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Multi-Timescale Optimization Scheduling of Distribution Networks Based on the Uncertainty Intervals in Source-Load Forecasting

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Energy Engineering 2025年第6期122卷 2417-2448页

作者： Huanan Yu Chunhe Ye Shiqiang Li He Wang Jing Bian Jinling Li Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012China

With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and *** for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution ***,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and ***,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast *** framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the ***,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration *** a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the ***,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.

关键词： Renewable energy distribution networks source-load uncertainty interval flexible scheduling soft actor-critic algorithm optimization model

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Robust Voltage control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network

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Journal of Modern Power systems and Clean Energy 2020年第6期8卷 1104-1114页

作者： Qianyu Zhao Wenlong Liao Shouxiang Wang Jayakrishnan Radhakrishna Pillai the Key Laboratory of Smart Grid of Ministry of Education and Tianjin Key Laboratory of Power System Simulation and Control Tianjin UniversityTianjinChina the Department of Energy Technology Aalborg UniversityAalborgDenmark

The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution *** this paper,a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network(IGAN).Firstly,both real and predicted data are used to train the IGAN consisting of a discriminator and a *** noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario ***,a new improved wolf pack algorithm(IWPA)is presented to solve the formulated robust voltage control model,since the accuracy of the solutions obtained by traditional methods is *** simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads,which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional ***,IWPA has a better performance than traditional methods in terms of convergence speed,accuracy,and stability for robust voltage control.

关键词： Robust voltage control uncertainty generative adversarial network wolf pack algorithm

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Targeting Ultra-Emitters for Effective Methane Mitigation in China’s Coal Sector

ACS ES&T Air

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ACS ES&T Air 2024年第7期1卷 734-742页

作者： Helan Wang Bo Zheng Hui Li Qiang Zhang Kebin He Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing 100084 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China

The energy system fuels climate change as the largest emitter of global greenhouse gases, while a lack of transparent, infrastructure-level information undermines efforts to cut emissions. Coal mining has been the largest emitter of methane (CH4) in China over the past 10 years, which is not tracked at the mine level, and effective mitigation pathways have yet to be established. Herein, we develop a state-of-the-art CH4emission database, comprising 3343 active coal mines in China, by harmonizing multisource information. The results suggest that China’s coal mines emitted 15.8 ± 5.1 million metric tons CH4yr–1in 2019. Those with CH4emission intensities >2 standard deviations above the national mean accounted for 4% of China’s coal production, yet they generated 23% of China’s coal CH4emissions. We project a steady decline in China’s coal CH4emissions by >75% in 2100 compared to 2019 levels under three different scenarios, due to reduced coal demand during energy transition. Targeting ultra-emitters for mine closure will accelerate emission reductions in the midterm and achieve deep CH4emission cuts by >92% in 2100, which buys us time to control global warming. Our idealized mine closure strategy serves as a cost-effective CH4mitigation strategy to enable swift climate action.

关键词： Biological databases Climate Coal Energy Hydrocarbons

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Visualization of CNN Transient Voltage Classification Based on Feature Recognition and Enhancement

Visualization of CNN Transient Voltage Classification Based ...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Li, Zhendong Liu, Yutian An, Jun Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan 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)9781665414395

The deep learning algorithm represented by CNN has achieved remarkable results in complex feature extraction, classification and regression. Since the CNN adopts an end-to-end training process, the carrier of acquisition knowledge is a high-dimensional non-linear function, which is hard to understand by humans. Therefore, it is difficult to be widely used in online decision-making engineering scenarios such as the fast assessment of power system transient voltage stability. In this paper, the key input elements extracted by CNN are identified by Grad-CAM, and the key features captured by CNN in the assessment process are enhanced based on AM, so as to finally obtain the visual expression of key areas and variables of the original input data. Besides, a sample construction method based on single line diagram is proposed to improve the visualization effect of Grad-CAM and AM. simulation results and analyses of IEEE 39bus test system demonstrates the effectiveness of the proposed method. © 2021 IEEE.

