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Corrigendum to ‘Optimised configuration of multi-energy systems considering the adjusting capacity of communication base stations and risk of network congestion’ [Energy volume 313 (2024) 133873]

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Energy 2025年 314卷

作者： Dongfeng Yang Xi Wang Yong Sun Jingying Yang Xiaojun Liu Chao Jiang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin 132012 China State Grid Jilin Electric Power Co. Ltd Changchun 30000 China

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Impact of China’s Air Pollution Prevention and control Action Plan on PM2.5 chemical composition over eastern China

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Science China Earth Sciences 2019年第12期62卷 1872-1884页

作者： Guannan GENG Qingyang XIAO Yixuan ZHENG Dan TONG Yuxuan ZHANG Xiaoye ZHANG Qiang ZHANG Kebin HE Yang LIU Department of Environmental Health Rollins School of Public HealthEmory UniversityAtlantaGeorgia 30322USA Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China Key Laboratory for Atmospheric Chemistry Chinese Academy of Meteorological SciencesCMABeijing 100081China

China promulgated the Air Pollution Prevention and control Action Plan(the Action Plan)in 2013 and developed stringent control measures to mitigate fine particulate matter(PM2.5) ***,we investigated the PM2.5 chemical composition changes over eastern China associated with the Action Plan during 2013-2017 using satellite-based PM2.5 chemical composition data derived using CMAQ simulations and satellite *** PM2.5 concentrations decreased considerably during this time as a result of the reductions in all chemical species in *** population-weighted mean concentrations over eastern China decreased from 11.1 to 6.7μgm-3 for SO42-,13.8-13.1μgm-3 for NO3-,7.4-5.8μgm-3 for NH4+,9.9-8.4μgm-3 for OM,4.6-3.8μg m-3 for BC and 12.9-9.6μg m-3 for other species in ***42-had the largest reduction of 40%,while NO3-had the lowest reduction of 5%,resulting in a greater fraction of NO3-and a smaller fraction of SO42-in *** the three key regions,Beijing-Tianjin-Hebei had the largest reduction in PM2.5 and its chemical *** decrease in SO42-concentrations was in line with the reduction of SO2 emissions,and the major driver of the SO2 emission reductions was the industrial *** decrease in NO3 concentrations was limited because the decrease in SO2 emissions and the stable NH3 emissions facilitated the formation of NO3-from HNO3,which partially offset the reduction in NOx emissions driven by the power *** mitigate PM2.5 pollution more effectively,future efforts are needed to reduce NH3 emissions.

关键词： PM25 chemical composition Air Pollution Prevention and control Action Plan Satellite remote sensing

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Deep Sequence to Sequence Attention Network Approach for Short-Term Electricity Price Forecasting

SSRN

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SSRN 2022年

作者： Chen, Haipeng Wu, Huayue Chen, Jian Kan, Tianyang Miao, Sen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Chengde Power Supply Company State Grid Jibei Electric Power Co. Ltd. Chengde067000 China

With increasing renewable energy penetration and the extension of the power system scale, accurate electricity price prediction has long become a challenging task. The complexity of electricity market information, insufficient model training, and the lack of model generalization ability limit the prediction accuracy of the existing electricity price forecasting methods. This paper proposes a Sequence-to-Sequence Attention algorithm based on the Double Deep Q Network optimization method for short-term electricity price prediction. This algorithm first conducts the maximum information coefficient correlation analysis to select the input dataset for the prediction model of electricity price. Secondly, the Sequence-to-Sequence Attention model is proposed for point forecasting and probabilistic forecasting of short-term electricity prices. Finally, the hyperparameters of the model are optimized by the Double Deep Q Network method, which improves the prediction accuracy and the generalization ability of the prediction model of electricity price. simulations are carried out on Pennsylvania–New Jersey–Maryland market data and the New South Wales electricity market. The results show that the proposed method has superior performance than the existing prediction methods, which is appliable for the short-term electricity price prediction and provides a powerful decision-making basis for market participants and regulators. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

arXiv

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arXiv 2022年

作者： Li, Yang Bu, Fanjin Li, Yuanzheng Long, Chao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Water Energy and Environment Cranfield University CranfieldMK43 0AL United Kingdom

Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater. © 2022, CC BY-NC-ND.

