Federated User Self-Decision Mechanism for Coupled Electricity and Carbon Market Considering Differentiated Objectives of Heterogeneous DERs

How to enable personalized objective and privacy protection on the user side while ensuring the model scalability, is quite challenging for the electricity and carbon (E&C) market at the distribution level. This paper proposes a user-side E&C market mechanism capable of accommodating heterogeneous distributed energy resources (DERs), whose personalized objectives are achieved by user-side self-decision and privacy-preserving procedures. Specifically, transactive operation models of multiple heterogeneous DERs are constructed, including the rarely unexplored metroway, charging station for aggregated electric vehicles, photovoltaic units, carbon emission units, and load aggregators. To keep in line with carbon emission reality on the user side, direct carbon emission models of six high-carbon enterprises are separately proposed. Further, a personalized federated learning algorithm with stochastic control variable (pFedScv) is proposed to deliver an efficient solution for the E&C market mechanism, which integrates a reinforcement learning algorithm called weighted twin-delayed deep deterministic policy gradient actor-critic network. Case studies on a real-world dataset show that the proposed E&C market mechanism can achieve a good trade-off between user-side trading costs and overall social welfare. The proposed pFedScv algorithm outperforms traditional federated learning algorithms in terms of convergence, stationarity, and computational performance.