关键词： Visualization

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Group equivalent modeling and analysis of group subsynchronous oscillation of direct-drive wind turbine 5

Group equivalent modeling and analysis of group subsynchrono...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Dong, Xuetao Zhao, Yue Zhai, Wenchao Wang, Zhenyang Liu, Kan Yan, Gangui Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Dept. Xinjiang Electric Power System Whole Process Simulation Key Laboratory Urumqi China Northeast Electric Power University Dept. Min. of Educ. Key Lab. of Modern Power System Simulation Control and Green Power Technology Jilin China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

Taking the direct drive wind farm connected to weak AC system as the research object, combined with the actual topology of a wind farm in Hami, Xinjiang, this paper establishes the detailed model and cluster aggregation model of direct drive wind farm on PSCAD / EMTDC platform, and comprehensively analyzes the effectiveness of aggregation model under sub synchronous oscillation (SSO). The results show that when SSO occurs, the cluster equivalent model can accurately reflect the output characteristics, damping characteristics and oscillation frequency of the wind farm. According to the actual topology of a wind power collection station in Hami, Xinjiang and the actual operation of a wind power collection station on a certain day, the equivalent clustering is carried out. © 2022 IEEE.

关键词： Wind power

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Electric Field Distribution Analysis During the Very Fast Transient Process Based on 3D Full Maxwell simulation 4th

Electric Field Distribution Analysis During the Very Fast Tr...

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4th International Conference on Power and Electrical Engineering, ICPEE 2023

作者： Sun, Qiang Gao, Wensheng Zhang, Ziwei Nie, Hongyu Lu, Guiyou He, Yufeng Qu, Jiaqi Department of Electrical Engineering State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua University Beijing China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China Ultra High Voltage Branch State Grid Sichuan Electric Power Company Chengdu China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China

ISBN: (纸本)9789819716739

Most simulations of electric field distribution during the very fast transient (VFT) process are based on equivalent waveforms, which affects the analysis of the electric field. In this paper, the actual VFT process is simulated based on the 3D full Maxwell finite element method (FEM). The electric field distribution at different points is measured. To investigate the influence of the induced electric field during the VFT process, and whether high amplitude or high steepness is the main factor affecting insulation during the VFT process, the simulation results are compared with the electrostatic simulation results. The results show that the induced electric field caused by the wave process does not play an important part. The electric field distribution is hardly influenced by the induced electric field. Therefore, the threat to insulation caused by VFTO is mainly due to its high amplitude, which leads to a stronger Coulomb electric field. Besides, the wave process only leads to a 6~8% voltage amplitude increase, which is much lower than the amplitude increase due to externally generated low-frequency oscillations. Therefore, the high amplitude of VFTO is mainly caused by low-frequency oscillations, rather than the high-frequency components during the VFT process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electric fields

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Economic Analysis of Energy Storage Peak Shaving Considering Full Life Cycle Cost 5

Economic Analysis of Energy Storage Peak Shaving Considering...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Li, Junhui An, Chenyu Zhang, Jingxiang Li, Cuiping Zhao, Haiji Yu, Jun Jin, Xuezhu Hou, Kaiyuan Xu, Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin132012 China Northeast Branch of State Grid Corporation of China Liaoning Shenyang110180 China Haidian District Beijing100084 China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

As an effective means to improve the wind power consumption capacity of power system, the economy of energy storage participation auxiliary service has received extensive attention from academic circles. In this paper, the cost composition of the whole life cycle of the electrochemical energy storage system is comprehensively considered, and the economic analysis of different Wheres of electrochemical energy storage participating in peak shaving auxiliary services is conducted according to the operation rules of the power market in northeast China. Firstly, four widely used electrochemical energy storage systems were selected as the representative, and the control strategy of source-side energy storage system was proposed for real-time peak modulation in wind farms. Secondly, the peak shaving economic model based on the life cycle cost of energy storage is constructed. Finally, by selecting the annual data of a wind farm in northeast China, the economic benefits of different Wheres of electrochemical energy storage are analyzed and compared, and the reasonable opinions on improving the benefits of energy storage are given. © 2022 IEEE.

