<![CDATA[ IEEE Transactions on Smart Grid - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 5165411 2017February 16<![CDATA[Table of contents]]>82C1514325<![CDATA[IEEE Transactions on Smart Grid publication information]]>82C2C275<![CDATA[Reactive Power Ancillary Service of Synchronous DGs in Coordination With Voltage Control Devices]]>825155273023<![CDATA[Cluster Control of Heterogeneous Thermostatically Controlled Loads Using Tracer Devices]]>825285361294<![CDATA[Applying Wind Simulations for Planning and Operation of Real-Time Thermal Ratings]]>825375471967<![CDATA[A Sparse-Data-Driven Approach for Fault Location in Transmission Networks]]>825485561459<![CDATA[Data-Driven Control for Interlinked AC/DC Microgrids Via Model-Free Adaptive Control and Dual-Droop Control]]>825575713123<![CDATA[Power System Risk Assessment in Cyber Attacks Considering the Role of Protection Systems]]>825725802064<![CDATA[Inhomogeneous Markov Models for Describing Driving Patterns]]>82581588591<![CDATA[Microgrids for Enhancing the Power Grid Resilience in Extreme Conditions]]>$ {K}$ measures the expected number of lines on outage due to extreme events. Index loss of load probability measures the probability of load not being fully supplied. Index expected demand not supplied measures the expected demand that cannot be supplied. Index $ {G}$ measures the difficulty level of grid recovery. The mechanism of extreme events affecting power grid operation is analyzed based on the proposed mesh grid approach. The relationship among transmission grid, distribution grid, and microgrid in extreme conditions is discussed. The Markov chain is utilized to represent the state transition of a power grid with integrated microgrids in extreme conditions. The Monte Carlo method is employed to calculate the resilience indices. The proposed power grid resilience analysis framework is demonstrated using the IEEE 30-bus and 118-bus systems assuming all loads are within microgrids.]]>825895971859<![CDATA[Adequacy Assessment of Power Distribution Network With Large Fleets of PHEVs Considering Condition-Dependent Transformer Faults]]>825986081237<![CDATA[Multi-Phase State Estimation Featuring Industrial-Grade Distribution Network Models]]>${ triangle }$ -connected loads, cumulative-type power measurements, line-to-line voltage magnitude measurements, and reversible line drop compensators. The enhanced modeling equips the estimator with capabilities that make it superior to a recently presented state-of-the-art distribution network load estimator that is currently used in real-life distribution management systems; comparative performance results demonstrate the advantage of the proposed estimator under practical measurement schemes.]]>826096181503<![CDATA[Cost-Friendly Differential Privacy for Smart Meters: Exploiting the Dual Roles of the Noise]]>82619626968<![CDATA[Competitive Charging Station Pricing for Plug-In Electric Vehicles]]>826276391294<![CDATA[A Modified Dynamic Synchrophasor Estimation Algorithm Considering Frequency Deviation]]>826406502539<![CDATA[Mitigation of Harmonics in Grid-Connected and Islanded Microgrids Via Virtual Admittances and Impedances]]>826516611806<![CDATA[Robust Worst-Case Analysis of Demand-Side Management in Smart Grids]]>82662673651<![CDATA[A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory]]>826746864393<![CDATA[Evaluating the Feasibility to Use Microgrids as a Resiliency Resource]]>82687696656<![CDATA[Immunity Toward Data-Injection Attacks Using Multisensor Track Fusion-Based Model Prediction]]>826977071077<![CDATA[Accurate Dynamic Phasor Estimation Based on the Signal Model Under Off-Nominal Frequency and Oscillations]]>827087192155<![CDATA[Trilevel Modeling of Cyber Attacks on Transmission Lines]]>82720729791<![CDATA[Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts]]>82730737929<![CDATA[A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids]]>827387481632<![CDATA[A Microgrid Monitoring System Over Mobile