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China International Electrical and energy Conference (CIEEC)

作者： Yang Cui Siyu Huang Huiquan Zhang Hongbo Li Key Laboratory of Modern Power Sysntem Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Ji Lin China State Grid Jilin Power Co. Ltd. Training Center Ji Lin China

ISBN: (数字)9781728116754

ISBN: (纸本)9781728116761

energy crisis and environmental pollution are becoming increasingly serious. Increasing the penetration rate of renewable energy in the power system can effectively solve this problem. Wind power and photovoltaic power generation are fluctuating power sources, and their large-scale interconnection will increase the difficulty of grid scheduling. Therefore, based on the regulation characteristics of concentrating solar power station, this paper proposes a green power dispatching strategy for power system with full renewable energy. The purpose of this strategy is to promote the consumption of renewable energy. The cost of power generation of each unit of the system and the security operation constraints of the grid are considered comprehensively. Finally, CPLEX optimization software was used to solve the model. The results verify the feasibility and validity of the proposed model.

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Remote Sensing Image Object Detection Based on Improved Sparse R-CNN

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1303-1320页

作者： Zhao, Li-Quan Chen, Chun-Lu Zhong, Tie Cui, Ying Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China School of Electric Power Engineering Northeast Electric Power University Jilin 132012 China Zhuhai Power Supply Bureau Guangdong Electric Power Corporation Zhuhai 519000 China School of Electrical and Information Engineering Beihua University Jilin 132013 China

A more precise sparse region convolutional neural network algorithm is developed to increase the accuracy of remote sensing object recognition. Firstly, a new intersection over union method is proposed to solve the problem that the intersection over union used in sparse region convolutional neural network cannot well measure the offset between the ground-truth box and the proposal box. It introduces multiple geo-metric factors that are overlap area, center point distance, vertex distance between the ground-truth box and the proposal box, and diagonal distance of minimum enclosing of the ground-truth box and the proposal box and into the new intersection over union. The proposed intersection over union can measure the offset between the ground-truth box and the proposal box to optimal the proposal box. Secondly, to improve the detection precision of sparse region convolutional neural network, a dual branches dynamic instance interaction head is designed. The proposed dynamic interaction head consists of a fully connected branch and a convolution branch. The fully connected head is used for both bounding box regression and object classification. The convolution branch is only used for bounding box regression. Compared with original sparse region convolutional neural network, the average mAP and recall are 3.55% and 5.75% higher, and the average detection speed is only 0.85 FPS slower. The proposed method realizes balance between detection precision and detection speed. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Remote sensing

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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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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage system

SSRN

引用

SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Shijiazhuang Gaocheng District Electric Supply Company No. 76 Changsheng South Street Gaocheng District Shijiazhuang052160 China

Large wind farms connected to the grid through MMC-HVDC systems can cause overloading of the non-faulty converter station after a DC fault. This short communication proposes a system fault ride-through method by coordinating and controlling the energy storage system, the non-faulty converter station and the wind farm to jointly consume the unbalanced power during the fault. The model is created in the PSCAD simulation platform, and the correctness and effectiveness of the method are verified by simulation. © 2023, The Authors. All rights reserved.

关键词： energy storage

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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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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage system

SSRN

引用

SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control& Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China State GridShijiazhuang Gaocheng District Electric Supply Company No. 76Changsheng SouthStreet Gaocheng District Shijiazhuang052160 China

关键词： energy storage

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Coordinated optimal configuration scheme of wind-solar ratio and energy storage considering wind-solar complementary characteristics

Coordinated optimal configuration scheme of wind-solar ratio...

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energy, power and Grid (ICEPG), International Conference on

作者： Xiuyu Yang Zhiwen Li Jian Peng Gang Miu Shaowei Yuan Zhengyin Zhu Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology of Ministry of Education Northeasst Electric Power University Jilin China State Grid Xinjiang Power Supply Company State Grid Urumqi Power Supply Company Urumqi City China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

This study proposes a collaborative optimization configuration scheme of wind-solar ratio and energy storage based on the complementary characteristics of wind and light. On the premise of maintaining the stability of the wind-solar hybrid power generation system, the optimal allocation model of wind-solar ratio and energy storage considering the complementary characteristics of wind-solar is established with the minimum total cost of planning. At the same time, three indicators of power fluctuation index, wind-solar permeability and new energy abandonment rate are proposed to evaluate the scheme.

