<![CDATA[ IEEE Transactions on Smart Grid - new TOC ]]>
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TOC Alert for Publication# 5165411 2018February 22<![CDATA[Table of contents]]>92C1511860<![CDATA[IEEE Transactions on Smart Grid publication information]]>92C2C275<![CDATA[Economic Impact Assessment of Topology Data Attacks With Virtual Bids]]>925125201387<![CDATA[A New Harmony Search Approach for Optimal Wavelets Applied to Fault Classification]]>925215291457<![CDATA[Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids]]>925305433315<![CDATA[Electricity Demand Forecasting by Multi-Task Learning]]>925445511051<![CDATA[Energy Function Inspired Value Priority Based Global Wide-Area Control of Power Grid]]>a priori schemes based on modal analysis of the power system. As these controllers cannot be tuned online, such schemes fail to perform well during severe dynamic system changes. In this paper, a wide-area control architecture designed based on reinforcement learning and optimal adaptive critic network is proposed, that learns and optimizes the system closed-loop performance. Also, a value priority scheme is designed using a derived Lyapunov energy function for prioritization of local and the proposed wide-area global controller which ensures coherent damping of local and inter-area oscillations. The method increases the reliability and allows for automatic tuning of stabilizing controllers especially in the presence of wide-area monitoring constraints. Simulation results on 8-bus 5-machine and 68-bus 16-machine IEEE test systems highlight the efficiency of the proposed method.]]>925525632799<![CDATA[Finding the Right Consumers for Thermal Demand-Response: An Experimental Evaluation]]>925645721306<![CDATA[PDE Modeling and Control of Electric Vehicle Fleets for Ancillary Services: A Discrete Charging Case]]>925735811570<![CDATA[Bilevel Optimization Framework for Smart Building-to-Grid Systems]]>925825931503<![CDATA[A Framework for Automatically Extracting Overvoltage Features Based on Sparse Autoencoder]]>925946041975<![CDATA[Energy Management Considering Load Operations and Forecast Errors With Application to HVAC Systems]]>926056141730<![CDATA[Market-Based Versus Price-Based Microgrid Optimal Scheduling]]>92615623952<![CDATA[Optimal Scheduling for Electric Vehicle Charging With Discrete Charging Levels in Distribution Grid]]>926246341306<![CDATA[Online Determination of External Network Models Using Synchronized Phasor Data]]>926356431902<![CDATA[Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Through Chance Constrained Mixed-Integer Programming]]>926446541492<![CDATA[Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part I: Stochastic Programming Model]]>92655666732<![CDATA[Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part II: Numerical Results]]>926676752972<![CDATA[On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization]]>92676683990<![CDATA[Stochastic Games for Power Grid Protection Against Coordinated Cyber-Physical Attacks]]>926846941672<![CDATA[A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model]]>926957022385<![CDATA[Profit Maximization for Geographically Dispersed Green Data Centers]]>92703711912<![CDATA[Profit-Maximizing Planning and Control of Battery Energy Storage Systems for Primary Frequency Control]]>927127231400<![CDATA[A Reconstruction of the WAMS-Detected Transformer Sympathetic Inrush Phenomenon]]>927247323643<![CDATA[Large Scale Control of Deferrable Domestic Loads in Smart Grids]]>927337421076<![CDATA[A Robust Optimization Approach for Demand Side Scheduling Considering Uncertainty of Manually Operated Appliances]]>927437551447<![CDATA[Optimal Operation for Community-Based Multi-Party Microgrid in Grid-Connected and Islanded Modes]]>927567651929<![CDATA[Real-Time Procurement Strategies of a Proactive Distribution Company With Aggregator-Based Demand Response]]>927667761147<![CDATA[Power System Structural Vulnerability Assessment Based on an Improved Maximum Flow Approach]]>927777851473<![CDATA[Improved Synchronverters with Bounded Frequency and Voltage for Smart Grid Integration]]>927867961707<![CDATA[Multiscale Adaptive Fault Diagnosis Based on Signal Symmetry Reconstitution Preprocessing for Microgrid Inverter Under Changing Load Condition]]>927978061730<![CDATA[High Frequency Impedance Based Fault Location in Distribution System With DGs]]>928078161682<![CDATA[Voltage and Current Controllability in Multi-Microgrid