Factorized solution of power system state estimation

In this thesis a general two-stage factorized solution for nonlinear WLS problems has been developed, with two main applications: a geographically distributed multilevel hierarchical state estimation algorithm, suitable for very large-scale power systems covering multiple control areas; and a factorized multi-stage version, which enhances the convergence speed and reduces the computational effort. In the multilevel hierarchical state estimation, the way the algorithm can be customized to the system decomposition is analyzed, particularizing the methodology for the distribution feeder, substation, and transmission or multi-area system levels. Tests are performed on benchmark and realistic large-scale networks, including the entire European transmission system. The main advantage of this method lies in the possibility of filtering raw measurements at the specific location where they are captured, and then sending only local estimates for further processing by higher level state estimators. This multilevel estimator will be of special interest in upcoming systems, where the increased introduction of ICTs at lower levels and widespread interconnections at the regional transmission level are leading to an explosion of information which could be hardly managed by a single energy management system. In the second case, different approaches are proposed, all of them sharing a first linear stage, clearly showing computational efficiency and enhanced convergence speed compared to the conventional estimator. After a two-stage algorithm, the dissertation develops a bilinear three-stage state estimation factorization which virtually eliminates the need to iterate yielding the same solution as that provided by the Gauss-Newton iterative method. This is also extended to the case in which equality constraints are to be enforcedPremio Extraordinario de Doctorado U

Security in the economic operation of power systems

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Efficient solvers for power flow equations : parametric solutions with accuracy control assessment

The Power Flow model is extensively used to predict the behavior of electric grids and results in solving a nonlinear algebraic system of equations. Modeling the grid is essential for design optimization and control. Both applications require a fast response for multiple queries to a parametric family of power flow problems. Different solvers have been introduced especially designed for the algebraic nonlinear power flow equations, providing efficient solutions for single problems, even when the number of degrees of freedom is considerably large. However, there is no existing methodology providing an explicit solution of the Parametric Power Flow problem (viz. a computational vademecum, explicit in terms of the parameters). This work aims precisely at designing algorithms producing computational vademecums for the Parametric Power Flow problem. Once these solutions are available, solving for different values of the parameters is an extremely fast (real-time) post-process and therefore both the optimal design and the control problem can readily be addressed. In a first phase, a new family of iteratives solvers for the non-parametric version of the problem is devised. The method is based on a hybrid formulation of the problem combined with an alternated search directions scheme. These methods are designed such that it can be generalized to deal with the parametric version of the problem following a Proper Generalized Decomposition (PGD) strategy. The solver for the parametric problem is conceived by performing the operations involving the unknowns in a PGD fashion. The algorithm follows the basic steps of the algebraic solver, but some operations are carried out in a PGD framework, that is requiring a nested iterative algorithm. The PGD solver is accompanied with an error assessment technique that allows monitoring the convergence of the iterative procedures and deciding the number of terms required to meet the accuracy prescriptions. Different examples of realistic grids and standard benchmark tests are used to demonstrate the performance of the proposed methodologies.El modelo de flujo de potencias se usa para predecir el comportamiento de redes eléctricas y desemboca en la resolución de un sistema de ecuaciones algebraicas no lineales. Modelar una red es esencial para optimizar su diseño y control. Ambas aplicaciones requieren una respuesta rápida a las múltiples peticiones de una familia paramétrica de problemas de flujo de potencias. Diversos métodos de resolución se diseñaron especialmente para resolver la versión algebraica de las ecuaciones de flujo de potencias. Sin embargo, no existe ninguna metodología que proporcione una solución explícita al problema paramétrico de flujo de potencias (esto quiere decir, un vademecum computacional explícito en términos de los parámetros). Esta tesis tiene como objetivo diseñar algoritmos que produzcan vademecums para el problema paramétrico de flujo de potencias. Una vez que las soluciones están disponibles, resolver problemas para diferentes valores de los parámetros es un posproceso extremadamente rápido (en tiempo real) y por lo tanto los problemas de diseño óptimo y control se pueden resolver inmediatamente. En la primera fase, una nueva familia de métodos de resolución iterativos para la versión algebraica del problema se construye. El método se basa en una formulación híbrida del problema combinado con un esquema de direcciones alternadas. Estos métodos se han diseñado para generalizarlos de forma que puedan resolver la versión paramétrica del problema siguiendo una estrategia llamada Descomposición Propia Generalizada (PGD). El método de resolución para el problema paramétrico calcula las incógnitas paramétricas usando la técnica PGD. El algoritmo sigue los mismo pasos que el algoritmo algebraico, pero algunas operaciones se llevan a cabo en el ambiente PGD, esto requiere algoritmos iterativos anidados. El método de resolución PGD se acompaña con una evaluación del error cometido permitiendo monitorizar la convergencia de los procesos iterativos y decidir el número de términos que requiere la solución para alcanzar la precisión preescrita. Diferentes ejemplos de redes reales y tests estándar se usan para demostrar el funcionamiento de las metodologías propuestas

