Federated learning assisted distributed energy optimization

The increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by multiple entities (agents). The transactive energy community serves as an established example of this transition. Distributed energy management approaches can effectively address the evolving grid's scalability, resilience, and privacy requirements. In this context, the accuracy of agents' estimations becomes crucial for the performance of distributed and multi-agent decision-making paradigms. This paper specifically focuses on integrating federated learning (FL) with the multi-agent energy management procedure. FL is utilized to forecast agents' local energy generation and demand, aiming to accelerate the convergence of the distributed decision-making process. To enhance energy aggregation in transactive energy communities, we propose an FL-assisted distributed consensus + innovations approach. The results demonstrate that employing FL significantly reduces errors in predicting net power demand. The improved forecast accuracy, in turn, introduces less error in the distributed optimization process, thereby enhancing its convergence behaviour.