关键词： Desalination

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Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yang Wang, Ruinong Li, Yuanzheng Zhang, Meng Long, Chao The Key Laboratory of Modern Power System Simulation Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China The State Grid Jilin Electric Power Co. Ltd Jilin Power Supply Company Chuanying District Jilin132001 China The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The School of National Key Laboratory of Science and Technology Vessel Integrated Power System Naval University of Engineering Wuhan430033 China The School of Water Energy and Environment Cranfield University United Kingdom

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Electric load dispatching

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Operation Risks Analysis and Stability control system Optimization for the Junction Area of Multiple HVDC Sending system

Operation Risks Analysis and Stability Control System Optimi...

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International Conference on Power system Technology (POWERCON)

作者： Yukun Zhu Yihua Zhu Dongxu Chang Jiawei Yu Shengnan Li Yong Chen State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid 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 Electric Power Research Institute Yunan Power Grid Co Ltd Kunming China

ISBN: (纸本)9781665407380

The successive commissioning of HVDC projects has enabled many substations or areas to connect with multiple sending ends of HVDC simultaneously, forming a junction area of multiple HVDC sending system. The operation risks in these areas have also changed accordingly. This paper focuses on the safe and stable operation of the junction area of multiple HVDC sending system, analyzing the current operation risks of the junction area and the adaptability of the regional stability control strategies at first. Then, an optimization scheme including regional stability control strategy design and structure configuration for the junction area is put forward, whose feasibility is verified through the stability control system of Huangping area, which is a typical junction area located in the sending ends of Jinguan-Guizhong and Xinsong-Dongfang HVDC. The Successful application in Huangping area also proves that the stability control system optamization scheme for junction area of multiple HVDC sending system proposed in this paper has good completeness and scalability, that is of great significance to the design for the stability control system of similar junction area.

关键词： Substations HVDC transmission systems operation Scalability Power system stability control systems Stability analysis

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Capacity Optimization Configuration of Wind-Solar-Hydrogen-Ammonia Coupling system Considering On/Off Grid Electricity Constraints

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Smart Power 2025年第1期53卷 75-81and97页

作者： Kong, Lingguo Jia, Zhonghua Shi, Zhenyu Kang, Jiandong Fang, Lei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China China Electric Power Research Institute Co. Ltd. Beijing100192 China State Grid Jiangxi Yingtan Power Supply Company Yingtan335000 China

To promote the large-scale development of "green hydrogen" for "green ammonia" production and enhance the economic viability of wind-solar-hydrogen-ammonia multi-factor coupling system，this paper proposes a capacity optimization configuration method for the wind-solar-hydrogen-ammonia multi-factor coupling system considering on/off grid electricity constraints. Firstly，the mathematical models of key equipments in hydrogen production by water electrolysis and ammonia synthesis framework are established. Then considering renewable energy on-grid and off-grid electricity constraints，the operation strategies for the wind-solar-hydrogen-ammonia multi-factor coupling system with various operating conditions are proposed based on net power conditions and time-of-use electricity pricing policies. Furthermore，the objective function of minimizing the levelized cost of the ammonia is combined with the operation strategies for the multi-factor coupling system to establish a refined capacity optimization configuration model，solving the model with an improved particle swarm optimization algorithm. Finally，a case study using wind and solar data from a region in Inner Mongolia is conducted to verify the economy and effectiveness of the proposed optimization configuration method. © 2025 Zhihui Dianli Bianjibu. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Dual-layer economic dispatch method for electric fused magnesia loads participating in peak shaving guided by price compensation

Dual-layer economic dispatch method for electric fused magne...

