Ancillary Services and Risk Assessment of Networked Microgrids Using Digital Twin

Abstract

The networked microgrids (NMGs) have become one of the better means of improving the resiliency and reliability of power system networks. NMGs need proper coordination of all microgrids in either grid-tied or isolated mode. This study proposes the use of a digital twin (DT) for the energy management of NMGs in the distribution system. The NMGs are virtually represented using a neural network (NN) by training the data from the different optimal results of unit commitment (UC). The UC formulation of NMGs considers risk assessment due to uncertainty of demand and renewable energy when the NMGs are operated in isolated mode and schedules ancillary service transactions when the NMGs are connected to the power grid. The NN predicts the schedule of diesel generators, fuel cell (FC) and the battery energy storage system (BESS) in both isolated and gird-tied modes. The merit of the proposed method is that the virtual model (NN) can interact with the physical model (NMGs) in a real-time manner that depends on its operational mode. Restated, the virtual model can provide real-time dispatch updates in the schedule of conventional generators, FC and BESS. A real-time digital simulator (Opal-RT eMegasim) was used to validate the proposed method by testing a modified CIGRE benchmark model and a modified IEEE 34-bus system.