Fault Diagnosis for Multi-energy Flows of Energy Internet: Framework and Prospects

Energy Internet (EI) is an inevitable development trend of energy systems under the background of technology development, environmental pressure and energy transition. Multi-energy flow coupling is one of the key characteristics of the EI, which enhances the interoperability of different types of energy flows while consequently increases the probability of cascading failures. Therefore it is of great significance to study the multi-energy flow fault diagnosis of the EI to ensure its safe and stable operation as well as the continuous energy supply. This paper introduces the concept of multi-energy flow cascading fault of the EI for the first time. The energy internet framework for multi-energy flow cascading fault diagnosis is firstly proposed, and then characteristics of various energy networks in the EI are analyzed from the perspective of fault diagnosis. Finally, future research prospects are discussed.National Natural Science Foundation of China 61703345National Natural Science Foundation of China 61472328National Natural Science Foundation of China 5160714

Nuevas técnicas para modelizar y analizar la vulnerabilidad de infraestructuras críticas de energía interdependientes

La interdependencia entre las redes de gas y electricidad es motivo de preocupación debido a la creciente utilización del gas para la generación de electricidad en centrales de ciclo combinado y al uso de energía eléctrica de los compresores en la red de gas. Estas redes están sujetas a riesgos de interrupción del suministro derivados de posibles problemas técnicos o amenazas intencionadas. Por lo tanto, resulta conveniente modelizar y analizar la vulnerabilidad de estas infraestructuras críticas de energía interdependientes.En esta tesis doctoral se presenta, en primer lugar, una metodología para analizar conjuntamente los flujos de electricidad y gas. El conjunto de ecuaciones no lineales que representan la operación del sistema de potencia se resuelve utilizando el método de Newton-Raphson, mientras que las ecuaciones en la red de gas se resuelven utilizando el enfoque de transformada análoga-lineal. Se presentan dos casos de estudio para demostrar la simplicidad de la metodología propuesta. Los resultados obtenidos se verifican contra el método Newton-Raphson tradicional con el fin de comprobar la solución alcanzada, encontrando un buen desempeño de la metodología conjunta aplicada. La aplicación del enfoque propuesto permite el análisis de la vulnerabilidad de las infraestructuras energéticas interdependientes. También, se desarrolla una metodología para evaluar la vulnerabilidad estructural de las redes de energía eléctrica y gas acopladas, considerando interdependencias en el proceso de fallos en cascada. La vulnerabilidad se evalúa empleando el índice de desconexión de carga y las medidas de centralidad de vulnerabilidad geodésica e impacto en la conectividad. El estudio muestra una elevada correlación entre el índice de desconexión de carga y el índice de vulnerabilidad geodésica. De esta manera, la teoría de grafos puede usarse como sustituto de los enfoques de flujos de carga que demandan un conocimiento detallado de los parámetros eléctricos e hidráulicos de los sistemas bajo estudio y son computacionalmente más intensivos que los métodos estadísticos de grafos. Como resultado, se propone un nuevo método para estimar la vulnerabilidad de las redes de energía eléctrica y gas conjuntas utilizando el índice de vulnerabilidad geodésica. Asimismo, se estudia el comportamiento de las redes de electricidad y gas natural de España, tanto de manera separada como conjunta. Los resultados muestran que la red de gas natural es menos robusta que la red eléctrica y que la red acoplada es más vulnerable que la red eléctrica ante fallos aleatorios y deliberados. Además, eliminar los nodos más fuertemente conectados de los dos sistemas independientes resultaría una estrategia de ataque eficaz para el rápido colapso de las infraestructuras acopladas interdependientes. Por último, se evalúa la robustez estructural de los planes de expansión de las infraestructuras de electricidad y gas natural en España. Los casos de estudio corresponden a las principales inversiones propuestas por los operadores de los sistemas en 2015-2020. Los resultados demuestran que la construcción de algunas instalaciones para la expansión de ambas redes no mejora la robustez estructural de la red acoplada; sin embargo, cuando se tiene en cuenta todo el programa de inversión se produce una mejora relativa de hasta un 6% con respecto al caso base. La metodología propuesta en esta tesis corrobora que la aplicación de la teoría de grafos es adecuada para analizar la planificación de activos de una infraestructura energética crítica, requiriendo únicamente la topología y el programa de inversiones para evaluar el desempeño de la red acoplada en caso de fallos en cascada. En suma, esta tesis doctoral pone de relieve la importancia de que los sistemas energéticos se aborden como redes acopladas debido a sus fuertes interacciones. Una perturbación en un sistema puede no ser crítica si las infraestructuras están separadas, pero dado que ambas redes son interdependientes, el impacto resultante podría causar fallos en el otro sistema. En otras palabras, las interdependencias aumentan el impacto de las perturbaciones.<br /