关键词： Life cycle

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Multi-Timescale Risk Scheduling for Transmission and Distribution Networks for Highly Proportional Distributed Energy Access

SSRN

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SSRN 2023年

作者： Wang, He Liu, Ziyue Bian, Jing Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

With the incorporation of more renewable energy sources, distribution networks are shifting from passive to active. The enhanced coupling of transmission and distribution networks is enhanced and bidirectional tides affect the reliable and secure operation of such networks. This paper proposes a multi-timescale risk dispatch study for transmission and distribution networks that have a high proportion of distributed energy access to meet the rising demand for coupling between the two types of networks. First, a day-ahead scheduling model that takes into account various operational risks is developed with the goal of minimizing total operating costs, and it optimizes the source network load and storage of multiple adjustable resources of the transmission network and distribution network jointly under the risk limit constraint. Second, an intraday scheduling model is built with the goal of minimizing the overall risk value of the transmission network and distribution network, and the day-ahead scheduling plan is modified to realize this, considering the magnitude of different operational risk values of the transmission network and distribution network. Finally, the effectiveness of the proposed multi-time-scale risk dispatch model is verified through the analysis of arithmetic cases, and it is proved that the model can enable the interaction and coordination between transmission and distribution networks as well as the synergistic optimization of operating costs and operating risks. © 2023, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Wind Power Ultra-Short-Term Prediction Method Based on Nwp Wind Speed Correction and Double Clustering Division of Transitional Weather Process

SSRN

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SSRN 2023年

作者： Yang, Mao Guo, Yunfeng Huang, Yutong Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China

Wind power prediction technology is important for building novel power systems with a high proportion of renewable energy. Numerical weather prediction (NWP) data quality has a significant impact on the accuracy of ultra-short-term wind power prediction (USTWPP). However, existing NWP do not reflect the adaptability of different weather processes, because of it’ s forecasting errors. In view of this, this paper proposes an USTWPP method based on NWP wind speed correction and transitional weather process division. The BILSTM-INFO-HKELM was first used to correct the NWP wind speed, and then the multiple clustering method was used to divide the transitional weather processes to establish a model for USTWPP based on different scenarios, the overall method was finally applied to a wind farm in west inner Mongolia, China. Compared to the pre-correction, the wind speed forecasted RMSE was reduced by 1.702 and the MAE by 1.366. Based on the wind power ultra-short-term prediction method proposed in this paper, the average reduction in RMSR is 4.96% and in MAE is 4.36% compared to the various comparison methods in this paper. The wind power ultra-short-term prediction method combining wind speed correction and double clustering division of weather scenarios can significantly improve prediction accuracy. © 2023, The Authors. All rights reserved.

关键词： Wind power

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Fault Location Method of Dc Distribution Network Based on Multivariate Information Fusion

SSRN

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SSRN 2023年

作者： Wang, He Zhang, Zekun Yu, Huanan Li, Shiqiang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

The accuracy of Direct Current (DC) distribution network fault location for a single electrical or switching quantity fault information is difficult to meet the requirements. This paper proposes a fault location method of DC distribution network based on multivariate information fusion. Firstly, the compressed sensing algorithm is used to reconstruct the fault transient electrical data to obtain the sparse characteristics of the high-frequency fault current. Secondly, the Bayesian network is established to obtain the fault probability of the faulty component based on the protection action inf0ormation generated by the faulty component, and then the sparse fault characteristics and fault probability of the fault current are normalized to obtain the fault degree of the electrical quantity and switching quantity. Finally, the traditional Dempster Shafer (DS) evidence theory fusion algorithm is improved to solve the problem of conflict of uncertain subsets by weight allocation method, and then the fault degree of electrical quantity and switching quantity is fused by combining Bayesian estimation method and weighting method, so as to achieve accurate fault location. simulation experiments conducted using MATLAB software demonstrate that the proposed method can effectively reduce the evidence conflict at the fault point, and the fault location accuracy is improved by 2% to 4% compared with the traditional DS evidence theory, which has a good engineering application prospect for the fault location of DC distribution network. © 2023, The Authors. All rights reserved.

关键词： Bayesian networks

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