关键词： Wind energy generation Costs Stability criteria Wind power generation power system stability Planning Permeability Resource management Optimization energy storage

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Pre-configuration Method and Scheduling Strategy of Distribution Network Mobile energy Storage Vehicle Considering Intermittent Load of Rural Network

Pre-configuration Method and Scheduling Strategy of Distribu...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Xiaoming Lu Yiwen Yao Yechen Xin Dingheng Wang Xinhong Wang Xin Xu Hao Li Economic and Technological Research Institute State Grid Jilin Electric Power Co. Ltd Changchun China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China State Grid Jilin Electric Power Co. Ltd Changchun China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

Aiming at the problem of heavy overload in the distribution station area during the peak period of energy consumption caused by intermittent loads such as agricultural motor wells, the mobile energy storage technology is considered to meet the agricultural power demand, and a two-stage mobile energy storage vehicle pre configuration and dynamic adjustment strategy of "Rural busy power energization" and "rural idle distributed energy storage" is proposed, taking into account the guarantee of load power demand and the efficiency and economy of energy storage. Firstly, the mathematical model of agricultural motor well load considering the time shift characteristics and the mobile energy storage vehicle model considering the power supply time constraint are established; Secondly, a multi-stage mobile energy storage configuration and scheduling strategy is established. In the Rural busy power generation stage, the mobile energy storage vehicle capacity is configured with the minimum daily power supply cost as the goal, and the power supply path is optimized with the highest charge state consistency at the completion of power supply as the goal; At the stage of distributed energy storage in rural slack, the collaborative optimization of mobile energy storage vehicles participating in multi scenario power services is carried out with the goal of maximizing the annual net income. Finally, the proposed two-stage strategy has certain economic benefits under the premise of meeting the power demand of the load, which is verified by combining the power consumption data of wells in Jilin Province and IEEE 33 bus system simulation.

关键词： energy consumption power demand Costs Distribution networks Motors Time factors Vehicle dynamics Optimization energy storage Load modeling

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

arXiv

引用

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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Ancillary service for frequency regulation based on multi-energy virtual power plant aggregating factory load

Ancillary service for frequency regulation based on multi-en...

引用

The 11th IET International Conference on Advances in power system control, Operation and Management (APSCOM 2018)

作者： Jiazhen Wang Xinwei Shen Yinliang Xu Qinglai Guo Hongbin Sun Yanbo Chen Shenzhen Environmental Science and New Energy Technology Engineering Laboratory Tsinghua-Berkeley Shenzhen Insitute Tsinghua University Shenzhen China State Key Lab of Control and Simulation of Power System and Generation Equipments Department of Electrical Engineering Tsinghua University Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University

ISBN: (数字)9781839530043

With the increasing proportion of renewable energy generation into the power grid, more challenges are coming to the safe and stable operation of the power system. In order to keep the balance in energy market, system operator has to purchase much service in ancillary service market to guarantee the stable operation of the power transmission. Virtual power plant (VPP) has been extensively studied as a concept that aggregates multiple resource forms to serve the electricity market. Meanwhile, with the development of energy Internet (EI) Error! Reference source not found., more attention has been focused on the optimization and operation of Integrated energy system (IES), therefore, the introduction and exploration of Multi-energy Virtual power Plant (MVPP) provide possibilities for IES to participate in the Electricity Market and Ancillary Services Market. This paper extracts the flexible electric and heat load in the industrial production process, combining the CHP units and Electric Boiler (EB), which are main heat energy supply devices as well as energy Storage system (ESS) as a MVPP to provide Frequency Regulation (FR) service to the Auxiliary Service Market. Model Predictive control (MPC) method is applied to control the industrial load, the operation of CHP, EB and ESS are gonging to follow the AGC signal issued by the FR market effectively while avoiding impacts on the production process of the factory. Finally, case study validates the effectiveness of the model.

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