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4th Energy Conversion and Economics Annual Forum (ECE Forum 2024)

作者： Junhui Li Tianzhen Fan Jing Xu Wei Zheng Chupeng Xiao Chaoyang Xu Cuiping Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin People's Republic of China State Grid Electric Power Research Institute Co. State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Co. Ltd. Hubei People's Republic of China State Grid Liaoning Electric Power Supply Co. State Grid Fushun Electric Power Supply Co. Ltd. Liaoning People's Republic of China

ISBN: (数字)9781837242924

To address the severe challenge of increasing fluctuations in supply and demand due to the expansion of new energy installations, this paper proposes a hierarchical optimization control strategy for electric fused magnesia loads participating in peak shav ing, guided by a price compensation mechanism. In the upper-layer system control, a price mechanism is designed to enhance the flexibility of electric fused magnesia loads in participating in peak shaving, considering the overall benefit of the system. In the lower-layer enterprise net profit optimization model, the product revenue, demand response income, operation and maintenance costs, and carbon emission costs of the electric fused magnesia enterprise are balanced to ensure the maximization of the enterprise’s net profit. Finally, numerical examples are used to verify the effectiveness of this method in improving the system's peak shaving capability.

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High-Efficient Removal of Mercury in Water by the Flexible Nh2-Mil-53(Al): Exploiting The Gate Opening Effect

SSRN

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SSRN 2023年

作者： Jian, Meipeng Yang, Xiaoqin Huang, Congxin Tang, Chaochun Zhang, Xiwang Liu, Ruiping Li, Haiyan Beijing Energy Conservation & Sustainable Urban and Rural Development Provincial Ministry Co-construction Collaboration Innovation Center School of Environment and Energy Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Key Laboratory of Urban Stormwater System and Water Environment Ministry of Education Beijing University of Civil Engineering and Architecture Beijing100044 China School of Civil Engineering and Architecture East China JiaoTong University Jiangxi Nanchang330013 China Monash University Australia Center for Water and Ecology State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China

Flexible metal-organic frameworks (MOFs) with the gate opening feature can optimize the removal efficiency on the adsorption behavior by accommodating to the specific target molecules. In this study, a flexible MOF denoted as NH2-MIL-53(Al) was investigated to remove Hg(II) from water, in which the adsorption capacities of large-pore NH2-MIL-53(Al) (NH2-MIL-53(Al)-LP) for Hg(II) reached 433.20 mg/g, which is about 4 times higher than narrow-pore NH2-MIL-53(Al) (112.75 mg/g). Further, the kinetic experiment also revealed that nearly 90% Hg(II) can be captured within 60 min for NH2-MIL-53(Al)-LP. Advanced characterizations and density functional theory calculations demonstrated that the strong coordination of the amino and oxygen centers on NH2-MIL-53(Al)-LP to Hg(II). Importantly, molecular dynamic simulation verified the gate opening effect of NH2-MIL-53(Al)-LP, as the faster diffusivity of Hg(II) ions in NH2-MIL-53(Al)-LP was observed. This work provides another opportunity in designing the pore geometry of porous materials for water remediation. © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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Power Output Monitoring and Anomaly Identification of Photovoltaic systems Using Graphical Modeling 4

Power Output Monitoring and Anomaly Identification of Photov...

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4th International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2020

作者： Chen, Yonghua Zhang, Xiaochen Liu, Gang Wu, Rui Liu, Chao Jiang, Dongxiang Nari Technology Co. Ltd Nanjing211106 China Tsinghua University Department of Energy and Power Engineering Beijing100084 China Tsinghua University State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Energy and Power Engineering Beijing100084 China

ISBN: (纸本)9781728170503

Photovoltaic cells are being increasingly installed worldwide as a type of important renewable resource for power generation. Among the installed photovoltaic systems, data are collected from the control and monitoring system, and the anomaly detection researches are mostly based on the individual photovoltaic cells and the systematic behavior in a group of photovoltaic systems is not well studied. Based on the assumption that the photovoltaic cells installed in the same location are with very close solar radiation, the power output of each subsystem is considered in this work for monitoring the operating status of the subsystem and identifying the anomalous subsystem, where spatiotemporal pattern network, a probabilistic graphical modeling approach, is applied. The results show that the presented framework is able to detect the anomaly and locate the anomalous subsystem. © 2020 IEEE.

关键词： Condition monitoring

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