Deep reinforcement learning for the optimized operation of large amounts of distributed renewable energy assets

Abstract

This study utilizes machine learning and, more specifically, reinforcement learning (RL) to allow for an optimized, real-time operation of large numbers of decentral flexible assets on private household scale in the electricity domain. The potential and current obstacles of RL are demonstrated and a guide for interested practitioners is provided on how to tackle similar tasks without advanced skills in neural network programming. For the application in the energy domain it is demonstrated that state-of-the-art RL algorithms can be trained to control potentially millions of small-scale assets in private households. In detail, the applied RL algorithm outperforms common heuristic algorithms and only falls slightly short of the results provided by linear optimization, but at less than a thousandth of the simulation time. Thus, RL paves the way for aggregators of flexible energy assets to optimize profit over multiple use cases in a smart energy grid and thus also provide valuable grid services and a more sustainable operation of private energy assets.