<![CDATA[ IEEE Transactions on Power Systems - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 59 2018April 19<![CDATA[Table of Contents]]>333C12320144<![CDATA[IEEE Power & Energy Society]]>333C2C2161<![CDATA[A Fast Local Search Scheme for Adaptive Coordinated Voltage Control]]>333232123301624<![CDATA[Comparing Different Regulation Offerings From a Battery in a Wind R&D Park]]>2) battery is charge rate limited, which affects the accuracy, especially at high states of charge. Offering a lower regulation capability increases the accuracy in the same state of charge range, but results in lower payments. Performing regulation behind the meter of a wind farm causes additional challenges due to the current electricity rate structure, which has a high demand charge during low wind generation periods. This causes the storage system to be limited to wind generation periods, resulting in regulation not being offered every hour. The battery achieved PJM performance scores above 90% while performing regulation, with month-long averages ranging from 59% to 63%, depending on the regulation offered. The maximum monthly revenue under the described operation, using PJM's regulation rates, is under 50% of the monthly cost of the Wind Energy Institute of Canada's USD 2.34 million dollar battery energy storage system.]]>333233123381551<![CDATA[Distributed Finite-Time Convergence Control of an Islanded Low-Voltage AC Microgrid]]>333233923481407<![CDATA[Simplified Sequential Simulation of Bulk Power System Reliability Via Chronological Probability Model of Load Supplying Capability]]>C), system state average duration ( D), and splitting percentage (P). The chronological probability model can simplify sequential simulation procedure no matter how system load pattern changes, and avoid the time-consuming optimal load curtailment model calculations by using the above-mentioned system state intrinsic properties. Thus, the proposed method can considerably accelerate simulation, especially when evaluating system reliability for different system load conditions. Besides, as the proposed method retains the chronological property of system state transition, it can not only obtain the probability distribution of reliability indices but also incorporate time-dependent renewable generations. RBTS, RTS79, and modified RTS79 with wind farm are utilized to verify the effectiveness of the proposed method.]]>33323492358636<![CDATA[Role of Outage Management Strategy in Reliability Performance of Multi-Microgrid Distribution Systems]]>33323592369834<![CDATA[Toward Efficient Cascading Outage Simulation and Probability Analysis in Power Systems]]>33323702382934<![CDATA[Optimal Placement and Sizing of Distributed Battery Storage in Low Voltage Grids Using Receding Horizon Control Strategies]]>333238323941798<![CDATA[Multi-Objective Coordinated Control of Reactive Compensation Devices Among Multiple Substations]]>333239524031256<![CDATA[Constrained Iterated Unscented Kalman Filter for Dynamic State and Parameter Estimation]]>a priori knowledge about the parameters other than a broad range which can be specified via appropriate constraints.]]>333240424142555<![CDATA[A Framework of Customizing Electricity Retail Prices]]>333241524281794<![CDATA[Multiobjective Dynamic VAR Planning Strategy With Different Shunt Compensation Technologies]]>N–1) contingency and load disturbance events. Shunt reactive power compensation devices considered include mechanically switched capacitor banks, static reactive power compensators and static synchronous compensators. The proposed strategy employs a large number of multitimescale time-domain simulations suitable for use with high performance computing clusters and a genetic algorithm to solve the mixed-integer nonlinear programming formulation using parallel computation capabilities. The method is applied to a New England IEEE 39-bus system with assumed high penetration of induction motors. A comprehensive study shows that performance enhancement and significant cost reduction can be achieved using an optimum combination of various shunt compensator technologies.]]