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This paper presents a procedure for estimating the systems state when considerable Information and Communication Technology (ICT) component outages occur, leaving entire system areas un-observable. For this task, a novel method for analyzing system observability is proposed based on the Manifold Boundary Ap-proximation Method (MBAM). By utilizing information geome-try, MBAM analyzes boundaries of models in data space, thus detecting unidentifiable system parameters and states based on available data. This approach extends local, matrix-based meth-ods to a global perspective, making it capable of detecting both structurally unidentifiable parameters as well as practically uni-dentifiable parameters (i.e., identifiable with low accuracy). Be-yond partitioning identifiable/unidentifiable states, MBAM also reduces the model to remove reference to the unidentifiable state variables. To test this procedure, cyber-physical system (CPS) simulation environments are constructed by co-simulating the physical and cyber system layers.

Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Gravitation Program “Materials Driven Regeneration” by the Netherlands Organization for Scientific Research (RegmedXB #024.003.013) and the Marie Skłodowska-Curie Actions (Grant agreement RESCUE #801540). The EU-funded project BRAV3 (H2020, ID:874827) Background Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes (iPSC-CMs) do not display all hallmarks of mature human primary cardiomyocytes: the ability to use fatty acids as an energy source, high mitochondrial mass, increased nuclei polyploidism, synchronized electrical conduction, and forceful contractions. Instead, their phenotype is similar to immature cardiomyocytes in the late fetal stage. This immaturity represents a bottleneck to their application in 1) disease modeling – as most cardiac (genetic) diseases have a middle-age onset – and 2) clinical use, where integration and functional coupling are key. So far, the mainly used methods to enhance iPSC-CM maturation include prolonged time-in-culture, 3D culture, cyclic mechanical stretch, and electrical stimulation with specialized media. However, these protocols are laborious, costly, and not easily scalable. Methods In this study, we developed a simple, low cost, and rapid protocol using two peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A/PGC-1α) activating small molecules: Asiatic Acid (AA) and GW501516 (GW) to promote cardiomyocyte maturity by inducing a metabolic switch to fatty acid utilization and increased mitochondrial biogenesis. Results Monolayers of iPSC-CMs were incubated with AA and GW every other day for 10 days resulting in increased expression of fatty acid-metabolism-related genes (5 and 10-fold increase in CPT1B gene expression, respectively), mitochondria biogenesis (protein expression of ATP5A) and fusion (50 and 100-fold increase in OPA1 gene expression, respectively). In addition, AA treated iPSC-CMs responded in the seahorse mitochondria stress test more rapidly to an artificial increase in mitochondrial activity and showed a higher flexibility in substrate utilization in the seahorse stress test. A more mature electrophysiological functionality was shown by increased ion channel gene expression (KCNA4, SCN5A, GJA1, CACNA1C, and SCN1B) and enhanced synchronous contraction in treated samples. Moreover, maturation was further shown by increased sarcomeric gene expression (5 and 7-fold increase in TNNI3 in AA and GW respectively) and nuclear polyploidism (>4N fold 2.16 and 1.48-fold increase in AA and GW respectively). Conclusions Collectively, these findings show that AA and GW trigger a metabolic switch and induce extensive maturation of iPSC-CMs, providing a rapid and cost-effective method to obtain iPSC-CMs that more closely resemble their adult counterparts.

The paper describes a manifold learning-based algorithm for big data classification and reduction, as well as parameter identification in real-time operation of a power system. Both black-box and gray-box settings for SCADA- and PMU-based measurements are examined. Data classification is based on diffusion maps, where an improved data-informed metric construction for partition trees is used. Data classification and reduction is demonstrated on the measurement tensor example of calculated transient dynamics between two SCADA refreshing scans. Interpolation/extension schemes for state extension of restriction (from data to reduced space) and lifting (from reduced to data space) operators are proposed. The method is illustrated on the single-phase Motor D example from a very detailed WECC load model, connected to the single bus of a real-world 441-bus power system.

