Abstract
A model for energy trading in microgrids (MGs) is proposed in this paper. Imperialist competitive algorithm is used as a powerful method to determine the optimal schedule of all generation units in MG. The optimal scheduling is determined over a planning horizon considering all the constraints of the MG elements and the load demands. Scheduling of distributed generations (DGs) in the MG affects the risk of blackout in the power system. Therefore, a new objective function is presented to investigate the effect of DGs scheduling on the risk of partial or total blackout. DGs generate the great portion of energy in a MG. Generated power by some of DGs is strongly dependent on the weather and ambient conditions. Therefore, the power generation forecast is the major concern for constructing the model for MG energy trading. In this paper, artificial neural network (ANN) is used to predict hourly power outputs of DGs in the MG. Based on the ANN-based forecast module, the imperialist competitive algorithm is developed to determine the MG scheduling, by which the sources can be managed and an optimal operation can be achieved.
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