Smart Distribution Systems]]>928178261762<![CDATA[Replicability Analysis of PLC PRIME Networks for Smart Metering Applications]]>928278351489<![CDATA[Distributed Noise-Resilient Networked Synchrony of Active Distribution Systems]]>928368462681<![CDATA[Distributed Energy Management for Networked Microgrids Using Online ADMM With Regret]]>928478561284<![CDATA[Small-Signal Model and Stability of Electric Springs in Power Grids]]>${K_{p}}$ and ${K_{i}}$ of the proportional-integral controllers of the ES to ensure the overall system stability of a weak grid is investigated and is achieved through the aid of simulation on an extended low-voltage (LV) network of IEEE 13-node test feeder. Both simulation and experimental results validate that as the number of ES in the isolated LV network (weak grid) increases, more stringent values of ${K_{p}}$ and ${K_{i}}$ are required to achieve system stability. Besides, a hypothesis that if the optimal values of ${K_{p}}$ and ${K_{i}}$ are adopted, a maximum number of ES can be stably installed over the grid is proposed. It is also shown experimentally that by eliminating the instability of voltage fluctuation in the distribution line, the subsequent frequency fluctuation of the power generation can also be eliminated.]]>928578652343<![CDATA[Performance-Based Settlement of Frequency Regulation for Electric Vehicle Aggregators]]>92866875895<![CDATA[Integration of Distributed PV in Existing and Future UFLS Schemes]]>928768851639<![CDATA[Risk Mitigation for Dynamic State Estimation Against Cyber Attacks and Unknown Inputs]]>safe measurements. Case studies are included to validate the proposed approach. Insightful suggestions, extensions, and open problems are also posed.]]>928868991219<![CDATA[Multi-Area Dynamic State Estimation With PMU Measurements by an Equality Constrained Extended Kalman Filter]]>929009101881<![CDATA[A Novel Weather Information-Based Optimization Algorithm for Thermal Sensor Placement in Smart Grid]]>929119221616<![CDATA[Frequency Control of an Isolated Micro-Grid Using Double Sliding Mode Controllers and Disturbance Observer]]>929239301584<![CDATA[A New LMP-Sensitivity-Based Heterogeneous Decomposition for Transmission and Distribution Coordinated Economic Dispatch]]>${M}_{ {c}}$ and ${M}_{ {p}}$ , are studied. ${M}_{{c}}$ is based on a centralized sensitivity equation, executed on the transmission side. ${M}_{{p}}$ is based on a probing mechanism and can be executed distributedly on the distribution side. The reason for the N-HGD’s improved convergency is theoretically analyzed, and the optimality is proven. Numerical tests show that N-HGD performs better than the original HGD, as well as a penalty-based modified HGD that needs a careful parameter-tuning process. Furthermore, compared with ${M}_{{c}}$ , an ${M}_{{p}}$ -based N-HGD is preferable because it has a much lower computational cost and no additional communicational cost.]]>929319411853<![CDATA[Optimal Power Flow Pursuit]]>929429521554<![CDATA[Explicit Conditions on Existence and Uniqueness of Load-Flow Solutions in Distribution Networks]]>92953962888<![CDATA[Stability and Performance of Coalitions of Prosumers Through Diversification in the Smart Grid]]>92963970909<![CDATA[Toward Optimal Operation of Internet Data Center Microgrid]]>929719791496<![CDATA[Fault Detector and Switch Placement in Cyber-Enabled Power Distribution Network]]>929809921918<![CDATA[Optimal Operation of Electric Railways With Renewable Energy and Electric Storage Systems]]>9299310011266<![CDATA[Harmonic Issues Assessment on PWM VSC-Based Controlled Microgrids Using Newton Methods]]>92100210111648<![CDATA[Distributed Supply Coordination for Power-to-Gas Facilities Embedded in Energy Grids]]>92101210221036<![CDATA[Detecting and Locating Non-Technical Losses in Modern Distribution Networks]]>92102310321737<![CDATA[On-Line Thévenin Impedance Estimation Based on PMU Data and Phase Drift Correction]]>92103310421664<![CDATA[Robust Scheduling of EV Charging Load With Uncertain Wind Power Integration]]>92104310541461<![CDATA[Application of Stochastic Decentralized Active Demand Response (DADR) System for Load Frequency Control]]>92105510621236<![CDATA[A Novel Method for Phasor Measurement Unit Sampling Time Error Compensation]]>92106310722701<![CDATA[Smart Deregulated Grid Frequency Control in Presence of Renewable Energy Resources by EVs Charging Control]]>92107310852836<![CDATA[Distributed Control of