Model Predictive Optimization for Energy Storage-Based Smart Grids

In recent years, energy storage systems (ESS) have started to play the role of an active electricity supplier so as to minimize overall electricity costs in a smart grid. However, ESS lifetime decreases with each cycle of charge/discharge. There is a tradeoff between ESS lifetime and electricity cost saving. As a solution, this work proposes a Model Predictive Optimization (MPO) method for distribution management in smart grids. Future energy states are predicted using an Autoregressive Integrated Moving Average (ARIMA) model. Based on the predicted electricity status, a near-optimal schedule for ESS usage is found using Genetic Algorithm such that a tradeoff is made between cost saving from electricity trading and the loss of life (LoL) in ESS. Experiment results show that the error rate of the prediction model is less than 10%. The MPO method achieves an overall cost saving of 0.85% and an ESS LoL reduction of 12.18%.

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