As an important part of the power industry, carbon reduction technology in substations plays a key role in supporting the implementation of national policy of carbon peak and reduction. In recent years, a series of carbon emission reduction measures have been developed in the operation phase of substations and promoted beneficial carbon emission reduction. This article summarizes the progress of related technologies and applications from the aspects of substation composition, carbon emission sources, carbon emission reduction technologies, and their effects. Firstly, the composition of carbon emissions from substations during operation is described. Secondly, various measures for carbon emission reduction are reviewed, including the selection of substation equipment types, the intelligent management of substation equipment, and the use of renewable energy. Finally, future technological directions for carbon reduction in substations are discussed, providing technical references and guidance for the sustainable construction and development of low-carbon substations.

Overhead transmission lines are crucial components of power grid construction, and their carbon emissions significantly impact the low-carbon construction of the power grid. This study adopts a cradle-to-gate life cycle assessment (LCA) method, defining the system boundary as the material production, transportation, and construction phases. Using the carbon-accounting software eFootprint and the emission factor method, we calculate and analyze the carbon emissions of a 500 kV double-circuit overhead transmission line project in Shantou, Guangdong Province, and systematically examine the emission characteristics from material production through construction. Results show that the material production phase dominates the carbon emissions of the project, accounting for 99.82% of the total emissions. Among them, conductors (49.41%) and tower materials (37.28%) are the core sources of carbon emissions, with a combined contribution of 86.69%. The findings highlight conductors and towers as key targets for emission reduction through strategies such as optimized material selection, adoption of high-strength lightweight alternatives, and modular construction techniques. However, this analysis has limitations: it is confined to a single subtropical coastal project, relies on industry-average emission factors from the CLCD database (with inherent methodological uncertainties), excludes operational and end-of-life phases, and should not be generalized without regional validation. While the study identifies key emission hotspots and potential mitigation levers, quantitative low-carbon design guidance requires project-specific data and full life-cycle assessment.

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.