Abstract
The traditional centralized power system is gradually being replaced by smart grids. However, an important design issue is how to perform accurate demand-response such that the power distribution management is effective. This includes two sub-problems, namely the accurate prediction of future electricity demand-response situations and the optimization of power distribution. In this work, we propose a novel Model Predictive Optimization (MPO) method for the advanced distribution management system in smart grids. Future electricity situations (surplus/deficit) are predicted using a customized Autoregressive Integrated Moving Average (ARIMA) model. Pairing between buyers and sellers of electricity are performed based on not only the current situation, but also considering future situations. As a result, trading pairs with overall near-optimal cost are found through concurrent and multiple instances of Particle Swarm Optimization (PSO), along with conflict resolution. Experimental results on 30 micro-grids show the error rate of the ARIMA prediction model to be less than 10%. The proposed MPO method saves totally 19.38% overall trading cost, if predictions are made for 4 future time slots.
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