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This paper investigates the potential impacts of geomagnetically induced current (GIC) on the wind farm operating conditions. A comprehensive harmonic analysis within the EMTP-RV program is done for the exposure of different components of the studied system to the generated harmonics due to GIC. In particular, the GIC effects on the wind farm main output transformer (MOT) and the doubly fed induction generator are investigated. A thermal analysis is also conducted to estimate the hot-spot heating of the MOT, when subjected to the GIC benchmark waveform with various magnitudes. The effects of a harmonic filter for limiting the harmonic distortions are investigated and compared with the case of using GIC blocking devices and the IEEE 519 standard limits. The simulated hot-spot heating due to GIC in the MOT structural parts and windings is also investigated and the simulation results are compared with the standard emergency temperature limits. The simulation results reveal that the hot-spot temperature of the MOT winding increases beyond the maximum standard level of 180 °C, when the peak of the benchmark GIC exceeds 70 A/phase. Thus, a loading capability curve is derived for the safe operation of MOT, during the geomagnetic disturbance. [ABSTRACT FROM AUTHOR]

Low-frequency high-voltage ac transmission scheme has recently been proposed as an alternative approach for bulk power transmission. This paper proposes a multi-period optimal power flow (OPF) for a multi-frequency HVac transmission system that interconnects both conventional 50/60-Hz and low-frequency grids using back-to-back converters with a centralized control scheme. The OPF objective is to minimize system losses by determining the optimal dispatch for generators, shunt capacitors, and converters. The OPF constraints include the operational constraints of all HVac grid and converter stations. The resulting mixed-integer nonlinear programing problem is solved using a proposed framework based on the predictor-corrector primal-dual interior-point method. The proposed OPF formulation and solution approach are verified using a multi-frequency HVac transmission system that is modified from the IEEE 57-bus system. The results with the optimal dispatch from the proposed method during a simulated day show a significant loss reduction and an improved voltage regulation compared to those when an arbitrary dispatch is chosen. [ABSTRACT FROM AUTHOR]

In recognizing significant advantages in achieving carbon emission reduction and mitigating variability of renewables, an increasing installation of combined-cycle (CC) units has been observed recently. This new shift in the generation portfolio has urged Independent System Operators (ISO) to continuously enhance CC unit models in their unit commitment (UC) tools, in order to derive physically feasible and operationally reliable unit commitment and generation dispatch schedules of CC units by accurately capturing their operation features. To this end, this paper accurately simulates startup and shutdown processes of individual turbines of a CC unit during the configuration transition procedure. A dynamic ramping model and the corresponding spinning reserve model for individual turbines are also proposed to further enhance the modeling accuracy. A modified IEEE 118-bus system with multiple CC units is adopted to evaluate the proposed models. [ABSTRACT FROM AUTHOR]

The identification of a tie-line power transmission region is crucial for the secure operation of interconnected power grids. The characterization of a tie-line power transmission region with regional operational constraints is inherently a multi-parametric programming problem, which greatly suffers from a heavy computational burden. In this paper, we propose a fast boundary search approach to efficiently characterize the tie-line power transmission region in AC/DC hybrid grids. The multi-parametric programming method is improved in the following three aspects: (1) the search direction is fixed along the boundary of a region, which avoids the search inside a region; (2) the abundant search of subregions that share a boundary facet is avoided; and (3) a fast guess of the Karush-Kuhn-Tucker (KKT) condition is presented, which reduces the number of optimization calculations. Furthermore, an expansion strategy is presented to enlarge the tie-line power transmission region via quantifying the impact of system parameters on the transmission region. Simulations based on the IEEE 118-bus test system and a 661-bus utility system show that (1) the proposed method can be over 30 times faster than existing methods and (2) the proposed method successfully expands the tie-line power transmission region by identifying the key parameters. [ABSTRACT FROM AUTHOR]

The utilization of voltage-source converter-based multi-terminal HVDC (VSC-MTDC) technology has been envisaged as a cost-effective solution to transmit bulk renewables from multiple sending ends to receiving-end AC systems. However, with complex configurations and various types of components, the reliability evaluation of a VSC-MTDC system is more complicated than that of a conventional HVDC system. In addition, due to stochastic failures and complex operating modes, the application of a VSC-MTDC system can have a significant influence on the reliability of hybrid AC/DC power systems. This paper presents a new technique for evaluating the nodal reliability of a hybrid AC/DC power system based on the VSC-MTDC system. The proposed technique is incorporated within a multi-state model for the VSC-MTDC system. Then, an optimization model for contingency management of a hybrid AC/DC power system is formulated. The model is established based on the optimal power flow technique for a VSC-MTDC based hybrid AC/DC power system. The objective of the model is to minimize the total system cost when determining the load curtailment and generation re-dispatch results for each contingency state. Nodal reliability indices for hybrid AC/DC power systems are proposed to evaluate the reliability performance of customers at each node. The effectiveness of the proposed methods is validated using a modified IEEE reliability test system. [ABSTRACT FROM AUTHOR]

