Under the new power system and the carbon peaking and carbon neutrality goals

virtual power plants (VPP) have become key entities for maintaining the balance between power supply and demand. Under increasingly stringent carbon constraints

achieving carbon balance has emerged as a pressing challenge. First

based on carbon-emission flow theory

distributed resources are partitioned and aggregated into virtual power plants by considering their carbon-balance potential and electrical distance. Then

the aggregated VPP is characterized using Zonotope

yielding its baseline power characteristics

maximum adjustable power boundary

maximum ramping rate

and carbon balance power characteristics. Finally

considering peer-to-peer trading and the carbon balance mechanism

a distributed low-carbon scheduling model for VPP is constructed

incorporating direct air capture

and solved using the alternating direction method of multipliers. Case studies demonstrate that the proposed model and methodology not only reduce the operating costs of virtual power plants but also effectively lower carbon costs

promote renewable energy integration

and enhance environmental benefits

thereby contributing to carbon neutrality.