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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1614-1624页

作者： Yanling Lin Xiaohu Zhang Jianhui Wang Di Shi Desong Bian geiri north america San JoseUSA Southern Methodist University DallasUSA

This paper proposes a voltage stability constrained optimal power flow(VSC-OPF)for an unbalanced distribution system with distributed generators(DGs)based on semidefinite programming(SDP).The AC optimal power flow(ACOPF)for unbalanced distribution systems is formulated as a chordal relaxation-based SDP model.The minimal singular value(MSV)of the power flow Jacobian matrix is adopted to indicate the voltage stability margin.The Jacobian matrix can be explicitly expressed by ACOPF state variables.The nonlinear constraint on the Jacobian MSV is then replaced with its maximal convex subset using linear matrix inequality(LMI),which can be incorporated in the SDP-based ACOPF formulation.A penalty technique is leveraged to improve the exactness of the SDP relaxation.Case studies performed on several IEEE test systems validate the effectiveness of the proposed method.

关键词： Voltage stability semidefinite programming Jacobian matrix optimal power flow

Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 669-681页

作者： Reza Bayani Saeed D.Manshadi Guangyi Liu Yawei Wang Renchang Dai San Diego State University San DiegoCA92182USA geiri north america San JoseCA95134USA

A total of 19%of generation capacity in California is offered by PV units and over some months,more than 10%of this energy is curtailed.In this research,a novel approach to reducing renewable generation curtailment and increasing system flexibility by means of electric vehicles'charging coordination is presented.The presented problem is a sequential decision making process,and is solved by a fitted Q-iteration algorithm which unlike other reinforcement learning methods,needs fewer episodes of learning.Three case studies are presented to validate the effectiveness of the proposed approach.These cases include aggregator load following,ramp service and utilization of non-deterministic PV generation.The results suggest that through this framework,EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times,as well as solar power generation are unknown beforehand.

关键词： Reinforcement learning electric vehicle curtailment reduction dispatchability scheduling

Active Power Correction Strategies Based on Deep Reinforcement Learning Part I:A Simulation-driven Solution for Robustness

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1122-1133页

作者： Peidong Xu Jiajun Duan Jun Zhang Yangzhou Pei Di Shi Zhiwei Wang Xuzhu Dong Yuanzhang Sun School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China geiri north america San JoseCA 95134USA

Employing the novel Deep Reinforcement Learning approach,this paper addresses the active power corrective control in modern power systems.Seeking to minimize the joint effect engendered by operation cost and blackout penalty,this correction strategy focuses on evaluating the robustness and adaptability aspects of the control agent.In Part I of this paper,where robustness is the primary focus,the agent is developed to handle unexpected incidents and guide the stable operation of power grids A Simulation-driven Graph Attention Reinforcement Learning method is proposed to perform robust active power corrective control.The aim of the graph attention networks is to determine the representation of power system states considering the topological features.Monte Carlo tree search is adopted to select the best suitable action set out of the large action space,including generator redispatch and topology control actions.Finally,driven by simulation,a guided training mechanism along with a long-short-term action deployment strategy are designed to help the agent better evaluate the action set while training and to operate more stably when deployed.The efficacy of the proposed method has been demonstrated in the“2020 I earning to Run a Power Network.Neurips Track 1”global competition and the associated cases.Part II of this paper deals with the adaptability case,where the agent is equipped to better adapt to a grid that has an increasing share of renewable energies through the years.

关键词： Active power corrective control deep reinforcement learning graph attention networks simulationdriven.

Risk-constrained Energy Management Strategy for a Commercial Campus Considering Comprehensive Reserves Against Islanding Conditions

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CSEE Journal of Power and Energy Systems 2023年第5期9卷 1797-1807页

作者： Zheming Liang Desong Bian Xiaohu Zhang Di Shi geiri north america San JoseCAUSA.Z.M.Liang is also with Huaneng Renewables Co.Ltd.Beijing 100036China

This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding capabilities.The goal is to minimize the total operation and maintenance costs,while maximizing comprehensive comfort levels for the occupants.A two-stage riskconstrained,scenario-based stochastic optimization approach is adopted to handle various uncertainties associated with the energy management process,such as power generation of rooftop solar panels,arrival state-of-charges,and arrival/departure time of plug-in electric vehicles,intermittent load demand,and uncertain grid-connection conditions.A conditional-valueat-risk method is introduced to provide a risk-averse energy management strategy.To face the challenge of both reducing the computational burden and maintaining the accuracy of the stochastic programming,an advanced scenario reduction method is leveraged.Extensive simulation results validate the effectiveness of the proposed energy management strategy for minimizing total operating and maintenance costs of commercial buildings with islanding capabilities,while maximizing comprehensive comfort levels of the occupants.

关键词： Comprehensive comfort levels CVaR energy management islanding capability reserve stochastic programming uncertainty

Enhanced Denoising Autoencoder-aided Bad Data Filtering for Synchrophasor-based State Estimation

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 640-651页

作者： Guanyu Tian Yingzhong Gu Zhe Yu Qibing Zhang Di Shi Qun Zhou Zhiwei Wang geiri north america San JoseCA 95134 USA geiri north america San JoseCA 95134 USA on leave from Department of Electrical and Computer EngineeringUniversity of Central FloridaOrlandoFL 32816 USA Grid Dispatch Center State Grid Jiangsu Electric Power Company Ltd.Nanjing 210024China Department of Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816 USA

Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)applications.However,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it supports.Bad data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and etc.To pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this paper.Bad data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder module.Two characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical measurements.We use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.