This paper proposes an interconnection and damping assignment passivity-based control (IDA-PBC) for multimachine power systems including hydro-turbine governing systems (HTGS) with surge tank. The main objective is to stabilize the rotor speed and regulate the terminal voltage of each synchronous machine in a power system. The proposed control is decentralized, thus avoiding challenges of communication between generators. Passivity theory is used since the open-loop of the HTGS presents a port-Hamiltonian structure. IDA-PBC allows a control law that maintains the passive structure in closed-loop, guaranteeing its asymptotic stability using Lyapunov's theory. The dynamics of each HTGS are described by an eleventh-order model, which can be reduced to a tenth-order. The proposed control is tested in a 12-bus test system and compared to a standard control, which considers a voltage regulator and exciter based on the IEEE type ST1A excitation system model and power system stabilizer IEEE-PSS1A. The governing system based on a PID control with static and transient droop is also employed. Additionally, the proposed controller is compared to a sliding mode controller. Time-domain simulations demonstrate the robustness and appropriate performance of the proposed decentralized control under different large disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we develop a data-driven framework for controlling distributed energy resources (DERs) in a balanced radial power distribution system with the objective of regulating voltages across the whole system. The objective is to determine optimal DER power injections that minimize the voltage excursions outside a desirable voltage range without knowing a complete model of the power distribution system. To this end, we approximate the nonlinear relationship between the voltage magnitudes and the power injections by a linear model. The parameters of this linear model—referred to as the voltage sensitivities—can be computed using information on the network topology and the line parameters, the values of which will be estimated. Assuming the knowledge of feasible network topology configurations and distribution line resistance-to-reactance ratios, we propose a framework for identifying the true network topology configuration and the corresponding line parameters using only a few measurements of voltage magnitudes and power injections. Utilizing the estimated voltage sensitivities, the optimal DER power injections can be readily determined by solving a convex optimization problem. Due to its data-driven nature, the proposed framework is intrinsically adaptive to changes in system conditions such as unknown topology reconfiguration. The effectiveness of the proposed framework is validated via numerical simulations on the IEEE 123-bus distribution test feeder. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of setting the tap positions of load tap changers (LTCs) for voltage regulation in power distribution systems. The objective is to find a policy that maps measurements of voltage magnitudes and topology information to LTC tap ratio changes so as to minimize the voltage deviation across the system. We formulate this problem as a Markov decision process (MDP), and propose a data and computationally efficient batch reinforcement learning (RL) algorithm to solve it. To circumvent the “curse of dimensionality” resulting from the large state and action spaces, we propose a sequential learning algorithm to learn an action-value function for each LTC, based on which the optimal tap positions can be directly determined. By taking advantage of a linearized power flow model, we propose an algorithm to estimate the voltage magnitudes under different tap settings, which allows the RL algorithm to explore the state and action spaces freely offline without impacting the system operation. The effectiveness of the proposed algorithm is validated via numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders. [ABSTRACT FROM AUTHOR]

This paper presents a new method of instability detection and subsequent stabilization of the power system network using a proposed multi-shot remedial action scheme (RAS) that can extemporaneously detect critical generators based on the dynamic states of generator computed from the terminal phasor measurement units. The instability detector is a moving window classifier that predicts impending instability using rate of change of individual generator transient energy indices evaluated from the d–q axis voltage as well as conventional severity indices based on generator angle and frequency. A comparative performance analysis of a spectral feature based ensemble decision tree classifier with a multivariate long short-term memory network is also presented. The proposed RAS identifies critical generators through individual machine transient energy formulation and recursive coherency matrix, evaluated solely from system-wide generator dynamic states, and maintains stability by tripping adaptively the run-away generators. Performance evaluation of the proposed scheme has been made on IEEE 39-bus network and it has been demonstrated that the proposed RAS is robust with regards to instability prediction and it can effectively identify critical generators and stabilize the network by tripping the same. [ABSTRACT FROM AUTHOR]

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time; however, tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines, our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions, and in particular Gaussian, for injections. We validate the algorithm through extensive experiments on ac three-phase IEEE distribution grid test cases. [ABSTRACT FROM AUTHOR]

Beamforming (BF) training is crucial to establishing reliable millimeter-wave communication connections between stations (STAs) and an access point. In IEEE 802.11ad BF training protocol, all STAs contend for limited BF training opportunities, i.e., associated BF training (A-BFT) slots, which results in severe collisions and significant BF training latency, especially in dense user scenarios. In this paper, we first develop an analytical model to evaluate the BF training protocol performance. Our analytical model accounts for various protocol components, including user density, the number of A-BFT slots, and protocol parameters, i.e., retry limit and contention window size. We then derive the average successful BF training probability, the BF training efficiency and latency. Since the derived BF training efficiency is an implicit function, to reveal the relationship between system parameters and BF training performance, we also derive an approximate expression of BF training efficiency. Theoretical analysis indicates that the BF training efficiency degrades drastically in dense user scenarios. To address this issue, we propose an enhancement scheme which adaptively adjusts the protocol parameters in tune with user density, to improve the BF training performance in dense user scenarios. Extensive simulations are carried out to validate the accuracy of the developed analytical model. In addition, simulation results show that the proposed enhancement scheme can improve the BF training efficiency by 35% in dense user scenarios. [ABSTRACT FROM AUTHOR]

Most V2X (Vehicle-to-Everything) applications rely on broadcasting awareness messages known as CAM (Cooperative Awareness Messages) in ETSI or BSM (Basic Safety Message) in SAE standards. A large number of studies have been devoted to guarantee their reliable transmission. However, to date, the studies are generally based on simplified data traffic models that generate awareness messages at periodic intervals or with a constant message size. These models do not accurately represent the real generation of CAM messages that follow specific mobility-based rules. Using simplified and unrealistic traffic models can significantly impact the results and validity of the studies, and hence accurate models for the generation of awareness messages are necessary. This paper proposes the first set of models that can realistically generate CAM messages. The models have been created from real traces collected by two car manufacturers in urban, sub-urban and highway test drives. The models are based on mth order Markov sources, and model the size of CAMs and the time interval between CAMs. The models are openly provided to the community and can be easily integrated into any simulator. [ABSTRACT FROM AUTHOR]

The IEEE 1588 standard known as Precision Time Protocol (PTP) was developed to provide highly accurate and synchronized clocks for a wide range of applications. In particular, it is critical for latency-sensitive applications, such as enterprise-class financial transactions. In this paper, we analyze some of the security risks associated with this protocol, in particular, cyberattacks that disrupt the synchronization between multiple PTP devices or the synchronization between system clock and PTP hardware. We construct an experimental PTP test bed and perform the baseline measurements of synchronization between multiple clocks. We then demonstrate successful attacks that disrupt PTP synchronization, including vulnerabilities based on the lack of message authentication between master and slave clocks. Proposed mitigations of these vulnerabilities are discussed, in an effort to improve the next-generation implementations of this timing protocol or recommend additional external authentication devices in the network architecture. [ABSTRACT FROM AUTHOR]

