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Mechanism Design for Ancillary Service Market considering Social Welfare and Fairness

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1000-1010页

作者： Zhi Wu Yuanxi Wu Wei Gu Zheng Xu Shu Zheng Jingtao Zhao School of Electrical Engineering Southeast UniversityNanjing 210096China nari technology co. Ltd.Nanjing 211106China IEEE CSEE

Increasing penetration of distributed energy resources in the distribution network(DN)is threatening safe operation of the DN,which necessitates setup of the ancillary service market in the DN.In the ancillary service market,distribution system operator(DSO)is responsible for safety of the DN by procuring available capacities of aggregators.Unlike existing studies,this paper proposes a novel market mechanism composed of two parts:choice rule and payment rule.The proposed choice rule simultaneously considers social welfare and fairness,encouraging risk-averse aggregators to participate in the ancillary service market.It is then formulated as a linear programming problem,and a distributed solution using the multi-cut Benders decomposition is presented.Moreover,successful implementation of the choice rule depends on each aggregator’s truthful adoption of private parameters.Therefore,a payment rule is also designed,which is proved to possess two properties:incentive compatibility and individual rationality.Simulation results demonstrate effectiveness of the proposed choice rule on improving fairness and verify properties of the payment rule.

关键词： Aggregator ancillary service market distributed mechanism fairness mechanism design

Learning Label correlations for Multi-Label Online Passive Aggressive Classification Algorithm

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Wuhan University Journal of Natural Sciences 2024年第1期29卷 51-58页

作者： ZHANG Yongwei Nanjing nari Intelligent Transport technology co. Ltd.Nanjing 210061JiangsuChina

Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label classification problems,batch learning deals with most issues,which consumes a lot of time and space resources.Unlike traditional batch learning methods,online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets.However,existing online learning research has done little to consider correlations between labels.On the basis of existing research,this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples.We evaluate the performance of the proposed algorithm on several public datasets.Experiments show the effectiveness of our algorithm.

关键词： label correlations passive aggressive multi-label classification online learning

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Optimal Initialization and Starting Approach of Synchronous condenser Integrated Power Grid

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 248-260页

作者： Puyu Wang Xing Liu Qingwen Mou Tao Yuan Tianming Gu Hemin Yang Xiao-Ping Zhang Zhengrong Xiang Chunyi Guo School of Automation Nanjing University of Science and TechnologyNanjing 210094China nari Group corporation/State Grid EPRI and also with nari technology co.ltd. Nanjing 211106China School of Engineering University of BirminghamBirminghamB152TTUnited Kingdom State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

Synchronous condensers(SCs)are generally used at the receiving-end stations of ultra-high-voltage direct current(UHVDC)transmission systems due to their strong reactive power support and flexible regulation of reactive power according to the interconnected grids operating conditions.In this paper,different starting control schemes of a SC integrated power grid are investigated providing four main contributions:1)The principle of reactive power support of the SC on the interconnected power grid is analytically studied,providing the establishment of mathematical models.2)Four different starting control schemes are developed for the initialization and SC integration,i.***.in Scheme 1,a preset initial falling speed is directly utilized without initialization;in Scheme 2,a black start sequential control approach with a static frequency converter(SFC)is proposed;in Scheme 3,PI/PD/PID controllers are respectively applied for the excitation device at the speed-falling stage;in Scheme 4,a pre-insertion approach of an energy absorption component with R/L/RL is utilized to suppress the surges at the SC integration instant.3)The dynamic behaviors of four different starting schemes at specific operating stages are evaluated.4)The success rate of SC integration is analyzed to evaluate starting control performance.Performance of the SC interconnected system with four different starting control schemes is evaluated in the timedomain simulation environment PSCAD/EMTDC^(TM).The results prove the superiority of the proposed starting control approach in Scheme 4.

关键词： Black start dynamic behaviors excitation system pre-insertion of energy absorption components starting control schemes static frequency converter(SFC) success rate of integration synchronous condenser(SC)

Distributed Robust Optimal Dispatch of Regional Integrated Energy Systems Based on ADMM Algorithm with Adaptive Step Size

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 852-862页

作者： Zhoujun Ma Yizhou Zhou Yuping Zheng Li Yang Zhinong Wei the college of Energy and Electrical Engineering Hohai University State Grid Jiangsu Electric Power co. Ltd. State Key Laboratory of Smart Grid Protection and control NARI Group Corporation

This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system(RIES). Our model regards the distribution network and each energy hub(EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic(PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers(ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.