This study proposes a novel flexible hybrid state estimation (SE) algorithm when a realistic communication system with its irregularities is taken into account. This system is modelled by the Network Simulator 2 software tool, which is also used to calculate communication delays and packet drop probabilities. Within this setup, the system observability can be predicted, and the proposed SE can decide between using the static SE (SSE) or the discrete Kalman filter plus SSE-based measurements and time alignment (Forecasting-aided SE). Flexible hybrid SE (FHSE) incorporates both phasor measurement units and supervisory control and data acquisition-based measurements, with different time stamps. The proposed FHSE with detailed modelling of the communication system is motivated by: (i) well-known issues in SSE (time alignment of the measurements, frequent un-observability for fixed SE time stamps etc.); and (ii) the need to model a realistic communication system (calculated communication delays and packet drop probabilities are a part of the proposed FHSE). Application of the proposed algorithm is illustrated for examples with time-varying bus load/generation on two IEEE test cases: 14-bus and 300-bus.

Very-high Energy (VHE) gamma-ray astroparticle physics is a relatively young field, and observations over the past decade have surprisingly revealed almost two hundred VHE emitters which appear to act as cosmic particle accelerators. These sources are an important component of the Universe, influencing the evolution of stars and galaxies. At the same time, they also act as a probe of physics in the most extreme environments known - such as in supernova explosions, and around or after the merging of black holes and neutron stars. However, the existing experiments have provided exciting glimpses, but often falling short of supplying the full answer. A deeper understanding of the TeV sky requires a significant improvement in sensitivity at TeV energies, a wider energy coverage from tens of GeV to hundreds of TeV and a much better angular and energy resolution with respect to the currently running facilities. The next generation gamma-ray observatory, the Cherenkov Telescope Array Observatory (CTAO), is the answer to this need. In this talk I will present this upcoming observatory from its design to the construction, and its potential science exploitation. CTAO will allow the entire astronomical community to explore a new discovery space that will likely lead to paradigm-changing breakthroughs. In particular, CTA has an unprecedented sensitivity to short (sub-minute) timescale phenomena, placing it as a key instrument in the future of multi-messenger and multi-wavelength time domain astronomy. I will conclude the talk presenting the first scientific results obtained by the LST-1, the prototype of one CTAO telescope type - the Large-Sized Telescope, that is currently under commission., PoS: Proceedings of Science, 395, ISSN:1824-8039, Proceedings of 37th International Cosmic Ray Conference (ICRC2021)

In this study, the modelling of arithmetical mean roughness after turning of C45 steel was performed. Four parameters of cutting tool geometry were varied, i.e.: corner radius r, approach angle κ, rake angle γ and inclination angle λ. After turning, the arithmetical mean roughness Ra was measured. The obtained values of Ra ranged from 0.13 μm to 4.39 μm. The results of the experiments showed that surface roughness improves with increasing corner radius, increasing approach angle, increasing rake angle, and decreasing inclination angle. Based on the experimental results, models were developed to predict the distribution of the arithmetical mean roughness using the response surface method (RSM), Gaussian process regression with two kernel functions, the sequential exponential function (GPR-SE) and Mattern (GPR-Mat), and decision tree regression (DTR). The maximum percentage errors of the developed models were 3.898 %, 1.192 %, 1.364 %, and 0.960 % for DTR, GPR-SE, GPR-Mat, and RSM, respectively. In the worst case, the maximum absolute errors were 0.106 μm, 0.017 μm, 0.019 μm, and 0.011 μm for DTR, GPR-SE, GPR-Mat, and RSM, respectively. The results and the obtained errors show that the developed models can be successfully used for surface roughness prediction.

The paper describes a data-driven system identification method tailored to power systems and demonstrated on models of synchronous generators (SGs). In this work, we extend the recent sparse identification of nonlinear dynamics (SINDy) modeling procedure to include the effects of exogenous signals and nonlinear trigonometric terms in the library of elements. We show that the resulting framework requires fairly little in terms of data, and is computationally efficient and robust to noise, making it a viable candidate for online identification in response to rapid system changes. The proposed method also shows improved performance over linear data-driven modeling. While the proposed procedure is illustrated on a SG example in a multi-machine benchmark, it is directly applicable to the identification of other system components (e.g., dynamic loads) in large power systems.

