Integrating data-driven forecasting and optimization to improve the operation of distributed energy storage

Abstract

Distributed energy technologies, such as residential energy storage, embedded generation, and microgrids, are likely to play an increasing role in future energy systems. Getting the most value from these distributed assets is often dependent on the ability to optimize their operation in a distributed manner. This distributed optimization, in turn, calls for effective short-term forecasts of the output of small-scale generating assets, and the demand of small-scale aggregations of users. This paper introduces the integration of data-driven forecasting and operational optimization methods into a single model, avoiding the need to explicitly produce forecasts. The method is tested against two e..