<![CDATA[ IEEE Transactions on Power Systems - new TOC ]]>
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TOC Alert for Publication# 59 2017June 22<![CDATA[Table of Contents]]>324C12492133<![CDATA[IEEE Power & Energy Society]]>324C2C2144<![CDATA[Wave Aspect of Power System Transient Stability—Part I: Finite Approximation]]>32424932500855<![CDATA[Wave Aspect of Power System Transient Stability—Part II: Control Implications]]>critical region for a stressed link near the end of a longitudinal system,” “dispersed control in a path,” “placement of controllers considering structure and load distribution in a system,” and “localized transient stability enhancement.” Good transient stability enhancement is successfully achieved by controllers in all test systems in terms of larger critical clearing time, which directly translates to increased maximum safe power transfer. The controller design is based on the transmission line analogy of traveling waves and load modulation effect. The concepts in this paper series can assist in improving the design of control strategies to enhance power system transient stability by considering the wave aspects.]]>324250125081019<![CDATA[Use of Polytopic Convexity in Developing an Adaptive Interarea Oscillation Damping Scheme]]>324250925201574<![CDATA[Sequential Coordination of Transmission Expansion Planning With Strategic Generation Investments]]>324252125341150<![CDATA[Efficient Uncertainty Quantification in Stochastic Economic Dispatch]]>324253525461455<![CDATA[Managing Wind Power Uncertainty Through Strategic Reserve Purchasing]]>324254725591477<![CDATA[A Common Information Model Oriented Graph Database Framework for Power Systems]]>324256025691931<![CDATA[Operational Reliability and Economics of Power Systems With Considering Frequency Control Processes]]>32425702580837<![CDATA[Optimization of the Event-Driven Emergency Load-Shedding Considering Transient Security and Stability Constraints]]>32425812592521<![CDATA[Endogenous Probabilistic Reserve Sizing and Allocation in Unit Commitment Models: Cost-Effective, Reliable, and Fast]]>32425932603635<![CDATA[Real-Time Contingency Analysis With Corrective Transmission Switching]]>324260426171062<![CDATA[Dynamic Robust Transmission Expansion Planning]]>32426182628411<![CDATA[Priority Ranking of Critical Uncertainties Affecting Small-Disturbance Stability Using Sensitivity Analysis Techniques]]>32426292639790<![CDATA[Hybrid Islanding Detection in Microgrid With Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network]]>${\text{PoI}}_{{\rm{ANN}}}$ is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it (${\text{PoI}}_{{\rm{FUZZY}}}$) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to ${\text{PoI}}_{{\rm{FUZZY}}}$. Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method.]]>324264026511782<![CDATA[Impact of Multi-terminal HVDC Grids on Enhancing Dynamic Power Transfer Capability]]>324265226621453<![CDATA[Receding Horizon Control of Wind Power to Provide Frequency Regulation]]>324266326721817<![CDATA[Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders]]>32426732681618<![CDATA[Distributed Energy Resources Topology Identification via Graphical Modeling]]>324268226941466<![CDATA[GPU-Based Fast Decoupled Power Flow With Preconditioned Iterative Solver and Inexact Newton Method]]>324269527031265<![CDATA[An Efficient Optimal Control Method for Open-Loop Transient Stability Emergency Control]]>324270427131406<![CDATA[Optimal Thermostat Programming for Time-of-Use and Demand Charges With Thermal Energy Storage and Optimal Pricing for Regulated Utilities]]>32427142723846<![CDATA[Model-Based Dispatch Strategies for Lithium-Ion Battery Energy Storage Applied to Pay-as-Bid Markets for Secondary Reserve]]>32427242734987<![CDATA[A Bayesian Approach for Parameter Estimation With Uncertainty for Dynamic Power Systems]]>a posteriori point of the parameters and their variance, which quantifies their uncertainty. Within this framework, we estimate several parameters of the dynamic power system, such as generator inertias, which are not quantifiable in steady-state models. We illustrate the performance of these approaches on a 9-bus power grid example and analyze the dependence on measurement frequency, estimation horizon, perturbation size, and measurement noise. We assess the computational efficiency, and discuss the expected performance when these methods are applied to large systems.]]