It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.

This study describes a new class of system identification procedures, tailored to electric power systems with renewable resources. The procedure described here builds on computational advances in differential geometry, and offers a new, global, and intrinsic characterisation of challenges in data-derived identification of electric power systems. The approach benefits from increased availability of high-quality measurements. The procedure is illustrated on the multi-machine benchmark example of IEEE 14-bus system with renewable resources, but it is equally applicable to identification of other components and systems (e.g. dynamic loads). The authors consider doubly-fed induction generators (DFIG) operating in a wind farm with system level proportional-integral controllers.

This paper describes a new class of system identification procedures that are tailored to electric power systems, in particular to synchronous generators (SGs) and other dynamic components. Our procedure builds on computational advances in differential geometry, and offers a new, global characterization of challenges frequently encountered in system identification of electric power systems. The approach also benefits from increasing availability of high-quality measurements. While the proposed procedure is illustrated on SG example in a multimachine benchmark (IEEE 14-bus and real-world 441-bus power systems), it is equally applicable to identification of other system components, such as loads.

This paper proposes a novel correlated intervals-based algorithm for calculation of distribution power flows. The non-conservative interval operations are proposed. It takes into account the uncertainties of renewable resource-based generations (wind generators, solar panels and other) and distribution network loads. Their correlations are calculated from historically recorded patterns of input variables and directly integrated into the adopted forward-backward distribution power flow algorithm. The proposed algorithm is tested on two distribution network test examples: (1) 6-bus with the aim of detailed illustration of some algorithmic steps, and (2) 1003-bus for verification of global performances of the proposed algorithm. Obtained results are verified by Monte Carlo sampling and power flow calculations.

This study addresses state estimation in three-phase distribution systems, and proposes a new method for bad area detection (BAD) and identification of bad data. The proposed BAD utilises key features of the system such as strong correlation among measurements to identify both bad measurement areas and phases, via decoupled Chi-squares test with newly proposed metrics. The bad data identification detects multiple bad measurements using the whitening (sphering) transformation for re-parameterisation of the measurement residuals. The proposed methodology is verified on two characteristic test examples: (i) modified IEEE 13-bus benchmark test system, and (ii) 186-bus real-world distribution utility feeder.

The paper describes a new model reduction procedure tailored to power systems. It uses measurement data to devise a family of reduced order nonlinear models while retaining physical interpretability of parameters and equations. The manifold boundary approximation method (MBAM) uses the Fisher information matrix calculated from measurements to identify the least relevant parameter combination in the original model. Next, it numerically constructs a geodesic on the corresponding statistical manifold originating from the initial parameters in the least relevant parameter direction until a manifold boundary is found. MBAM then identifies a limiting approximation in the mathematical form of the model and removes one parameter combination. The simplified model is recalibrated by fitting its behavior to that of the original model, and the process is repeated as appropriate. MBAM is demonstrated on the example of a synchronous generator (SG), which has been treated extensively in the literature. Implications of the proposed model reduction procedure on large power system models are illustrated on a 441-bus, 72-SG dynamical model.

Distribution utilities are facing a new set of challenges for efficient and optimal operation of modern (active and more dynamic) distribution networks. To address these challenges, utilities are relying on sophisticated algorithms and software that require accurate phase connectivity models and three-phase state estimation. Without this data, the operational benefits of optimization software packages are reduced and limited. This paper presents a method for verification and estimation of phase connectivity for a predefined set of nodes with three-, two- and single-phase connections. These nodes are formulated as accurate nodes with questionable phases. Accordingly, the bus injections (loads and/or distributed generations) and overall network’s operation condition are estimated. The proposed method requires the minimum set of real-time measurements and utilizes all other available quasi real-time and pseudo measurements. The method is simulated on two characteristic test systems: 1) modified IEEE 13-bus benchmark network, and 2) real-world 186-bus distribution feeder through realistic study cases of distribution network model coordinator.

