Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning
This work addresses the problem of sustainable and scalable energy trading for prosumer communities in smart grids, though it appears incremental as it builds on existing technologies like federated learning and 5G networks.
The paper tackles the challenge of integrating prosumers into smart grid energy markets by addressing issues like communication overhead and data privacy, proposing a multi-level framework that uses 5G wireless networks and Federated Edge Learning to enable efficient energy trading and collaborative model training, with evaluations showing high accuracy and reduced communication overhead.
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.