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Different from most existing studies that focus on offline demand side management (DSM) in microgrids (MGs) while neglecting forecasting errors of uncertain renewable generations, this paper studies online DSM. A two-stage real-time DSM method for an MG including different time scales, integrated with schedulable ability (SA) and uncertainties, is proposed. In the first stage, a novel internal pricing model is developed. On this basis, a model predictive control-based dynamic optimization is applied to minimize the operation cost and maintain the power balance considering the uncertainties imposed by both supply and demand sides in the MG system. In the second stage, we define the concept of SA for response executors (REs) and also establish an SA evaluation system taking the real-time and history information of the REs into account. In doing so, a faster-time scale online power allocation among REs is carried out in the framework of dynamic optimization to further compensate for the uncertainties in real-time, based on the evaluated SA values of the REs and the required compensation power. Numerical simulations on a residential MG show the reasonableness and effectiveness of the proposed method.
Yang et al. (Tue,) studied this question.
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