基于碳感知调制扩散模型的虚拟电厂低碳调度场景生成方法OA

Under the trend of widely integrating clean energy resources such as photovoltaics(PV)and electric vehicles(EVs)into the low-carbon scheduling of virtual power plants(VPPs),how to generate high-quality scenarios that are suitable for low-carbon scheduling under source-load uncertainty conditions has become a key issue in enhancing the economic and low-carbon nature of VPPs.However,existing scenario generation methods generally lack effective modeling of source-load joint perturbations and their sensitivity structure with respect to carbon allowances,resulting in generated samples being insufficiently aligned with the scheduling objectives.To address this issue,this paper proposes a scenario generation method for low-carbon scheduling of VPPs based on a carbon-aware modulated diffusion model.First,a low-carbon scheduling model is constructed to reveal the sensitivity propagation paths of carbon allowances under source-load perturbations.Then,the carbon sensitivity features are extracted from historical data and embedded into the noise injection,network structure,and loss function of a denoising diffusion probabilistic model,enabling structured modeling and modulation of key perturbation paths and introducing conditional variables to construct source-load joint scenarios specific to the target scheduling day.Finally,simulation experiments are conducted using actual data to verify the significant advantages of the proposed method in terms of scenario generation quality and low-carbon scheduling adaptability.The proposed method can serve as an input scenario generation tool for low-carbon scheduling of VPPs,enhancing the practicality and adaptability of scenarios in uncertainty modeling and decision-making support.