>333242924391699<![CDATA[Synchronous DG Planning to Help High Voltage Systems]]>333244024511672<![CDATA[Security-Constrained Design of Isolated Multi-Energy Microgrids]]>333245224621107<![CDATA[Model-Free MLE Estimation for Online Rotor Angle Stability Assessment With PMU Data]]>333246324761668<![CDATA[Lossy DC Power Flow]]>33324772485406<![CDATA[Looping Radial Distribution Systems Using Superconducting Fault Current Limiters: Feasibility and Economic Analysis]]>333248624951207<![CDATA[Clearing and Pricing for Coordinated Gas and Electricity Day-Ahead Markets Considering Wind Power Uncertainty]]>33324962508792<![CDATA[An Optimization-Based DC-Network Reduction Method]]>333250925171079<![CDATA[Probabilistic Voltage Sensitivity Analysis (PVSA)—A Novel Approach to Quantify Impact of Active Consumers]]>33325182527683<![CDATA[SSR Mitigation of Series-Compensated DFIG Wind Farms by a Nonlinear Damping Controller Using Partial Feedback Linearization]]>333252825382854<![CDATA[Optimal Transmission Line Switching Under Geomagnetic Disturbances]]>33325392550967<![CDATA[A Novel Fast and Flexible Holomorphic Embedding Power Flow Method]]>33325512562983<![CDATA[Mitigation of Geomagnetically Induced Currents Using Corrective Line Switching]]>33325632571818<![CDATA[Transmission Grid Topology Control Using Critical Switching Flow Based Preventive Stabilizing Redispatch]]>333257225821205<![CDATA[Distribution Network Expansion Planning With an Explicit Formulation for Reliability Assessment]]>333258325961134<![CDATA[An Incremental Reliability Assessment Approach for Transmission Expansion Planning]]>33325972609801<![CDATA[A Bayesian Inference Approach to Unveil Supply Curves in Electricity Markets]]>333261026201234<![CDATA[A Distributed Transmission-Distribution-Coupled Static Voltage Stability Assessment Method Considering Distributed Generation]]>333262126321107<![CDATA[Allocation of Resources Using a Microgrid Formation Approach for Resilient Electric Grids]]>33326332643769<![CDATA[Coordinated Control Method for DFIG-Based Wind Farm to Provide Primary Frequency Regulation Service]]>333264426592217<![CDATA[On the Nature of Voltage Impasse Regions in Power System Dynamics Studies]]>${-}$1) relative rotor angles. Once the post-fault trajectory enters a VIR, voltage magnitude solutions become complex or negative, the algebraic Jacobian becomes singular, and the behaviour of a system becomes undefined. The case study has been carried out using a simple 3-machine-1-load system with static load models. In the study, VIR appeared and enlarged as the non-linear (constant power and constant current) load increased. Furthermore, the non-convergence of time-domain solution occurred exactly at VIR, thereby confirming that the problem is of structural nature.]]>333266026702924<![CDATA[Can Merchant Demand Response Affect Investments in Merchant Energy Storage?]]>333267126831282<![CDATA[A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning]]>33326842695308<![CDATA[Stochastic and Chance-Constrained Conic Distribution System Expansion Planning Using Bilinear Benders Decomposition]]>33326962705402<![CDATA[Power System Operational Adequacy Evaluation With Wind Power Ramp Limits]]>33327062716825<![CDATA[Security-Constrained Unit Commitment for AC-DC Grids With Generation and Load Uncertainty]]>$l_1$-norm regularization term to the objective function, and then use convex relaxation techniques to transform the problem into a semidefinite program (SDP). We develop an algorithm based on the iterative reweighted $l_1$-norm approximation that involves solving a sequence of SDPs. Simulations are performed on an IEEE 30-bus test system. Results show that the proposed algorithm returns a solution within 2% gap from the global optimal solution for the underlying test system. When compared with the multi-stage algorithm in the literature, our algorithm has a lower running time and returns a solution with a smaller gap from the global optimal solution.]]>333271727321386<![CDATA[Symmetrical Component Decomposition of DC Distribution Systems]]>33327332741932<![CDATA[Optimal Location Planning of Renewable Distributed Generation Units in Distribution Networks: An Analytical Approach]]>$4.2%$ of the long-term average cost and $80.59%$ of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by $99.51%$ on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches.]]>333274227531039<![CDATA[Optimal Purchase Strategy for Demand Bidding]]>33327542762736<![CDATA[A Second-Order Cone Programming Model for Planning PEV Fast-Charging Stations]]>333276327771028<![CDATA[Multiobjective Automated and Autonomous Intelligent Load Control for Smart Buildings]]>333277827911801<![CDATA[The Interdependence Between Transmission Switching and Variable-Impedance Series FACTS Devices]]>333279228031228<![CDATA[Retrofit Control of Wind-Integrated Power Systems]]>333280428151523<![CDATA[Coordinated Design of Droop Control in MTDC Grid Based on Model Predictive Control]]>$Delta P/Delta {U_{dc}}$. Minimize the $Delta {U_{dc}}$ means $Delta P$ is minimized. So if necessary, the active power of a particular droop VSC can be fixed at a constant level by setting the lower and upper constraint on the corresponding droop gain to be equal. Tests are conducted on the system with six-terminal converters. The performance of the proposed grid controller is compared with those of fixed droop scheme. Simulation results have demonstrated the superiority and flexibility of the proposed grid controller under various operating conditions.]]