>32427352743385<![CDATA[An Operational State Aggregation Technique for Transmission Expansion Planning Based on Line Benefits]]>32427442755439<![CDATA[Power System Sensitivity Identification—Inherent System Properties and Data Quality]]>324275627661556<![CDATA[Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation]]>32427672778996<![CDATA[On Estimation and Sensitivity Analysis of Distribution Circuit's Photovoltaic Hosting Capacity]]>324277927891760<![CDATA[Modeling Protection Systems in Time-Domain Simulations: A New Method to Detect Mis-Operating Relays for Unstable Power Swings]]>324279027982236<![CDATA[Modeling of Type 3 Wind Turbines With df/dt Inertia Control for System Frequency Response Study]]>f/dt inertia control is developed for studying frequency dynamics in power systems. A simplified small-signal Type 3 WT model with df/dt control is first constructed based on the mass-spring-damping concept, such that the physical properties and frequency response of a Type 3 WT can be clearly understood, besides the frequency-domain expressions of the available inertia and the corresponding damping coefficient can be directly derived. The manifested inertia is apparently controllable and frequency-dependent, but differs from the constant inertia featured in a conventional synchronous generator (SG). Furthermore, the frequency response model of a generic two-machine system, composed of an SG and an aggregate Type 3 WTs, is established. The model synthetically considers the effects of the WTs’ different controller parameters, operating points, and the SG's governor response on system frequency characteristics. Then, time-domain simulations on the studied two-machine system are performed in MATLAB/Simulink. The simulated results verify that the proposed model is effective for analyzing system frequency dynamics, and that the test system frequency characteristics can be improved based on tuning the mass-spring- damping coefficients of Type 3 WTs.]]>324279928091554<![CDATA[A Method for Filtering Low Frequency Disturbance in PMU Data Before Coordinated Usage in SCADA]]>324281028161897<![CDATA[Online Detection of Low-Quality Synchrophasor Measurements: A Data-Driven Approach]]>324281728272228<![CDATA[Alternative LP and SOCP Hierarchies for ACOPF Problems]]>32428282836244<![CDATA[A General Unified AC/DC Power Flow Algorithm With MTDC]]>324283728462101<![CDATA[Resilience Enhancement With Sequentially Proactive Operation Strategies]]>324284728571793<![CDATA[Robust Operation of Microgrids via Two-Stage Coordinated Energy Storage and Direct Load Control]]>32428582868551<![CDATA[Head Dependence of Pump-Storage-Unit Model Applied to Generation Scheduling]]>32428692877823<![CDATA[Gaussian Mixture Based Probabilistic Load Flow For LV-Network Planning]]>32428782886953<![CDATA[Risk Assessment of Multi-Timescale Cascading Outages Based on Markovian Tree Search]]>32428872900883<![CDATA[Decentralized Reactive Power Sharing and Frequency Restoration in Islanded Microgrid]]>324290129121882<![CDATA[Examination of Three Different ACOPF Formulations With Generator Capability Curves]]>324291329231179<![CDATA[Fast and Reliable Primary Frequency Reserves From Refrigerators With Decentralized Stochastic Control]]>324292429412604<![CDATA[Probabilistic Forecast of PV Power Generation Based on Higher Order Markov Chain]]>324294229521053<![CDATA[Robust Defense Strategy for Gas–Electric Systems Against Malicious Attacks]]>32429532965939<![CDATA[An Iterative Method for Determining the Most Probable Bifurcation in Large Scale Power Systems]]>324296629731376<![CDATA[An Improved IRA Algorithm and Its Application in Critical Eigenvalues Searching for Low Frequency Oscillation Analysis]]>32429742983948<![CDATA[An Efficient Tri-Level Optimization Model for Electric Grid Defense Planning]]>32429842994781<![CDATA[Open and Closed-Loop Residential Load Models for Assessment of Conservation Voltage Reduction]]>324299530054096<![CDATA[Optimal Location-Allocation of TCSCs and Transmission Switch Placement Under High Penetration of Wind Power]]>32430063014359<![CDATA[Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming]]>32430153026612<![CDATA[Advanced Control Strategies of PMSG-Based Wind Turbines for System Inertia Support]]>32430273037913<![CDATA[Consideration of Existing Capacity in Screening Curve Method]]>324303830481380<![CDATA[Contingency-Constrained Unit Commitment With Intervening Time for System Adjustments]]>$N$-1-1 contingency reliability criterion considers the consecutive loss of two components in a power system, with intervening time for system adjustments between the two losses. In this paper, we consider the problem of optimizing generation unit while ensuring the $N$-1-1 criterion. Due to the coupling of time periods associated with consecutive component losses, the resulting problem yields a very large-scale mixed-integer linear optimization model. For efficient solution, we introduce a novel branch-and-cut algorithm using a temporally decomposed bilevel separation oracle. The model and algorithm are assessed using multiple IEEE test systems, and a comprehensive analysis is performed to compare system performance across different contingency criteria. Computational results demonstrate the value of considering intervening time for system adjustments in terms of total cost and system robustness.]]