Modeling, Simulation, and Analysis of Cascading Outages in Power Systems

Interconnected power systems are prone to cascading outages leading to large-area blackouts. Modeling, simulation, analysis, and mitigation of cascading outages are still challenges for power system operators and planners.Firstly, the interaction model and interaction graph proposed by [27] are demonstrated on a realistic Northeastern Power Coordinating Council (NPCC) power system, identifying key links and components that contribute most to the propagation of cascading outages. Then a multi-layer interaction graph for analysis and mitigation of cascading outages is proposed. It provides a practical, comprehensive framework for prediction of outage propagation and decision making on mitigation strategies. It has multiple layers to respectively identify key links and components, which contribute the most to outage propagation. Based on the multi-layer interaction graph, effective mitigation strategies can be further developed. A three-layer interaction graph is constructed and demonstrated on the NPCC power system.Secondly, this thesis proposes a novel steady-state approach for simulating cascading outages. The approach employs a power flow-based model that considers static power-frequency characteristics of both generators and loads. Thus, the system frequency deviation can be calculated under cascading outages and control actions such as under-frequency load shedding can be simulated. Further, a new AC optimal power flow model considering frequency deviation (AC-OPFf) is proposed to simulate remedial control against system collapse. Case studies on the two-area, IEEE 39-bus, and NPCC power systems show that the proposed approach can more accurately capture the propagation of cascading outages when compared with a conventional approach using the conventional power flow and AC optimal power flow models.Thirdly, in order to reduce the potential risk caused by cascading outages, an online strategy of critical component-based active islanding is proposed. It is performed when any component belonging to a predefined set of critical components is involved in the propagation path. The set of critical components whose fail can cause large risk are identified based on the interaction graph. Test results on the NPCC power system show that the cascading outage risk can be reduced significantly by performing the proposed active islanding when compared with the risk of other scenarios without active islanding

Real-time Prediction of Cascading Failures in Power Systems

Blackouts in power systems cause major financial and societal losses, which necessitate devising better prediction techniques that are specifically tailored to detecting and preventing them. Since blackouts begin as a cascading failure (CF), an early detection of these CFs gives the operators ample time to stop the cascade from propagating into a large-scale blackout. In this thesis, a real-time load-based prediction model for CFs using phasor measurement units (PMUs) is proposed. The proposed model provides load-based predictions; therefore, it has the advantages of being applicable as a controller input and providing the operators with better information about the affected regions. In addition, it can aid in visualizing the effects of the CF on the grid. To extend the functionality and robustness of the proposed model, prediction intervals are incorporated based on the convergence width criterion (CWC) to allow the model to account for the uncertainties of the network, which was not available in previous works. Although this model addresses many issues in previous works, it has limitations in both scalability and capturing of transient behaviours. Hence, a second model based on recurrent neural network (RNN) long short-term memory (LSTM) ensemble is proposed. The RNN-LSTM is added to better capture the dynamics of the power system while also giving faster responses. To accommodate for the scalability of the model, a novel selection criterion for inputs is introduced to minimize the inputs while maintaining a high information entropy. The criteria include distance between buses as per graph theory, centrality of the buses with respect to fault location, and the information entropy of the bus. These criteria are merged using higher statistical moments to reflect the importance of each bus and generate indices that describe the grid with a smaller set of inputs. The results indicate that this model has the potential to provide more meaningful and accurate results than what is available in the previous literature and can be used as part of the integrated remedial action scheme (RAS) system either as a warning tool or a controller input as the accuracy of detecting affected regions reached 99.9% with a maximum delay of 400 ms. Finally, a validation loop extension is introduced to allow the model to self-update in real-time using importance sampling and case-based reasoning to extend the practicality of the model by allowing it to learn from historical data as time progresses