>333281628281536<![CDATA[Convex Relaxations of Chance Constrained AC Optimal Power Flow]]>33328292841789<![CDATA[Amalgam Power Flow Controller: A Novel Flexible, Reliable, and Cost-Effective Solution to Control Power Flow]]>33328422853581<![CDATA[Short-Term Frequency Regulation and Inertia Emulation Using an MMC-Based MTDC System]]>333285428632786<![CDATA[An Optimization Model for the Electricity Market Clearing Problem With Uniform Purchase Price and Zonal Selling Prices]]>33328642873588<![CDATA[Electric Power Distribution System Model Simplification Using Segment Substitution]]>33328742881911<![CDATA[Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains]]>333288228941182<![CDATA[Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter]]>333289529052176<![CDATA[Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms]]>33329062918352<![CDATA[Optimal Planning and Design of Low-Voltage Low-Power Solar DC Microgrids]]>333291929281130<![CDATA[Tuningless Load Frequency Control Through Active Engagement of Distributed Resources]]>333292929391493<![CDATA[Identification of Critical Protection Functions for Transient Stability Studies]]>33329402948916<![CDATA[Input-to-State Stability Based Control of Doubly Fed Wind Generator]]>333294929612220<![CDATA[Hierarchical Interactive Risk Hedging of Multi-TSO Power Systems]]>333296229743799<![CDATA[New Electric Shipboard Topologies for High Resiliency]]>$text{eight}$ simultaneous worst-case attacks. Based on the study of structural dependence of resiliency, two new topologies are designed that can survive up to $text{14}$ worst-case attacks each. It is to be noted that the new topologies are designed with the same number of circuit breakers, DC buses, and lines as the nominal topologies.]]>33329752983969<![CDATA[An Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies]]>333298429941170<![CDATA[A Novel Cascading Faults Graph Based Transmission Network Vulnerability Assessment Method]]>33329953000895<![CDATA[Three-Phase Power Imbalance Decomposition Into Systematic Imbalance and Random Imbalance]]>a priori judgment to classify any set of three-phase power series into one of four scenarios, depending on whether there is a definite maximum phase, a definite minimum phase, or both. Then, this paper proposes a new method to decompose three-phase power series into a systematic imbalance component and a random imbalance component as the closed-form solutions of quadratic optimization models that minimize random imbalance. A degree of power imbalance is calculated based on the systematic imbalance component to guide phase swapping. Case studies demonstrate that 72.8% of 782 low-voltage substations have systematic imbalance components. The degree of power imbalance results reveal the maximum need for phase swapping, and the random imbalance components reveal the minimum need for demand-side management, if the three phases are to be fully rebalanced.]]>333300130122436<![CDATA[Market Mechanisms for Cooperative Operation of Price-Maker Energy Storage in a Power Network]]> centralized mechanism according to which all batteries are centrally operated to minimize the social cost, 2) the semi-centralized mechanism under which the batteries are centrally operated subject to the constraints specified by a single storage owner (on the maximum amount of withdrawn and charged energy in each period), and 3) the deregulated mechanism according to which the storage owner can freely operate batteries so as to maximize her profit. Under some mild assumptions, we establish the equivalence between the semi-centralized and the deregulated mechanisms: The two mechanisms result in the same storage operation and the same dispatch of generation. Motivated by this equivalence result, we propose a modified version of the semi-centralized mechanism so as to better tradeoff social cost and storage owner's profit by imposing an additional regulatory constraint. We conduct numerical experiments on (modified) IEEE 14- and 57-bus test systems to demonstrate the established theoretical results.]]