Analysis of the Robustness Behavior of Modern Power Grids
現代電力網絡的穩健性分析
Student thesis: Doctoral Thesis
Author(s)
Related Research Unit(s)
Detail(s)
Awarding Institution | |
---|---|
Supervisors/Advisors |
|
Award date | 22 Jul 2024 |
Link(s)
Permanent Link | https://scholars.cityu.edu.hk/en/theses/theses(d5a94f79-00b8-4d0f-81de-4b6c689896eb).html |
---|---|
Other link(s) | Links |
Abstract
Power systems serve as one of the most indispensable infrastructures in modern society. The disruption of the electricity supply can lead to huge economic and societal consequences. Blackouts can disrupt public services, affect commercial activities, and cause economic losses. According to the World Bank policy research working paper titled 'Underutilized potential: The business costs of unreliable infrastructure in developing countries', blackouts can cause 38 billion dollars in loss per year in developing countries. Moreover, climate change is amplifying the occurrence and severity of extreme weather events, which may disrupt the operation of power grids. Between 2000 and 2021, approximately 83% of major outages reported in the U.S. were attributed to weather-related incidents, and such outages occur more frequently today.
There is an urgent need to analyze events occurring during large disturbances. Cascading failure processes involve rapid transitions of system operating points, which may encompass various failure events such as out-of-step protection of synchronous generators, voltage instability events, and overload protection of transmission lines. The interactions among these failure mechanisms pose challenges for cascading failure analysis. Furthermore, with the increasing integration of inverter-based resources (IBRs) for renewable generation, analyzing cascading failure processes becomes crucial to providing valuable insights for power system engineers to design and improve the robustness of power grids.
Acquiring real-world blackout data is challenging due to its rarity. Therefore, cascading failure models are essential to analyze the propagation patterns of cascading failures. These models enable statistical analysis involving a large amount of simulation data. The identification of propagation mechanisms facilitates the development of effective mitigation strategies. In this study, we propose a cascading failure model that achieves a balance between computational efficiency and accuracy. We incorporate the continuation algorithm to track the steady-state variation of equilibrium points in cascading failure processes. Hence, the proposed model allows for the detection of voltage instability events, which have a significant influence on the accurate reproduction of cascading failure processes. The proposed model provides reliable voltage information on system components. The voltage information is adopted to model the power controllers of system components. We differentiate between synchronous generators and IBRs based on their power control capabilities. Additionally, our model considers the interaction between local primary frequency control and system-level frequency control, ensuring a comprehensive representation of power system dynamics.
By utilizing an appropriate cascading failure model, our analysis of cascading failure propagation focuses on two key aspects: the failure pattern of transmission lines and the interaction of voltage instability events. First, we incorporate uncertainties into linear sensitivity analysis and derive transmission line outage risks under the penetration of renewable energy resources. Then, we introduce algorithms that employ network flow theory to cluster power system components. This clustering approach helps us identify source generators and sink loads, with each source and its corresponding sinks forming a cluster. We determine the proportion of power from a generator that serves a specific load within a cluster with the power-sharing method. Based on the identified power system clusters, our observations indicate that faults triggering local power imbalances can lead to remote power transfers, while faults that do not cause local power imbalances result in local power redistribution. These observations enable us to propose two cascading failure mitigation strategies, namely, the adaptive power balance restoration and the selective edge protection. Furthermore, we investigate the interdependence among buses in a power system experiencing voltage instability events. We achieve this by deriving interaction graphs, which are constructed using successive transition probability matrices and the Markov transition matrix. Our findings reveal patterns in voltage instability events. A group of closely interdependent buses is identified as a voltage instability chain. We propose cascading failure mitigation strategies based on voltage instability chains including power flow dispatch strategies, intentional branch removal strategies, and substituting synchronous generators with IBRs.
