A Two-stage Peer-to-peer Energy Trading Model for Distribution Systems with Participation of Utility

With the growing penetration of distributed energy resources(DER)in distribution systems,the traditional utility dominated tariff-based business model may no longer meet the need for further development.As a result,the transformation from the traditional tariff-based business model to the emerging peer-to-peer energy trading model has been acknowledged by researchers and policy makers.In this paper,a two-stage peer-to-peer energy trading model is proposed while considering the role of the utility.Specifically,energy transactions between buyers and sellers are optimized in the first stage;the cleared transactions are submitted to the utility for approval in the second stage,which solves a transaction approval model to verify the transactions from the perspective of secure system operations.Indeed,certain transactions mav be disapproved to ensure that all network constraints,such as voltage and line flow limitations,are satisfied.In addition,a comprehensive trading tariff is designed to recover the hidden costs of the utility,such as those associated with network usage,system losses,and ancillary service provision.A modified 33-bus distribution system is adopted to verify the proposed model.

PFL-DSSE:A Personalized Federated Learning Approach for Distribution System State Estimation

A centralized framework-based data-driven framework for active distribution system state estimation(DSSE)has been widely leveraged.However,it is challenged by potential data privacy breaches due to the aggregation of raw measurement data in a data center.A personalized federated learningbased DSSE method(PFL-DSSE)is proposed in a decentralized training framework for DSSE.Experimental validation confirms that PFL-DSSE can effectively and efficiently maintain data confidentiality and enhance estimation accuracy.