>333301330281130<![CDATA[Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling]]>33330293039637<![CDATA[Application of Type 3 Wind Turbines for System Restoration]]>P–f and Q–V) relationships. These analyses provide the basic guidance for subsequent control updating. Afterwards, to allow Type 3 WTs to provide black-starting capacity, a concomitant control strategy and a restoration sequence are proposed. The uncovered P–f relationship is reinforced by self-maintained frequency control for the subsequent stand-alone loaded stage. The mechanism of frequency matching among multiple WTs is also discussed. Furthermore, a mathematical analysis for the impacts of controller parameters on the frequency characteristics of a Type 3 WTs-based stand-alone system is developed, thereby providing the guidance for control design. Eventually, a specific islanded system, merely consisting of Type 3 WTs and some passive local loads, is constructed to verify the feasibility of Type 3 WTs for system restoration and to validate the analytical results.]]>333304030511588<![CDATA[Cost-Effective Upgrade of PMU Networks for Fault-Tolerant Sensing]]>$boldsymbol{SA}$ ) at a bus is the fraction of time the time-synchronized current/voltage phasors are correctly present for real-time usage. An algorithm that achieves the above objectives is developed. The algorithm switches between a placement step solved via integer programming and an $boldsymbol{SA}$ evaluation step that uses a bus-centric stochastic model to efficiently determine the need for another placement step with a tightened constraint. The algorithm is shown to subsume the phasor observability and is equally applicable PMU network upgrade and creation. Scalability and computational efficiency of the algorithm are analyzed. The results of tests of the algorithm applied to the IEEE 118-bus system are reported, which are performed under a variety of availability specifications and random event arrival rates associated with communication interruptions, equipment faults, and their removals. Superior accuracy and fault tolerance in state estimation are achieved by PMU networks meeting more stringent ${boldsymbol{SA}}$.]]>33330523063845<![CDATA[Coordinated Expansion Planning of Natural Gas and Electric Power Systems]]>33330643075506<![CDATA[Optimizing Power–Frequency Droop Characteristics of Distributed Energy Resources]]>333307630862105<![CDATA[Fractional Order Sliding Mode Based Direct Power Control of Grid-Connected DFIG]]>333308730962541<![CDATA[Avoiding Frequency Second Dip in Power Unreserved Control During Wind Power Rotational Speed Recovery]]>333309731061940<![CDATA[Nonlinear Adaptive Excitation Control for Structure Preserving Power Systems]]>333310731174225<![CDATA[Stability Analysis of SSR in Multiple Wind Farms Connected to Series-Compensated Systems Using Impedance Network Model]]>333311831282142<![CDATA[Remote-Controlled Switch Allocation Enabling Prompt Restoration of Distribution Systems]]>33331293142633<![CDATA[Small-Signal Stability of an AC/MTDC Power System as Affected by Open-Loop Modal Coupling Between the VSCs]]>333314331521563<![CDATA[Efficient Estimation of Component Interactions for Cascading Failure Analysis by EM Algorithm]]>33331533161555<![CDATA[Automatic Identification of Power System Load Models Based on Field Measurements]]>333316231711477<![CDATA[Positive Sequence Model for Converter-Interfaced Synchronous Generation With Finite DC Capacitance]]>333317231801729<![CDATA[Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders]]>33331813188167<![CDATA[Preprocessing of Multi-Time Instant PV Generation Data]]>33331893191462<![CDATA[Development and Analysis of a Sensitivity Matrix of a Three-Phase Voltage Unbalance Factor]]>ph) voltages and, consequently, 3-ph unbalanced load demand. To demonstrate its utility, we performed simulation case studies and compared the estimates made using the matrix to those from a power flow calculation algorithm. The VUF sensitivity matrix is expected to have a variety of practical applications for power systems, such as optimal scheduling and real-time control, which enables improvement in network power quality.]]>33331923195443<![CDATA[Performance Accuracy Scores in CAISO and MISO Regulation Markets: A Comparison Based on Real Data and Mathematical Analysis]]>33331963198356<![CDATA[A Novel Decentralized Responsibilizing Primary Frequency Control]]>33331993201529<![CDATA[Identification of Var-Voltage Characteristics Based on Ambient Signals]]>33332023203348<![CDATA[On the Computational Complexity of Tariff Optimization for Demand Response Management]]>Simple Multi-Period Energy Tariff Optimization Problem (SMETOP) and prove its NP-hardness. The result naturally applies to many models in the literature that generalize SMETOP, and whose complexity status has been unknown to date.]]