Intelligent Economic Alarm Processor (IEAP)

The advent of electricity market deregulation has placed great emphasis on the availability of information, the analysis of this information, and the subsequent decision-making to optimize system operation in a competitive environment. This creates a need for better ways of correlating the market activity with the physical grid operating states in real time and sharing such information among market participants. Choices of command and control actions may result in different financial consequences for market participants and severely impact their profits. This work provides a solution, the Intelligent Economic Alarm Processor to be implemented in a control center to assist the grid operator in rapidly identifying the faulted sections and market operation management. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Next, in order to better feed the FRPN model with more accurate inputs, the failure rates of the protections devices are analyzed. A new approach to assess the circuit breaker’s life cycle or deterioration stages using its control circuit data is introduced. Unlike the traditional “mean time” criteria, the deterioration stages have been mathematically defined by setting up the limits of various performance indices. The model can be automatically updated as the new real-time condition-based data become available to assess the CB’s operation performance using probability distributions. The economic alarm processor module is discussed in the end. This processor firstly analyzes the fault severity based on the information retrieved from the fault section estimation module, and gives the changes in the LMPs, total generation cost, congestion revenue etc. with electricity market schedules and trends. Then some suggested restorative actions are given to optimize the overall system benefit. When market participants receive such information in advance, they make estimation about the system operator's restorative action and their competitors' reaction to it

Collaborative, Trust-Based Security Mechanisms for a National Utility Intranet

This thesis investigates security mechanisms for utility control and protection networks using IP-based protocol interaction. It proposes flexible, cost-effective solutions in strategic locations to protect transitioning legacy and full IP-standards architectures. It also demonstrates how operational signatures can be defined to enact organizationally-unique standard operating procedures for zero failure in environments with varying levels of uncertainty and trust. The research evaluates layering encryption, authentication, traffic filtering, content checks, and event correlation mechanisms over time-critical primary and backup control/protection signaling to prevent disruption by internal and external malicious activity or errors. Finally, it shows how a regional/national implementation can protect private communities of interest and foster a mix of both centralized and distributed emergency prediction, mitigation, detection, and response with secure, automatic peer-to-peer notifications that share situational awareness across control, transmission, and reliability boundaries and prevent wide-spread, catastrophic power outages

Emergency response system for electric power systems

Complicated cascading disturbances do occur in power systems and cause extreme social and economic impacts. A new Emergency Response System (ERS) is developed to defend power systems against these severe situations. Compared with traditional System Protection Schemes (SPS), an ERS has the 4 significant advantages. (1) The remedial action designed by ERS is more adaptive to system configuration and operating conditions, since it conducts computation online and takes real-time operating conditions as input. (2) Its initiating event set is much larger than that in traditional SPS, because it uses Dynamic Decision Event Tree (DDET) technique and dynamically increases the number of initiating events to be analyzed. (3) It detects, by incorporating long-term simulation and protective relay modeling into the simulator, many power system failures that were \u27hidden\u27 during traditional SPS remedial action design process. (4) The optimal action identification process is adaptive to power market information---By communicating with power market database, the most up-to-date cost factor is used to determine the optimal action.;This work also generalized the basic remedial action design features in such a way so as to make what has heretofore been highly application-specific technology---the SPS action logic design---into an automated and intelligent decision process. This contribution is important, because it is the fundamental enabler for the ERS, making it effective in an emergency scenario where response must be very fast. This contribution is also important because it serves to encapsulate, in a formalized way, the SPS design process; as a result it will be useful to SPS designers in reflecting on and improving upon what they do.;A demonstration system for this generalized remedial action logic design process is developed for proof-of-concept. The results on a test power system show that this approaches is feasible and very effective. Out of 41 initiating events in the test system that would result in power system failure, only 3 can be detected thus prevented by traditional SPS. The rest would rely on the ERS for identifying effective remedial actions

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

Adaptive Three-Stage Controlled Islanding to Prevent Imminent Wide-area Blackouts

