Hierarchical Electricity and Carbon Trading in Transmission and Distribution Networks Based on Virtual Federated Prosumer

TL;DR

This paper introduces a hierarchical energy and carbon trading mechanism using virtual federated prosumers, optimizing market interactions to enhance energy efficiency and lower emissions through a novel game-theoretic approach.

Contribution

It proposes a new hierarchical market framework with a virtual federated prosumer model and develops algorithms for equilibrium pricing and budget allocation.

Findings

Effective reduction in carbon emissions demonstrated in case studies

Improved energy efficiency through hierarchical market coordination

Validated algorithms converge to equilibrium in practical scenarios

Abstract

Facing the dilemma of growing energy demand and mitigating carbon emissions, this paper proposes an energy sharing mechanism based on virtual federated prosumers (VFPs) with budget allocation for joint electricity and carbon market to incentivize distributed energy resources to participate in the hierarchical market and reduce carbon emissions. At the transmission level, the regional transmission operator coordinates transactions between two markets, the inter-VFP energy sharing market and the wholesale market, intending to minimize the overall cost of VFPs. The energy sharing market clearing problem is formulated as a generalized Nash game, for which we develop a first-order response algorithm to obtain the equilibrium. At the distribution level, the VFPs play the role of selfless auctioneer that leverage discriminatory weights and benchmark prices to allocate the electricity-carbon budget among entities in the VFP to maximize social welfare. The Nash game is exploited to characterize the budget allocation problem, for which a distributed feedback allocation algorithm is proposed. The entire hierarchical electricity and carbon trading is modeled as an equilibrium problem and is solved iteratively. Case studies based on a practical regional grid verify the effectiveness of the proposed algorithm and show that the mechanism is effective in improving energy efficiency and reducing carbon emissions.