State Estimation in Power Distribution Network Operation

The majority of power distribution networks were planned, designed and built as a passive but reliable link between the bulk power transmission point and the in- dividual customer. Enough latent capacity in cables and lines to accommodate anticipated demand growth was allowed and so the system was left unmonitored. Following the signicant development in business regulation, technology evolutions and various government policies towards low carbon renewable generation, it has become necessary to operate the distribution systems efficiently and in a controlled manner. This obviously needs state estimation for network control functions. State estimation is the core function of any energy management system in transmission networks. However little emphasis have been given to the distribution system state estimation, mainly due to the absence of adequate network measurements and also lack of rigorous methodology and tools that could be applied on restricted measure- ments. The scarcity of measured information offers formidable challenge to the state estimator to provide reasonably meaningful estimates of the system states. This introduces bottlenecks in carrying out a range of substation and feeder automa- tion tasks that rely on the quality of the state estimator and opens up many issues like modelling of demand, identification of suitable estimator and placement of new measurements etc. This thesis attempts to address these issues. Thus, the objec- tives of this research are to model the demand as pseudo measurement, identify the state estimation methodology to suite the distribution scenarios and find the effec- tive locations for placing measurements for improving the quality of the estimated quantities. The thesis discusses in detail the criterion for identifying suitable solvers for the distribution system state estimation and stochastic optimisation methods to model the demand. It also discusses a probabilistic technique for identifying effective locations for measurement placement. The robustness of the state estimation algorithm against changes in network topology has been addressed in a statistical framework. All the concepts have been demonstrated on 12-bus radial and 95-bus UKGDS network models

Integration of Large PV Power Plants and Batteries in the Electric Power System

The declining cost of renewables, the need for cleaner sources of energy, and environmental protection policies have led to the growing penetration of inverter-based resources such as solar photovoltaics (PV), wind, and battery energy storage systems (BESS) into the electric power system. The intermittent nature of these resources poses multiple challenges to the power grid and substantial changes in the conventional generation and electrical power delivery practices will be required to accommodate the large penetration of these renewable power plants. The impact of large solar PV penetration on both generation and transmission systems, and the use of BESS to mitigate some of the challenges due to solar PV penetration has been studied in this dissertation. One of the major challenges in evaluating the impact of inverter-based resources (IBR) such as solar PV systems is developing an equivalent model adequate to represent its operation. This work proposes a detailed solar PV model suitable for analyzing the configurations, design, and operation of multi-MW grid connected PV systems. This model which takes into account the contributions of the power electronics control and operation was used to evaluate the impact of transient changes in solar PV power on an example transmission system. The benefits of a battery system configuration connected to the grid through an independent inverter were analyzed and its operation during transient conditions was also evaluated. After developing a detailed solar PV and BESS modules for analyzing the effect of IBR on transmission systems, an innovative approach for evaluating the impact of solar PV plants on both generation and transmission system based on a practical minute-to-minute economic dispatch model was proposed. The study demonstrates that large solar PV penetration may lead to both over- and under-generation violations, and substantial changes to conventional generation dispatch and unit commitment will be required to accommodate the growing renewable solar PV penetration. The terminal voltage of a battery pack varies based on multiple parameters and cannot be modeled as a constant voltage source for a detailed analysis BESS operation. A novel approach for estimating the equivalent circuit parameters for utility-scale BESS using equipment typically available at the installation site was proposed in this dissertation. This approach can be employed by utilities for monitoring energy storage system operation, ensure safety and avoid lithium-ion battery thermal runaway . The new methods developed, configurations and modules proposed in this dissertation may be directly applicable or extended to a wide range of utility practices for evaluating the impact of renewable resources and estimating the maximum solar PV capacity a service area can accommodate without significant upgrades to existing infrastructures

Distributed state estimation with the measurements of Phasor Measurement Units

The world-wide application of Phasor Measurement Units (PMUs) brings great benefit to power system state estimation. The synchronised measurements from PMUs can increase estimation accuracy, synchronise states among different systems, and provide greater applicability of state estimation in the transient condition. However, the integration of synchronised measurements with state estimation can introduce efficiency problems due to the substantial burden of data. The research is divided into two parts: finding a solution to cope with the computational efficiency problem and developing a transient state estimation algorithm based on synchronised measurements from PMUs. The computational efficiency problems constitute important considerations in the operation of state estimation. To improve the low computational efficiency, two distributed algorithms are proposed in Chapters 4 and 5. In these two algorithms, the modelling, structure, and solution are described, and the corresponding procedures of bad data processing are presented. Numerical results on the IEEE 30-bus, 118-bus and 300-bus systems can verify the effectiveness of the two proposed algorithms. A novel transient state estimation algorithm based on synchronised measurements is proposed in Chapter 6. Considering the scanning cycle and sampling rate of PMU measurements, the proposed algorithm can estimate transient states in a practical way. The performance of the proposed algorithm is demonstrated in a transient simulation on the IEEE 14-bus system

