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Eutrophication in cold-arid lakes: molecular characteristics and transformation mechanism of DOM under microbial action at the ice-water interface

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Carbon Research 2024年第1期3卷 1-17页

作者： Wang, Tengke Feng, Weiying Liu, Jing Fan, Wenhong Li, Tingting Song, Fanhao Yang, Fang Liao, Haiqing Leppäranta, Matti School of Materials Science and Engineering Beihang University Beijing100191 China Environment Research Institute Shandong University Qingdao266273 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Institute for Atmospheric and Earth System Research University of Helsinki Helsinki00014 Finland

During freezing periods, nutrients (carbon and organic matter, etc.) are enriched in the water and sediment of lakes in cold-arid regions, leading to potential algal bloom outbreaks and other health risks to the ecosystem. Particularly, dissolved organic matter (DOM) is a critical component of the nutrients and plays an important role in the global carbon cycle. However, the mechanisms of DOM transfer between ice and water remain unclear. This study analyzed the influence of microbial community on DOM composition using 16 s RNA, 3DEEM, and FT-ICR MS in Daihai Lake and Wuliangsuhai Lake in the Yellow River Basin, China. According to the spectral analysis, the content of endogenous organic matter, such as humus, accounted for 40% of the total DOM in water, while the content of tryptophan and tyrosine accounted for 80% of the total DOM in ice. The results of mass spectrometry showed that lignin was the main component, and the content of organic matter in the ice was less than that in the water. Molecular structures of seven DOM coexisting in the lake ice and water were elucidated with adapted Kendrick-analogous network visualization, which clearly illustrates that long-chain DOM molecules are derived from small molecules, while other heteroatoms are complexed with the side groups. The positive correlations between CHO, CHNO, CHOS, CHOS and Actinomyces indicate that DOM actively interacted with the microbial community. 44% of CHO compounds have the same molecular formula in water, the content of CHOS in the water of the two lakes was closed to 7% higher than that in the ice. Meanwhile, DOM dynamically migrate between ice and water via interstitial water because of the solubility changes under microbial transformation, which has been proved by the decrease in the contents of the humus and tryptophan-like substances in the ice from the bottom to the surface and lower contents of carbohydrate and unsaturated aromatic hydrocarbon in the water than the ice. This study helps

关键词： Aromatization

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Real-time Optimal Energy Management of Combined Cooling, Heating and Power systems Based on Knowledge conlusion 6

Real-time Optimal Energy Management of Combined Cooling, Hea...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： He, Chunjiang Wang, Yijian Xu, Yang Cui, Yang China Electric Power Research Institute State Key Laboratory of Power Grid Security and Energy Conservation Beijing China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin China

ISBN: (纸本)9798350346671

Combined Cooling, Heating and Power(CCHP) system is an important way to improve the efficiency of energy utilization and wind power consumption. In this case, increasing the operational flexibility of CCHP systems can help increase renewable energy consumption and reduce carbon emissions. So, it is necessary to provide real-time optimal energy management(OEM) for CCHP systems. In this paper. Firstly, a new training method is proposed for the LightGBM to improve the accuracy of real-time wind power prediction. In addition, to solve the nonlinear OEM of CCHP in real-time, a novel deep reinforcement learning algorithm-Soft Actor-Critic, is used in this paper. By combining the mechanism of CCHP with the reward function through knowledge fusion, the non-convex models can be translated into Markov Decision Processes(MDP), and then according to the Bellman optimal equation, the real-time optimized operation of the CCHP system is realized. Finally, the effectiveness of the proposed policy is verified by the actual operation data of a CCHP system in Northwest China. © 2023 IEEE.

关键词： Reinforcement learning

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Enhanced Transient Stability Strategy for Grid-Forming Converter Based on Current Limiting

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CSEE Journal of Power and Energy systems 2025年第3期11卷 960-971页

作者： Zhenglong Sun Zhifeng He Naiyuan Liu Rui Zhang Bo Wang Guowei Cai Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China

With the sustained increase in the penetration rate of grid-forming (GFM) converters in power systems, problems such as overcurrent and transient instability during system faults have drawn significant attention. This paper proposes an enhanced dynamic dq-axis current-limiting strategy (ED-CLiS) for GFM converters, which improves transient stability across all stages before and after current limiting. First, a dynamic dq-axis current-limiting strategy (D-CLiS) is proposed. This strategy regulates the power angle to prevent it from going out of control during current limiting, which is achieved by controlling the $d$ - and $q$ -axis current reference values to reshape the ${P}-\delta^{\prime}$ curve. Additionally, a large reverse acceleration area can induce excessive angle $\delta^{\prime}$ swings, causing the D-CLiS to be triggered repeatedly and resulting in converter instability after the control exit, even without faults. A reference power control structure (RPCS) is proposed, which suppresses the phenomenon by adjusting the power reference value to control the converter's reverse acceleration area. Finally, the control strategy is verified through simulations.