Voltage Regulating Devices in the Presence of High PV Penetration to Mitigate Ramp-Rate Issues]]>92108610951338<![CDATA[Integrating EV Charging Stations as Smart Loads for Demand Response Provisions in Distribution Systems]]>92109611061758<![CDATA[Sizing and Coordinating Fast- and Slow-Response Energy Storage Systems to Mitigate Hourly Wind Power Variations]]>92110711171987<![CDATA[Agent-Based Distributed Security Constrained Optimal Power Flow]]>92111811301556<![CDATA[Modeling of Lithium-Ion Battery Degradation for Cell Life Assessment]]>9211311140978<![CDATA[Impact of GPS Signal Loss and Its Mitigation in Power System Synchronized Measurement Devices]]>92114111491915<![CDATA[A Novel Multi-Agent Decision Making Architecture Based on Dual’s Dual Problem Formulation]]>92115011601777<![CDATA[Toward Optimal Energy Management of Microgrids via Robust Two-Stage Optimization]]>92116111741180<![CDATA[A Two-Stage Approach for Network Constrained Unit Commitment Problem With Demand Response]]>^{1} along with the unit commitment schedule and ac load flow solution. Here, the objective is to maximize the social welfare which is expressed as the total utility of the demand side minus the total generation cost. The second stage solves an incentive or penalty minimization problem to determine the demand shifting and demand curtailment across the 24-h period at each DR bus, offering DR, based on the hourly net demand changes obtained during the first stage. The proposed formulation shows how demand shift and demand curtailment happening at different DR buses can be traced back to the hourly net demand changes occurring at the system level. The results, presented for a six-bus system and IEEE 118 bus system, show the benefits of including DR into the network-constrained unit commitment problem according to the proposed formulation.

Any bus which is capable of offering DR will be referred to as DR bus. It should not be mistaken for PQ bus referred in the load flow analysis.

]]>92117511831134<![CDATA[Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability]]>$boldsymbol {kappa }$ , UKF-modified, UKF-$boldsymbol {Delta Q}$ , and the square-root UKF (SR-UKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKF-schol, UKF-$boldsymbol {kappa }$ , and UKF-$boldsymbol {Delta Q}$ do not work well in some estimations while UKF-GPS works well in most cases. UKF-modified and SR-UKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.]]>92118411961451<![CDATA[Performance Recovery of Voltage Source Converters With Application to Grid-Connected Fuel Cell DGs]]>$(boldsymbol {epsilon })$ achieves more reliable performance in compare to the conventional current control scheme. The results also verified that the redesigned controller is quite successful in improving the startup and tracking responses along with enhancing the overall robustness of the system.]]>92119712042259<![CDATA[A Cyber-Physical Control Framework for Transient Stability in Smart Grids]]>92120512151155<![CDATA[Distributed Energy Management for Comprehensive Utilization of Residential Photovoltaic Outputs]]>92121612277815<![CDATA[Coordinated Transmission and Distribution AC Optimal Power Flow]]>92122812401591<![CDATA[Optimal Design of Serious Games for Consumer Engagement in the Smart Grid]]>^{1} we introduce the problem of optimal serious-game design for achieving specific energy-consumption reduction goals. We consider a serious game, where a serious-game designer entity presents publicly to all consumers a list of top-${K}$ consumers and a list of bottom-${M}$ consumers according to their respective energy-consumption reduction at peak hours. The driving forces of this serious game are the user discomfort due to demand load reduction, the user desire for social approval and the user sensitivity to social outcasting. We formulate the problems of the serious-game designer as an operational-cost minimization one for the utility company and that of each consumer as a utility-maximization one. The serious-game-design problem is to decide on ${K}$ , ${M}$ , and on the feedback provided to the consumers, while the consumer-side problem amounts to selecting the behavioral change to energy consumption that maximizes the expected user utility. By a series of simulations, we show how the choices of ${K}$ , ${M}$ affect the energy consumption reduction for different types of customers.

Part of this work was published in IEEE SmartGridComm 2014 [1].