On power system automation: a Digital Twin-centric framework for the next generation of energy management systems

The ubiquitous digital transformation also influences power system operation. Emerging real-time applications in information (IT) and operational technology (OT) provide new opportunities to address the increasingly demanding power system operation imposed by the progressing energy transition. This IT/OT convergence is epitomised by the novel Digital Twin (DT) concept. By integrating sensor data into analytical models and aligning the model states with the observed system, a power system DT can be created. As a result, a validated high-fidelity model is derived, which can be applied within the next generation of energy management systems (EMS) to support power system operation. By providing a consistent and maintainable data model, the modular DT-centric EMS proposed in this work addresses several key requirements of modern EMS architectures. It increases the situation awareness in the control room, enables the implementation of model maintenance routines, and facilitates automation approaches, while raising the confidence into operational decisions deduced from the validated model. This gain in trust contributes to the digital transformation and enables a higher degree of power system automation. By considering operational planning and power system operation processes, a direct link to practice is ensured. The feasibility of the concept is examined by numerical case studies.The electrical power system is in the process of an extensive transformation. Driven by the energy transition towards renewable energy resources, many conventional power plants in Germany have already been decommissioned or will be decommissioned within the next decade. Among other things, these changes lead to an increased utilisation of power transmission equipment, and an increasing number of complex dynamic phenomena. The resulting system operation closer to physical boundaries leads to an increased susceptibility to disturbances, and to a reduced time span to react to critical contingencies and perturbations. In consequence, the task to operate the power system will become increasingly demanding. As some reactions to disturbances may be required within timeframes that exceed human capabilities, these developments are intrinsic drivers to enable a higher degree of automation in power system operation. This thesis proposes a framework to create a modular Digital Twin-centric energy management system. It enables the provision of validated and trustworthy models built from knowledge about the power system derived from physical laws, and process data. As the interaction of information and operational technologies is combined in the concept of the Digital Twin, it can serve as a framework for future energy management systems including novel applications for power system monitoring and control, which consider power system dynamics. To provide a validated high-fidelity dynamic power system model, time-synchronised phasor measurements of high-resolution are applied for validation and parameter estimation. This increases the trust into the underlying power system model as well as the confidence into operational decisions derived from advanced analytic applications such as online dynamic security assessment. By providing an appropriate, consistent, and maintainable data model, the framework addresses several key requirements of modern energy management system architectures, while enabling the implementation of advanced automation routines and control approaches. Future energy management systems can provide an increased observability based on the proposed architecture, whereby the situational awareness of human operators in the control room can be improved. In further development stages, cognitive systems can be applied that are able to learn from the data provided, e.g., machine learning based analytical functions. Thus, the framework enables a higher degree of power system automation, as well as the deployment of assistance and decision support functions for power system operation pointing towards a higher degree of automation in power system operation. The framework represents a contribution to the digital transformation of power system operation and facilitates a successful energy transition. The feasibility of the concept is examined by case studies in form of numerical simulations to provide a proof of concept.Das elektrische Energiesystem befindet sich in einem umfangreichen Transformations-prozess. Durch die voranschreitende Energiewende und den zunehmenden Einsatz erneuerbarer Energieträger sind in Deutschland viele konventionelle Kraftwerke bereits stillgelegt worden oder werden in den nächsten Jahren stillgelegt. Diese Veränderungen führen unter anderem zu einer erhöhten Betriebsmittelauslastung sowie zu einer verringerten Systemträgheit und somit zu einer zunehmenden Anzahl komplexer dynamischer Phänomene im elektrischen Energiesystem. Der Betrieb des Systems näher an den physikalischen Grenzen führt des Weiteren zu einer erhöhten Störanfälligkeit und zu einer verkürzten Zeitspanne, um auf kritische Ereignisse und Störungen zu reagieren. Infolgedessen wird die Aufgabe, das Stromnetz zu betreiben anspruchsvoller. Insbesondere dort wo Reaktionszeiten erforderlich sind, welche die menschlichen Fähigkeiten übersteigen sind die zuvor genannten Veränderungen intrinsische Treiber hin zu einem höheren Automatisierungsgrad in der Netzbetriebs- und Systemführung. Aufkommende Echtzeitanwendungen in den Informations- und Betriebstechnologien und eine zunehmende Menge an hochauflösenden Sensordaten ermöglichen neue Ansätze für den Entwurf und den Betrieb von cyber-physikalischen Systemen. Ein vielversprechender Ansatz, der in jüngster Zeit in diesem Zusammenhang diskutiert wurde, ist das Konzept des so genannten Digitalen Zwillings. Da das Zusammenspiel von Informations- und Betriebstechnologien im Konzept des Digitalen Zwillings vereint wird, kann es als Grundlage für eine zukünftige Leitsystemarchitektur und neuartige Anwendungen der Leittechnik herangezogen werden. In der vorliegenden Arbeit wird ein Framework entwickelt, welches einen Digitalen Zwilling in einer neuartigen modularen Leitsystemarchitektur für die Aufgabe der Überwachung und Steuerung zukünftiger Energiesysteme zweckdienlich einsetzbar macht. In Ergänzung zu den bereits vorhandenen Funktionen moderner Netzführungssysteme unterstützt das Konzept die Abbildung der Netzdynamik auf Basis eines dynamischen Netzmodells. Um eine realitätsgetreue Abbildung der Netzdynamik zu ermöglichen, werden zeitsynchrone Raumzeigermessungen für die Modellvalidierung und Modellparameterschätzung herangezogen. Dies erhöht die Aussagekraft von Sicherheitsanalysen, sowie das Vertrauen in die Modelle mit denen operative Entscheidungen generiert werden. Durch die Bereitstellung eines validierten, konsistenten und wartbaren Datenmodells auf der Grundlage von physikalischen Gesetzmäßigkeiten und während des Betriebs gewonnener Prozessdaten, adressiert der vorgestellte Architekturentwurf mehrere Schlüsselan-forderungen an moderne Netzleitsysteme. So ermöglicht das Framework einen höheren Automatisierungsgrad des Stromnetzbetriebs sowie den Einsatz von Entscheidungs-unterstützungsfunktionen bis hin zu vertrauenswürdigen Assistenzsystemen auf Basis kognitiver Systeme. Diese Funktionen können die Betriebssicherheit erhöhen und stellen einen wichtigen Beitrag zur Umsetzung der digitalen Transformation des Stromnetzbetriebs, sowie zur erfolgreichen Umsetzung der Energiewende dar. Das vorgestellte Konzept wird auf der Grundlage numerischer Simulationen untersucht, wobei die grundsätzliche Machbarkeit anhand von Fallstudien nachgewiesen wird