Power blackouts are a recurring problem worldwide, and research in this area continues to focus on developing improved methods for their prediction and prevention. Controlled islanding has been proposed as a last resort action to save the network before imminent blackouts when the usual means fail in an unexpected manner. Successful controlled islanding has to deal with three important issues that are involved in the implementation of islanding: when to island, where to island and what to do after islanding is implemented in each island. This thesis presents a framework that combines all three issues to achieve successful islanding based on wide area measurement systems (WAMS). In addition, this thesis focuses on the question of when to island. This question is critical to the success of the three-stage controlled islanding scheme because the possible issues of false dismissal and false alarm have to be handled. In false dismissal, islanding is triggered too late. However, the potentially unstable system is still allowed to operate, and this unstable system, which could have survived, may cause uncontrolled cascading blackouts. In false alarm, islanding is triggered too early, and an originally stable system is forced to split into islands, resulting in unnecessary disruption and economic loss. Thus, the early recognition and identification of “the point of no return” before blackout is inevitable. The single machine equivalent (SIME) method is adopted online to predict transient stability during cascading outages that would shortly lead to blackouts, giving support in decisions about when to island in terms of transient instability. SIME also evaluates dynamic stability after islanding and ensures that the selected island candidates are stable before action is taken. Moreover, in this thesis, the power flow tracing-based method provides all possible islanding cutsets, and SIME helps to identify the one that has the best transient stability and minimal power flow disruption. If no possible island cut set exists, corrective actions through tripping critical generators or load shedding are undertaken in each island. The IEEE 10-generator, 39-busbar power system and 16-generator 68-busbar system are used to demonstrate the entire framework of the controlled islanding scheme. The performance of each methodology involved in each stage is then presented

Data generation and model usage for machine learning-based dynamic security assessment and control

The global effort to decarbonise, decentralise and digitise electricity grids in response to climate change and evolving electricity markets with active consumers (prosumers) is gaining traction in countries around the world. This effort introduces new challenges to electricity grid operation. For instance, the introduction of variable renewable energy generation like wind and solar energy to replace conventional power generation like oil, gas, and coal increases the uncertainty in power systems operation. Additionally, the dynamics introduced by these renewable energy sources that are interfaced through converters are much faster than those in conventional system with thermal power plants. This thesis investigates new operating tools for the system operator that are data-driven to help manage the increased operational uncertainty in this transition. The presented work aims to an- swer some open questions regarding the implementation of these machine learning approaches in real-time operation, primarily related to the quality of training data to train accurate machine- learned models for predicting dynamic behaviour, and the use of these machine-learned models in the control room for real-time operation. To answer the first question, this thesis presents a novel sampling approach for generating ’rare’ operating conditions that are physically feasible but have not been experienced by power systems before. In so doing, the aim is to move away from historical observations that are often limited in describing the full range of operating conditions. Then, the thesis presents a novel approach based on Wasserstein distance and entropy to efficiently combine both historical and ’rare’ operating conditions to create an enriched database capable of training a high- performance classifier. To answer the second question, this thesis presents a scalable and rigorous workflow to trade-off multiple objective criteria when choosing decision tree models for real-time operation by system operators. Then, showcases a practical implementation for using a machine-learned model to optimise power system operation cost using topological control actions. Future research directions are underscored by the crucial role of machine learning in securing low inertia systems, and this thesis identifies research gaps covering physics-informed learning, machine learning-based network planning for secure operation, and robust training datasets are outlined.Open Acces

MMI, SCADA and ALARM philosophy for disturbed state operating conditions in an electrical utility

