Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning

TL;DR

This paper proposes a federated learning-based framework utilizing 5G wireless networks to enable efficient, privacy-preserving energy trading among prosumer communities in smart grids, addressing communication, scalability, and sustainability challenges.

Contribution

It introduces a multi-level decision framework combined with federated learning and 5G communication to enhance prosumer energy trading efficiency and privacy in smart grids.

Findings

Federated learning achieves high prediction accuracy for energy resources.

The framework reduces communication overhead compared to centralized models.

Prosumers can make proactive energy trading decisions using local predictions.

Abstract

The exponential growth of distributed energy resources is enabling the transformation of traditional consumers in the smart grid into prosumers. Such transition presents a promising opportunity for sustainable energy trading. Yet, the integration of prosumers in the energy market imposes new considerations in designing unified and sustainable frameworks for efficient use of the power and communication infrastructure. Furthermore, several issues need to be tackled to adequately promote the adoption of decentralized renewable-oriented systems, such as communication overhead, data privacy, scalability, and sustainability. In this article, we present the different aspects and challenges to be addressed for building efficient energy trading markets in relation to communication and smart decision-making. Accordingly, we propose a multi-level pro-decision framework for prosumer communities to achieve collective goals. Since the individual decisions of prosumers are mainly driven by individual self-sufficiency goals, the framework prioritizes the individual prosumers' decisions and relies on the 5G wireless network for fast coordination among community members. In fact, each prosumer predicts energy production and consumption to make proactive trading decisions as a response to collective-level requests. Moreover, the collaboration of the community is further extended by including the collaborative training of prediction models using Federated Learning, assisted by edge servers and prosumer home-area equipment. In addition to preserving prosumers' privacy, we show through evaluations that training prediction models using Federated Learning yields high accuracy for different energy resources while reducing the communication overhead.