The power sector is the single industry with the largest carbon emission in China, the carbon emission of which accounts for more than 40% of China’s total carbon emissions. In relevant research on the simulation of power system operation, current studies focus more on energy conservation and economical operation, while few consider the low-carbon optimization of the power system from the perspective of carbon emissions. In addition, in relevant research on carbon reduction in the power system, current studies focus more on controlling the direct carbon emission of the source side and less on the indirect carbon emissions of the load side, which focus on the reverse effect of a user’s electricity consumption behavior on the carbon reduction goals of the power system. This article delved into a deterministic simulation model of power system operation based on time series load curves and proposed a carbon reduction mechanism called the low-carbon demand response mechanism, which guides users to actively respond and reduce the carbon emission of power systems. In addition, this article conducted an empirical analysis based on the planning data of the North China Power Grid. To minimize carbon emissions, a simulation of low-carbon optimization operation for the North China Power Grid in 2040 was carried out. Then, based on the simulation results, an analysis of the carbon reduction benefits of low-carbon demand response was carried out. Ultimately, the empirical analysis verified that low-carbon optimization operation and low-carbon demand response technology possess significant carbon reduction potential for the power system.

Fair sharing of carbon emission responsibilities is an important direction for the low carbonization of power systems. This paper proposes a new low-carbon dispatch method (N-LCD), which takes node carbon emissions as a constraint for power system decision-making, so as to adapt to the current management needs of power carbon footprint. The N-LCD model is built on the traditional optimal power flow (OPF) or low-carbon dispatch (LCD) model, integrating carbon flow equations and constraints and introducing a bidirectional power flow component to solve the problem of unknown carbon flow direction in power flow optimization. Empirical analysis proves the effectiveness of the N-LCD model in ensuring the fair share of carbon emission responsibilities and solving low-carbon unit commitment optimization based on carbon nodal emission control.

Introduction: Accurate and rapid carbon accounting method for the power industry is crucial to support China’s low-carbon transformation. Currently, carbon emission accounting methods are based on slowly updated fuel statistics or expensive monitoring equipment, resulting in high costs and delays in carbon emission estimation. Power data offers high real-time availability, accuracy, and resolution, and exhibits a strong correlation with carbon emissions. These characteristics provide a pathway for achieving rapid and precise annual carbon emission accountings. However, carbon emission data inherently exhibits small sample characteristics, making these methods less effective in small sample conditions and leading to lower accounting accuracy.Methods: Therefore, this paper proposes an augmented pre-training-based “electricity-to-carbon” method under small sample conditions.Results: This approach utilizes the correlation between electricity and carbon data as well as the autocorrelation characteristics of carbon emission data to construct a machine learning-based electricity-carbon fitting model for rapid and accurate carbon emission estimation. To address the challenges of small sample learning, this paper introduces an interpolation pre-training method to optimize the model’s hyperparameters and conserve samples for model training, thereby improving the model’s generalization and robustness.Discussion: Case studies on a real dataset verifies the effectiveness of the proposed method. The findings of this study can promote the development of carbon measurement technology and facilitate the low-carbon transition of developing countries.

The low-carbon transition of the power sector is fundamental to achieving “Dual Carbon” goals, where demand-side management plays an increasingly vital role in transforming flexible loads into renewable energy accommodation and active emission-reduction resources. However, existing low-carbon demand response mechanisms based on dynamic carbon emission factors only reflect average system states and fail to quantify the incremental carbon impact of marginal load changes. To address this limitation, this paper proposes a novel marginal carbon emission factor-driven low carbon demand response mechanism. Unlike traditional methods, the proposed mechanism utilizes marginal carbon emission factors as a high-sensitivity guiding signal to inform users of the real-time emission and renewable energy consumption variations caused by their consumption adjustments. Furthermore, considering the forecasting errors of high-penetration renewable energy, the uncertainty of marginal carbon emission factors is explicitly considered. Case studies are conducted to compare the proposed method with the conventional method through comparative analyses based on the modified PJM-5 system. Results demonstrate that the MCEF-driven approach provides more precise carbon-reduction and renewable energy utilization signals to achieve superior system-wide decarbonization performance and sustainable development.

Sustainable power-system operation requires carbon-reduction strategies that are emission-effective, physically deliverable, economically feasible, and compatible with user-side decarbonization claims. As Scope 2 carbon accounting increasingly emphasizes temporal, spatial, and physical consistency, dispatch models need to link user-level carbon claims with network-constrained power delivery. This paper proposes a User-Centric Carbon Cost-Constrained Low-Carbon Dispatch (CCC-LCD) framework that integrates carbon emission flow (CEF), nodal carbon intensity (NCI), network-constrained optimal dispatch, and endogenous demand response. A PTDF-based DC-OPF model represents active-power deliverability, while dual virtual flow variables determine carbon-flow directions endogenously. The model minimizes the target user’s physically traced Scope 2 emissions under a cost-tolerance budget and flexible-load constraints. Case studies on a modified IEEE 14-bus system show that nodal decarbonization is topology-dependent: high-load and high-NCI nodes obtain larger reductions from source-side generation substitution, whereas renewable-adjacent nodes exhibit limited marginal gains. The CEF-DR strategy outperforms single-mechanism cases, indicating the value of coordinating physical carbon-flow constraints with flexible demand. From a sustainability perspective, the proposed framework supports verifiable low-carbon electricity consumption, improves the economic feasibility of user-side decarbonization, and provides a practical dispatch tool for sustainable energy transition and corporate Scope 2 emission reduction.