>32430493059857<![CDATA[A Loss Minimization Method on a Reactive Power Supply Process for Wind Farm]]>32430603068739<![CDATA[Griddle: Video Gaming for Power System Education]]>n = 178) of Griddle's significant transformative impact, with the goal of validating the game-based learning approach and sharing “lessons learned” with designers of related tools. We find that Griddle is effective at engaging students and presents evidence that it helps students integrate key concepts, and we identify areas where further development and study are needed.]]>32430693077523<![CDATA[Probabilistic Framework for Transient Stability Assessment of Power Systems With High Penetration of Renewable Generation]]>324307830882648<![CDATA[Detection and Correction of Systematic Errors in Instrument Transformers Along With Line Parameter Estimation Using PMU Data]]>32430893098888<![CDATA[Three- or Two-Stage Stochastic Market-Clearing Algorithm?]]>32430993110951<![CDATA[Flywheel Energy Storage Model, Control and Location for Improving Stability: The Chilean Case]]>324311131192341<![CDATA[Distribution Load Capability With Nodal Power Factor Constraints]]>32431203126214<![CDATA[LTS-Based Robust Hybrid SE Integrating Correlation]]>32431273135676<![CDATA[Voltage Control Strategies for Solid Oxide Fuel Cell Energy System Connected to Complex Power Grids Using Dynamic State Estimation and STATCOM]]>324313631452008<![CDATA[Propagating Uncertainty in Power-System DAE Models With Semidefinite Programming]]>324314631562291<![CDATA[Load Following of Multiple Heterogeneous TCL Aggregators by Centralized Control]]>324315731671476<![CDATA[Observability Analysis for Dynamic State Estimation of Synchronous Machines]]>32431683175976<![CDATA[Predictive Analysis of Microgrid Reliability Using a Probabilistic Model of Protection System Operation]]>324317631841514<![CDATA[Local and Remote Estimations Using Fitted Polynomials in Distribution Systems]]>324318531942564<![CDATA[Analysis and Damping of Mechanical Resonance of Wind Power Generators Contributing to Frequency Regulation]]>324319532041781<![CDATA[A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation]]>324320532161318<![CDATA[Hopf Bifurcation Control of Power System Nonlinear Dynamics via a Dynamic State Feedback Controller–Part I: Theory and Modeling]]>32432173228633<![CDATA[Hopf Bifurcation Control of Power Systems Nonlinear Dynamics Via a Dynamic State Feedback Controller—Part II: Performance Evaluation]]>324322932362363<![CDATA[Generalized Δ-Circuit Concept for Integration of Distributed Generators in Online Short-Circuit Calculations]]>32432373245660<![CDATA[Reliable Renewable Generation and Transmission Expansion Planning: Co-Optimizing System's Resources for Meeting Renewable Targets]]>$n-K$ security criteria is presented. Three case studies are proposed to illustrate the applicability of the proposed model. A case study with realistic data from the Chilean system is presented and solutions obtained with different levels of security are tested against a set of 10 000 simulated scenarios of renewable injections and system component outages.]]>32432463257627<![CDATA[Grid Structural Characteristics as Validation Criteria for Synthetic Networks]]>32432583265481<![CDATA[Voltage Stability Monitoring From a Transmission Bus PMU]]>324326632741177<![CDATA[Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity]]>324327532864652<![CDATA[Dependency Analysis and Improved Parameter Estimation for Dynamic Composite Load Modeling]]>a priori information about parameter values. Effectiveness of the proposed dependence analysis and parameter estimation scheme is validated using both synthetic and real measurement data during faults. Albeit focused on CMPLDW, the proposed approaches can be readily used for composite load modeling in general.]]>324328732974389<![CDATA[A Novel Method of Polynomial Approximation for Parametric Problems in Power Systems]]>Taylor expansion at a given operation point. Although the method is general, for simplicity and good readability, we introduce the detailed process in its application to load flow problems. Case studies from 6-, 118-, and 1648-bus system show that the proposed method provides approximation more efficiently than traditional Galerkin method does, and 3-order polynomials can give very accurate results.]]>32432983307987<![CDATA[Dual Pricing Algorithm in ISO Markets]]>3243308331073<![CDATA[Impact of Coupled Transmission-Distribution on Static Voltage Stability Assessment]]>3243311331257<![CDATA[Wind Farm Power Distribution Function Considering Wake Effects]]>32433133314354<![CDATA[Machine Learning Based Power Grid Outage Prediction in Response to Extreme Events]]>32433153316165<![CDATA[The 2015 Ukraine Blackout: Implications for False Data Injection Attacks]]>32433173318216<![CDATA[Existence and Uniqueness of Load-Flow Solutions in Three-Phase Distribution Networks]]>3243319332077<![CDATA[Maximum Wind Energy Extraction for Variable Speed Wind Turbines With Slow Dynamic Behavior]]>32433213322104<![CDATA[Information for Authors]]>324C3C3117<![CDATA[Blank page]]>324C4C42