Carbon-aware peer-to-peer energy trading within virtual power plants under networked constraints

Abstract

Virtual Power Plants enable decentralized prosumers to participate in power system operations. However, the geographical dispersion and heterogeneity of prosumers may lead to violations of networked constraints and higher carbon emissions. This paper addresses these challenges by developing a carbon-aware peer-to-peer energy trading approach within VPPs under networked constraints. First, a green matching trading mechanism is presented to sort and match orders from prosumers based on low-carbon energy priorities. Second, a built-in load vector matrix is integrated into a P2P energy trading model to determine the feasibility boundaries for trading among prosumers, which are coordinated to respond to scheduling commands from VPPs in a distributed manner. Third, a scaled alternating direction method of multipliers is used to solve the proposed model, in which networked constraints are rewritten as the augmented Lagrangian terms. The approach dynamically updates trading orders on an hourly basis, with results from each period influencing subsequent trading strategies. Simulation results show that the proposed approach outperforms traditional methods by achieving faster convergence and reducing VPPs’ costs, improving trading efficiency, optimizing resource allocation, and supporting low-carbon operations, offering a more effective framework for future energy markets.