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We consider multistage stochastic optimization problems involving multipleunits. Each unit is a (small) control system. Static constraints couple unitsat each stage. We present a mix of spatial and temporal decompositions totackle such large scale problems. More precisely, we obtain theoretical boundsand policies by means of two methods, depending whether the couplingconstraints are handled by prices or by resources. We study both centralizedand decentralized information structures. We report the results of numericalexperiments on the management of urban microgrids. It appears thatdecomposition methods are much faster and give better results than the standardStochastic Dual Dynamic Programming method, both in terms of bounds and ofpolicy performance.

Mixed Spatial and Temporal Decompositions for Large Scale Multistage Stochastic Optimization Problems