Yeongung Seo  Seungyoung Park  Myungjin Kim  Sungbin Lim

10.5626/JOK.2019.46.11.1165

Energy Storage System peak demand Machine Learning Time-Series Data

In recent years, blackouts have become more likely in South Korea as the peak demand has sharply increased. In order to address this issue, an energy storage system (ESS) operation scheduling technique has been investigated for its ability to reduce the peak demand by utilizing the power stored in the ESS. If the power demand information is known in advance, an optimal ESS operation scheduling technique can be applied in consideration of both the power stored in the ESS and the power demand to be generated in the future. However, it is difficult to predict the peak demand in advance because it only occurs in a relatively short time period, and the instance of its occurrence differs substantially from day-to-day. Therefore, it is very difficult to implement an optimal ESS operation scheduling technique that requires exact information on power demands in advance. Thus, in this paper, we proposed an ESS operation scheduling method with which to reduce the peak demand by using only historical power demands. Specifically, we employed a long short-term memory (LSTM) network and trained it using the historical power demands and their corresponding optimal ESS discharge powers. Then, we applied the trained network to approximate the optimal ESS operation scheduling. We showed the validity of the proposed method through computer simulations using historical power demand data from four customers. In particular, it was shown that the proposed scheme reduced the peak demand per year by up to about 82.42% compared to the optimal scheme that is only feasible when the exact future power demands are available.