Optimized ESS Operation for Peak Shaving based on Probabilistic Load Prediction

With an increase in the mount of photovoltaic (PV) generators in a distribution network, the network requires flexible energy storage systems (ESSs) to accept additional PVs and prevent power overflow. This paper aims to find the best ESS operation schedule to minimize the peak load on the feeder. The load on the feeder needs to be predicted to determine the optimized ESS schedule; however, one deterministically predicted scenario is not reliable because the prediction contains certain error. In this paper, the forecasting error is reflected when the ESS schedule is optimized by using probabilistic load prediction. The proposed method doesn't provide only one scenario but shows various scenarios with probability of peak load to distributed network operators (DNOs) as a result of ESS operation. The DNOs can choose the best scenario based on their principles. In the simulation, based on the probabilistic load prediction, the peak load is shaved and three scenarios - optimistic, realistic, and pessimistic - are shown.