Green Base Station Battery Dispatchable Capacity Modeling and Optimization

Abstract

With the innovation of energy harvesting(EH) tech-nology and energy storage technology, renewable energy with energy storage batteries provides a new way to power future mobile communication base stations (BSs). However, a large number of BSs distributed energy storage resources are idle in most cases. In order to cope with this phenomenon, this study divides the battery energy storage zone into backup area and dispatchable capacity area according to the relationship between renewable energy collection and base station(BS) local load. On this basis, the battery control model and battery schedulable model are established to obtain the battery dispatchable capacity. In addition, deep Q learning (DQL) algorithms in machine learning are explored to optimize the model and maximize battery schedulable capacity. Finally, experimental cases show that battery energy dispatching is a win-win move for communication operators and distribution networks. Increasing the battery capacity can effectively smooth the local load curve of the distribution network.