A project report submitted to the Faculty of Engineering, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg 1995.Advances in digital computing technology make it possible to improve the design of the Man Machine Interface (MMI), SCADA and ALARM modules used in electrical utility control centres. to overcome the problem of control staff data overloading. A possible solution is proposed, based on-an explicit representation of a disturbed power system state in addition to quiescent conditions. The structure of modem SCADA, installations is analysed in terms of the computing power of full graphic workstations, the quantities of element data delivered to the control room and the capabilities of intelligent remote terminal units. This analysis indicates that existing designs for the presentation of SCADA data need to change to solve the data overloading-problem. The proposed philosophy moves the focus of attention from the element level up to the device level by grouping and dividing all elements into categories at the RTU and linking them to their parent device, Control staff are notified graphically on the one-line displays, next to the device in question, of the existence of abnormal elements by category. The element state details for the device are only displayed on demand, resulting in a 95% reduction of alarm text messages. Suggestions are made as to the software functions needed at tbe RTU and the workstation to assist with the display of system data. Lastly racommendations are offered to reduce maintenance by standardising and pre-ordering device element data.AC201

Optimal and Secure Electricity Market Framework for Market Operation of Multi-Microgrid Systems

Traditional power systems were typically based on bulk energy services by large utility companies. However, microgrids and distributed generations have changed the structure of modern power systems as well as electricity markets. Therefore, restructured electricity markets are needed to address energy transactions in modern power systems. In this dissertation, we developed a hierarchical and decentralized electricity market framework for multi-microgrid systems, which clears energy transactions through three market levels; Day-Ahead-Market (DAM), Hour-Ahead-Market (HAM) and Real-Time-Market (RTM). In this market, energy trades are possible between all participants within the microgrids as well as inter-microgrids transactions. In this approach, we developed a game-theoretic-based double auction mechanism for energy transactions in the DAM, while HAM and RTM are cleared by an optimization algorithm and reverse action mechanism, respectively. For data exchange among market players, we developed a secure data-centric communication approach using the Data Distribution Service. Results demonstrated that this electricity market could significantly reduce the energy price and dependency of the multi-microgrid area on the external grid. Furthermore, we developed and verified a hierarchical blockchain-based energy transaction framework for a multi-microgrid system. This framework has a unique structure, which makes it possible to check the feasibility of energy transactions from the power system point of view by evaluating transmission system constraints. The blockchain ledger summarization, microgrid equivalent model development, and market players’ security and privacy enhancement are new approaches to this framework. The research in this dissertation also addresses some ancillary services in power markets such as an optimal power routing in unbalanced microgrids, where we developed a multi-objective optimization model and verified its ability to minimize the power imbalance factor, active power losses and voltage deviation in an unbalanced microgrid. Moreover, we developed an adaptive real-time congestion management algorithm to mitigate congestions in transmission systems using dynamic thermal ratings of transmission lines. Results indicated that the developed algorithm is cost-effective, fast, and reliable for real-time congestion management cases. Finally, we completed research about the communication framework and security algorithm for IEC 61850 Routable GOOSE messages and developed an advanced protection scheme as its application in modern power systems

Dynamic security assessment processing system

The architecture of dynamic security assessment processing system (DSAPS) is proposed to address online dynamic security assessment (DSA) with focus of the dissertation on low-probability, high-consequence events. DSAPS upgrades current online DSA functions and adds new functions to fit into the modern power grid. Trajectory sensitivity analysis is introduced and its applications in power system are reviewed. An index is presented to assess transient voltage dips quantitatively using trajectory sensitivities. Then the framework of anticipatory computing system (ACS) for cascading defense is presented as an important function of DSAPS. ACS addresses various security problems and the uncertainties in cascading outages. Corrective control design is automated to mitigate the system stress in cascading progressions. The corrective controls introduced in the dissertation include corrective security constrained optimal power flow, a two-stage load control for severe under-frequency conditions, and transient stability constrained optimal power flow for cascading outages. With state-of-the-art computing facilities to perform high-speed extended-term time-domain simulation and optimization for large-scale systems, DSAPS/ACS efficiently addresses online DSA for low-probability, high-consequence events, which are not addressed by today\u27s industrial practice. Human interference is reduced in the computationally burdensome analysis