Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks

Abstract

An increasing amount of volatile infrastructure, such as solar power plants, wind power plants, battery storages, or electric vehicles, will be added to the distribution grid. Such volatile infrastructure adds strain to the existing infrastructure and may lead to high transmission load on lines and corrupt power grid states. One way to resolve these issues is to establish regional energy markets. Such energy markets try to coordinate energy consumption or production regionally, based on estimates of future power grid states, thus reducing stress on the power grid. For these markets, it is crucial to know possible threatening grid states in advance to allow the market operators to keep the power grid in a healthy grid state with the help of regionally consumed or produced energy. In this work, we propose three different approaches to forecast future grid states with deep neural networks based on a learned numerical weather prediction representation. We evaluate these approaches with experimental results and discuss the remaining challenges that have to be addressed to enable regional energy markets. Furthermore, we provide the dataset to allow other researchers to reimplement our work.