Prediction of Residual Power Peaks in Industrial Microgrids Using Artificial Neural Networks

The main goal of an industrial microgrid during grid-connected operation is maximal cost saving for the microgrid owner. Many industrial companies do not only pay for the amount of electrical energy, but also for the maximum electrical power, which they have drawn from the distribution grid within the billing period. Under these conditions two basic options of cost saving exists utilizing the local energy storage systems inside the microgrid: reduction of the maximal power peak (peak shaving) and increase of self-consumption. For maximal cost saving, an operation strategy which combine both is desirable, but the combination requires information about the further residual power flow. A favorite option is the extrapolation of the residual power flow into the future. Unfortunately, it was found that errors in the extrapolation of the residual power lead to bad results in crucial situations. Therefore, this paper presents an additional artificial neural network (ANN) trained to predict residual power peaks, which will work in parallel to the extrapolation. This application-specific enhancement minimizes the effects of extrapolation errors and improves the original strategy in outcome and reliability. For an exemplary application, the self-consumption of the industrial microgrid is thereby increased by approx. 27% compared to the original result without peak power prediction.