This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information.Peer ReviewedPostprint (author's final draft

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In this work, we introduce an approach for the reactive scheduling of production systems with bounded uncertain parameters. The proposed method follows a state-space representation for the scheduling problem, and relies on the use of a rolling horizon framework and multiparametric programming techniques. We show that by considering as uncertain parameters the set of variables that describe the state of the system at the beginning of the prediction horizon, we can effectively formulate a set of state-space multiparametric programming problems that are solved just once and off-line. In contrast to existing approaches, the repetitive solution of a new multiparametric problem after each disruptive event is avoided. The results of the parametric optimization are used in a rolling horizon basis without the need for online optimization. The proposed multiparametric programming rolling horizon (mp-RH) approach is applied in the scheduling problem of a network of combined heat and power units (i.e., a unit commitment problem type). Several case studies are solved, potential extensions of the proposed method are provided, and challenging areas wherein research is necessary are discussed.

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