This paper explores power system network observability while taking into account realistic communication network behavior. The overall information is obtained by combining SCADA- and phasor measurement unit-derived data, where time stamping (based on Global Positioning System or an equivalent local clock) for all measurements is assumed. Based on simulations performed in communication Network Simulator 2, empirical cumulative distribution functions can be associated with transfer times of measurement packets, which will reflect communication parameters and irregularities. This is further used to form an algorithm which maximizes the number of successful network observability checks, and thus the number of possible state estimations, in a certain time period. Application is demonstrated on the IEEE 14-bus test power system example.

We describe a method for simultaneously identifying and reducing dynamic power systems models in the form of differential-algebraic equations. Often, these models are large and complex, containing more parameters than can be identified from the available system measurements. We demonstrate our method on transient stability models, using the IEEE 14-bus test system. Our approach uses techniques of information geometry to remove unidentifiable parameters from the model. We examine the case of a networked system with 58 parameters using full observations throughout the network. We show that greater reduction can be achieved when only partial observations are available, Including reduction of the network itself.

This paper describes a manifold learning algorithm for big data classification and parameter identification in real-time operation of power systems. We assume a black-box setting, where only SCADA-based measurements at the point of interest are available. Data classification is based on diffusion maps, where an improved data-informed metric construction for partition trees is used. Data reduction is demonstrated on an hourly measurement tensor example, collected from the power flow solutions calculated for daily load/generation profiles. Parameter identification is performed on the same example, generated via randomly selected input parameters. The proposed method is illustrated on the case of the static part (ZIP) of a detailed WECC load model, connected to a single bus of a real-world 441-bus power system.

The paper explores interleaved and coordinated refinement of physics- and data-driven models in describing transient phenomena in large-scale power systems. We develop and study an integrated analytical and computational data-driven gray box environment needed to achieve this aim. Main ingredients include computational differential geometry-based model reduction, optimization-based compressed sensing, and a finite approximation of the Koopman operator. The proposed two-step procedure (the model reduction by differential geometric (information geometry) tools, and data refinement by the compressed sensing and Koopman theory based dynamics prediction) is illustrated on the multi-machine benchmark example of IEEE 14-bus system with renewable sources, where the results are shown for doubly-fed induction generator (DFIG) with local measurements in the connection point. The algorithm is directly applicable to identification of other dynamic components (for example, dynamic loads).

This paper proposes a tractable and scalable algorithm to identify and analyze bifurcation points of a large-scale power system model, which are directly related to dynamic voltage instability problems. Different types of bifurcations are analyzed, including: saddle-node (fold), Hopf, singularity-induced and limit-induced. An algorithm that combines optimization and predictor-corrector procedure is proposed for equilibrium tracing. The algorithm is based on calculation of only critical (rightmost and closest-to-zero) eigenvalues. The proposed algorithm is extended to the case of dynamic voltage stability assessment for power systems with optimized topology (simultaneously subjected to the topology control changes and generation re-dispatch). The proposed approach is illustrated on two (medium- and large-scale real-world) test power systems.

This paper proposes a probabilistic extension to flexible hybrid state estimation (FHSE) for cyber-physical systems (CPSs). The main goal of the algorithm is improvement of the system state tracking when realistic communications are taken into account, by optimizing information and communication technology (ICT) usage. These advancements result in: 1) coping with ICT outages and inevitable irregularities (delay, packet drop and bad measurements); 2) determining the optimized state estimation execution frequencies based on expected measurement refresh times. Additionally, information about CPSs is gathered from both the phasor measurement units (PMU) and SCADA-based measurements. This measurement transfer introduces two network observability types, which split the system into observable (White) and unobservable (Grey) areas, based on 1) deployed measuring instruments (MIs) and 2) received measurements. A two-step bad data detection (BDD) method is introduced for ICT irregularities and outages. The proposed algorithm benefits are shown on two IEEE test cases with time-varying load/generation: 14-bus and 300-bus.

