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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Nan Wu Mao Yang Chenglian Ma Yunpeng Jia Guozhong Fang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Jilin Power Supply Company of State Grid Jilin Electric Power Co. Ltd Jilin China State Gird Northeast China Branch Jilin China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

With the gradual acceleration of the world's energy transformation, the construction of new energy is becoming more and more large-scale. At the same time, the proportion of new energy in the power grid is increasing, and the high volatility of wind power poses certain challenges to the stability and reliability of the power system. Therefore, accurate large-scale wind power prediction is very necessary. This paper presents a multi-task learning predicting method for wind power clusters. Firstly, the wind power cluster is divided into subgroups with similar output characteristics, and then the different subgroup power is used to predict the multi-task targets. Meanwhile, the Kepler optimization algorithm (KOA) is used to optimize the model parameters. When the method is applied to a wind power cluster in China, RMSE and MAE are reduced by 2.07% and 1.92% compared with the traditional multi-task learning method, which proves the effectiveness of the proposed method.

关键词： Learning systems Clustering algorithms system integration Wind power generation power system stability Multitasking Prediction algorithms Stability analysis Reliability Optimization

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Optimization Charging Method of Lithium-Ion Battery Based on Multi-Objective Bbo Algorithm

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SSRN 2024年

作者： Duan, Shuangming Xia, Kuifeng Li, Junhui Zhao, Zhiqiang Liu, Haojun Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology Ministry of Education Northeast Electricity Power University Jilin Jilin132012 China State Grid Hami Power Supply Company Xinjiang Uygur Autonomous Region Hami839000 China

Currently, the charging strategy of lithium-ion battery is usually an open-loop method, which lacks feedback in the charging process. In order to obtain a suitable charging strategy for lithium-ion battery, a simple and easy PID closed-loop constant temperature control system is proposed, which takes the temperature of lithium-ion battery as the feedback quantity, and improves the charging speed under the condition that the maximum temperature rise of lithium-ion battery is consistent. Secondly, SOC-based Multistage Constant Current (SMCC) based on SOC segmentation is proposed and orthogonal experiments are constructed by Taguchi method to determine the constant current values of each SMCC segment. Then, taking charging time, charging efficiency and SOH (state of Health) decay as the charging objectives to be optimized, multi-objective biogeographic-based optimization (BBO) is used. The MBBO algorithm optimizes each charging current under different weight coefficient combinations. According to the optimization results, Pareto optimal frontier is used to select the most balanced charging strategy from the six proposed charging strategies. Finally, SMCC charging current optimized by MBBO algorithm is connected to PID closed-loop constant temperature control system, and compared with SMCC charging strategy without constant temperature control and traditional 1C-CCCV method, the results show that compared with 1C-CCV charging strategy, The algorithm optimized SMCC can significantly reduce the charging time and SOH loss of lithium-ion batteries, and the SMCC charging current can be connected to PID closed-loop constant temperature control to further improve the charging speed, and keep the charging efficiency basically equal to the SOH attenuation. © 2024, The Authors. All rights reserved.

关键词： Taguchi methods

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Partial Discharge Fault Diagnosis in power Transformer Based on Symplectic Geometry Mode Decomposition and Optimized Convolutional Neural Networks

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SSRN 2023年

作者： Shang, Haikun Liu, Zhidong Huang, Tao Zhang, Ranzhe Li, Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Electric Power Research Institute Xinjiang830011 China

To address challenges in feature extraction, such as difficulty in accurately extracting valuable information and low recognition accuracy during pattern recognition, this paper proposes a novel method for power transformer PD fault diagnosis based on symplectic geometry mode decomposition (SGMD) and optimized Convolutional Neural Networks (CNN). First, three different types of power transformer PD fault signals are extracted, namely floating discharge (FD), corona discharge (CD), and bubble discharge (BD), in a laboratory environment. Second, PD signals are decomposed using SGMD to obtain different symplectic geometric components (SGCs). Afterwards, approximate entropy (ApEn) values of each SGC are calculated as PD feature parameters. Subsequently, the sparrow search algorithm (SSA) algorithm is utilized to optimize and configure the CNN parameters to enhance the diagnostic accuracy and stability. Finally, the obtained feature parameters are fed into the optimized CNN for fault diagnosis. simulation and experimental analysis verify the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Mode decomposition

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Exergic Perspective of Low Carbon Economy Dispatch of Carbon Capture power Plant and power to Gas Synergistic Operation