关键词： Transient analysis Power system stability Grid forming Stability criteria Voltage control Circuit stability Limiting Mathematical models Circuit faults Synchronous generators

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Optimal Scheduling of Pumped Storage Power Station with Double-Fed Variable Speed Unit Based on Reservoir Regional Operation Strategy 4

Optimal Scheduling of Pumped Storage Power Station with Doub...

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4th Asia-Pacific Conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Ma, Yuhan An, Yining Ge, Xiaohui Zhao, Bo State Grid Zhejiang Electric Power Co. Ltd. Research Institute Zhejiang Hangzhou China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin Jilin China

ISBN: (纸本)9798350359985

The conventional pumping unit lacks the ability to continuously adjust input power during pumping operations, and exhibits poor adaptability when integrating new energy sources into the grid. In this study, we propose a novel 'domain operation' strategy that allocates resources based on real-time reservoir conditions across all levels of hydropower stations, while optimizing operations through the implementation of doubly fed units. © 2024 IEEE.

关键词： Hydroelectric power

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Research on the Application of Big Data in Transient Stability simulation of Power systems 4

Research on the Application of Big Data in Transient Stabili...

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4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Zhang, Jie Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou510663 China National Energy Power Grid Technology R&d Centre Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou5106633 China

ISBN: (纸本)9798331518066

With the rapid expansion of smart grids, there is a growing complexity in power system dynamics, which increases the availability of real-time operational data and underscores the need for advanced transient stability simulations. Traditional transient stability analysis methods are constrained by their computational demands and may compromise accuracy. Furthermore, deep learning-based methods often focus on the application of a single algorithm, lacking a systematic consideration of the entire simulation process. To address these challenges, this study proposes a novel approach that leverages big data to enhance transient stability simulations. By integrating deep learning and ensemble learning techniques, the model significantly improves the accuracy and efficiency of predicting transient instabilities. The methodology utilizes a multi-source data acquisition and fusion scheme to ensure data comprehensiveness and a distributed storage architecture to handle large-scale datasets effectively. Advanced visualization technologies are also employed to enhance the interpretability of the simulation results. This comprehensive framework aims to support the safe and stable operation of power systems amid increasing complexity and data availability. © 2024 IEEE.

关键词： Transient stability

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A semantic segmentation method for UAV image based on improved BiseNetv2 5

A semantic segmentation method for UAV image based on improv...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Guo, Fengjuan Han, Sen Zhao, Jiahang Han, Jiale Xin, Jing Shaanxi Key Laboratory of Integrated and Intelligent Navigation The 20th Research Institute of China Electronics Technology Corporation Shaanxi Xi’an710068 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9781510668621

Aiming at the problem of low semantic segmentation accuracy of tiny targets in UAV aerial images, a bilateral semantic segmentation network T-BiseNetv2 is proposed. The proposed segmentation network is based on BiseNetv2, and the Transformer structure is introduced into semantic branch, and the detail branch and feature fusion module are reconstructed, which improves the network’s capacity to capture both global context information and local semantic information. Several semantic segmentation experiments are conducted on the standard semantic segmentation dataset UAVID, experimental results show that the proposed semantic segmentation network T-BiseNetv2 has a MIoU of 69.52%, which is about 2.2% higher than the original BiseNetv2 network. Especially, for "Human", the IoU of TBiseNetv2 is about 4.3% higher than that of the popular UNetFormer. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Emergency control methods for power systems based on improved deep reinforcement learning 6

Emergency control methods for power systems based on improve...

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2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhang, Jie Zhu, Yihua Liang, Zhuohang Ma, Qinfeng Zhang, Qingqing Liu, Mingshun An, Su Pu, Qingxin Dai, Jiang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangdong Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong Guangzhou510663 China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangdong Guangzhou510663 China Power Dispatching and Control Center of Guizhou Power Grid Co. Ltd. Guizhou Guiyang550000 China

In order to achieve fast and accurate transient stability analysis and emergency control, this paper proposes a transient stability emergency control method based on improved deep reinforcement learning. In order to fully explore the temporal and spatial variation trend of transient response, a multi-dimensional feature containing information such as transient situation energy is constructed, and the deep reinforcement learning model is transformed based on the time-space graph neural network. On this basis, an emergency control model is constructed, and the power grid knowledge is integrated into the emergency control decision-making scheme to reduce the exploration of invalid decision-making and improve the performance of the model. The effectiveness of the proposed method is verified in the IEEE-39 system. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Deep reinforcement learning

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Fault Condition Testing of Photovoltaic Inverter controller Based on Active Support Technology using Hardware-In-the-Loop in RTDS 1

Fault Condition Testing of Photovoltaic Inverter Controller ...

