It is generally accepted that the impact of weather variation is gradually increasing in modern distribution networks with the integration of high-proportion photovoltaic (PV) power generation and weather-sensitive loads. This article analyzes power flow using a novel stochastic weather generator (SWG) based on statistical machine learning (SML). The proposed SML model, which incorporates generative adversarial networks (GANs), probability theory, and information theory, enables the generation and evaluation of simulated hourly weather data throughout the year. The GAN model captures various weather variation characteristics, including weather uncertainties, diurnal variations, and seasonal patterns. Compared to shallow learning models, the proposed deep learning model exhibits significant advantages in stochastic weather simulation. The simulated data generated by the proposed model closely resemble real data in terms of time-series regularity, integrity, and stochasticity. The SWG is applied to model PV power generation and weather-sensitive loads. Then, we actively conduct a power flow analysis (PFA) on a real distribution network in Guangdong, China, using simulated data for an entire year. The results provide evidence that the GAN-based SWG surpasses the shallow machine learning approach in terms of accuracy. The proposed model ensures accurate analysis of weather-related power flow and provides valuable insights for the analysis, planning, and design of distribution networks.

This paper reviews scenario generation techniques for modeling uncertainty in wind and photovoltaic (PV) power generation, a critical component as renewable energy integration into power systems grows. Scenario generation enables the simulation of variable power outputs under different weather conditions, serving as essential inputs for robust, stochastic, and distributionally robust optimization in system planning and operation. We categorize scenario generation methods into explicit and implicit approaches. Explicit methods rely on probabilistic assumptions and parameter estimation, which enable the interpretable yet parameterized modeling of power variability. Implicit methods, powered by deep learning models, offer data-driven scenario generation without predefined distributions, capturing complex temporal and spatial patterns in the renewable output. The review also addresses combined wind and PV power scenario generation, highlighting its importance for accurately reflecting correlated fluctuations in multi-site, interconnected systems. Finally, we address the limitations of scenario generation for wind and PV power integration planning and suggest future research directions.

The stochastic production simulation of photovoltaic (PV) power is crucial for the analysis of power balance in power planning, annual or monthly operational planning, and long-term transactions in the electricity market, especially in power systems with a high share of PVs. To model the uncertainty and temporal characteristics inherent in PV power, this letter introduces the style transfer and innovatively establishes bi-directional long short-term memory generative adversarial networks (GAN). Simulation results confirm the advantages of the proposed GAN over traditional convolutional neural network-based GANs in simulating the diversity and temporal characteristics of PV power.

Accurate multi-site ultra-short term photovoltaic (PV) power forecasting is essential for grid stability and efficient energy management. Existing methods are limited by coarse-resolution weather data and insufficient modeling of spatiotemporal dependencies. We propose a novel framework that combines bias-corrected high-resolution weather data with an Adaptive Fourier Graph Neural Network (PV-AFGNN) to capture complex spatial and temporal patterns. A Gated recurrent unit (GRU)-based encoder-decoder module refines Numerical Weather Prediction forecasts using local meteorological data, generating site-specific inputs for PV-AFGNN, which operates in the Fourier domain to model correlations efficiently. By jointly optimizing weather data correction and spatiotemporal learning, the framework achieves high accuracy even under challenging forecasting scenarios. Experiments on real-world multi-site PV datasets show that our method consistently outperforms state-of-the-art benchmarks, offering a scalable and robust solution for reliable energy scheduling, and enhanced integration of PV power into modern electricity systems.

A multi-view autostereoscopic three-dimensional (3D) system is built by using a 2D display screen and a customized parallax-barrier shutter (PBS) screen. The shutter screen is controlled dynamically by address driving matrix circuit and it is placed in front of the display screen at a certain location. The system could achieve densest viewpoints due to its specially optical and geometric design which is based on concept of “eye space”. The resolution of 3D imaging is not reduced compared to 2D mode by using limited time division multiplexing technology. The diffraction effects may play an important role in 3D display imaging quality, especially when applied to small screen, such as iPhone screen etc. For small screen, diffraction effects may contribute crosstalk between binocular views, image brightness uniformity etc. Therefore, diffraction effects are analyzed and considered in a one-dimensional shutter screen model of the 3D display, in which the numerical simulation of light from display pixels on display screen through parallax barrier slits to each viewing zone in eye space, is performed. The simulation results provide guidance for criteria screen size over which the impact of diffraction effects are ignorable, and below which diffraction effects must be taken into account. Finally, the simulation results are compared to the corresponding experimental measurements and observation with discussion.

The output of photovoltaic power generation exhibits significant randomness, volatility, and intermittency, and its uncertainty will have an impact on the planning assessment, dispatch decision-making, and real-time operation of the power system. To systematically understand the development status of modeling methods for the uncertainty of photovoltaic power generation, this paper conducts a review around the generation of annual scenarios and multi-timescale power prediction of photovoltaic power, and analyzes the correlations between photovoltaic output, influencing factors, and system applications from the perspective of statistical relationships and artificial intelligence. For the annual scale, the focus is on the generation methods of meteorological-driven scenarios for long-term sequences, including probability statistical methods, deep generation methods, constraint relations and engineering application evaluation issues; for the day-ahead scale, the historical power, meteorological variables and numerical weather forecasts are used to explore feature extraction, probability prediction and robust modeling methods; for the intraday scale, the signal decomposition, deep learning, regional collaborative modeling and multi-source perception methods for short-term power fluctuations perception are summarized. On this basis, further analysis is conducted on subsequent research directions such as multi-timescale collaborative modeling, multi-source heterogeneous information fusion, controllable generative modeling, extreme scenario characterization, and engineering closed-loop verification. This paper can serve as a reference for scenario generation, power prediction, and power system operation analysis under the condition of a high proportion of photovoltaic power integration.