]]>92124112491036<![CDATA[Development of Phasor Estimation Algorithm for P-Class PMU Suitable in Protection Applications]]>${L_{2}}$ -norm. Though IEEE C37.118.1a-2014 standard does not specify the accuracy requirements of phasor under transient condition, the performance of phasor estimator is tested under different dynamic conditions as per IEEE C37.118.1a-2014 standard. The effectiveness of proposed algorithm has also been verified on modified two area power system during fault along with the data generated by the experimental setup in laboratory. The results revealed that the proposed algorithm estimates the phasor accurately irrespective of distortion present in the sinusoidal signals. Furthermore, the proposed estimator inherently filters harmonics, immune to decaying dc components, detects sharp changes in a signal during faults, and effectively works under complex modulated conditions. The above scenario appears frequently in a power system with distributed energy sources. The simplicity, robustness, and generality of the proposed algorithm suits for wide area measurement systems to measure the voltage and current phasors during disturbance in the smart power system networks.]]>92125012603288<![CDATA[Hardware-Oriented Authentication for Advanced Metering Infrastructure]]>92126112702363<![CDATA[A Partially Observable Markov Decision Process Approach to Residential Home Energy Management]]>92127112811080<![CDATA[Intelligent Demand Response Contribution in Frequency Control of Multi-Area Power Systems]]>92128212911647<![CDATA[Advanced Power Sharing Method to Improve the Energy Efficiency of Multiple Battery Energy Storages System]]>92129213002445<![CDATA[Islanding-Aware Robust Energy Management for Microgrids]]>9213011309652<![CDATA[Parallel Three-Phase Interfacing Converters Operation Under Unbalanced Voltage in Hybrid AC/DC Microgrid]]>92131013222960<![CDATA[A Multiagent-Based Hierarchical Energy Management Strategy for Multi-Microgrids Considering Adjustable Power and Demand Response]]>92132313331817<![CDATA[Impacts of Ramping Inflexibility of Conventional Generators on Strategic Operation of Energy Storage Facilities]]>92133413441429<![CDATA[Fault Location in Distribution Networks Through Graph Marking]]>92134513531767<![CDATA[Distributed Load Sharing of an Inverter-Based Microgrid With Reduced Communication]]>92135413641994<![CDATA[Optimal Coordination of Directional Overcurrent Relays in Microgrids by Using Cuckoo-Linear Optimization Algorithm and Fault Current Limiter]]>92136513753101<![CDATA[New EMS to Incorporate Smart Parking Lots Into Demand Response]]>92137613862040<![CDATA[Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines]]>92138713961714<![CDATA[Water-Filling Exact Solutions for Load Balancing of Smart Power Grid Systems]]>92139714071343<![CDATA[A Framework for Optimal Coordinated Primary-Secondary Planning of Distribution Systems Considering MV Distributed Generation]]>92140814151098<![CDATA[An Incentive-Compatible Scheme for Electricity Cooperatives: An Axiomatic Approach]]>9214161424810<![CDATA[Multi-Stage Planning of Active Distribution Networks Considering the Co-Optimization of Operation Strategies]]>92142514331143<![CDATA[Comprehensive Study on Different Possible Operations of Multiple Grid Connected Microgrids]]>92143414412089<![CDATA[Resilience Enhancement Strategy for Distribution Systems Under Extreme Weather Events]]>92144214511452<![CDATA[Fisher Information-Based Meter Placement in Distribution Grids via the D-Optimal Experimental Design]]>92145214612052<![CDATA[Stability Assessment and Optimization Methods for Microgrid With Multiple VSG Units]]>92146214713160<![CDATA[Geo-Routing Algorithms and Protocols for Power Line Communications in Smart Grids]]>92147214811137<![CDATA[Energy Cooperation Optimization in Microgrids With Renewable Energy Integration]]>energy cooperation among microgrids by enabling their energy exchange for sharing is an appealing new solution. In this paper, we consider the energy management problem for two cooperative microgrids each with individual renewable energy generator and ESS. First, by assuming that the microgrids’ renewable energy generation/load amounts are perfectly known ahead of time, we solve the off-line energy management problem optimally. Based on the obtained solution, we study the impacts of microgrids’ energy cooperation and their ESSs on the total energy cost. Next, inspired by the off-line optimization solution, we propose online algorithms for the real-time energy management of the two cooperative microgrids. It is shown via simulations that the proposed online algorithms perform well in practice, have low complexity, and are also valid under arbitrary realizations of renewable energy generations/loads. Finally, we present one method to extend our proposed online algorithms to the general case of more than two microgrids based on a clustering approach.]]>92148214931383<![CDATA[Stochastic Dynamic Pricing for EV Charging Stations With Renewable Integration and Energy Storage]]>92149415051367<![CDATA[An Innovative Two-Level Model for Electric Vehicle Parking Lots in Distribution Systems With Renewable Energy]]>92150615202056<![CDATA[Effects of PEV Traffic Flows on the Operation of Parking Lots and Charging Stations]]>92152115301719<![CDATA[Forecast System Inertia Condition and Its Impact to Integrate More Renewables]]>9215311533508<![CDATA[Cyber-Attack on Overloading Multiple Lines: A Bilevel Mixed-Integer Linear Programming Model]]>9215341536276<![CDATA[IEEE Power Engineering Society information for authors]]>92C3C354<![CDATA[[Blank page]]]>92C4C43