Platforms]]>827497582097<![CDATA[Placement of EV Charging Stations—Balancing Benefits Among Multiple Entities]]>827597681288<![CDATA[A Stochastic Shortest Path Framework for Quantifying the Value and Lifetime of Battery Energy Storage Under Dynamic Pricing]]>82769778598<![CDATA[SARAA: Semi-Supervised Learning for Automated Residential Appliance Annotation]]>82779786974<![CDATA[Decentralized Stochastic Optimal Power Flow in Radial Networks With Distributed Generation]]>827878011623<![CDATA[A Novel Dispatching Control Strategy for EVs Intelligent Integrated Stations]]>828028111527<![CDATA[Approach in Nonintrusive Type I Load Monitoring Using Subtractive Clustering]]>$K$ -mean-based NIALM method.]]>828128211272<![CDATA[Distributed Finite-Time Economic Dispatch of a Network of Energy Resources]]>828228321273<![CDATA[Joint Investment and Operation of Microgrid]]>828338451505<![CDATA[Real-Time Charging Navigation of Electric Vehicles to Fast Charging Stations: A Hierarchical Game Approach]]>828468561393<![CDATA[Toward Power Quality Management in Hybrid AC–DC Microgrid Using LTC-L Utility Interactive Inverter: Load Voltage–Grid Current Tradeoff]]>828578674203<![CDATA[Development of a Self-Healing Strategy to Enhance the Overloading Resilience of Islanded Microgrids]]>828688802994<![CDATA[Stability Analysis and Controller Design of DC Microgrids With Constant Power Loads]]>828818881101<![CDATA[Power System Reliability Evaluation Considering Load Redistribution Attacks]]>828899011650<![CDATA[Robust Aggregator Design for Industrial Thermal Energy Storages in Smart Grid]]>829029162181<![CDATA[Efficient and Autonomous Energy Management Techniques for the Future Smart Homes]]>829179261190<![CDATA[Energy Storage System Control for Prevention of Transient Under-Frequency Load Shedding]]>829279361255<![CDATA[Smart Loads for Voltage Control in Distribution Networks]]>829379461432<![CDATA[Identification and Estimation for Electric Water Heaters in Direct Load Control Programs]]>829479551416<![CDATA[A Probability Model for Grid Faults Using Incomplete Information]]>829569681313<![CDATA[Coupling Neighboring Microgrids for Overload Management Based on Dynamic Multicriteria Decision-Making]]>2 emissions in the alternative MGs. Moreover, the frequency and voltage deviation in the system of coupled MGs are considered in the selection. A dynamic multicriteria decision-making algorithm is developed for this purpose. To contemplate the uncertainties in the considered distribution network, a cloud theory-based probabilistic analysis is deployed as the research framework and the performance of the developed technique is evaluated in MATLAB.]]>829699832648<![CDATA[Robust Frequency Regulation Capacity Scheduling Algorithm for Electric Vehicles]]>829849972612<![CDATA[Multi-Time Scale Coordination of Distributed Energy Resources in Isolated Power Systems]]>829981005967<![CDATA[Integrated Model Considering Effects of Zero Injection Buses and Conventional Measurements on Optimal PMU Placement]]>8210061013976<![CDATA[Opportunities for Smart Electric Thermal Storage on Electric Grids With Renewable Energy]]>82101410226509<![CDATA[Cyber Attacks Against the Economic Operation of Power Systems: A Fast Solution]]>8210231025260<![CDATA[Parallel and Distributed Computation for Dynamical Economic Dispatch]]>8210261027290<![CDATA[Closure to Discussion on “A New Framework for Detection and Identification of Network Parameter Errors”]]>1 interest in our work.^{2} The concerns the discusser raised mainly involved the basic idea and formulation of the Normalized Lagrange Multiplier (NLM) test, which was first proposed in [1], and was only briefly reviewed in this paper. In order to better clarify, the logic and procedure of this part will be presented in greater detail below.]]>821029103067<![CDATA[Scholarship Plus Initiative]]>82103110311553<![CDATA[The power of information]]>82103210321433<![CDATA[IEEE Power Engineering Society information for authors]]>82C3C3120<![CDATA[Blank page]]>82C4C43