Physics-Based and Data-Driven Analytics for Enhanced Planning and Operations in Power Systems with Deep Renewable Penetration

This dissertation is motivated by the lack of combined physics-based and data-driven framework for solving power system challenges that are introduced by the integration of new devices and new system components. As increasing number of stochastic generation, responsive loads, and dynamic measurements are involved in the planning and operations of modern power systems, utilities and system operators are in great need of new analysis framework that could combine physical models and measuring data together for solving challenging planning and operational problems. In view of the above challenges, the high-level objective of this dissertation is to develop a framework for integrating measurement data into large physical systems modeled by dynamical equations. To this end, the dissertation first identifies four critical tasks for the planning and operations of the modern power systems: the data collection and pre-processing, the system situational awareness, the decision making process, as well as the post-event analysis. The dissertation then takes one concrete application in each of these critical tasks as the example, and proposes the physics-based/data-driven approach for solving the challenging problems faced by this specific application. To this end, this dissertation focuses on solving the following specific problems using physics-based/data-driven approaches. First, for the data collection and pre-processing platform, a purely data-driven approach is proposed to detect bad metering data in the phasor measurement unit (PMU) monitoring systems, and ensure the overall PMU data quality. Second, for the situational awareness platform, a physics-based voltage stability assessment method is presented to improve the situational awareness of system voltage instabilities. Third, for the decision making platform, a combined physics-based and data-driven framework is proposed to support the decision making process of PMU-based power plant model validation. Forth, for the post-event analysis platform, a physics-based post-event analysis is presented to identify the root causes of the sub-synchronous oscillations induced by the wind farm integration. The above problems and proposed solutions are discussed in detail in Section 2 through Section 5. The results of this work can be integrated to address practical problems in modern power system planning and operations

Physics-Based and Data-Driven Analytics for Enhanced Planning and Operations in Power Systems with Deep Renewable Penetration

This dissertation is motivated by the lack of combined physics-based and data-driven framework for solving power system challenges that are introduced by the integration of new devices and new system components. As increasing number of stochastic generation, responsive loads, and dynamic measurements are involved in the planning and operations of modern power systems, utilities and system operators are in great need of new analysis framework that could combine physical models and measuring data together for solving challenging planning and operational problems. In view of the above challenges, the high-level objective of this dissertation is to develop a framework for integrating measurement data into large physical systems modeled by dynamical equations. To this end, the dissertation first identifies four critical tasks for the planning and operations of the modern power systems: the data collection and pre-processing, the system situational awareness, the decision making process, as well as the post-event analysis. The dissertation then takes one concrete application in each of these critical tasks as the example, and proposes the physics-based/data-driven approach for solving the challenging problems faced by this specific application. To this end, this dissertation focuses on solving the following specific problems using physics-based/data-driven approaches. First, for the data collection and pre-processing platform, a purely data-driven approach is proposed to detect bad metering data in the phasor measurement unit (PMU) monitoring systems, and ensure the overall PMU data quality. Second, for the situational awareness platform, a physics-based voltage stability assessment method is presented to improve the situational awareness of system voltage instabilities. Third, for the decision making platform, a combined physics-based and data-driven framework is proposed to support the decision making process of PMU-based power plant model validation. Forth, for the post-event analysis platform, a physics-based post-event analysis is presented to identify the root causes of the sub-synchronous oscillations induced by the wind farm integration. The above problems and proposed solutions are discussed in detail in Section 2 through Section 5. The results of this work can be integrated to address practical problems in modern power system planning and operations

Improving distribution network model accuracy using impedance estimation from micro-synchrophasor data

An accurate network model is essential for performing detailed analysis of a power system. The quality of many distribution network models is very diverse, especially for low voltage (LV) networks. To help identify areas where the model is incomplete or incorrect, Micro Phasor Measurement Units (μPMUs) can be integrated into a network. These μPMUs would work together, with a trusted cloud back-end system. The basis for this paper is to determine how the data collected by μPMUs can be used, and what can be calculated from this data to help recognize areas where the network model is inaccurate and may need resurveyed. As a preliminary investigation to determine the feasibility of the approach, this paper discusses the calculation of the impedance of both a transformer and line, and compares the values obtained from μPMU data to the level of value expected on the network

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

Machine Learning Based Detection of False Data Injection Attacks in Wide Area Monitoring Systems

The Smart Grid (SG) is an upgraded, intelligent, and a more reliable version of the traditional Power Grid due to the integration of information and communication technologies. The operation of the SG requires a dense communication network to link all its components. But such a network renders it prone to cyber attacks jeopardizing the integrity and security of the communicated data between the physical electric grid and the control centers. One of the most prominent components of the SG are Wide Area Monitoring Systems (WAMS). WAMS are a modern platform for grid-wide information, communication, and coordination that play a major role in maintaining the stability of the grid against major disturbances. In this thesis, an anomaly detection framework is proposed to identify False Data Injection (FDI) attacks in WAMS using different Machine Learning (ML) and Deep Learning (DL) techniques, i.e., Deep Autoencoders (DAE), Long-Short Term Memory (LSTM), and One-Class Support Vector Machine (OC-SVM). These algorithms leverage diverse, complex, and high-volume power measurements coming from communications between different components of the grid to detect intelligent FDI attacks. The injected false data is assumed to target several major WAMS monitoring applications, such as Voltage Stability Monitoring (VSM), and Phase Angle Monitoring (PAM). The attack vector is considered to be smartly crafted based on the power system data, so that it can pass the conventional bad data detection schemes and remain stealthy. Due to the lack of realistic attack data, machine learning-based anomaly detection techniques are used to detect FDI attacks. To demonstrate the impact of attacks on the realistic WAMS traffic and to show the effectiveness of the proposed detection framework, a Hardware-In-the-Loop (HIL) co-simulation testbed is developed. The performance of the implemented techniques is compared on the testbed data using different metrics: Accuracy, F1 score, and False Positive Rate (FPR) and False Negative Rate (FNR). The IEEE 9-bus and IEEE 39-bus systems are used as benchmarks to investigate the framework scalability. The experimental results prove the effectiveness of the proposed models in detecting FDI attacks in WAMS

Making Distribution State Estimation Practical: Challenges and Opportunities

In increasingly digitalized and metered distribution networks, state estimation is generally recognized as a key enabler of advanced network management functionalities. However, despite decades of research, the real-life adoption of state estimation in distribution systems remains sporadic. This systematization of knowledge paper discusses the cause for this while comparing industrial and academic experiences and reviewing well- and less-established research directions. We argue that to make distribution system state estimation more practical and applicable in the field, new perspectives are needed. In particular, research should move away from conventional approaches and embrace generalized problem specifications and more comprehensive workflows. These, in turn, require algorithm advancements and more general mathematical formulations. We discuss lines of work to enable the delivery of tangible research.Comment: 10 page