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SSRN 2023年

作者： Wang, Xiuyun Zhang, Wanyu Wang, Rutian Wen, Xiangyun Cui, Benwang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Tianjin Electric Power Company Tianjin300010 China

In order to improve the system economy and low carbon, the power to gas-carbon capture power plants joint operation system is introduced in the integrated energy system to reduce carbon emissions while increasing the consumption of renewable energy. From the perspective of exergy, both the quantity and quality of energy were taken into account, the calculation methods of different exergy values of energy flow were refined, and the sub-model of exergy flow calculation was established. To further achieve low carbon emission reduction, the costs of carbon emission trading, carbon capture energy consumption and carbon storage were included in the non-energy costs, and the exergoeconomic cost distribution model was established. Compared with the traditional pricing method, this paper considers the impact of carbon cost and energy grade difference on unit exergic cost of cooling, heating and electricity in the system, reflecting the principle of higher quality and higher price. Finally, the constructed model is solved with the total cost as the objective function. The simulation example shows that the proposed scheduling method can improve the low-carbon emission reduction capacity of the system and achieve reasonable cost distribution. © 2023, The Authors. All rights reserved.

关键词： Carbon capture

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Basic reason for the accumulation of charge on the surface of polymer dielectrics

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Science China Materials 2022年第10期65卷 2884-2888页

作者： Tianyu Wang Xiaofen Li Boya Zhang Dayu Li Jianbo Liu Guixin Zhang State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong UniversityShaanxi 710049China

Due to their outstanding electrical insulation performance,low energy loss and high reliability,polymer dielectrics are widely used in flexible electronics,capacitors and large-scale power transmission equipment[1–5].However,polymer dielectrics accumulate large amounts of charge on their surface during use,which causes local electric field distortion and induces flashover,explosion,and equipment damage[6–9].Although researchers have attempted to develop methods to reduce the accumulation of surface charge through material modification,such as plasma treatment[10],direct fluorination[11],and coating[7,9,12],each of these methods has drawbacks;thus,the bottleneck has yet to be overcome.

关键词：电荷量第一性原理计算聚合物表面电介质电场畸变深能级缺陷电荷测量电荷特性

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simulation Testing of Large-Scale Grid-Connected Wind power with Energy Storage system Based on Active Support Performance Evaluation Indices

Simulation Testing of Large-Scale Grid-Connected Wind Power ...

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Electrical Engineering and Automation (IFEEA), International Forum on

作者： Yihua Zhu Yun Hu Liang Tu Mingzhang Su Chao Luo State Key Laboratory of HVDC Electric Power Research Institute CSG Guangzhou China National Energy Power Grid Technology R&D Centre 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

This paper establishes simulation modeling for large-scale grid connected wind power with energy storage system (ESS). The conventional grid-following (GFL) based ESS is used as a reference object to test the active support performance of large-scale grid-connected wind power with grid-forming (GFM) based ESS with active support performance evaluation indices in different scenarios. (1) Small disturbance evaluation indices: The point of common coupling (PCC) frequency/voltage drop depth and recovery time. (2) Large disturbance evaluation indices: The PCC frequency/voltage drop depth and rise height after fault removal. The output active/reactive power drop depth and rise height/recovery time after fault removal. (3) Island capability evaluation indices: The PCC frequency/voltage fluctuation amplitude at the moment of switching and system stability after switching.

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Research on Distributed Photovoltaic Marketization Trading Model Considering Auxiliary Services

Research on Distributed Photovoltaic Marketization Trading M...

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Energy Engineering and power systems (EEPS), International Conference on

作者： Tao Meng Dexin Li Chenggang Li Wei Wang State Grid Jilin Electric Power Co. Ltd. Electric Power Research Institute Changchun Jilin Province China The Key Laboratory of Modern Power System Simulation and Control Ministry of Education Renewable Energy Technology (Northeast Electric Power University) Jilin Jilin Province China

ISBN: (数字)9798350366914

ISBN: (纸本)9798350366921

Constrained by the features of distributed photovoltaic (PV) power generation, as more distributed photovoltaic systems connect to the power grid, the demand for auxiliary services resulting from PV integration into the distribution network will significantly increase. Thus, when conducting market-oriented transactions, it becomes essential to consider not only the electricity market but also the auxiliary services required for distributed photovoltaics. This paper discusses the future distributed photovoltaic trading platform, price models, and the pricing and allocation of auxiliary services, aiming to achieve a virtuous cycle in distributed photovoltaic market-oriented trading. Furthermore, an example of distributed photovoltaic auxiliary services is provided, demonstrating that neglecting auxiliary services may allow distributed photovoltaic transactions to proceed, but they may fail to cover the cost of auxiliary services. Hence, the consideration of auxiliary services for distributed photovoltaics is crucial when organizing market-oriented transactions.

