Search

The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations. [ABSTRACT FROM AUTHOR]

Non-Gaussian dynamic processes are ubiquitous due to the presence of non-Gaussian distributed variables. Therefore, fault detection of non-Gaussian dynamic processes plays a vital role to maintain the safe operation of systems and symmetry of data distribution. In this paper, a dynamic generalized likelihood ratio (DGLR)-based fault detection method is proposed for non-Gaussian dynamic processes. Different from the conventional principal component analysis (PCA)-based, dynamic PCA-based, and PCA-based GLR fault detection methods, the novelty of the proposed method is that the GLR is extended to non-Gaussian dynamic processes, and the randomized algorithm is integrated for threshold setting to attenuate the influence of non-Gaussian. The application scope of these methods is also discussed. The proposed method is compared with four existing fault detection methods on a numerical simulation and the continuous stirred-tank reactor (CSTR) process. The achieved results show that the proposed method is able to significantly improve the detection performance in terms of fault detection rate and prompt response to faults. [ABSTRACT FROM AUTHOR]