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Given the huge calculation burden and long-time consumption of the existing point-to-point feasible region search method， a new analytical modeling method of feasible regions for power generation is proposed in this paper. Firstly， the construction process of feasible regions for power generation under cross-section safety constraints is introduced， and the boundary characteristics of the feasible regions are analyzed. Secondly， it is deduced mathematically that there is a linear relationship between the output of sensitive generators and the system load at the boundary of the feasible region. An analytical modeling method for the feasible region boundary is proposed based on the linear relationship. Afterwards， when the output power of balanced generators exceeds their limits， a piecewise linear modeling method for the feasible region boundary is introduced. Finally， with a provincial power grid as an example， the feasible region boundary under different cross-section safety constraints is calculated， and the effectiveness of the proposed method is verified through comparison with the existing point-by-point search method.

The ground‐based laser heterodyne radiometer (LHR), which exhibits the advantages of small size, high spectral resolution, and easy integration, has been used for the remote sensing detection of several gases to meet a wide range of needs. This study aims to optimize the laser heterodyne system for detecting CO2 gas by focusing on existing research. Firstly, using the all‐fiber laser heterodyne detection system built by our research group, the power spectrum associated with the radio frequency signals of the detection system is discussed under different conditions: under no irradiation, under sunlight only, under sunlight and laser irradiation at the absorption peak, and under a filter in the spectrum range of 185–270 MHz. Signal‐to‐noise ratios (SNRs) of the high‐resolution spectrum have been obtained using different filter bands of 185–270, 225–270, and 225–400 MHz. Finally, the filter in the 225–270 MHz band, which has the highest SNR, is selected. Consequently, the resolution is improved and the system is further optimized. Furthermore, an optical fiber attenuator is used to change the power of the local oscillator light entering the system, and hyperspectral spectra with varying percentages of input energy and total energy are obtained. When the laser attenuation reaches 40%, the optimal SNR of the system is 486 and can be further improved to meet the expected requirements. This study will provide insights for improving the applicability of laser heterodyne technology in atmospheric sounding. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]