This paper presents an analytical framework which lays the foundation for distributed massive multiple-input multiple-output (MIMO) supported wireless power transfer for wearable devices. In our approach, we consider multiple users, each wearing a number of wireless sensors along with one body worn hub which acts as a relay to forward information from the on-body sensors to nearby access points (APs). Each AP is equipped with a large number of antennas and is not only responsible for receiving data sent from the hubs, but also supplying wireless power to them. Interaction between hubs and APs is not exclusive to a single pairing, with APs assigned to supply energy to and receive data from more than one hub. More precisely, APs perform the downlink energy transmission and the uplink data transmission using maximum-ratio combining. Analytical approximations of the outage probability and spectral efficiency are derived. Based on these analytical results, two modes of operation are investigated. These are outage probability prioritized and spectral efficiency prioritized. For each mode, max-min power controls are proposed to ensure a uniformly good service throughout the area of coverage. By contrasting with the IEEE 802.15.6 standard, the numerical results illustrate that while using collocated massive MIMO may provide unsatisfactory performance, the distributed setting shows much promise for enabling wireless power transfer for wearable devices. [ABSTRACT FROM AUTHOR]

In recent years, software defined radio and digital signal processing have been widely used in communication and radar. As a result, the hardware and RF front-end for radar and wireless communication tends to be similar. Thus, using the same RF and hardware platform for joint radar-communication becomes viable. Joint radar-communication would bring more efficient plan and usage for the radio spectral resource. Furthermore, it could enable new applications that require information exchange and precise localization at the same time. In this paper, cyclic prefixed single carrier (CP-SC) and its variations are chosen as the waveforms for joint radar-communication. CP-SC waveform and its variations are popular in wireless communication and have been chosen by a few standards like IEEE 802.11ad and LTE-advanced. Efficient algorithms are proposed to use such waveforms for range and speed detection/estimation of targets. The proposed algorithms are derived from the maximum likelihood (ML) principle and have low computational complexity. Simulations show that the estimation performance of the proposed method is almost the same as that of ML and is much better than that of the channel estimation based method. [ABSTRACT FROM AUTHOR]

Minimal linear codes have significant applications in secret sharing schemes and secure two-party computation. There are several methods to construct linear codes, one of which is based on functions over finite fields. Recently, many construction methods for linear codes from functions have been proposed in the literature. In this paper, we generalize the recent construction methods given by Tang et al. in [IEEE Transactions on Information Theory, 62(3), 1166-1176, 2016] to weakly regular plateaued functions over finite fields of odd characteristic. We first construct three-weight linear codes from weakly regular plateaued functions based on the second generic construction and then determine their weight distributions. We also give a punctured version and subcode of each constructed code. We note that they may be (almost) optimal codes and can be directly employed to obtain (democratic) secret sharing schemes, which have diverse applications in the industry. We next observe that the constructed codes are minimal for almost all cases and finally describe the access structures of the secret sharing schemes based on their dual codes. [ABSTRACT FROM AUTHOR]

The compressive sensing theory is implemented by means of analog-to-information converter (AIC) that provides a digitized and compressed output $y$ directly from its analog input signal $x$. A reconstruction algorithm, in turn, is needed to obtain a reconstructed signal $\hat {x}$ that estimates $x$ from $y$. This algorithm can be far away from the AIC, linked through a communication channel, and embedded in a remote machine, for instance. In general, $\hat {x}$ is evaluated in order to test the AIC itself and, in consequence, the performance of the reconstruction algorithm affects the test result of the AIC-under-test and, as a result, can invalidate the AIC test at all. Few state-of-the-art works have proposed the test methods that take into account only the AIC output $y$. However, they take into consideration a single-sine test signal to follow the IEEE Standard 1241 for analog-to-digital converter testing. In this paper, two new AIC test methods are introduced in which only the AIC outputs, that is, only the compressed outputs $y$ , are taken in account. In the first one, multisine signals can be applied and, in the second one, four-parameters sine-wave fit methods, as recommended by the IEEE Standard 1241, can be applied. Experimental results were obtained for two different AIC implementations (namely, random demodulator and random modulation preintegrator) and the signal-to-noise and distortion ratio (SINAD) of both was measured directly from the AIC output proving that the proposed AIC test methods are effective. [ABSTRACT FROM AUTHOR]

The goal of this paper is to develop an experimental hardware testbed for verifying the compliance of the medium access mechanism of commercially available Wi-Fi certified units with the IEEE 802.11 standard. The core of this testbed is based on a Universal Software Radio Peripheral (USRP), which is used to capture and downconvert the Wi-Fi frames to the baseband. The data captured by the USRP are used to extract the key medium access parameters of the commercial unit under test (UUT). Our measurement methodology involves a novel compliance metric based on the Kullback–Leibler distance between probability distributions. This metric is in the form of a single scalar, which will hence enable us to rank the UUTs based on their level of compliance. Furthermore, we develop a method based on the harmonized European Standard ETSI EN 301 893 to identify UUTs with noncompliant access mechanisms that tend to monopolize the wireless medium and UUTs for which compliance is an option that deviates from default settings. [ABSTRACT FROM AUTHOR]

Phasor estimates produced by the Taylor-based weighted least square filter (TWLS) can be erroneous if the reference frequency is not close to its real value. On the other hand, online calculation of the TWLS coefficients leads to the significantly increased computational cost. In this paper, a low complexity algorithm is proposed, that aims to enhance the phasor and frequency estimation at off-nominal frequencies. The proposed algorithm utilizes a symmetric TWLS (STWLS) filter to iteratively estimate the parameters of the positive image of the fundamental component. Due to the symmetry in the STWLS frequency response, an estimation accuracy at off-nominal frequencies can be significantly improved by adjusting the filter center frequency, which slightly affects the computational complexity. In a variety of scenarios, simulation results confirm the favorable performance of the proposed algorithm over the TWLS. [ABSTRACT FROM AUTHOR]

Over the last years, extreme weather events have caused extensive damages in power systems, leaving millions of customers without electricity and therefore highlighting the necessity to enhance power system resilience. This paper proposes a resilience constrained day-ahead unit commitment framework for increasing resiliency of a power system exposed to an extreme weather event. The weather-dependent failure probabilities of the transmission lines are taken into account in order to decide the scheduling of generators that minimizes load shedding in the most efficient way, while respecting operating limits of the system. The problem is formulated as a tri-level optimization problem that is transformed to a bi-level problem using duality theory and linearization techniques. The problem is solved as a two-stage robust optimization problem using a Column & Constraint Generation based decomposition algorithm. The master problem provides the unit commitment and the subproblem identifies the worst damage scenario due to weather event. A Sequential Monte Carlo simulation of a modified IEEE Reliability Test System and IEEE 118-bus System is applied to illustrate and validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

With the growth of renewable generation (RG) and the development of associated ride through curves serving as operating limits, during disturbances, on violation of these limits, the power system is at risk of losing large amounts of generation. In order to identify preventive control measures that avoid such scenarios from manifesting, the power system must be modeled as a constrained dynamical system. For such systems, the interplay of feasibility region (man-made limits) and stability region (natural dynamical system response) results in a positively invariant region in state space known as the constrained stability region (CSR). After the occurrence of a disturbance, as it is desirable for the system trajectory to lie within the CSR, critical clearing time (CCT) must be defined with respect to the CSR instead of the stability region as is done traditionally. The sensitivity of CCT to system parameters of constrained systems then becomes beneficial for planning/revising protection settings (which impact feasible region) and/or operation (which impact dynamics). In this paper, we derive the first order CCT sensitivity of generic constrained power systems using the efficient power system trajectory sensitivity computation, pioneered by Hiskens and Pai in [“Trajectory sensitivity analysis of hybrid systems,” IEEE Trans. Circuits Syst. Fundam. Theory Appl., vol. 47, no. 2, pp. 204–220, Feb. 2000]. The results are illustrated for a single-machine infinite-bus (SMIB) system as well as a multi-machine system in order to gain meaningful insight into the dependence between ability to meet constraints, system stability, and changes occurring in power system parameters, such as, mechanical power input and inertia. [ABSTRACT FROM AUTHOR]

Identifying network dynamic topology changes with limited measurement data is a primary challenge for power distribution system analysis. Existing methods require the measurement of the nodal voltage (both amplitudes and phase angles) of a majority of nodes, which means that large-scale installation of advanced monitors is necessary. In this paper, a distribution network dynamic topology awareness method that only requires a synchronized voltage amplitude measurement of a limited number of nodes is proposed. The key idea of the design is to relate network topology changes (including line topology changes, node topology changes and switch actions) to the resultant perturbations in the voltage amplitude covariance matrix. Because perturbations can be identified with incomplete observations, the corresponding topology changes can be identified with limited measurements of voltage amplitude data. With no need for phase-angle measurement, only ordinary root mean square (RMS) based monitors are needed, which can be made available with a relatively minor investment. Simulation tests are performed with the IEEE 123-node system and 8500-node system. Remarkably, 80% of the topology changes can be detected with only 10% of the nodes equipped with monitors, and a 100% correct localization rate can be achieved with 50% of the nodes equipped with monitors. [ABSTRACT FROM AUTHOR]

This paper proposes a variable proportion coefficient based control scheme for the doubly-fed induction generator based wind turbines (DFIG-WTs) to implement frequency support by regulating rotor speed. To reduce the adverse impact caused by regulating DFIG-WT on the dynamic characteristic of the grid frequency, a two-stage switching control scheme is employed for the proposed method. In the first stage, the variable proportion coefficient is designed for emulating the virtual inertia of the DFIG-WT to provide inertia response. In the second stage, a fuzzy control scheme is employed to design the variable proportion coefficient for both quickly restoring the maximum power point tracking (MPPT) operation of DFIG-WTs and avoiding the secondary frequency drop to system. Case studies are undertaken based on WSCC 9-bus and IEEE 39-bus power systems, respectively. Simulation results show that the proposed control scheme can not only make grid-connected DFIG-WTs provide the friendly frequency support, but also help them to fully use the frequency regulation ability of synchronous generators for quickly restoring the MPPT operation. [ABSTRACT FROM AUTHOR]

The rapid increase in the installation of renewable energy sources, particularly solar photovoltaic (PV) sources associated with unbalanced features of distribution systems (DS), disturbs the classic control strategy of voltage regulation devices and causes voltage violation problems. This paper proposes an effective control strategy for voltage regulators in the DS based on the voltage sensitivity using a multi-agent system (MAS) architecture. The features of the unbalanced distribution system (UDS) with the PV and different types and configurations of voltage regulators are considered in the proposed strategy. The novelty of the proposed method lies in realizing both the control optimality of minimizing voltage violations and the flexibility to accommodate changes in the DS topology using an MAS scheme. An advantageous feature of using the MAS scheme is the robust control performance in normal operation and against system failure. Simulation studies have been conducted using IEEE 34-node and 123-node distribution test feeders considering high PV penetration and different sun profiles. The results show that the proposed voltage control strategy can optimally and effectively manage the voltage regulators in the UDS, which decrease their operation stresses and minimize the overall voltage deviation. [ABSTRACT FROM AUTHOR]

Modern grid codes demand the integration of voltage support capability with photo-voltaic (PV) generators to ensure a secure operation of power systems. On the other hand, short-term voltage instability (STVI) of distribution networks (DNs) is one of the key issues to be addressed. It can occur due to high proportion of induction motor loads. However, the existing literature lacks a thorough analysis of short-term voltage stability (STVS) of DNs following an asymmetrical fault. Furthermore, an effective voltage-support strategy for PV units, which can improve the STVS while mitigating the excessive voltage swell, is essential for secure operation. Hence, firstly this paper thoroughly investigates the STVS of DNs subjected to asymmetrical faults. It is perceived that voltage support through conventional methods can increase the risk of STVI and excessive voltage swell. Secondly, a new voltage-support strategy is proposed based on the negative sequence voltage at point of common coupling (PCC) aimed at mitigating the STVI and regulating the phase voltages within the margins. The key features of the proposed strategy are, fast and accurate estimation of the network's impedance at PCC is not required and it can be easily re-designed considering the network's dynamic behaviors. The efficacy of the proposed method is validated on two IEEE benchmark test systems, and the results demonstrate the effectiveness in improving the STVS and alleviating over voltage issues. [ABSTRACT FROM AUTHOR]

High renewable energy penetration profoundly increases the diversity of operating statuses for the power system. Therefore, massive operating scenarios need to be considered in transmission expansion planning (TEP) to fully reflect the impact of renewable energy on power system operation. Usually, representative scenarios need to be selected to reduce the computational burden of TEP. However, the impact of abandoning many scenarios is unclear because investment decisions are highly non-linear with respect to the input scenarios. In this paper, we propose a TEP model to effectively consider massive scenarios without reduction. We use Benders decomposition to divide the TEP problem into an investment master problem and many operation subproblems. Multiple parametric linear programming (MPLP) is applied to cluster the operation subproblems in each iteration. Here, only one operation subproblem needs to be solved in each cluster, and the results of other subproblems in the same cluster can be analytically obtained. The clustering process is objective-based and self-updated dynamically during each iteration so that the effects of all the scenarios on investment decisions are considered. The efficiency improvement and effectiveness of the proposed method are illustrated through case studies on the modified Garver's 6-bus, IEEE RTS-79, IEEE RTS-96, and realistic-sized test system. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical-hybrid architecture for Volt/Var control of distribution grids in the presence of high penetration of distributed generators (DGs). The architecture includes three layers with specific operational goals associated with each layer based on data resolution, communication network, and control device response time. The top layer carries out central optimization and seeks to minimize power losses via optimal 15-minute scheduling of load tap changers (LTCs), voltage regulators (VRs), and capacitor banks (CBs), and DGs. The middle layer ensures voltage regulation using a distributed approach for reactive power provisioning by fast response DGs. The bottom layer carries out local decision making that enlists edge intelligence to operate LTCs, VRs and CBs to cope with fast and real-time changes in DGs and loads. The top layer generates overall references for the lower layers optimized over a 24-hour horizon, updated at 15-minute intervals, which are then suitably corrected at faster time-scales of seconds in the middle and bottom layers. Appropriate coordination is introduced between each layer so as to meet combined goals of optimization and accommodation of load and generation uncertainties in near real-time. The proposed method is validated using a modified IEEE 34 bus test feeder and at 80% penetration of DGs. [ABSTRACT FROM AUTHOR]

Stalling of residential air conditioners (RACs) following a transient fault can delay the voltage recovery ranging from 3 to 20 seconds. This phenomenon is referred to as fault-induced delayed voltage recovery (FIDVR). Several aggregated RAC models have been presented to replicate the actual FIDVR events. However, they usually have two main drawbacks as: i) considering independent stalling voltage and stalling time for all RACs, ii) resulting in only two ultimate modes; either 100% stalled or 100% normal running. This paper amends the abovementioned shortcomings by proposing a simple and effective performance-based RAC model. The proposed model utilizes a simple explicit relation between the stalling voltage and its required time to stall the RAC, furthermore, it can stall a fraction of RACs based on the voltage dip characteristics. The experimental results support the proposed model and computer simulations employing the IEEE 118-bus test system and Iran's electricity grid reveal the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Power systems with high penetration of wind resources must cope with significant uncertainties originated from wind power prediction error. This uncertainty might lead to wind power curtailment and load shedding events in the system as a big challenge. Efficient modeling and incorporation of wind power uncertainty in generation and reserve scheduling can prevent these events. This paper presents a new framework for wind power cumulative distribution function (CDF) modeling and its incorporation in a new chance-constrained economic dispatch (CCED) problem. The proposed CDF modeling uses few moments of wind power random samples. To validly capture the actual features of the wind power distribution such as main mass, high skewness, tails, and especially boundaries from the moments, an efficient moment problem is presented and solved using the beta kernel density representation (BKDR) technique. Importantly, a new polynomial cost function for efficient modeling of wind power misestimation costs is proposed for the CCED problem that eliminates the need for an analytical CDF and enables the use of an accurate piecewise linearization technique. Using this technique, the non-linear CCED problem is converted to a mixed-integer linear programming (MILP)-based problem that is convex with respect to the continuous variables of the problem. Therefore, it is solved via off-the-shelf mathematical programming solvers to reach more optimal results. Numerical simulations using the IEEE 118-bus test system show that compared with conventional approaches, the proposed MILP-based model leads to lower power system total cost, and thereby is suggested for practical applications. [ABSTRACT FROM AUTHOR]

The paper introduces a robust method to detect random errors and cyber-attacks targeting alternating current (AC) dynamic state estimation through false data injection. The dynamic state estimator uses measurements that are delivered at a high reporting rate from synchrophasors or phasor measurement units (PMUs). A statistical outlier detection algorithm using the S-estimator is implemented on successive batch-mode regression representations of the extended Kalman filter (EKF). The S-estimator is a robust regression estimator that resists the effect of outliers and bad measurements. Theoretically, an S-estimator offers an improved detection of outliers compared with other methods implemented in the smart grid literature such as the Huber M-estimator and the Generalized Huber M-estimator (GM-estimator). The S-estimator has the advantage of offering a high breakdown point and can resist both observation outliers and leverage points. The introduced S-based EKF accurately tracks the fast variations of states when the PMU data is either clean or contaminated by bad measurements and cyber attacks. The proposed filter also accurately tracks the states when errors and cyber attacks target the topology of the grid. The simulations are conducted on the IEEE 14-bus system. [ABSTRACT FROM AUTHOR]

This paper proposes a Kriging assisted strategy to expedite evolutionary computation for solving Optimal Power Flow (OPF) problems. First, two algorithms were developed–a Kriging Assisted Genetic Algorithm (KAGA) and a Kriging Assisted Particle Swarm Optimization (KAPSO) - and tested using unconstrained benchmark functions; it was found that both algorithms provided reliable and robust solutions. Accordingly, KAGA and KAPSO were selected and tested on the IEEE 30 and 118 bus systems for minimizing generation costs and active power losses. It is shown that the proposed KAPSO outperforms other algorithms, especially in terms of the computation time. In reference to the solution quality yielded by KAGA and KAPSO, the proposed Kriging assisted strategy offers a promising method to improve the performance of evolutionary based computation when solving OPF problems. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of synchrophasor estimation algorithms in Under-Frequency Load-Shedding (UFLS) and Load-Restoration (LR) schemes, relying on frequency and Rate-of-Change-of-Frequency (ROCOF) measurements produced by Phasor Measurement Units (PMUs). We compare two consolidated window-based synchrophasor estimation algorithms, as representative approaches based on static and dynamic signal models, with a focus on the appropriateness of utilizing PMU-based ROCOF measurements. The performance of the proposed relaying scheme is assessed by means of a Real-Time Simulator implementing the time-domain full-replica dynamic model of the IEEE 39-Bus power system. [ABSTRACT FROM AUTHOR]

In this paper, we analytically study the performance of opportunistic multi-channel bonding protocol supporting delay-sensitive multimedia services. We consider a multi-channel system shared by IEEE 802.11ac users who can transmit over multiple channels and legacy users who can only transmit over one single channel. By analyzing the channel bonding behavior of IEEE 802.11ac users and the random access of legacy users, bonding probability and successful bonding probability of IEEE 802.11ac users can be derived. Furthermore, the access delays of both legacy and 802.11ac users are analyzed. According to the analytical results, the network capacity which quantifies the maximum number of multimedia flows that can be supported with guaranteed delay is then presented. Additionally, the impacts of different parameters such as traffic data rate on the network capacity are investigated. Our analytical results show that channel bonding is favorable when the secondary channels are underutilized. But channel bonding should be disabled when there are already intense contentions from legacy users. Based on the analytical results, we propose a heuristic bonding policy which can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. Extensive simulations have been conducted to validate the analytical results. [ABSTRACT FROM AUTHOR]

The use of social robots has been proposed for the delivery of therapy to autistic children. The aim of such projects, of which the DREAM project is an example, is to replace therapists by robots, operating in sensory environments that enable them to detect and respond to feedback from the child. This article considers the ethical concerns of autonomy, community, transparency, identity, value, and empathy to evaluate the ethics of such deployment of robots. In doing so it provides a response to the Richardson et al. article in IEEE Technology and Society Magazine, Mar. 2018 [20]. This article concludes that deployment of robots to control the behavior of autistic children is ethically suspect and should be questioned. The use of robots with children should be evaluated on the basis of the purpose of and process by which such robots are deployed, rather than on the basis of the technology itself. Particularly important is the roboticist's empathy with the user of the robot, and gaining an understanding of the individual child. The paper suggests how an understanding of the autistic child might lead to sensitive deployment of a robot to help the child manage social environments through supporting the child's regulation of emotions. [ABSTRACT FROM AUTHOR]

The wind speeds of each wind turbine generator (WTG) are the basis for establishing an aggregated model of a wind farm for power system transient simulation. However, the commonly obtained operation data of the wind farm do not include the wind speeds of each WTG. Thus, a modeling method for the wind speeds of the WTGs of same moment inside the wind farm was proposed. A wind speed combination model (WSCM) is established via K-means clustering from the sorted field-measured wind speed data, which can provide reasonable wind speeds for each WTG when aggregate modeling the wind farm. Subsequently, an improved two-step clustering method of the WTGs was proposed, by which the WTGs are initially clustered according to whether they enter low voltage ride through (LVRT) mode or not. An “N-1 VS One” aggregated model and the binary search algorithm were used to quickly and correctly predict whether a WTG in the wind farm enters LVRT mode under a grid fault. The case study demonstrates the satisfactory performance of the WSCM and the necessity of clustering the WTGs according to whether they enter or do not enter LVRT mode for obtaining an accurate aggregated model of the wind farm. All the data, models, and programs that are used in this paper can be found at IEEE Dataport, DOI: 10.21227/dqyk-t852. [ABSTRACT FROM AUTHOR]

This paper presents a new online detection approach for rolling bearing’s incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm is first proposed based on singular value decomposition (SVD) and Kurtosis criterion. Based on the assessment results, a kind of deep learning algorithm, i.e., stacked denoising autoencoder (SDAE), is introduced to extract the common deep features of normal state and early fault state. Support vector data description (SVDD) is applied to establish the offline detection model using the obtained features. At the online stage, a self-adaptive matching strategy with 1-Dimensional anchor is proposed. By utilizing the SDAE model established at offline stage, this strategy can extract more representative deep features of the target bearing via generating various proposal fragments and then determining the fault occurrence time in a self-adaptive way by feeding the online features into the SVDD model. Experiments run on the bearing data set of IEEE prognostic and health management (PHM) Challenge 2012. The results show the proposed approach has good detection performance in real time and much lower false alarm rate, with no need to acquire fault characteristic frequency in advance. [ABSTRACT FROM AUTHOR]

The increasing proliferation of distributed generators (DGs) demands a robust and comprehensive design of the communication-based adaptive protection scheme, which must be able to not only deal with the known changes in the DGs-distribution system but also handle the unknown changes due to variations in the operating mode, network configuration, and number, type, and size of DGs. On one side, with the increasing installations of DGs, the deployment of the communication system in protection is increasing to monitor the system status and select the relays’ settings accordingly. However, on the other side, if information flow does not happen due to the presence of pre-fault failure of the relay-agents and/or communication links, a protection scheme may fail to provide the suitable protection. This paper proposes a centralized adaptive protection scheme to deal with such failures and varying operating conditions. It provides optimal relays settings for varying operating conditions of the DGs-distribution system with the aim of providing fast, comprehensive, and self-adaptive protection. In order to achieve these objectives, two algorithms have been developed: the first algorithm determines optimal hybrid pick-up settings of the relays, and the second algorithm determines the adaptive hierarchies of the relays. With the inclusion of these two algorithms, the relay coordination problem has been formalized linearly. The performance of the proposed scheme has been tested and validated for various known and unknown events with different types of short circuit faults of different fault impedances on the 37-bus IEEE test distribution system in a MATLAB simulation and coding environment. The results show that the proposed method not only provides the optimal relay's coordination in varying situations but also effectively reduces the protection latency due to pre-fault failures of the relays and communication links, and low level of fault currents. [ABSTRACT FROM AUTHOR]

Recent Federal Energy Regulatory Commission (FERC) Order 841 requires that Independent System Operators (ISOs) facilitate the participation of energy storage systems (ESSs) in energy, ancillary services, and capacity markets, by including ESS bidding parameters that represent the physical and operational characteristics. However, in the existing market frameworks that allow Battery Energy Storage Systems (BESSs) to participate, the bids and offers do not explicitly represent the physical and operational characteristics such as the state of charge (SOC), discharge rate, degradation, etc. This paper proposes a novel BESS operational cost model considering degradation cost, based on depth of discharge and discharge rate. The model is developed considering Lithium-ion batteries, and the approach can be applied to other conventional electrochemical batteries, but not flow batteries. A detailed bid/offer structure based on the proposed BESS operational cost functions is formulated. Thereafter, a new framework and mathematical model for BESS participation in an LMP based, co-optimized, energy and spinning reserve market, are developed. Three case studies are presented to investigate the impact of BESS participation on system operation and market settlement. The proposed model is validated on the IEEE Reliability Test System (RTS) to demonstrate its functionalities. [ABSTRACT FROM AUTHOR]

The reliability evaluation of integrated power-gas systems (IPGS) becomes critical due to the high dependency of the two energy systems. Once a contingent incident happens in one system, the other system will accordingly be affected. In this paper, a detailed model for the IPGS is presented with the consideration of the power-to-gas devices and gas storages. Furthermore, a sequential Monte Carlo (SMC) simulation is utilized to evaluate the reliability of the IPGS. In particular, the gas storage life reliability model is considered to characterize the charging and discharging performance. Moreover, an optimal load shedding model is used to coordinate the load shedding of the IPGS. What's more, new reliability indices are given to display the reliability of the IPGS. Finally, the proposed model is tested on an integrated IEEE 24-bus power system and 20-node gas system and an integrated IEEE RTS 96 power system and 40-node gas system. The results show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

An electric power distribution system is operated in several distinct radial topologies by opening and closing of system's sectionalizing and tie switches. The estimation of the system's current operational topology is a precursor to implementing any optimal control actions (during normal operation) or restorative actions (during outage condition). This paper presents a mathematical programming approach to estimate the operational topology of a three-phase unbalanced power distribution system under both outage and normal operating conditions. Specifically, a minimum weighted least absolute value estimator is proposed that uses the line flow measurements, historical/forecasted load data, and ping measurements and solves a mixed-integer linear program (MILP) to estimate the operational topology and any outaged sections simultaneously. We thoroughly validate the accuracy of the proposed approach using IEEE 123-bus and a 1069-bus three-phase multi-feeder test system with the help of numerous simulations. It is observed that the approach performs well even at high percentages of measurement errors. [ABSTRACT FROM AUTHOR]

This paper proposes a new state estimation approach for large-scale power systems called spanning tree maximum exponential absolute value (ST-MEAV). The novel state estimator is developed based on the combination of the maximum exponential absolute value (MEAV) and a fast-linear solver. An overall algorithm is presented to show the process. A modified ST-MEAV called ST0-MEAV is also proposed to improve computational efficiency. Furthermore, the state estimation of the two FACTS devices called interphase power controllers (IPC) and unified interphase power controllers (UIPC) is addressed. The new formulations minimize the number of additional variables needed for the state estimation to reduce the computational load and to simplify implementation compared to previous methods presented in the literature for similar FACTS devices like UPFC or IPFC. The state estimation approach incorporates detailed steady-state models of the devices including IPC and UIPC constraints. The ST-MEAV algorithm is modified based on the new formulations for IPC and UIPC. Two modified IEEE test systems are used to verify the performance of ST-MEAV in the presence of IPC and UIPC. Application tests of ST-MEAV on two real power grids are also presented to evaluate the state estimator performance in large-scale power systems. The simulation results of the proposed method compare favorably with those for weighted least square-largest normal residual (WLS-LNR) and MEAV with reduced correction equation (MEAV-RCE). [ABSTRACT FROM AUTHOR]

The imbalance of generation and load caused by the increasing integration of volatile generations poses challenges on frequency regulation. AGC is required to respond to the generation fluctuations without violating operational and security constraints. Explicit model predictive control (EMPC) provides an approach to reaching such requirements, which calculates the control laws of MPC in an explicit form, allowing for offline validation of the controller and enabling fast online computation. However, the partition number of EMPC's piecewise affine control laws grows exponentially with the number of constraints and prediction/control horizons, which hinders its application in large systems. In this paper, we propose an alternative explicit control approach for AGC by approximating the control laws of EMPC using Legendre polynomial series expansions, thus entirely eliminating the partition issue. The Galerkin method is applied to the KKT conditions of EMPC's multiparametric quadratic programming (mp-QP) problem to compute the approximation. Case studies in an illustrative system and IEEE 118-Bus System verify the performance and efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Networked microgrids (MGs) are considered as an emerging grid design for the future distribution system (DS). The coordination of the networked MGs is critical in order to further enhance the operation efficiency and reliability of the system. In this paper, a transactive energy (TE) framework is proposed for the coordinated energy management of networked MGs in DS. Instead of direct coordination signals and fixed pricing schemes, the distribution network operator (DNO) organizes a transactive market with the MGs to coordinate the energy management in the operation. Further, a distributionally robust optimization (DRO)-based algorithm is developed to provide a robust solution of the detailed scheduling decisions in the proposed TE framework under uncertainty without being too conservative. Case studies with the proposed framework were conducted with the IEEE 33-bus system with three MGs and the IEEE 123-bus system with nine MGs. The results of the case studies show that the proposed TE-based framework can effectively coordinate the energy scheduling of the MGs. The operational cost of the DS is reduced significantly. Meanwhile, the proposed DRO-based algorithm provides a robust but not over-conservative solution for the operation decisions of the DNO and MGs in the proposed framework. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal allocation of the electric spring (ES) smart load device in radial distribution systems for voltage regulation. An ES can transform a connected noncritical load into a smart load providing voltage support and voltage suppression functions. A risk-limiting ES planning method is proposed to obtain the optimal ES configuration (number, locations, capacities, and types) to mitigate the voltage violations caused by uncertainties in renewable distributed generation. The forecasting uncertainties are placed into multi-state discrete levels to reduce scenario numbers. The voltage regulation capability of different types of ES are quantified and compared to determine the optimal allocation model. The optimal ES installation plan is identified as per the minimal total cost and lowest voltage violation risk. The proposed optimal allocation method is validated on a modified IEEE 15-bus distribution network. [ABSTRACT FROM AUTHOR]

Volatile voltage profiles in distribution systems caused by the fluctuating nature of renewable distributed generation (DG) are attracting growing concern. In this paper, we develop a new formulation of network reconfiguration to mitigate voltage volatility. It provides new insights into the voltage regulation problem in distribution systems with high renewable penetration, which is commonly addressed by power electronic controllers. From the linear DistFlow equations, we first propose a novel index that measures the voltage volatility of each bus in the system. This index is a function of distribution network parameters that characterizes the role of network structure in voltage volatility. Then, we formulate a new reconfiguration model that minimizes the network loss and restricts the voltage volatility indices with the coordination of switched capacitor banks. A Benders decomposition-based approach is designed to solve the problem using mixed-integer quadratic programming. The simulations on the IEEE 69-bus system show that the reconfiguration scheme is able to: first, minimize network loss when DG outputs are as predicted; and second, significantly reduce the risk of voltage violations when DG outputs deviate from the prediction. The proposed formulation unleashes the distinctive power of network reconfiguration in reducing voltage volatility, by which the cost of power electronic controllers can be saved. [ABSTRACT FROM AUTHOR]

Efficient solution methods for large-scale unit commitment (UC) problems have long been an important research topic and a challenge, especially in market clearing computation. For large-scale UC, the Lagrangian relaxation methods (LR) and the mixed integer programming methods (MIP) are most widely adopted. However, LR usually suffers from slow convergence; and the computational burden of MIP is heavy when the binary variable number is large. In this paper, a variable reduction method is proposed. First, the time-coupled constraints in the original UC problem are relaxed, and many single-period UC problems (s-UC) are obtained. Second, LR is used to solve the s-UCs. Different from traditional LR with iterative subgradient method, the optimal multipliers and the approximate UC solutions of the s-UCs are obtained by solving linear programs. Third, a criterion for choosing and fixing the UC variables in the UC problem is established; hence, the number of binary variables is reduced. Finally, the UC with reduced binary variables is solved to obtain the final UC solution. The proposed method is tested on the IEEE 118-bus system and a 6484-bus system. The results show the method is very efficient and effective. [ABSTRACT FROM AUTHOR]

The limited predictability and controlability of distributed energy resources has resulted in an increasing need for managing the available demand side flexibility from proactive end-users. In this regard, distribution system operators are expected to take a coordinating role in facilitating the utilization of the available flexibility. This paper proposes a novel optimization method to solve the optimal flexibility dispatch problem for a system operator such that it deploys the demand response resource effectively. We use the second-order cone relaxation of the problem to keep the problem convex, tractable and representing electric flows to a high accuracy. The analytical solution to the problem gives the locational pricing of flexibility services. To demonstrate the applicability and scalability of the proposed framework, it is applied to two case studies. We study a stylized 9-bus distribution network and a modified version of the IEEE 30-Bus System. Simulation results are interpreted in economic terms and show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for generating restoration sequences in distribution systems and microgrids with emphasis on deriving optimal switching times for remote control switches. Microgrid architecture considered are assumed to be operating in islanded single master mode. Control methods for handling the unbalance inherent in distribution systems using single-phase controlled PQ inverters were developed and incorporated in the proposed method. The problem of considering simultaneously both energy optimization and dynamic stability of the system, which is lacking in existing methods, was addressed by studying the transient behavior of microgrids based on extended electromagnetic transients program (EMTP) simulations and then approximate and incorporate these behaviors into the optimization formulation for restoration. The approach is based on multi-objective optimization of final restored load and total restoration time. The methodology was studied on a modified IEEE 123 node test feeder. An EMTP simulation in PSCAD of the formed microgrids was used to validate the accuracy of the dynamic and steady-state characteristics of the resulting systems. By considering the transient behavior of distributed generators (DGs) during restoration, a better restoration sequence that does not trigger protection relays could be realized. [ABSTRACT FROM AUTHOR]

The increasing power/load demand is becoming a new concern for the grid operators as it can potentially overload transmission lines. Among advantages of a multi-terminal dc (MTDC) grid, its high controlability makes it a promising asset for ac line overload alleviation. This paper proposes a model predictive control based strategy, which regularly updates the power setpoints of the MTDC converter stations and ac generators to alleviate ac line overloads. To compute and update the power setpoints, necessary measurements are received by the controller within regular time intervals. The proposed controller ensures that subsequent to any ac line overload, changing the power setpoints of the MTDC converter station does not cause any operation violation in the MTDC grid. Furthermore, it ensures that the ac voltage limits and voltage stability criteria are met to avoid voltage instability. Performance and effectiveness of the proposed controller are evaluated by simulation studies on two systems, i.e., the 39-bus New England system integrated with a 5-bus MTDC system and the IEEE 118-bus system connected to a 6-bus MTDC system. Simulation results confirm that the MTDC grid can play a major role in controlling active powers of most of the ac transmission lines. [ABSTRACT FROM AUTHOR]

Recent advancement of distributed renewable generation has motivated microgrids to trade energy directly with one another, as well as with the utility, in order to minimize their operational costs. Energy trading among microgrids, however, confronts challenges such as reaching a fair trading price, maximizing participants’ profit, and satisfying power network constraints. In this paper, we formulate the direct energy trading among multiple microgrids as a generalized Nash bargaining (GNB) problem that involves the distribution network's operational constraints (e.g., power balance equations and voltage limits). We prove that solving the GNB problem maximizes the social welfare and also fairly distributes the revenue among the microgrids based on their market power. To address the nonconvexity of the GNB problem, we propose a two-phase approach. The first phase involves solving the optimal power flow problem in a distributed fashion using the alternative direction method of multipliers to determine the amount of energy trading. The second phase determines the market clearing price and mutual payments of the microgrids. Simulation results on an IEEE 33-bus system with four microgrids show that the proposed framework substantially reduces total network cost by 37.2%. Our results suggest direct trading need be enforced by regulators to maximize the social welfare. [ABSTRACT FROM AUTHOR]