EcoBlock: Grid Impacts, Scaling, and Resilience

Widespread deployment of EcoBlocks has the potential to transform today's electricity system into one that is more resilient, flexible, efficient and sustainable. In this vision, the system will consist of self- su cient, renewable-powered, block-scale entities that can deliberately adjust their net power exchange and can optimize performance, maintain stability, support each other, or disconnect entirely from the grid as needed. This report is intended as an independent analysis of the potential relationships, both constructive and adverse, between EcoBlocks and the grid

Increasing resilience to cascading events: The M.OR.D.OR. scenario

The growing complexity of global interconnected risk suggests that a shift has occurred in the way emergency planners need to improve preparedness and response to cascading events. With reference to the literature from the physical, social and political sciences, this paper analyses extreme space weather events and cyberattacks. The goal of this work is to produce a replicable scenario-building process, based on cross-disciplinary understanding of vulnerability, that could be complementary to probabilistic hazard assessment. Our hypothesis is that the technological and human component of critical infrastructure could be the primary vector for the escalation of secondary emergencies. While not themselves having direct implications in terms of loss of life, elements that are common to different risks could provide particular challenges for disaster management. Our findings identify some vulnerable nodes, such as Global Navigation Satellite System technology and remote-control systems, that could act as paths for the escalations of events. We suggest that these paths may be common to various known and unknown threats. We propose two scenarios of Massive, OveRwhelming Disruption of OpeRations (M.OR.D.OR.) that could be used for testing emergency preparedness strategies, and increasing the response to highly complex, unknown events. The conclusions highlight the open challenges of seeking to increase societal resilience. The limitations of this work are described, as are the possible challenges for future research

Optimizing resilience decision-support for natural gas networks under uncertainty

2019 Summer.Includes bibliographical references.Community resilience in the aftermath of a hazard requires the functionality of complex, interdependent infrastructure systems become operational in a timely manner to support social and economic institutions. In the context of risk management and community resilience, critical decisions should be made not only in the aftermath of a disaster in order to immediately respond to the destructive event and properly repair the damage, but preventive decisions should to be made in order to mitigate the adverse impacts of hazards prior to their occurrence. This involves significant uncertainty about the basic notion of the hazard itself, and usually involves mitigation strategies such as strengthening components or preparing required resources for post-event repairs. In essence, instances of risk management problems that encourage a framework for coupled decisions before and after events include modeling how to allocate resources before the disruptive event so as to maximize the efficiency for their distribution to repair in the aftermath of the event, and how to determine which network components require preventive investments in order to enhance their performance in case of an event. In this dissertation, a methodology is presented for optimal decision making for resilience assessment, seismic risk mitigation, and recovery of natural gas networks, taking into account their interdependency with some of the other systems within the community. In this regard, the natural gas and electric power networks of a virtual community were modeled with enough detail such that it enables assessment of natural gas network supply at the community level. The effect of the industrial makeup of a community on its natural gas recovery following an earthquake, as well as the effect of replacing conventional steel pipes with ductile HDPE pipelines as an effective mitigation strategy against seismic hazard are investigated. In addition, a multi objective optimization framework that integrates probabilistic seismic risk assessment of coupled infrastructure systems and evolutionary algorithms is proposed in order to determine cost-optimal decisions before and after a seismic event, with the objective of making the natural gas network recover more rapidly, and thus the community more resilient. Including bi-directional interdependencies between the natural gas and electric power network, strategic decisions are pursued regarding which distribution pipelines in the gas network should be retrofitted under budget constraints, with the objectives to minimizing the number of people without natural gas in the residential sector and business losses due to the lack of natural gas in non-residential sectors. Monte Carlo Simulation (MCS) is used in order to propagate uncertainties and Probabilistic Seismic Hazard Assessment (PSHA) is adopted in order to capture uncertainties in the seismic hazard with an approach to preserve spatial correlation. A non-dominated sorting genetic algorithm (NSGA-II) approach is utilized to solve the multi-objective optimization problem under study. The results prove the potential of the developed methodology to provide risk-informed decision support, while being able to deal with large-scale, interdependent complex infrastructure considering probabilistic seismic hazard scenarios