Forecast-driven stochastic optimization scheduling of an energy management system for an isolated hydrogen microgrid

Abstract

Balancing supply and demand constitutes the most important and challenging task in an isolated microgrid. Accordingly, it is essential to develop an optimization scheduling strategy for an energy management system of an isolated microgrid operation. In this study, a novel forecast-driven stochastic scheduling strategy was devised for the optimal operation of an isolated hydrogen microgrid. First, the change in wind power and load over 24 h was forecast using a bidirectional and long short-term memory convolutional neural network modeled end-to-end. To the best of the authors’ knowledge, this is the first application of end-to-end modeling for wind-power forecasting. Based on the forecast results, the stochastic optimization scheduling of the energy management system was resolved through deep reinforcement learning to minimize the microgrid lifecycle cost. Deep reinforcement learning combines the advantages of deep learning and reinforcement learning and uses statistical models to effectively solve sequence decisions of features of high-dimensional spaces. In addition, stochastic scenarios were generated using Monte Carlo simulations to analyze the uncertainties in wind and load. Furthermore, the energy capacity degradation of the energy storage system was considered. Finally, the effectiveness of the proposed approach was validated based on comparisons of different benchmark models and the latest models. The proposed scheduling strategy can realize high operational efficiency and reliable energy management system scheduling and is expected to serve as a reference for future research in this area.