Deep Reinforcement Learning based Renewable Energy Error Compensable Forecasting (Papers Track)
Jaeik Jeong (Sogang University); Hongseok Kim (Sogang University)
Recently, renewable energy is rapidly integrated into the power grid to prevent climate change, and accurate forecasting of renewable generation becomes critical for reliable power system operation. However, existing forecasting algorithms only focused on reducing forecasting errors without considering error compensability by using a large-scale battery. In this paper, we propose a novel strategy called error compensable forecasting. We switch the objective of forecasting from reducing errors to making errors compensable by leveraging a battery. Specifically, we propose a deep reinforcement learning based framework having forecasting in the loop of control. Extensive simulations show that the proposed one-hour ahead forecasting achieves zero error for more than 98% of time while reducing the operational expenditure by up to 44%.