Double Robust Federated Digital Twin Modeling in Smart Grid

Abstract

Harnessing the advantage of digital twin (DT) technology, smart grid provides tempting prospects for efficient management of energy manufacturing, conservation, demand forecasting, pricing, and scheduling. However, the development of smart grid is challenged by volatility of individual users, cyber attacks, and hesitation about data sharing. In this study, a lightweight blockchain-enhanced federated learning (FL) framework is proposed to address the above issues and promote efficient collaborative DT modeling. Through encrypted blockchain retrieval, data aggregation and outlier detection, a Proof of Distribution consensus algorithm is developed to periodically update a conformal score for each edge user over time. The conformal score measures conformity of one user with patterns of the overall data distribution at current stage and is further utilized as basis for stochastic participant selection and model averaging, which improves stability and pertinence of FL in an uncertain environment with heterogeneity and concept drift. In the meantime, based on average learning loss of users with top-ranked conformal scores and engagement of users in the learning process, a reputation score is updated in real time for each user, which offers timely detection of malicious behaviors in the cooperation. The transmission of models and data are both implemented with blockchain, which guarantees privacy preservation and makes the process auditable. Through simulation of residential load forecasting based on real-world scenarios, double robustness of our framework against data volatility and adversarial attacks is verified.

Topics & Concepts