关键词： Uncertainty costs Power systems Stochastic processes Renewable energy sources Optimization methods Adaptation models

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

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Global Energy Interconnection 2024年第2期7卷 179-189页

作者： Yongrong Zhou Zhaoxing Ma Hao Chen Ruihua Wang State Key Laboratory of technology and Equipment for Defense against Power System Operational Risks Nanjing 211106P.R.China nari Group co. Ltd.Nanjing 211106P.R.China Qingdao University of technology School of Information and Control EngineeringQingdao 266520P.R.China State Grid Nanjing Power Supply company Nanjing 211000P.R.China

High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

关键词： High voltage circuit breaker Signal separation Monitor Multiple faults Power system

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Multi-stage co-planning Model for Power Distribution System and Hydrogen Energy System Under Uncertainties

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 80-93页

作者： Qirun Sun Zhi Wu Wei Gu Pengxiang Liu Jingxuan Wang Yuping Lu Shu Zheng Jingtao Zhao School of Electrical Engineering Southeast UniversityNanjing 210096China nari technology development co.ltd NanjingChina

The increased deployment of electricity-based hydrogen production strengthens the coupling of power distribution system(PDS)and hydrogen energy system(HES).considering that power to hydrogen(PtH)has great potential to facilitate the usage of renewable energy sources(RESs),the coordination of PDS and HES is important for planning purposes under high RES penetration.To this end,this paper proposes a multi-stage co-planning model for the PDS and HES.For the PDS,investment decisions on network assets and RES are optimized to supply the growing electric load and PtHs.For the HES,capacities of PtHs and hydrogen storages(HSs)are optimally determined to satisfy hydrogen load considering the hydrogen production,tube trailer transportation,and storage constraints.The overall planning problem is formulated as a multistage stochastic optimization model,in which the investment decisions are sequentially made as the uncertainties of electric and hydrogen load growth states are revealed gradually over periods.Case studies validate that the proposed co-planning model can reduce the total planning cost,promote RES consumption,and obtain more flexible decisions under long-term load growth uncertainties.

关键词： Power distribution system vehicle-based hydrogen energy system co-planning model multi-stage stochastic programming uncertainty

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Study on the Preparation,the Magnetic Performance,and the Oxygen Evolution Reaction of LaMnO_(3+δ)

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Journal of Electronic Research and Application 2024年第3期8卷 97-103页

作者： Peng Fan Qirui Wu Geming Wang Rahman Sheikh Tamjidur Chendong Shao Wuhan nari Limited Liability company State Grid Electric Power Research InstituteNARI Group(State Grid Electric Power Research Institute)Co.Ltd.Wuhan 430074Hubei ProvinceChina Provincial Key Laboratory of Plasma Chemistry and Advanced Materials Wuhan Institute of TechnologyWuhan 430073Hubei ProvinceChina

In this paper,a series of LaMnO_(3+δ)(LMOs)were successfully prepared by adjusting the sintering temperature using the sol-gel method with ABO3-type LMO oxides as the object of study.The results showed that with the increase of sintering temperature,the O_(ads),oxygen vacancies,and Mn^(4+)content in the system gradually decreased,and the oxygen evolution reaction(OER)was subsequently weakened.Although the suitable Mn^(3+)/Mn^(4+)valence ratio(2.15:1)of the LMO700 sample created a strong ferromagnetic double-exchange effect,the high concentration of oxygen vacancies in LMO700 disturbed this effect and weakened its macro magnetism.This paper serves to contribute to the design and development of new magnetic perovskite electrocatalysts.

关键词： Manganese oxides Microstructure Magnetic properties Oxygen evolution reaction

Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning

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computers, Materials & continua 2024年第6期79卷 5105-5124页

作者： Shuming Sha Naiwang Guo Wang Luo Yong Zhang Nanjing nari Information&communication technology co. Ltd.Nanjing210032China State Grid Electric Power Research Institute Nanjing211106China State Grid Shanghai Municipal Eleciric Power company Shanghai200122China

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the ave

关键词： Reinforcement learning workflow task scheduling load balancing

Short-TermWind Power Prediction Based on combinatorial Neural Networks

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Intelligent Automation & Soft computing 2023年第8期37卷 1437-1452页

作者： Tusongjiang Kari Sun Guoliang Lei Kesong Ma Xiaojing Wu Xian School of Electrical Engineering Xinjiang UniversityUrumqi830017China Anhui nari Jiyuan Electric Power System Tech co.ltd Hefei230088China

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

关键词： Wind power prediction wavelet transform back propagation neural network bi-directional long short term memory