The paper explores the effects of sensor behavior and communication system (CS) irregularities on power system state estimation (SE). CS are modeled in Network Simulator 2 (NS-2), allowing the quantification of irregularities, including delays and dropped packets. The overall information is obtained combining SCADA measurements with phasor measurement unit (PMU) derived data, where time stamping (based on GPS or an equivalent local clock) for all measurements is assumed. To fully analyze the effects of irregularities, a detailed analysis of sensitivities to different communication system parameters is provided as well. Using the co-simulation environment PiccSIM, a SE with these irregularities is quantified for CS parameter variation, with detailed models of power and communication flows.

This paper describes a geometric approach to parameter identifiability analysis in models of power systems dynamics. When a model of a power system is to be compared with measurements taken at discrete times, it can be interpreted as a mapping from parameter space into a data or prediction space. Generically, model mappings can be interpreted as manifolds with dimensionality equal to the number of structurally identifiable parameters. Empirically it is observed that model mappings often correspond to bounded manifolds. We propose a new definition of practical identifiability based the topological definition of a manifold with boundary. In many ways, our proposed definition extends the properties of structural identifiability. We construct numerical approximations to geodesics on the model manifold and use the results, combined with insights derived from the mathematical form of the equations, to identify combinations of practically identifiable and unidentifiable parameters. We give several examples of application to dynamic power systems models.

The paper describes a global identification procedure for dynamic power system models in the form of differential and algebraic equations. Power system models have a number of features that makes their improvement challenging – they are multi-level, multi-user and multi-physics. Not surprisingly, they are nonlinear and time varying, both in terms of states (memory variables) and parameters, and discrete structures, such as graphs, are strongly blended with continuous dynamics, resulting in network dynamics. The transient stability models are used as a prototypical example. Our method is based on information geometry, and uses advances in computational differential geometry to characterize high-dimensional manifolds in the space of measurements. In the case of network parameters, a comparison is presented with circuit-theoretic techniques. The results are illustrated on the case of IEEE 14-bus test system with 58 parameters in our realization.

In this paper we propose approximate bisimulation relations and functions for reduction of power system dynamic models in differential-algebraic (descriptor) form. The full-size dynamic model is obtained by linearization of the nonlinear transient stability model. We generalize theoretical results on approximate bisimulation relations and bisimulation functions, originally derived for a class of constrained linear systems, to linear systems in descriptor form. An algorithm for transient stability assessment is proposed and used to determine whether the power system is able to maintain the synchronism after a large disturbance. Two benchmark power systems are used to illustrate the proposed algorithm and to evaluate the applicability of approximate bisimulation relations and bisimulation functions for reduction of the power system dynamic models.

The paper proposes a scalable and tractable algorithm for dynamic topology optimization of power systems involving changes in branch on/off status, while respecting small-signal stability (SSS) constraints. A procedure for fast updates of the system matrices (in descriptor form) and without additional full matrix inversions is proposed. To additionally reduce the computation time, only critical eigenvalues (right-most or those in a specified damping ratio and frequency range) are calculated. A quadratic optimization approach is proposed for optimized generation re-dispatch to satisfy SSS constraints. The approach is applied to two (medium- and large-scale) real-world test power systems.

Summary A need to minimize the duration of outages in weakly meshed distribution networks leads to the intensive application of single-phase switching equipment and temporary elements. The result creates a wide range of allowed, but not typical, network topologies such as cross-phase jumpers, meshed islands, pseudo loops, and bidirectional branches, which cannot be processed in a simple/efficient way with existing algorithms for topological analyses. This paper proposes improvement of the traditional practice in distribution network modeling and topological analysis by using the phase granular adjacent matrix model with static and dynamic data, which provides a simple and efficient update of the mathematical model after switching and temporary element application. Additionally, the proposed algorithm provides a wide range of topological information such as network-layered structure, tracing, islands, energization, and active network phases. The results of the proposed algorithm are shown in an 84-bus test and 502,113-bus real-world schemes of unbalanced distribution networks.