Modelling and control techniques for multiphase electric drives: a phase variable approach

Multiphase electric drives are today one of the most relevant research topics for the electrical engineering scientific community, thanks to the many advantages they offer over standard three-phase solutions (e.g., power segmentation, fault-tolerance, optimized performances, torque/power sharing strategies, etc...). They are considered promising solutions in many application areas, like industry, traction and renewable energy integration, and especially in presence of high-power or high-reliability requirements. However, contrarily to the three-phase counterparts, multiphase drives can assume a wider variety of different configurations, concerning both the electrical machine (e.g., symmetrical/asymmetrical windings disposition, concentrated/distributed windings, etc...) and the overall drive topology (e.g., single-star configuration, multiple-star configuration, open-end windings, etc…). This aspect, together with the higher number of variables of the system, can make their analysis and control more challenging, especially when dealing with reconfigurable systems (e.g., in post-fault scenarios). This Ph.D. thesis is focused on the mathematical modelling and on the control of multiphase electric drives. The aim of this research is to develop a generalized model-based approach that can be used in multiple configurations and scenarios, requiring minimal reconfigurations to deal with different machine designs and/or different converter topologies, and suitable both in healthy and in faulty operating conditions. Standard field-oriented approaches for the analysis and control of multiphase drives, directly derived as extensions of the three-phase equivalents, despite being relatively easy and convenient solutions to deal with symmetrical machines, may suffer some hurdles when applied to some asymmetrical configurations, including post-fault layouts. To address these issues, a different approach, completely derived in the phase variable domain, is here developed. The method does not require any vector space decomposition or rotational transformation but instead explicitly considers the mathematical properties of the multiphase machine and the effects of the drive topology (which typically introduces some constraints on the system variables). In this thesis work, the proposed approach is particularized for multiphase permanent magnet synchronous machines and for multiphase synchronous reluctance machines. All the results are obtained through rigorous mathematical derivations, and are supported and validated by both numerical analysis and experimental tests. As proven considering many different configurations and scenarios, the main benefits of the proposed methodology are its generality and flexibility, which make it a viable alternative to standard modelling and control algorithms