Reinforcement Learning and Stochastic Optimization with Deep Learning-Based Forecasting on Power Grid Scheduling

Abstract

The emission of greenhouse gases is a major contributor to global warming. Carbon emissions from the electricity industry account for over 40% of the total carbon emissions. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable environment is challenging. To address the issue, we propose a grid dispatching technique that combines deep learning-based forecasting technology, reinforcement learning, and optimization technology. Deep learning-based forecasting can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the optimization method, we simplify the complex electricity environment to speed up the solution. The combination of proposed deep learning-based forecasting and stochastic optimization with online data augmentation is used to address the uncertainty of the dispatch system. A multi-agent reinforcement learning method is proposed to utilize team reward among energy storage devices. At last, we achieved the best results by combining reinforcement and optimization strategies. Comprehensive experiments demonstrate the effectiveness of our proposed framework.