Furthermore, topological properties play a crucial role in determining the robustness of a grid, but they are often overlooked. The transition of modern power grids towards clean energy introduces new characteristics. We propose indexes to investigate these new characteristics quantitatively. First, distributed generation has drawn more attention recently. We propose metrics for assessing the level of distributed generation. Hence, we can analyze the impact of distributed generation on cascading failures in power systems quantitively. Second, with the increasing penetration of IBRs in power grids, we explore their influence on the synchronization performance of the grid. To achieve this, we introduce two indexes, namely, the inertia clustering coefficient and the inertia centrality index, which measure the distribution of inertia in power networks. These indexes provide insights into the impact of IBRs on synchronization.
There is an urgent need to analyze events occurring during large disturbances. Cascading failure processes involve rapid transitions of system operating points, which may encompass various failure events such as out-of-step protection of synchronous generators, voltage instability events, and overload protection of transmission lines. The interactions among these failure mechanisms pose challenges for cascading failure analysis. Furthermore, with the increasing integration of inverter-based resources (IBRs) for renewable generation, analyzing cascading failure processes becomes crucial to providing valuable insights for power system engineers to design and improve the robustness of power grids.
Acquiring real-world blackout data is challenging due to its rarity. Therefore, cascading failure models are essential to analyze the propagation patterns of cascading failures. These models enable statistical analysis involving a large amount of simulation data. The identification of propagation mechanisms facilitates the development of effective mitigation strategies. In this study, we propose a cascading failure model that achieves a balance between computational efficiency and accuracy. We incorporate the continuation algorithm to track the steady-state variation of equilibrium points in cascading failure processes. Hence, the proposed model allows for the detection of voltage instability events, which have a significant influence on the accurate reproduction of cascading failure processes. The proposed model provides reliable voltage information on system components. The voltage information is adopted to model the power controllers of system components. We differentiate between synchronous generators and IBRs based on their power control capabilities. Additionally, our model considers the interaction between local primary frequency control and system-level frequency control, ensuring a comprehensive representation of power system dynamics.
By utilizing an appropriate cascading failure model, our analysis of cascading failure propagation focuses on two key aspects: the failure pattern of transmission lines and the interaction of voltage instability events. First, we incorporate uncertainties into linear sensitivity analysis and derive transmission line outage risks under the penetration of renewable energy resources. Then, we introduce algorithms that employ network flow theory to cluster power system components. This clustering approach helps us identify source generators and sink loads, with each source and its corresponding sinks forming a cluster. We determine the proportion of power from a generator that serves a specific load within a cluster with the power-sharing method. Based on the identified power system clusters, our observations indicate that faults triggering local power imbalances can lead to remote power transfers, while faults that do not cause local power imbalances result in local power redistribution. These observations enable us to propose two cascading failure mitigation strategies, namely, the adaptive power balance restoration and the selective edge protection. Furthermore, we investigate the interdependence among buses in a power system experiencing voltage instability events. We achieve this by deriving interaction graphs, which are constructed using successive transition probability matrices and the Markov transition matrix. Our findings reveal patterns in voltage instability events. A group of closely interdependent buses is identified as a voltage instability chain. We propose cascading failure mitigation strategies based on voltage instability chains including power flow dispatch strategies, intentional branch removal strategies, and substituting synchronous generators with IBRs.
Furthermore, topological properties play a crucial role in determining the robustness of a grid, but they are often overlooked. The transition of modern power grids towards clean energy introduces new characteristics. We propose indexes to investigate these new characteristics quantitatively. First, distributed generation has drawn more attention recently. We propose metrics for assessing the level of distributed generation. Hence, we can analyze the impact of distributed generation on cascading failures in power systems quantitively. Second, with the increasing penetration of IBRs in power grids, we explore their influence on the synchronization performance of the grid. To achieve this, we introduce two indexes, namely, the inertia clustering coefficient and the inertia centrality index, which measure the distribution of inertia in power networks. These indexes provide insights into the impact of IBRs on synchronization.