids Under Stochasticity

Microgrids are recognized as a relevant tool to absorb decentralized renewable energies in the energy mix. However, sequential handling of multiple stochastic productions and demands, storage, make their management delicate issue. We add another layer complexity by considering microgrids where different buildings stand at nodes network connected arcs; some host local production storage capabilities, can exchange with others surplus. formulate problem multistage optimization problem, corresponding minimization expected temporal sum operational costs, while satisfying demand each node, for all time. The resulting mathematical has large-scale nature, exhibiting both spatial couplings. displays structure that makes it amenable mix decomposition-coordination decomposition methods. conduct numerical simulations on sizes topologies, up 48 64 state variables. Decomposition methods faster provide more efficient policies than state-of-the-art Stochastic Dual Dynamic Programming algorithm. Moreover, they scale almost linearly dimension, making them promising address complex microgrid optimal problems.