关键词： Autoencoder bad data processing linear state estimation PMU

Doubly-fed Deep Learning Method for Bad Data Identification in Linear State Estimation

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1140-1150页

作者： Yingzhong Gu Zhe Yu Ruisheng Diao Di Shi IEEE geiri north america San JoseCA 95134USA

With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.

关键词： Bad data identification linear state estimation preprocessing deep neural network wide-area monitoring system(WAMS)

A Robust Real-time Low-frequency Oscillation Detection and Analysis (LFODA) System with Innovative Ensemble Filtering

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CSEE Journal of Power and Energy Systems 2020年第1期6卷 174-183页

作者： Desong Bian Zhe Yu Di Shi Senior Ruisheng Diao Zhiwei Wang geiri north america San JoseCA 95134.

Low-frequency oscillations are hazardous to power system operation,and can lead to cascading failures if not detected and mitigated in a timely manner.This paper presents a robust and automated real-time monitoring system for detecting grid oscillations and analyzing their mode shapes using PMU measurements.A novel Extended Kalman Filtering(EKF)based approach is introduced to detect and analyze oscillations.To further improve the accuracy and efficiency of the presented software system,the EKF approach takes advantages of three effective signal processing methods(including Prony’s Method,Hankel Total Least Square(HTLS)Method,EKF)and adopts a novel voting schema to significantly reduce the computation cost.Results from these methods are processed through a timeseries filter to ensure the consistency of detected oscillations and reduce the number of false alarms.The Density-based Spatial Clustering of Applications with Noise(DBSCAN)method is used to accurately classify oscillation modes and the PMU measurement channels.The LFODA system has been functioning well in the State Grid Jiangsu Electric Power Company with 176 PMUs and 1000+channels since February 2018,demonstrating outstanding performance in reducing false alarms with much less computational cost.

关键词： Extended Kalman filter HTLS low frequency oscillation PMU Prony

Online coherence identification using dynamic time warping for controlled islanding

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Journal of Modern Power Systems and Clean Energy 2019年第1期7卷 38-54页

作者： Hasan Ul BANNA Zhe YU Di SHI Zhiwei WANG Dawei SU Chunlei XU Sarika Khushalani SOLANKI Jignesh M.SOLANKI geiri north america Department of CSEE West Virginia University Grid Dispatch Center State Grid Jiangsu Electric Power Company

Controlled islanding is considered to be the last countermeasure to prevent a system-wide blackout in case of cascading failures.It splits the system into self-sustained islands to maintain transient stability at the expense of possible loss of load.Generator coherence identification is critical to controlled islanding scheme as it helps identify the optimal cut-set to maintain the transient stability of the post-islanding systems.This paper presents a novel approach for online generator coherency identification using phasor measurement unit(PMU) data and dynamic time warping(DTW).Results from the coherence identification are used to further cluster non-generator buses using spectral clustering with the objective of minimizing power flow disruptions.The proposed approach is validated and compared to existing methods on the IEEE39-bus system and WECC 179-bus system, through which its advantages are demonstrated.

关键词： Coherence identification Constrained spectral clustering Controlled islanding Dynamic time warping Phasor measurement unit measurement

Deep Learning in Power Systems Research:A Review

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 209-220页

作者： Mahdi Khodayar Guangyi Liu Jianhui Wang Mohammad E.Khodayar Global Energy Interconnection Research Institute north america(geiri north america) San JoseCAUSA Department of Computer Science at the University of Tulsa TulsaOKUSA Khodayar are with the Department of Electrical and Computer Engineering of Southern Methodist

With the rapid growth of power systems measurements in terms of size and complexity,discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction,demand response,energy disaggregation,and state estimation is considered a crucial challenge.In recent years,deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms.This study explores the theoretical advantages of deep representation learning in power systems research.We review deep learning methodologies presented and applied in a wide range of supervised,unsupervised,and semi-supervised applications as well as reinforcement learning tasks.We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders.The theoretical and experimental analysis of deep neural networks in this study motivates longterm research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research.

关键词： Autoencoder convolution neural network deep learning discriminative model deep belief network generative architecture variational inference

Sizing battery storage for islanded microgrid systems to enhance robustness against attacks on energy sources

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Journal of Modern Power Systems and Clean Energy 2019年第5期7卷 1177-1188页

作者： Kexing LAI Yishen WANG Di SHI Mahesh S.ILLINDALA Yanming JIN Zhiwei WANG geiri north america San JoseUSA Department of ECE Ohio State UniversityColumbusUSA State Grid Energy Research Institute BeijingChina

Power system security against attacks is drawing increasing attention in recent years.Battery energy storage systems(BESSs)are effective in providing emergency support.Although the benefits of BESSs have been extensively studied earlier to improve the system economics,their role in enhancing the system robustness in overcoming attacks has not been adequately investigated This paper addresses the gap by proposing a new battery storage sizing algorithm for microgrids to limit load shedding when the energy sources are attacked.Four participants are considered in a framework involving interactions between a robustness-oriented economic dispatch model and a bilevel attacker-defender model.The proposed method is tested with the data from a microgrid system in Kasabonika Lake of Canada.Comprehensive case studies are carried out to demonstrate the effectiveness and merits of the proposed approach.

关键词： Battery storage sizing Bilevel optimization Economic dispatch(ED) Microgrids Attack-defender(AD) Robustness