Photovoltaic (PV) generation has high impact on the decarbonization pathways of power systems. Accuracy of day‐ahead PV power forecasting has become crucial in the operation and control of power system with high PV penetration. This paper develops a hybrid approach based on generative adversarial network (GAN) combined with convolutional autoencoder (CAE) to improve PV power forecasting accuracy. Self‐organizing map method is first utilized as data pre‐processing to classify target days into different weather types based on solar irradiance. With the ability of GAN to reduce the burden of loss and the advantages of CAE to extract multi‐scale effective features from the weather and PV power, PV power forecasting model consisting of GAN and CAE is proposed. The developed method has been tested on a real dataset in a Chinese PV station and compared with base reference PV forecasting methods. Numerical testing results demonstrate the effectiveness of our method with high accuracy.
To achieve its carbon neutrality goal, China has invested broadly in energy infrastructure and the emerging integrated energy stations (IESs) projects will bring enormous opportunities. Accurate carbon emission accounting for IESs is challenging in view of the complexity of the manufacturing process and uncertainty in construction and operation processes. To overcome these challenges, this paper develops a novel quantitative carbon footprint analysis model for IESs from a lifecycle perspective, with production and materialization, construction, operation and maintenance, and disposal and recycling phases considered. The method is applied on a 110 kV wind power IES project in China, to analyze and calculate lifecycle carbon emissions, identify the key influence factors of carbon footprints and provide suggestions for carbon reduction. The findings can identify key influence factors and provide suggestions for carbon reduction for the development of IES projects.
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