>33332043206160<![CDATA[Compensation in Complex Variables for Microgrid Power Flow]]>33332073209164<![CDATA[Identifying Redundant Flow Limits on Parallel Lines]]>33332103212199<![CDATA[An Intrainterval Security Risk Regarding Regulation Burden Due to Wind Variation in High-Wind-Penetrated Power Systems]]>33332133216529<![CDATA[Corrections to “Purchase Bidding Strategy for a Retailer With Flexible Demands in Day-Ahead Electricity Market”]]>3333217321721<![CDATA[Corrections to “Price-Maker Bidding in Day-Ahead Electricity Market for a Retailer With Flexible Demands”]]>3333217321721<![CDATA[Blank Page]]>333B3218B32203<![CDATA[Table of Contents]]>33332213222122<![CDATA[Guest Editorial for the Special Section on Enabling Very High Penetration Renewable Energy Integration Into Future Power Systems]]>3333223322659<![CDATA[Influence of Stochastic Dependence on Small-Disturbance Stability and Ranking Uncertainties]]>33332273235788<![CDATA[Power System Voltage Stability Evaluation Considering Renewable Energy With Correlated Variabilities]]>33332363245985<![CDATA[Probabilistic Risk Assessment of Power Quality Variations and Events Under Temporal and Spatial Characteristic of Increased PV Integration in Low-Voltage Distribution Networks]]>333324632542527<![CDATA[Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV]]>33332553264747<![CDATA[Model-Free Renewable Scenario Generation Using Generative Adversarial Networks]]>333326532751278<![CDATA[Forecasting the High Penetration of Wind Power on Multiple Scales Using Multi-to-Multi Mapping]]>333327632842294<![CDATA[Probabilistic Flexibility Evaluation for Power System Planning Considering Its Association With Renewable Power Curtailment]]>333328532951608<![CDATA[Two-Stage Optimization of Battery Energy Storage Capacity to Decrease Wind Power Curtailment in Grid-Connected Wind Farms]]>333329633051111<![CDATA[Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty]]>33333063314694<![CDATA[Stochastic Unit Commitment With Topology Control Recourse for Power Systems With Large-Scale Renewable Integration]]>33333153324906<![CDATA[Distributed Dispatch Approach for Bulk AC/DC Hybrid Systems With High Wind Power Penetration]]>333332533361258<![CDATA[Robust Optimization for Hydroelectric System Operation Under Uncertainty]]>33333373348704<![CDATA[A Novel Generation Rescheduling Algorithm to Improve Power System Reliability With High Renewable Energy Penetration]]>33333493357759<![CDATA[A Multi-State Model for Exploiting the Reserve Capability of Wind Power]]>333335833721653<![CDATA[LMP-Based Pricing for Energy Storage in Local Market to Facilitate PV Penetration]]>333337333821147<![CDATA[Real-Time Coordinated Voltage Control of PV Inverters and Energy Storage for Weak Networks With High PV Penetration]]>333338333951746<![CDATA[Network Partition and Voltage Coordination Control for Distribution Networks With High Penetration of Distributed PV Units]]>333339634072368<![CDATA[Virtual Multi-Slack Droop Control of Stand-Alone Microgrid With High Renewable Penetration Based on Power Sensitivity Analysis]]>333340834171406<![CDATA[Formal Analysis of Networked Microgrids Dynamics]]>333341834272002<![CDATA[Temporary Frequency Support of a DFIG for High Wind Power Penetration]]>333342834371075<![CDATA[Frequency Response Assessment and Enhancement of the U.S. Power Grids Toward Extra-High Photovoltaic Generation Penetrations—An Industry Perspective]]>333343834491330<![CDATA[Impact of Inertia Control of DFIG-Based WT on Electromechanical Oscillation Damping of SG]]>333345034592746<![CDATA[Oscillation Analysis and Wide-Area Damping Control of DFIGs for Renewable Energy Power Systems Using Line Modal Potential Energy]]>${rm{H}}_{{rm{infty }}}$ control is designed, and this design is shown to have a certain anti-interference ability. The proposed method can be used for submode analysis and oscillation suppression. Finally, the correctness of the LMPE method is verified through the analysis and simulation of a four-generator, two-area system and an actual power system in China .]]>333346034712134<![CDATA[An Oscillatory Stability Criterion Based on the Unified <inline-formula><tex-math notation="LaTeX">$dq$ </tex-math></inline-formula>-Frame Impedance Network Model for Power Systems With High-Penetration Renewables]]>dq frame impedance network model (INM), with which different converters as well as traditional generators/HVDCs can be incorporated to form an integrated s-domain model of a practical system. As the INM is aggregated into a lumped impedance matrix, a new criterion is then proposed to quantify the oscillatory stability, just by analyzing the frequency characteristics of the determinant of the matrix. Finally, the modeling method and the stability criterion are applied to a real-world system with a very high share of renewables and a realistic risk of oscillatory instability. Their effectiveness has been validated by both field measurements and electromagnetic transient simulations.]]>333347234852298<![CDATA[Introduction the IEEE PES Resource Center]]>33334863486495<![CDATA[Information for Authors]]>333C3C3117<![CDATA[Blank page]]>333C4C42