The paper proposes a power system state estimation algorithm in the presence of irregular sensor sampling and random communication delays. Our state estimator incorporates Phasor Measurement Units (PMU) and SCADA measurements. We use an Extended Kalman filter based algorithm for time alignment of measurements and state variables. Time stamps are assumed for PMU, SCADA and state estimation. Application of the proposed algorithm is illustrated for hourly/daily load/generation variations on two test examples: 14-bus and 118-bus.

Load modeling has been extensively studied in power systems. The problem is intrinsically hard, as a simple description is sought for a large collection of heterogeneous physical devices. One aspect of model simplification has to do with the number of parameters needed to describe a dynamic load. With the rich tapestry of methods proposed in the literature as a backdrop, this paper introduces a new approach to simplify the load models and estimate the parameters. Our method is based on information geometry which combines information theory with computational differential geometry to derive global estimation results and shed a new light on difficulties commonly encountered when fitting widely used models to the measurement data. The results are compared with the literature using simulations on the IEEE 14 bus benchmark system.

SUMMARY This paper examines the influence of correlated pseudo measurements on the three-phase (sequence component-based) micro-grid state estimation. Pseudo measurements are used as the external inputs to replace the unavailable real-time measurements on distributed generation (DG) units and loads to provide the minimum bus observability. Output powers of unmonitored DG (photovoltaic and wind-based) units and loads are evaluated using the weather (either measured or forecasted) data, historically recorded state estimation patterns and available real-time measurements. The historical data are classified into clusters by the Self-Organization Map Artificial Neural Network (SOM ANN). The correlation coefficients between dependent pseudo measurements are calculated from clustered weather data and corresponding powers from DG units or loads, where the Feed Forward Artificial Neural Networks (FF ANNs) with backpropagation are used for approximating the output active power of unmonitored elements. The results and practical aspects of the proposed three-phase state estimation methodology with correlated measurements are demonstrated on two (benchmark and real-world) micro-grids. Copyright © 2014 John Wiley & Sons, Ltd.

This paper proposes an algorithm for transmission expansion planning (TEP) which minimizes the congestion surplus calculated from optimized nonlinear (AC) Optimal Power Flow (OPF) and Locational Marginal Prices (LMPs). Uncorrelated and correlated uncertainties related to operating conditions of the future transmission network and expected costs of the submitted energy bids to the energy market are constrained by bounding hyper-ellipsoid around base case AC OPF solution, with assumption of additive uncertainties. Perturbed uncertain points inside a hyper-ellipsoid are selected by proposed quasi-random sampling algorithm. For these points, the linearized OPF around base case AC OPF solution is proposed. The Genetic Algorithm (GA) does selection of lines and years for transmission expansion, where the increments of the fitness function are calculated by proposed linearized AC OPF model. The results and practical aspects of the proposed methodology are illustrated on 12- and 118-bus test power system examples.

This paper proposes a new approach to physics-based reduction of large-scale power system dynamic models, described by a set of differential and algebraic equations (DAEs). The proposed technique, based on the balanced realization theory (BRT), replaces the original model by one of reduced order, while preserving the physical meaning of retained state variables. Input and output variables of the model are selected in accordance with the goal of a particular study. State variables removed from the model are those that do not dominantly affect the nature of the phenomenon to be analyzed, as evidenced by the magnitude of the frequency response. The proposed technique provides insight into steps of the mathematical description of reduction for the considered phenomenon. In a given study, the number of state variables that can be eliminated depends on the power system model itself and on the required accuracy. Described approach is applied to two (benchmark and real-world) power systems.

In this paper the methodology for solving Locational Marginal Price (LMP) differences (inconsistency of LMPs) that arise at the boundary buses between separate power markets is proposed. The algorithm developed enables us to obtain consistent LMP values at the boundary buses between interconnected ISOs. A Primal-Dual Interior Point based optimal power flow (OPF) is applied, with complete set of power system physical limit constraints, to solve a regional spot market. The OPF is implemented such that producer and consumer behaviors are modeled simultaneously, while the welfare is maximized. In this paper a generalized methodology for multiple ISOs case is proposed and later it is practically applied on two interconnected independent entities. The algorithm for approximation of cost coefficients of generators and dispatchable loads for neighboring ISOs is proposed. The developed algorithm enables participating ISOs to obtain LMPs at the boundary buses with other interconnected ISOs. By controlling interchange of electric power at the scheduled level, regional spot markets are resolved eliminating possible exercise of market power by individual interconnected ISOs. Results of proposed methodology are tested on the IEEE 118-bus power system.

