Peng Zhang

To improve the economy and stability of data center green power direct supply, the capacity configuration optimization of wind–light–load storage based on improved particle swarm optimization (PSO) is conducted. According to wind speed, the Weibull distribution of wind output is established, while the Beta distribution of solar output is established according to light intensity. Furthermore, by conducting the correlation analysis, it is indicated that there is a negative correlation between wind and solar output, which is helpful to optimize the mix of wind and solar output. To minimize the yearly average cost of wind–light–load storage, the capacity configuration optimization model is established, where the constraints include wind and solar output, energy storage capacity, balance between wind and solar output and data center load. To solve the capacity configuration optimization model, the improved PSO is adopted, compared to other optimization algorithms, like differential evolution (DE), genetic algorithm (GA) and grey wolf optimizer (GWO); by adjusting the inertia weight factor dynamically, the improved PSO is more likely to escape the local optimal solution. To validate the feasibility of data center green power direct supply with wind–light–load storage, a case study is conducted. By solving the capacity configuration optimization model of wind–light–load storage with the improved PSO, the balance rate between wind–solar output and data center load is improved by 12.5%, while the rate of abandoned wind and solar output is reduced by 17.5%, which is helpful to improve the economy and stability of data center green power direct supply.

With the development of the power system, the ability to present a comprehensive and reasonable evaluation of its coordination level has become important for the collaborative optimization of source-grid-load-storage. By identifying uncertain risk factors fully, the present work develops a multi-level coordination-level evaluation of source-grid-load-storage based on AHP-entropy weighting. Building on previous studies, the present work reflects interactive characteristics of the collaborative optimization of source-grid-load-storage. Meanwhile, to determine the indicator weighting more reasonably, AHP-entropy weighting is adopted; this method combines the advantages of subjective AHP weighting and objective entropy weighting. Firstly, the multi-level coordination-level evaluation of source-grid-load-storage is introduced and includes both direct factors and indirect factors. Next, based on AHP-entropy weighting, the indicator weighting of the multi-level coordination-level evaluation is determined. Lastly, a case study is conducted that involves evaluating the coordination levels of the power systems of three regions. Additionally, the effectiveness of the multi-level coordination-level evaluation of source-grid-load-storage is validated.

To maximize the consumption of renewable energy and improve the reliability of power systems, the development of wind–photovoltaic–hydropower complementary power systems is promising, utilizing the dynamic regulation characteristics of hydropower. Firstly, the complementarity of wind–photovoltaic–hydropower is analyzed, where output characteristics of wind, photovoltaics, and hydropower are introduced, and output models related to wind speed, light intensity, and water level are established. Furthermore, the probability distribution of wind and photovoltaic output is calculated with the maximum likelihood method, and a correlation analysis between wind and photovoltaic output is conducted, where a wind–photovoltaic joint output model is established. Lastly, the k-means clustering algorithm is adopted to process typical scenarios of wind–photovoltaic joint output, and a case study is conducted to validate the wind–photovoltaic–hydropower joint output model. In configuring wind and photovoltaic output reasonably, the amount of abandoned wind and abandoned light is decreased significantly, while the reliability of the power system is improved.

Ensuring secure and efficient water level monitoring is critical for the intelligent management of hydropower plants, especially in challenging indoor environments. Existing methods, which are tailored for open areas with optimal conditions (adequate lighting, absence of debris interference, etc.), frequently falter in scenarios characterized by poor lighting, water vapor, and confined spaces. To address this challenge, this study introduces a robust indoor water level monitoring framework specifically for hydropower plants. This framework integrates a temporal super-resolution technique with an improved Yolov5 model. Specifically, to enhance the quality of indoor monitoring images, we propose a temporal super-resolution enhancement module. This module processes low-resolution water-level images to generate high-resolution outputs, thereby enabling reliable detection even in suboptimal conditions. Furthermore, unlike existing complex model-based approaches, our enhanced, lightweight Yolov5 model, featuring a small-scale feature mapping branch, ensures real-time monitoring and accurate detection across a variety of conditions, including daytime, nighttime, misty conditions, and wet surfaces. Experimental evaluations demonstrate the framework’s high accuracy, reliability, and operational efficiency, with recognition speeds reaching O(n). This approach is suitable for deployment in emerging intelligent systems, such as HT-for-Web analysis software 0.2.3 and warning platforms, providing vital support for hydropower plant security and emergency management.