Distributed Optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks

Abstract

This paper proposes a distributed optimization-based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles and heat pumps. The DDT method employs a decomposition-based optimization method to have aggregators explicitly participate in congestion management, which gives more certainty and transparency compared to the normal DT method. With the DDT method, aggregators reveal their final aggregated plan and respect the plan during operation. By establishing an equivalent overall optimization, it is proven that the DDT method is able to minimize the overall energy consumption cost and line loss cost, which is different from the previous decomposition-based methods such as multiagent system methods. In addition, a reconditioning method and an integral controller are introduced to improve convergence of the distributed optimization where challenges arise due to multiple congestion points, multiple types of flexible demands, and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks.