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Dielectric testing techniques, in both time and frequency domains, are currently widely used by power utilities for assessment of the condition of transformer oil-paper insulation systems. However, it has been reported that results of these tests are highly influenced by the operating temperature during measurements. The distribution, migration and equilibrium of moisture between oil and paper in a complicated insulation system is highly temperature dependent. It requires adequate experience and proper understanding to interpret the dielectric response results in the presence of temperature variations and thermal instability. Proper analysis of the dielectric test result is only possible with an understanding of the physical behavior of the insulation system in response to temperature. A circuit model, which describes the dielectric behavior of the transformers main insulation system, has been investigated in this paper. The values of the parameters of the model have been identified from the dielectric tests. A correlation has been observed between the operating temperature and the equivalent model parameters that can be used as additional information for better interpretation of the dielectric test results. This paper thus reports a detailed study on the effects of temperature on dielectric measurements of a transformer under controlled laboratory conditions. Some results of practical on-site testing are also presented to demonstrate the possibility of errors that may be introduced in dielectric test results analysis unless temperature effects are taken into consideration. [ABSTRACT FROM AUTHOR]

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach. [ABSTRACT FROM AUTHOR]

The renewable energy-based hydrogen production can lead to the integrated electric power and hydrogen system (IPHS) and offer a pathway to a sustainable energy utilization. Hydrogen is mainly transported via hydrogen tube trailers (HTs), making the hydrogen energy system (HES) operation quite different from those of other energy technologies. This paper proposes an optimal IPHS operation strategy which utilizes HT for transportation. The proposed strategy coordinates hydrogen generation, transportation, and storage stages considering constrained operations of electric power system (EPS), transportation system, and variable renewable energy. The proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the respective solutions are coordinated accordingly. The case studies using the modified IEEE-RTS79 have verified the validity of the proposed IPHS model and its solution method and confirmed the necessity of considering HES in enhancing the EPS operation. The synergies between EPS and HES are studied via numerical examples and the impact of the flexibilities in hydrogen generation, transportation and demand are highlighted. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for reliability evaluation of composite power systems by combining Monte Carlo simulation and multi label k-nearest neighbor (MLKNN) algorithm. MLKNN is a classification technique in which target vector of each instance is assigned into multiple classes. In this paper, MLKNN is used to classify states (failure or success at system or bus level) of a complete power system without requiring optimal power flow (OPF) analysis, except in the training phase. As a result, the computational burden to perform OPF is reduced dramatically. For illustration, the proposed method is applied to the IEEE 30 BUS Test System and IEEE Reliability Test System. The obtained results from various case studies demonstrate that MLKNN based reliability evaluation provides promising results in both classification accuracy and computation time in evaluating the composite power system reliability. [ABSTRACT FROM AUTHOR]

The co-optimization of security-constrained unit commitment and dispatchable transmission network is a large-scale and computationally complex optimization problem that brings big challenges for the traditional centralized and/or master–slave based decomposition approaches. This paper proposes a fully parallel approach to achieve an efficient and fast solution while considering post-contingency corrective actions. In this paper, the original co-optimization problem is decomposed into six major solution modules for unit commitment, optimal power flow, and transmission switching in base case and contingencies, respectively. Moreover, each major solution module is further decomposed by unit, period, dispatchable line, and/or contingency to make the proposed approach favorable in a fully parallel computing environment. The effectiveness and efficiency of the proposed fully parallel approach are justified using numerical cases on a high-performance computing (HPC) platform. [ABSTRACT FROM AUTHOR]

The integration of variable renewable energy (VRE) generation, i.e., wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch ability through the use of thermal energy storage and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: first, exploring how CSP plants operate in high renewable penetrated power systems; and second, analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

To encourage and support innovation, synthetic electric grids are fictional, designed systems that mimic the complexity of actual electric grids but contain no confidential information. Synthetic grid design is driven by the requirement to match wide variety of metrics derived from statistics of actual grids. In order to scale these systems to 10,000 buses or more, robust reactive power planning is needed, accounting for power flow convergence issues. This paper addresses reactive power planning and power flow convergence in the context of large synthetic power grids. The iterative algorithm presented by this paper supplements a synthetic transmission network that has been validated by a dc power flow with a realistic set of voltage control devices to meet a specified voltage profile, even with the constraints of difficult power flow convergence for large systems. The algorithm is illustrated with an example new synthetic 10,000 bus system, geographically situated in the western United States, which is publicly available and useful for a variety of research studies. An analysis is shown validating the synthetic system with actual grid characteristics. [ABSTRACT FROM AUTHOR]

The unit commitment is still a widely studied problem, especially when more renewable energy of stochastic character is being added to power systems. This paper proposes a model for weekly planning of systems involving hydro, wind, and thermal energy under a stochastic perspective. The proposed model follows the endogenous reserve determination criterion to achieve a simultaneous optimization of energy and reserves considering wind uncertainty, forced outages of equipment, a dc Flow model of the network, and cascaded head sensitive hydro systems, among others. The paper also develops a practical solution methodology based on outer approximation and benders decomposition. Testing has been conducted over four systems, and computational results demonstrate that the proposed model and solution are useful and effective. [ABSTRACT FROM AUTHOR]

This paper proposes a new filter design method that can smooth the wind power generation output while keeping the energy capacity and power rating of the energy storage system (ESS) small. Wind power generation causes frequency fluctuations in power systems. Therefore, in many cases, the ESS is needed for smoothing the fluctuations of wind generator output. In order to improve the performance of the ESS controlled with the conventional low-pass filter (LPF), this paper investigates other types of filters. First, the relations between filter and energy capacity/power rating of the ESS are disclosed through the worst-case study. Then, the frequency fluctuation analysis of the power system with respect to the wind power generation is carried out in order to realize a pin-point smoothing. Finally, a metaheuristic optimization method is proposed for the multiobjective design of the filter based on these relations so as to achieve an optimal tradeoff between the output smoothing and energy capacity/power rating of the ESS. It is shown via simulation analysis with real wind data that the proposed method can suppress the frequency fluctuation of the power system effectively with the ESS having smaller energy capacity and power rating. [ABSTRACT FROM PUBLISHER]

In hydroelectric power plants, which are characterized by particularly high short-circuit current levels and high time constants, conventional current transformer specifications can result in increased equipment dimensions, thus complicating their installation, especially during refurbishment projects. This paper presents a study to assess the potential of various low-power instrument transformers (LPITs) for generator protection applications. Two types of LPITs were evaluated: the optical current transformer (OCT) and the stand alone merging unit (SAMU). To test the OCT, a 65-kA-peak test source was developed using a real-time simulator. This source was used to inject symmetrical and asymmetrical currents into LPITs for the assessment of protection accuracy class. Low-current tests were also performed to assess their metering classes. This paper presents the design of a high-current test bench, measurement uncertainty analysis, and analysis algorithms used for device evaluation. Analysis results are then presented and discussed in terms of their applicability to generator differential protection using SAMUs and OCTs. [ABSTRACT FROM AUTHOR]

In this paper, dynamic performance of automatic generation control (AGC) of two area thermal–thermal power system is determined through the steam chest and reheater constant trajectories. In earlier AGC studies, steam chest and reheater time constants denoted as T\text{SC} and T\text{RH} are considered to be constant, irrespective of plant generation schedules. However, it is discovered that T\text{SC} and T\text{RH} follow the specific trajectories with the plant generation schedules. These time constant trajectories are determined by the power output control strategies being employed to change the plant generation schedules. These control strategies are the different combinations of steam pressure versus flow rate curves used to operate the plant at different generation schedules. In this paper, time constant versus generation schedule trajectories of T\text{SC} and T\text{RH} have been derived for different power output control strategies. Furthermore, these time constant trajectories have been utilized to investigate the dynamic performance of two area reheat thermal power system. [ABSTRACT FROM AUTHOR]

As electric power system operators shift from conventional energy to renewable energy sources, power distribution systems will experience increasing fluctuations in supply. These fluctuations present the need to not only design online decentralized power allocation algorithms, but also characterize how effective they are given fast-changing consumer demand and generation. In this paper, we present an online decentralized dual descent (OD3) power allocation algorithm and determine (in the worst case) how much of observed social welfare can be explained by fluctuations in generation capacity and consumer demand. Convergence properties and performance guarantees of the OD3 algorithm are analyzed by characterizing the difference between the online decision and the optimal decision. We demonstrate validity and accuracy of the theoretical results in the paper through numerical experiments using real power generation data. [ABSTRACT FROM PUBLISHER]

The increasing penetration of variable energy resources has led to more uncertainties in power systems. Flexible Ramping Products (FRP) have been adopted by several electricity markets to manage the uncertainties. We reveal that neglected line congestion for FRP may not only cause infeasibility, but also result in a failure of cost recovery. To address the deliverability issues on FRP, this paper proposes a new concept, Deliverable Robust Ramping Products (DRRP), in real-time markets. The DRRP includes generation ramping reserve and generation capacity reserve. The DRRP is deliverable and immunized against any predefined uncertainty. It also fully addresses the bid cost recovery issue caused by the line congestion in existing FRPs. The prices of DRRP are derived within the Affine Adjustable Robust Optimization (AARO) framework. These prices can be used to identify valuable reserves among available reserves and quantify the values of flexible resources that provide reserves. This paper also proposes a general approach to obtaining the time-decoupled prices for DRRP and generation, which can be used for the market settlement of the first interval only in real-time markets. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of DRRP and the advantages of DRRP compared to existing FRP. [ABSTRACT FROM PUBLISHER]

Solar energy is one of the most important and widely utilized renewable energy resource. Despite the many attractive features of solar-based renewables, the wide scale integration of solar generation into electric power systems presents significant challenges to network planners and operators, mainly due to the intermittent nature of solar energy. Most published work in this area is, however, focused on short-term forecasting studies used for unit commitment and energy market decisions with very little attention paid to solar irradiance probability density estimation needed for network planning and design studies. In this paper, a reliable nonparametric model of solar irradiance probability density is proposed based on the application of local linear regression in tandem with a root transformation method, introduced here for the first time. The performance of the proposed estimator is assessed via comparisons with the parametric Beta distribution (conventionally employed to model solar irradiance probability density) and two nonparametric kernel density estimation models; using the Kolmogorov–Smirnov (K–S) goodness-of-fit test, coefficient of determination ($R^2$), and a number of error metrics. Results confirm the suitability and accuracy of the proposed method for solar irradiance probability density estimation. [ABSTRACT FROM AUTHOR]

The screening curve method (SCM) is an intuitive and fast model that estimates the least-cost generation mix for generation planning purposes. It calculates an economically adapted generation mix for a target load duration curve within a few seconds, but lacks many detailed considerations of the generation system. In order to improve SCM, many developments have been made in recent years. However, one of the biggest limitations had always been that the SCM assumed all capacity to be new with no existing capacity. This drawback made SCM less useful compared to other generation planning models. In this paper, we develop a direct way to model existing capacity in SCM. The proposed method is studied in this paper and is illustrated with an example. Finally, an ERCOT year 2030 case is simulated. [ABSTRACT FROM AUTHOR]

This paper describes the application of remote isolation applied in mining industry conveyance systems. Remote isolation removes personnel from potentially dangerous electrical lockout tasks by removing the element of human error. Safe verified isolation of electrical systems has proved to increase production output by reducing the required time to electrically isolate industrial systems where zero energy is required prior to performing work. Time saved to both isolate and de-isolate is converted to production time and increased manufacturing throughput. This paper describes the application of remote isolation systems, discusses implementation of enhanced safety systems and the necessary engagement with local regulating authorities. Finally, a case study of an actual installation discusses specific details where these systems have been installed. [ABSTRACT FROM AUTHOR]

Large-scale integration of wind generation changes the power swing characteristics of a power system and may result in the misoperation of legacy power swing protection schemes. This paper presents a qualitative study on the impact of wind generation on power swing protection. The objective is to provide an understanding of the problem through case studies and present possible solutions and adjustments in protection schemes to ensure the efficiency of protection under large-scale integration of wind generation. The misoperation of power swing protection functions, namely power swing blocking and out-of-step tripping (OST), as a result of increased wind generation levels, are shown through case studies. It is also shown that the electrical center of a power system may move due to wind generation. In this case, it would be necessary to revise the optimal location of the OST protection. Finally, the impact of various factors, such as wind generator type, control scheme and fault-ride-through function, and wind generation level and capacity are investigated to determine the key features that need to be accounted for in practical protection studies. [ABSTRACT FROM AUTHOR]

Linearized models of power systems are often desirable to formulate tractable control and optimization problems that still reflect real-world physics adequately under various operating conditions. In this paper, we propose an approach that can make use of known data concerning the distribution of demand, and/or intermittent supply, to minimize expected model inconsistency with respect to the original non-linear model. The optimal linearization is obtained by approximating a generalized moment problem with a hierarchy of sparse semi-definite relaxations. The output is a linearization point that minimizes the expected absolute constraint violation with respect to the uncertain supply and demand, under optimal power flow solutions. Numerical results for different power systems demonstrate the accuracy and scalability of our linearization method. [ABSTRACT FROM AUTHOR]

Future changes in the generation mix may reduce the overall amount of system inertia. It becomes crucial to provide response services more quickly to secure postfault transient frequency dynamics. This paper proposes a novel mixed integer linear programming unit commitment formulation that simultaneously optimizes energy production and the allocation of inertial response (IR), enhanced frequency response (EFR), and primary response (PR) against largest plant outage. A set of linearized inertia-dependent and multispeed constraints on frequency evolution explicitly recognizes the quicker provision of EFR compared to PR and their mutual interplay with IR. The proposed model is applied to analyze a typical 2030 GB low-carbon scenario. Results demonstrate the value, as reduction of system operational cost, when battery storage provides EFR, facilitating a cost-efficient transition toward the low-carbon electricity sector. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm for parameter identification of a dynamic equivalent for an external subsystem, based solely on the available online measurements in boundary buses and branches. Static equivalent part is represented by equivalent impedances from boundary buses (ones that separate the internal and external subsystems) and calculated using the modified (minimum loss) radial, equivalent, and independent method. Parameter identification of synchronous generator (SG)-based equivalent (for predominantly production external areas), dynamic load (DL)-based equivalent (for predominantly load external areas), or (SG + DL)-based equivalent (for mixed external areas) in fictitious buses is performed by Levenberg–Marquardt weighted least-square nonlinear optimization, which minimizes the variances between available online measurements and transient responses of the reduced power system. The IEEE 14-bus and 441-bus real-world test systems are used to illustrate and test the proposed power system equivalent derivation technique. [ABSTRACT FROM AUTHOR]

This paper proposes a bilevel programming model for the planning of tidal current farms (TCFs). The micrositing strategy of tidal current turbines (TCTs) and the collector system planning scheme are coordinated to achieve a better balance of energy yields, TCF capital investment and power systems economic operation using the proposed method. The power output of the TCF is modeled considering the characteristics of tidal current velocity and wake effects. A coordinated planning model consisting of one upper level model and two lower level models is developed to maximize comprehensive profit. Not only the investment and maintenance costs of TCTs and submarine cables, but also the operation cost of the collector system and the impact of the TCF integration on the operation of power systems are all taken into account to ensure the long-term benefits of both TCF owners and power systems. An efficient solution for the proposed planning model is developed by combining a genetic algorithm with a mixed integer programming. The effectiveness and adaptability of the proposed method are demonstrated using the measured data of tidal current velocity with distinct characteristics and the IEEE 30- and 118-bus test systems. [ABSTRACT FROM AUTHOR]

A shortage of large power system data sets, as well as the frequent restrictions on sharing such models, have led to newfound interest in creating synthetic data that can be easily shared among researchers. This paper considers the problem of forming a power system test case from the constituent parts of realistic power grid samples. Starting from a topology, and samples of generation, load, and branches, we assemble systems while respecting the constraints imposed by a typical Optimal Power Flow problem. Expressed in this manner, the problem involves solving for permutations of the input data. Since permutations matrices are binary, the problem is linearized, allowing for a Mixed Integer Linear Problem formulation. The problem is further decomposed using the Alternating Direction Method of Multipliers as well as an Evolutionary Algorithm to facilitate scaling to larger system sizes. A post processing step is used to add shunt elements for reactive power planning. The resulting systems demonstrate statistically similar power flow behavior to reference systems. Finally, new analysis avenues, opened by synthesizing test cases according to the proposed method, are briefly introduced by creating fictitious systems with different topology models and examining how these affect power flow behavior. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines brings challenges to the DG planning problem. This paper proposes a novel probability-weighted robust optimization (PRO) method to allocate DG units including microturbines and wind turbines in microgrids, aiming to maximize the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind turbine output and load demand during a long-term planning horizon which can fully cover the uncertainty spectrum and accurately represent the uncertainty probability distribution. Then, the PRO method optimizes DG sizing and locating under the worst uncertainty cases considering their occurrence probabilities. Therefore, the planning decision is robust against any possible uncertainty realization. Besides, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed method can achieve full operating robustness. [ABSTRACT FROM AUTHOR]

To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days, or weeks. However, the validity of these time-period aggregation approaches to determine the capacity expansion plan of future power systems is arguable, as they fail to capture properly the mid-terms dynamics of renewable power generation and to model accurately the operation of electricity storage. In this paper, we propose a new time-period clustering method that overcomes the aforementioned drawbacks by maintaining the chronology of the input time series throughout the whole planning horizon. Thus, the proposed method can correctly assess the economic value of combining renewable power generation with interday storage devices. Numerical results from a test case based on the European electricity network show that our method provides more efficient capacity expansion plans than existing methods while requiring similar computational needs. [ABSTRACT FROM AUTHOR]

Maintenance activities help prevent costly generator breakdowns but because generators under maintenance are typically unavailable, the impact of maintenance schedules is significant and their cost must be accounted for when planning maintenance. In this paper, we address the generator maintenance scheduling problem in hydropower systems. We propose a mixed-integer programing model that considers the time windows of the maintenance activities, as well as the nonlinearities and disjunctions of the hydroelectric production functions. Because the resulting model is hard to solve, we also propose an extended formulation, a set reduction approach that uses logical conditions for excluding unnecessary set elements from the model, and valid inequalities. We performed computational experiments using a variety of instances adapted from a real hydropower system in Canada, and the extended formulation with set reduction achieved the best results in terms of computational time and optimality gap. [ABSTRACT FROM AUTHOR]

A novel method is proposed to locate the source of events in power distribution systems by using distribution-level phasor measurement units, a.k.a., micro-PMUs. An event in this paper is defined rather broadly to include any major change in any component across the distribution feeder. The goal is to enhance situational awareness in distribution grid by keeping track of the operation (or misoperation) of various grid equipment, assets, distribution energy resources, loads, etc. The proposed method is built upon the compensation theorem in circuit theory to generate an equivalent circuit to represent the event by using voltage and current synchrophasors that are captured by micro-PMUs. Importantly, this method makes critical use of not only magnitude but also synchronized phase angle measurements, thus, it justifies the need to use micro-PMUs, as opposed to ordinary RMS-based voltage and current sensors. The proposed method can work with data from as a few as only two micro-PMUs. The effectiveness of the developed method is demonstrated through computer simulations on the IEEE 123-bus test system, and also on micro-PMUs measurements from a real-life 12.47 kV test feeder in Riverside, CA. The results verify that the proposed method is accurate and robust in locating the source of different types of events on power distribution systems. [ABSTRACT FROM AUTHOR]

The demand response provides an opportunity for load serving entities (LSEs) that operate retail electricity markets (REMs) to strategically purchase energy and provide reserves in wholesale electricity markets (WEMs). This paper concerns with the problem of simultaneously determining the optimal energy bids and reserve offers an LSE submits to the WEM as well as the optimal energy and reserve prices it sets in the REM so as to maximize its profit. To this end, we explicitly model the trilayer market structure that consists of a WEM, a REM, and a set of end user customers, so as to capture the coupling between the bidding problem and the pricing problem. Based on the trilayer market model, we then formulate the joint bidding and pricing problem as a bilevel programming problem and further transform it into a single-level mixed integer linear programming problem, which can be solved efficiently. Numerical studies using the IEEE test cases are presented to illustrate the application of the proposed methodology as well as to reveal several interesting characteristics of the LSE's profit-seeking behavior. [ABSTRACT FROM AUTHOR]

A probabilistic power flow (PPF) study is an essential tool for the analysis and planning of a power system when specific variables are considered as random variables with particular probability distributions. The most widely used method for solving the PPF problem is Monte Carlo simulation (MCS). Although MCS is accurate for obtaining the uncertainty of the state variables, it is also computationally expensive, since it relies on repetitive deterministic power flow solutions. In this paper, we introduce a different perspective for the PPF problem. We frame the PPF as a probabilistic inference problem, and instead of repetitively solving optimization problems, we use Bayesian inference for computing posterior distributions over state variables. Additionally, we provide a likelihood-free method based on the approximate Bayesian computation philosophy, that incorporates the Jacobian computed from the power flow equations. Results in three different test systems show that the proposed methodologies are competitive alternatives for solving the PPF problem, and in some cases, they allow for reduction in computation time when compared to MCS. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient solution approach based on Benders’ decomposition to solve a network-constrained ac unit commitment problem under uncertainty. The wind power production is the only source of uncertainty considered in this paper, which is modeled through a suitable set of scenarios. The proposed model is formulated as a two-stage stochastic programming problem, whose first-stage refers to the day-ahead market, and whose second-stage represents real-time operation. The proposed Benders’ approach allows decomposing the original problem, which is mixed-integer nonlinear and generally intractable, into a mixed-integer linear master problem and a set of nonlinear, but continuous subproblems, one per scenario. In addition, to temporally decompose the proposed ac unit commitment problem, a heuristic technique is used to relax the inter-temporal ramping constraints of the generating units. Numerical results from a case study based on the IEEE one-area reliability test system (RTS) demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Recent years have witnessed significant interest in convex relaxations of the power flows, with several papers showing that the second-order cone relaxation is tight for tree networks under various conditions on loads or voltages. This paper shows that ac-feasibility, i.e., to find whether some generator dispatch can satisfy a given demand, is NP-hard for tree networks. [ABSTRACT FROM AUTHOR]

Oscillations threaten the stability of a power system. Timely detecting oscillations can improve operators' situational awareness of system stability and enable remedial reactions. To detect oscillations during their early stages, this paper proposes a cross-coherence method using multiple-channel phasor measurement unit (PMU) data. Oscillations are related to the peaks in coherence spectra and can be detected by visual inspection and setting up a threshold. Using simulation data, it is shown that the proposed coherence method can detect oscillations even under low signal-to-noise ratios. Three algorithms for estimating coherence spectra are implemented and evaluated. Their performances are compared using Monte Carlo methods. Based on the comparison, this paper makes some recommendations for proper use of the algorithms. [ABSTRACT FROM AUTHOR]

In this paper we address some shortcomings in existing nonlinear dynamic models of power system experiencing symmetrical and unsymmetrical faults in their transmission/distribution networks. The shortcomings relate to: 1) approximations relating to stator dynamics, internal voltage of the positive sequence network and the dynamics of the negative and zero sequence networks, and 2) existing models are time -variant, which may not be suitable for system studies such as stability and fault detection studies that require time invariant model. The approximations outlined in 1) adversely affect the modeling accuracy, especially that of the system response during the period immediately after the occurrence of faults. We present in this paper a full fault-dependent time-invariant dynamical model of power systems experiencing symmetrical and unsymmetrical faults in their transmission/distribution networks and verify it through simulation studies mimicking real life scenarios. IEEE 30 bus power system is considered as the study case. The simulation results are compared to Matlab's “SimPower” and PSCAD/EMTDC software packages. [ABSTRACT FROM AUTHOR]

Power generators contain control systems that are able to regulate system frequency, but the frequency setpoint values are rarely modified from nominal values. This paper describes design considerations for a communication system from generators to frequency-sensitive distributed energy resources (FS-DER) using changes to frequency setpoint values of generators. Signaling discrete system states by generating off-nominal system frequency values can be used as a novel narrowband unidirectional broadcast communications channel. This paper describes two protocols for utilizing off-nominal frequencies to carry information: First, a protocol for dispatching blocks of FS-DER that is suitable for systems restricted to relatively slow rates of change of frequency (ROCOF). Second, for systems that allow higher ROCOF values, the feasibility of using power generation resources as a power-line communication transmitter is shown. Data from an operating islanded power system with diesel generators are analyzed to demonstrate the feasibility of the proposed communication system in systems fed by rotating machines. The feasibility of the proposed communication system in systems fed by voltage-source inverters is shown with laboratory tests of a 20-kVA inverter. The inverter was found to have a maximum ROCOF of 2.2 Hz/s, sufficient to enable its use as a power-line communication transmitter. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of determining the maximum distributed energy resource (DER) capacity that can be connected to low-voltage (LV) networks without creating voltage-rise problems. That maximum capacity depends on the network characteristics and on the candidate sites for DER connection. When sites are multiple and LV networks are poorly characterized, such maximum capacity cannot be determined with power-flow or other admittance-matrix-based approaches. This paper proposes an approach to estimate voltage rise caused by DER under reduced network characterization. Such approach is built upon customary voltage and impedance measurements to be made at the sites of DER injection and gives sufficiently accurate results under most practical situations. The paper validates the accuracy of the proposed approach against exact power-flow results for well-characterized networks and load situations and provides guidelines for DER connection based on maximum capacity nodal allocation. [ABSTRACT FROM PUBLISHER]

Prioritizing equipment maintenance activities has been an important and challenging task in developing an equipment maintenance strategy for electric power utility companies. This paper presents a RankBoost-based data-driven approach for determining the maintenance priority of circuit breakers. Compared with conventional methods, the proposed method can consider both equipment-level condition-monitoring parameters and system-level reliability impacting indices, automatically create the rational weights among the multiple parameters/indices, and handle missing and invalid data. The simulation results based on the data of 33 circuit breakers indicate that the maintenance priority list obtained using the proposed method is closer to reality than the ranking lists obtained using a subjective weighting method or only using system-level reliability indices. [ABSTRACT FROM AUTHOR]

This paper presents a new generation adjustment method for achieving economical operation of power systems. Uncertainty in the loads and renewable energy sources is inevitable in power systems. The participation factor, which can be calculated using the incremental cost, is generally used to manage such unexpected variations. The incremental cost considers only the generators; however, owing to line loss, the location of the variation may also affect the operation performance. Herein, we introduce a bus-dependent participation factor (BDPF) to precisely reflect the line loss. The BDPF is calculated based on the generation-load incremental cost (GLIC), which is the cost variation for a specific generator and load. The GLIC can fairly reflect line loss changes; in addition, the second-order loss sensitivity is used to improve the accuracy of the BDPF. Using the BDPF, power generation can be instantly adjusted under unexpected variations, after which re-adjustment can be performed, if operation constraint violations exist. The proposed method is tested on the IEEE 14-bus and 118-bus systems, and the simulation results validate its effectiveness. [ABSTRACT FROM AUTHOR]

Knowledge of the impact of uncertain inputs is valuable, especially in power systems with large amounts of stochastic renewable generations. A global sensitivity analysis (GSA) can determine the impact of input uncertainties on the output quantity of interest in a certain physical or mathematical model. The GSA has not been widely employed in power systems due to the prohibitively computational burden. In this paper, it is demonstrated that, via the implementation of a basis-adaptive sparse polynomial chaos expansion, a GSA can be applied to the power system with numerous uncertain inputs. The performance of the proposed method is tested on both the IEEE 13-bus test feeder and the IEEE 123-node test system, in presence of a large amount of independent or correlated uncertain inputs. The possible application of a GSA on the basis of the basis-adaptive sparse polynomial chaos expansion in power systems are discussed in terms of various sensitivities. The findings cannot only be used to rank the most influential input uncertainties with respect to a specific output, such as variances of the nodal power, but also to identify the most sensitive or robust electrical variables such as the bus voltage with respect to input uncertainties. [ABSTRACT FROM AUTHOR]

Power systems are making a transition from purely technical, centrally planned systems to market based, decentralized systems. The need for balancing power and frequency control reserves are increasing, partially due to variable renewable production, which gives an opportunity for new incomes but also a challenge in terms of changed modes of operation with risk for reduced lifetime for controllable power plants. This paper investigates how the allocation of a sold volume of frequency control reserves within a large hydropower production fleet can affect the costs of providing primary and secondary reserves, in terms of its impact on wear and fatigue, production losses, and the quality of the delivered frequency control. The results show that for primary control, low static gain in the governors results in poor quality and a large amount of load cycles of the units. High static gain, on the other hand, increases the production losses. The control work of the fleet can be reduced by using a proper balance of primary and secondary control gain on each unit, although the intuitive results from linear models exaggerate this effect. Automatic secondary control improves the system frequency quality but also increases the wear. [ABSTRACT FROM PUBLISHER]

High-level wind power integration can dramatically affect a power system's dynamic performance and introduce significant uncertainties to system's operation. This paper proposes a robust dispatch method to optimize the power system's operation state while sustaining its transient stability with highly variable and stochastic wind power generation. The problem is first modeled as an augmented optimal power flow model with uncertain variables and differential-algebraic equations. Then, the stability constraints are converted to approximately-equivalent algebraic equations based on one-machine-infinite-bus equivalence technique and trajectory sensitivity analysis. Next, the uncertain wind power generation is represented by a small number of strategically selected testing scenarios. Finally, a decomposition-based computation strategy is developed to divide the original model into a master problem and a series of slave problems which are solved iteratively. Using industry-grade system dynamic models and simulation software, the proposed method is tested on the New England 39-bus system and Nordic32 system, showing high performance on economic optimality, stability robustness, and computational efficiency. [ABSTRACT FROM PUBLISHER]

This paper presents a mixed integer linear programming model for the hourly energy and secondary regulation reserve scheduling of a price-taker and closed-loop variable speed pumped-storage hydropower plant, considering the energy losses due to the use of electronic frequency converters. The plant participates in the day-ahead energy market and in the secondary regulation service of the Iberian electric power system. The model is utilised to compare the income of the plant with and without considering the variable speed technology, with synchronous or asynchronous machines, with and without bypassing the frequency converter in generating mode, and with and without perfect information of the electric power system data. Numerical testing results demonstrate that the operation with the variable speed technology could help notably to enlarge the income of the power plant and that the secondary regulation reserve market might be the main source of revenue for the power plant. [ABSTRACT FROM PUBLISHER]

The paper introduces a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation. The framework is based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation. In addition to identifying unstable generator groups, the developed clustering methodology also facilitates identification of the sequence in which the groups lose synchronism. The framework is illustrated on a modified version of the IEEE 68 bus test network incorporating significant portion of renewable generation. [ABSTRACT FROM PUBLISHER]

This paper provides a reformulation of the scenario-based two-stage unit commitment problem under uncertainty that allows finding unit-commitment plans that perform reasonably well both in expectation and for the worst case. The proposed reformulation is based on partitioning the sample space of the uncertain factors by clustering the scenarios that approximate their probability distributions. The degree of conservatism of the resulting unit-commitment plan (that is, how close it is to the one provided by a purely robust or stochastic unit-commitment formulation) is controlled by the number of partitions into which the said sample space is split. To efficiently solve the proposed reformulation of the unit-commitment problem under uncertainty, we develop two alternative parallelization and decomposition schemes that rely on a column-and-constraint generation procedure. Finally, we analyze the quality of the solutions provided by this reformulation for a case study based on the IEEE 14-node power system and test the effectiveness of the proposed parallelization and decomposition solution approaches on the larger IEEE 3-Area RTS-96 power system. [ABSTRACT FROM PUBLISHER]

This paper describes an integrated operational simulation tool that combines various stochastic unit commitment and economic dispatch models together that consider stochastic loads and variable generation at multiple operational timescales. The tool includes four distinct configurable sub-models within: day-ahead security-constrained unit commitment (SCUC), real-time SCUC, real-time security-constrained economic dispatch (SCED), and automatic generation control (AGC). The unit commitment and dispatch sub-models within can be configured to meet multiple load and variable generation (VG) scenarios with configurable first stage and second-stage decisions determined where first-stage decisions are passed on and second-stage decisions are later determined by other sub-models in a continuous manner. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies, considering various configurations of stochastic and deterministic sub-models are conducted in low wind and high wind penetration scenarios to highlight the advantages of the stochastic programming during different decision-making processes. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and short-term reliability metrics to provide a broader view of its impact at different timescales and decision-making processes. [ABSTRACT FROM PUBLISHER]

This paper presents a method to derive the optimal offering/bidding strategy of a power producer who owns a hybrid power station (HPS), comprising wind farms (WFs) and pumped-hydro storage. The method is in line with the provisions of the Greek grid code for non-interconnected islands regarding the operation of HPSs. The goal of the HPS producer is to construct a single energy offer/bid for the total daily production/absorption, which maximizes his profits in the day-ahead market and minimizes any imbalance penalties during real-time operation. To this purpose, a two-stage stochastic bilevel optimization model is formulated, where the uncertainty lies in the wind power production of both the WFs belonging to the hybrid producer (internal WFs–IWFs) and the WFs belonging to other independent producers (WFs external to the HPS–EWFs). The upper level of the proposed bilevel model represents the hybrid producer's profit maximization, while the lower level represents the system operator's (SO) market clearing problem. This stochastic bilevel model is finally converted to a mixed-integer linear programming model in order to be tractable. Finally, simulation results from two test cases are thoroughly discussed and useful conclusions are drawn. [ABSTRACT FROM PUBLISHER]

With high renewable power penetration, uncertainty and intermittence of renewable sources become major concerns of power system planning and operation. Large and fast wind speed change may cause great variation of active power generation, which may lead to system stability and reliability problems. The response speeds of the committed conventional generators (CGs) for frequency regulations are critical for system reliable and stable operation. The slow response of the committed CGs may result in power shortage or surplus, which may affect system frequency. This paper proposes a technique to evaluate operational reliability and efficiency problems of power systems with high wind power penetration from frequency aspect. Energy unnecessarily consumed and less supplied during system frequency control processes are modeled in detail. The reliability and economic indexes are formulated with considering system dynamic frequency control processes. The IEEE-RTS79 is used to verify the proposed models and method. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs), similar to other types of flexible loads, can be controlled to provide additional flexibility to power system operators, which is needed to transition from primarily fossil fuel based electricity generation to more renewable generation. Existing electricity markets already provide economic incentives to offer flexibility. So-called aggregators in charge of controlling EV charging could take advantage of these incentives. However, random driving behavior and area-specific market rules complicate the market participation of EV aggregators in energy and reserve markets. The goal of this paper is to investigate the design and performance of a system that would enable EV aggregators to participate in wholesale electricity markets. We consider a certain type of market environment, which includes an intraday energy market and pay-as-bid reserve markets that require exact reserve delivery and have long operating intervals. We therefore propose a novel approach for concurrent market participation that can deal with these constraints, even when faced with highly uncertain reserve activation and EV behavior. [ABSTRACT FROM PUBLISHER]

This paper proposes a computationally effective method for optimal operation of multiple distributed generations (DGs) in dc distribution systems to improve their efficiency. There have been many research works to select the optimal sizes of multiple DGs. However, these optimal sizes are valid only in the particular operating point. For optimal operations of power systems, the DG sizes must be determined all the time according to the various load conditions. The proposed method can maintain the distribution system operating in its optimal condition with only single optimization process, without regard to load variations. Therefore, it can moderate the serious problems, derived from inaccurate load forecasting. To verify the performance of the proposed method, the dc system is first implemented with the real distribution system of Do-gok area in Seoul, Korea. Then, the real hourly-load data, acquired from SCADA system in Korea, are used to simulate the practical load variations. [ABSTRACT FROM PUBLISHER]

In many industrial settings, momentary power disruptions commonly occur, resulting in tripping of large electric machines, which then have to be brought to zero speed before the machine can be restarted. This can result in frequent interruptions in an industrial process, which can have negative effects on productivity. A more practical approach would restart the machine back to the original speed as soon as power is restored, not having to wait for the machine to be at a standstill. This concept is known as flying restart. In this paper, we propose an approach to implement the flying restart for permanent magnet synchronous machines (PMSM) by using an identification algorithm that determines the speed and position of the machine back-electromotive force so that the correct voltage vector can be applied, thus minimizing the inrush current during the restart. The novelty of the proposed method is that its implementation only requires nameplate machine parameters and needs no machine-specific tuning, making the approach suitable for implementation with both vector- and scalar-controlled PMSM. [ABSTRACT FROM PUBLISHER]

This paper develops a modeling method for robust stability analysis of nonlinear electrical power systems over a range of operating points and under parameter uncertainties. Standard methods can guarantee stability under nominal conditions, but do not take into account any uncertainties of the model. In this study, stability is assessed by using structured singular value (SSV) analysis, also known as $\mu$ analysis. This method provides a measure of stability robustness of linear systems against all considered sources of structured uncertainties. The aim of this study is to apply the SSV method for robust small-signal analysis of nonlinear systems over a range of operating points and parameter variations. To that end, a modeling methodology is developed to represent any such system with an equivalent linear model that contains all system variability, in addition to being suitable for $\mu$ analysis. The method employs symbolic linearization around an arbitrary operating point. Furthermore, in order to reduce conservativeness in the stability assessment of the nonlinear system, the approach takes into account dependences of operating points on parameter variations. The methodology is verified through $\mu$ analysis of the equivalent linear model of a 4-kW permanent magnet machine drive, which successfully predicts the destabilizing torque over a range of different operating points and under parameter variations. Further, the predictions from $\mu$ analysis are validated against experimental results. [ABSTRACT FROM PUBLISHER]

In power systems with a large integration of wind power, setting the adequate operating reserve levels is one of the main concerns of system operators (SO). The integration of large shares of wind generation in power systems led to the development of new forecasting methodologies, including probabilistic forecasting tools, but management tools able to use those forecasts to help making operational decisions are still needed. In this paper, a risk evaluation perspective is used, showing that it is possible to describe the consequences of each possible reserve level through a set of risk indices useful for decision making. The new reserve management tool (RMT) described in the paper is intended to support the SO in defining the operating reserve needs for the daily and intraday markets. Decision strategies like setting an acceptable risk level or finding a compromise between economic issues and the risk of loss of load are explored. An illustrative example based on the Portuguese power system demonstrates the usefulness and efficiency of the tool. [ABSTRACT FROM PUBLISHER]