Carbon-flow Aware VPP Optimal Scheduling and Carbon Trading Framework

Accurately calculating carbon emissions within virtual power plants (VPP) is critical to ensure environmentally responsible and economically efficient operation in today’s low-carbon energy landscape. VPPs have become key platforms for aggregating distributed energy resources through advanced control and communication technologies. However, existing studies treat carbon emission flow (CEF) as a post-scheduling evaluation, ignoring its real-time impact on operational decisions, and the carbon penalty strategies adopted are often overly simplistic. To address this limitation and enable cost-effective carbon emission reduction during the scheduling phase, this paper proposes an integrated CEF-VPP optimal scheduling framework that includes a non-convex problem formulation, McCormick relaxation to make the problem solvable, and an iterative algorithm to alleviate relaxation error. A stepped carbon penalty pricing strategy is incorporated into the problem formulation to represent the carbon pricing mechanism more realistically. Furthermore, a profit-oriented framework developed allows obtaining the marginal cost of carbon for VPP to enable informed participation in carbon markets. Realistic case studies validate the effectiveness of the proposed model and framework. The results indicate that integrating CEF and a stepped carbon pricing mechanism into the scheduling process can significantly reduce carbon emissions. Particularly, in the case study considered, carbon emissions were reduced by 10.6% compared to the original level. Additionally, under the proposed market participation strategy, the VPP can achieve an additional 18.77% of daily profit, as a result of CEF-informed VPP scheduling.