The evolving complexity of distribution networks with higher levels of uncertainties is a new challenge faced by system operators. This paper introduces the use of Gaussian mixtures models as input variables in stochastic power flow studies and state estimation of distribution networks. These studies are relevant for the efficient exploitation of renewable energy sources and the secure operation of network assets.

In contemporary power system practice, the stability limits are typically computed off-line and stored in databases to be monitored by system operators (dispatchers) in the real-time environment. Several sources of uncertainty affect such computations and consequently reasonable stability margins must be taken into account when determining operation limits. Despite these precautions, unplanned outages and planned switching actions (such as topology control [TC] actions) may cause operational conditions not considered at operation planning stages, and consequently system operators are left with no pertinent stability information. Online stability assessment has been proposed as an additional line of defense in which stability limits are computed based on the actual power system condition, which decreases the uncertainty, thus providing more accurate stability operation limits. The online stability assessment can be performed for the (real-time) operating point only, or additionally for a region around this condition [1].

The paper describes a REI-based procedure for estimating parameters of a dynamic model from measurements in the boundary buses/branches. Parameter identification of equivalent synchronous generators in fictitious buses is performed by Weighted Least Square (WLS) nonlinear optimization to minimize the difference between online measurements and transient responses of reduced power system.

The paper describes a new class of system identification procedures that are tailored to electric power systems with renewable sources. Our procedure builds on computational advances in differential geometry, and offers a new, global, intrinsic characterization of challenges frequently encountered in system identification of electric power systems. The approach also benefits from increased availability of high-quality measurements. While the proposed procedure is illustrated on renewable source IEEE 14-bus based example in a multi-machine benchmark power system, it is equally applicable to identification of other system components (for example, dynamic loads).

This paper analyzes optimal tuning of power system stabilizers (PSSs) as the main resource for small-signal stability enhancement in power systems. The procedure is based on dynamic power system response and its frequency amplitude spectrum. Since the optimization model is very complex, there are difficulties in defining the algebraic relation between optimization criteria and PSS parameters and the authors concluded that classical optimization techniques are inappropriate for application in practice. To avoid these problems, application of artificial neural networks (ANNs) as efficient functional approximators is proposed. Optimal PSS parameters are determined by trust region based optimization, where the ANN represents an input function. Robustness of the optimization is ensured with the proposed ANN structure which considers an arbitrary number of different power system operating conditions (including single contingencies). For verification of the proposed methodology, two test systems are used: the New England-New York 68-node, 16-machine test system and the 75-machine dynamic model of the Serbian power system. Poorly damped modes of oscillation are identified and damped by installation of PSSs at appropriate locations with ANN-based optimally tuned parameters.

In this paper is proposed a novel branch flow and weighted least square (WLS) based algorithm for state estimation in three phase distribution networks with distributed generation (DG) units. The basic formulation is simplified by the use of radial property of distribution network and later will be extended to meshed networks. Unmonitored (or partially monitored) loads are initially estimated from normalized daily load profiles (NDLPs) with lower weights (treated as the pseudo measurements). Also, the different types of monitored, partially monitored, or unmonitored DG units are included in state estimation. Their initial power outputs are calculated on the basis of external inputs, such as wind, sun and water inflow forecasts etc. (depending on DG unit type) or by normalized daily generation profiles (NDGPs) obtained from historical generation data. The pseudo measurements obtained by Initial Load/Generation Allocation are re-adjusted additionally by Optimal Load/Generation Reallocation procedure to fit real-time measurements inside WLS-based state estimation. The results and practical aspects of the proposed methodology are demonstrated on two real-life distribution networks.

