This paper addresses the joint stochastic energy and reserve scheduling problem in microgrids (MGs). The established approach proposes a novel high-performance energy management system (EMS) making use of automatically controlled switches (ACSs). Accordingly, besides the optimal scheduling of active elements namely distributed generations (DGs) and responsive loads (RLs), the optimal topology of the network for each of the scheduling intervals is determined as well. Likewise, the effects of the reconfiguration process in probable variations of the scheduled energy patterns in DGs, RLs, and grid purchases are thoroughly assessed to highlight the alterations in unallocated capacities of these resources. Moreover, the uncertainties associated with both the load and wind speed forecasting errors are suitably accommodated through the reserve allocations. The proposed optimization procedure is formulated as a mixed-integer non-linear problem and resolved using a genetic algorithm (GA). The effectiveness of the projected framework is verified utilizing a typical MG, and the obtained numerical results are discussed in depth. Copyright © 2016 John Wiley & Sons, Ltd.

A comprehensive stochastic energy management system in reconfigurable microgrids

SIANO, PIERLUIGI
2016-01-01

Abstract

This paper addresses the joint stochastic energy and reserve scheduling problem in microgrids (MGs). The established approach proposes a novel high-performance energy management system (EMS) making use of automatically controlled switches (ACSs). Accordingly, besides the optimal scheduling of active elements namely distributed generations (DGs) and responsive loads (RLs), the optimal topology of the network for each of the scheduling intervals is determined as well. Likewise, the effects of the reconfiguration process in probable variations of the scheduled energy patterns in DGs, RLs, and grid purchases are thoroughly assessed to highlight the alterations in unallocated capacities of these resources. Moreover, the uncertainties associated with both the load and wind speed forecasting errors are suitably accommodated through the reserve allocations. The proposed optimization procedure is formulated as a mixed-integer non-linear problem and resolved using a genetic algorithm (GA). The effectiveness of the projected framework is verified utilizing a typical MG, and the obtained numerical results are discussed in depth. Copyright © 2016 John Wiley & Sons, Ltd.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4674804
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