The high penetration level of renewable energy is thought to be one of the basic characteristics of future smart grids. Wind power, as one of the most increasing renewable energy, has brought a large number of uncertainties into the power systems. These uncertainties would require system operators to change their traditional ways of decision-making. This article provides a comprehensive survey of computational intelligence techniques for wind power uncertainty quantification in smart grids. First, prediction intervals (PIs) are introduced as a means to quantify the uncertainties in wind power forecasts. Various PI evaluation indices, including the latest trends in comprehensive evaluation techniques, are compared. Furthermore, computational intelligence-based PI construction methods are summarized and classified into traditional methods (parametric) and direct PI construction methods (nonparametric). In the second part of this article, methods of incorporating wind power forecast uncertainties into power system decision-making processes are investigated. Three techniques, namely, stochastic models, fuzzy logic models, and robust optimization, and different power system applications using these techniques are reviewed. Finally, future research directions, such as spatiotemporal and hierarchical forecasting, deep learning-based methods, and integration of predictive uncertainty estimates into the decision-making process, are discussed. This survey can benefit the readers by providing a complete technical summary of wind power uncertainty quantification and decision-making in smart grids.

Although the integration of solar photovoltaic (PV) systems is gaining widespread acceptance, the intermittency and instability of PV power generation lead to several operational challenges. PV power generation can be impacted by multiple environmental factors, such as the soiling of solar PV panels. There are some conventional methods proposed to deterministically estimate the solar power loss caused by soiling. However, the error of deterministic estimation cannot be eliminated due to the inherent volatility of solar power. Therefore, this paper proposes a probabilistic quantification method, namely SolarQRNN, to estimate the power loss by leveraging images captured by surveillance cameras. Specifically, the proposed model employs a novel quantile loss function and deep learning structures (backbone networks based on residual convolution units), which combines quantile regression and computer vision models for the first time. The proposed method has been extensively tested on a solar panel soiling image dataset. Test results indicate that SolarQRNN outperforms benchmark classification models with at least 51% improvements in evaluating metrics.

Solar panels are exposed to various pollutants in outdoor environments, such as dust, sediment, and bird excrement, which can cause the power generated by the panels to drop by up to 50%. To accurately estimate the power generated by photovoltaic (PV) systems, it is necessary to take into account the effects of soiling on the panels. In this article, we propose a deep learning approach that uses edge devices such as micro-controllers to estimate the power loss due to soiling based on images captured by surveillance cameras. The proposed model, called SoilingEdge, is based on MobileNet and has been implemented on several platforms, including CPUs, edge tensor processing units (EdgeTPUs), field programmable gate arrays (FPGAs), and vision processing units (VPUs). We present a comparative study of the performance of SoilingEdge on these different platforms and show that FPGA offers a balanced compromise between cost and inference performance, while VPU is the most cost-effective option, but has a slower execution time. Furthermore, the article also investigates the interpretability of the model through the visualization of attention maps, thus providing useful insights for researchers and engineers in understanding the model behaviors.

Quantifying the uncertainty in the wind power forecasting process is of great significance to the stability, security and reliability of the power system. The uncertainty can be effectively quantified by the interval prediction. In this paper, a novel interval prediction method based on skip-connection-based gate recurrent unite (skip-GRU) network and block bootstrap is proposed to predict short-term wind power. Both model parameters and data noise are concerned in uncertainty quantification. A large number of subsets are generated by different bootstrap methods firstly, and then skip-GRU models are trained by these subsets. The prediction intervals (PIs) on test datasets are generated with three confidence levels ranging from 85% to 95%. The results of the case studies demonstrate that the proposed method is able to obtain satisfactory PIs, and the overall quality of the PIs is higher than other three bootstrap methods and benchmark models.