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2024 1st International Conference on Smart Grids and Power systems, ICSGPS 2024

作者： Li, Shengnan Hu, Yun He, Xin Zhu, Yihua He, Tingyi Tu, Liang Deng, Can Luo, Chao Su, Mingzhang Zeng, Guanming Yunnan Electric Power Research Institute Kunming China National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

Photovoltaic power generation is an important component of sustainable power generation and has been widely utilized. Grid connected inverters are key equipment in photovoltaic integrated systems, which directly affect the stability of the system. In this paper, the transient model of photovoltaic inverter controller based on active support technology is derived. The transient mathematical model and corresponding self-closing loop control strategy are analyzed. Based on the RTDS, a hardware controller semi-physical simulation platform is established to simulate and test photovoltaic inverters under different fault and control methods. Finally, the simulation data obtained from the self-closing loop control method and the hardware control method are compared to verify the stability and reliability of the photovoltaic inverter hardware controller. © 2024 Published under licence by IOP Publishing Ltd.

关键词： controllers

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Short-Term Prediction of Wind Farm Cluster Power Based on Load Risk Scenarios and Improved Weighted Loss During Peak and Valley Load Periods 7

Short-Term Prediction of Wind Farm Cluster Power Based on Lo...

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7th IEEE Conference on Energy Internet and Energy system Integration, EI2 2023

作者： Yang, Mao Guo, Yunfeng Wang, Bo Wang, Zhao Ministry of Education Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin132012 China China Electric Power Research Institute National Key Laboratory of Renewable Energy Grid-Integration Beijing100192 China

ISBN: (纸本)9798350345094

Load peak and valley periods as the key scheduling scenarios, the effectiveness of wind power prediction becomes extremely important, in order to minimize the impact of wind power on the power system due to prediction errors, a short-term prediction method of wind farm cluster power considering risk scenarios and improved loss function is proposed. Firstly, the coupled correlation mechanism between meteorological type and load demand is explored, and the load risk scenario and meteorological correlation are derived. Second, according to the risk scenarios of load demand, the traditional loss function is improved and used to train the power prediction model under the risk scenarios. Then, a dynamic division strategy of wind farm clusters considering the meteorological similarity of the previous day is proposed to obtain the sub-clusters of similar output. Finally, the model with improved weighted loss function is used to predict the wind power for different regional sub-clusters and extended to wind farm clusters. The above method is validated using the wind farm clusters in West Inner Mongolia, China. and the results show that in the low-risk load scenario, the accuracy of the model with improved loss is higher, and the accuracy decreases with the increase of the load risk level, but the overall risk indexes are significantly lower than the method with traditional loss function, which proves the validity of the proposed method. © 2023 IEEE.

关键词： Risk assessment

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New WHO global air quality guidelines help prevent premature deaths in China

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National Science Review 2022年第4期9卷 10-12页

作者： Tao Xue Guannan Geng Xia Meng Qingyang Xiao Yixuan Zheng Jicheng Gong Jun Liu Wei Wan Qiang Zhang Haidong Kan Shiqiu Zhang Tong Zhu Institute of Reproductive and Child Health/National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking University State Key Joint Laboratory of Environmental Simulation and Pollution Control School of EnvironmentTsinghua University School of Public Health Key Laboratory of Public Health Safety of the Ministry of Educationand Key Laboratory of Health Technology Assessment of the Ministry of HealthFudan University Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning College of Environmental Sciences and Engineering Peking University School of Energy and Environmental Engineering University of Science and Technology Beijing Clean Air Asia Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua University

The air quality guidelines （AQGs） issued by the World Health Organization （WHO） in 2005 are key references for the National Ambient Air Quality Standards （NAAQSs） in *** 22September 2021,the WHO issued new AQGs （NAQGs）[1],which propose more stringent targets to control exposure to multiple air pollutants,including fine particulate matter (PM2.5) and ozone （O3）,to which,in 2019,451 million premature deaths,globally,were attributable [2].

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