SUMMARY This paper proposes an extension of the traditional DC-based power flow model for obtaining the linear power transmission distribution factors (PTDFs) that includes the influence of the branch resistances. The proposed model is adopted for the fast contingency analysis, including cases of injection changes, branch outages and their combination. Proposed formulation saves the efficiency of the DC-based model, such as linearity, fast matrix inversion by matrix inversion lemma for branch outages as well as fast block matrix inversion. This model is appropriate for subtransmission and transmission networks, where neglecting the branch resistances and other approximations introduced in DC-based power model is not acceptable. The proposed generalized linear PTDF-based model is successfully verified on the basis of the results for two examples: small (for educational purposes) test system and large-scale (for industry applicability purposes) test system of Continental Europe Synchronous Area (ex-UCTE) for characteristic contingencies (including the generation/load changes and branch outages). Copyright © 2011 John Wiley & Sons, Ltd.

We look at the effect of modeling branch-outage contingencies on locational marginal prices. To model contingencies in the day-ahead auction, we formulate a two-stage stochastic program. Rather than follow the current practice of including a list of possible contingencies that must be satisfied, we incorporate a larger set of contingencies in the model and allow contingencies to result in load reductions/outages at a cost. The model can be used and interpreted in two ways. One is to look at the tradeoff between reliability and outage costs. Another is to consider the load losses resulting from a contingency to be consumer offers of load reductions in response to line outages as part of the day-ahead auction. In analyzing the model structure, we find that the prices in the model closer in definition to those currently used in the day-ahead auction do not maximize expected surplus because the day-ahead auction produces prices that assume shortages will never occur. This raises issues with the design of auctions with important stochastic elements in the market. We present results for a 68-node grid with 86 branches (lines and transformers) to illustrate how prices and expected values change as the costs of outages are varied.

Planning for contingencies typically results in the use of more expensive facilities before disruptions. It leads to different prices and energy availability at various network locations depending on how the contingency analysis is performed. In this paper we present a two-stage stochastic programming model for incorporating contingencies. The model is computationally demanding, and made tractable by using an interior-point log-barrier method coupled with Benders decomposition. The second-stage optimal recourse function (RF) defines the most economically efficient actions in the post-contingency state for returning the system back to normal operating conditions. The approach is illustrated with for two examples: small (with 8 buses/11 branches) and IEEE medium-scale (with 300 buses/411 branches).

The paper presents a computational method that approximates feasible sets specified by linear or convex inequalities. This numerically efficient approach to power system optimization is based on computational geometry of multidimensional ellipsoids and is potentially applicable to problems with high dimensions, as it builds on recent advances in convex optimization. In an important application, it provides ranges in which nodal (generator) injections can vary without violating operational constraints in security analysis. The model is applied to two important problems in deregulated power systems: optimal economic dispatch (OED) and calculation of locational marginal prices (LMPs) in a day-ahead power market. Optimization problem with convex (ellipsoid-based) constraints is solved by a linear matrix inequality (LMI)-based procedure. The method is verified on the benchmark example with 68 buses, 16 generators, and 86 lines.

This paper deals with mutually connected problems of reactive power compensation and voltage control in radial distribution systems. The paper presents a simple iterative procedure for optimal coordination of actions of main means for reactive compensation (shunt capacitors) and voltage control (variable turn ratio transformers), within the decoupled solution of these two subproblems. The procedure is based on the static optimization, specified as a non-linear programming-type problem. The paper presents the derivation of a method for the solution of specified control problems and gives an illustrative application of this method on the test example of a one-source radial distribution system, consisting of 53 nodes and 52 branches.

This paper proposes a scalable algorithm to identify and analyze bifurcation points of a large power system model of dynamic voltage instability. Different types of bifurcations are analyzed, such as: saddle-node (fold), Hopf, singularity-induced, and limit-induced bifurcations. The interval bisection based algorithm is proposed for determination of the steady-state equilibrium point. The proposed algorithm is based on calculation of critical eigenvalues (least damped or closest-to-zero) only. The approach is applied to two (medium- and large-scale real-world) test power systems.