关键词： Photovoltaic systems Costs Electricity Pricing Distribution networks Electricity supply industry power grids Resource management

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Ultra-short-term power Prediction of Photovoltaic based on CNN-AM-BiLSTM

Ultra-short-term Power Prediction of Photovoltaic based on C...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Junhui Li Mingxuan Du Haozheng Yu Jie Ma Youwen Zhang Jiatao Gan Xingxu Zhu Cuiping Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China AnYang Power Supply Company State Grid HeNan Electric Power Company AnYang China Economic and Technical Research Institute State Grid HeNan Electric Power Company ZhengZhou China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The power of photovoltaic power generation is affected by a variety of factors such as weather, radiation and temperature, exhibit a high degree of randomness and uncertainty. To address this problem, this paper proposes a ultra-short-term photovoltaic power prediction model based on Convolutional Neural Networks (CNN) and attention mechanism and bidirectional long short-term memory (Bi-LSTM). Firstly, Pearson's correlation coefficient was used to filter out the meteorological factors which have significant influence on the historical power. Subsequently, in order to capture the spatial features of the input data, a convolutional neural network is utilized for feature extraction and an attention mechanism is introduced to strengthen the model's ability to discriminate key features. Meanwhile, the Bi-LSTM network is utilized to capture the temporal dependencies in the time series data, and finally the PV power prediction value is obtained. Finally, the validation is based on data from actual PV power plants, and the results show that the proposed Convolutional Neural Networks and attention mechanism (CA) and Bi-LSTM Ultra-short-term PV power prediction model outperforms the single model in terms of prediction accuracy, reflecting the effectiveness and superiority of the hybrid model.

关键词： Photovoltaic systems Accuracy Attention mechanisms Time series analysis Weather forecasting Predictive models Feature extraction Data models Hybrid power systems Convolutional neural networks

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Operational Optimization of Photovoltaic-Storage system Considering Demand Response

Operational Optimization of Photovoltaic-Storage System Cons...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Xuefeng Gao Wenwen Qin He Wang Yiwen Yao Cuiping Li Hao Peng Xingxu Zhu Junhui Li State Grid Jilin Electric Power Company Limited Economic and Technical Research Institute Changchun China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University China Songyuan Power Supply Company State Grid Jilin Electric Power Company Limited Songyuan China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Aiming to address the stability challenges in power systems caused by the volatility and uncertainty of photovoltaic (PV) power generation, an optimization strategy for PV-storage systems incorporating demand response (DR) is proposed. Which is based on PV generation characteristics, Time-of-Use Pricing (TUP), and demand side flexibility. A multi-objective optimization model is established to balance the operational and environmental costs of PV-storage configuration. Building on dynamic adjustments at the demand side, a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with dynamic inertia weighting is introduced to enhance global search capability for optimized energy storage (ES) configuration. In case study, two scenarios are designed to validate the effectiveness of the proposed model in reducing costs and improving PV utilization rates.

关键词： Photovoltaic systems Costs Uncertainty Heuristic algorithms Optimization models power system dynamics power system stability Demand response Optimization Load modeling

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Research on Wind Turbine control Hardware in Loop Real-time simulation and its Application

Research on Wind Turbine Control Hardware in Loop Real-time ...

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Sustainable power and Energy Conference (iSPEC)

作者： Guanming Zeng Liang Tu Yihua Zhu Chengxiang Li Mingqun Liu Peng He State Key Laboratory of HVDC and CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid and National Energy Power Grid Technology R&D Centre and Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou Guangdong Yunnan Power Grid Co. Ltd. Electric Power Research Institute Kunming Yunnan

With the construction of new power system, high-capacity renewable energy sources including wind power are integrated into the power system, whose transient response characteristics have a significant impact on the safe and stable operation of both renewable energy sources and power system. Real-time simulation has become an effective method to study interaction between high-capacity renewable energy sources and power system because of its high accuracy and reliability, and precise wind turbine real-time simulation model is of great significance in research mentioned above. Therefore, the wind turbine control hardware in loop (CHIL) real-time simulation method is proposed in the paper, whose feasibility and accuracy are also verified by comparing simulation data from CHIL platform and on-site testing data. It’s further used in the analysis of large-scale offshore wind farm cluster and connected power system as a practical reference case.

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