Infoscience

Thesis

Vulnerability Assessment of Electric Power Supply under Extreme Weather Conditions

This thesis analyses and models the vulnerability of the electricity power supply under extreme weather conditions. The system under study is the electric supply system that includes major power plants to main load centers. Extreme weather conditions can cause common mode contingencies (CMCs) of overhead power lines, which endanger the security of electricity supply. Planning and operation of transmission systems are subject to N-1 criterion, which requires that all single failures of network elements do not cause a breach of safety limits. This criterion does not guarantee the security of electricity supply at the time of extreme weather conditions. The objective of this research is to identify critical and plausible CMCs, taking into account space-time correlations of extreme weather conditions and possible states of the network. The most vulnerable zones are focused on to determine appropriate countermeasures for reducing vulnerability. In the past, extreme weather events have caused major disruptions. For example, the blackout in New York in 1977 was initiated by three impacts of lightning on high voltage lines. In 1999, hurricane Lothar caused damage to power grids in several countries, leaving hundreds of thousands of people in darkness. These examples demonstrate the vulnerability of power systems to CMCs. Current transmission networks are expected to undergo significant changes in response to developments such as increases in consumption and newly installed capacity. These changes provide an opportunity to strengthen the security of electricity supply in the perspective of extreme weather conditions and even improve the resilience of electric supply systems. The use of the proposed methodology allows reducing the level of vulnerability by reinforcing only few points or change of the topology of the network. Faced with uncertainties about the evolution of networks and plausible extreme weather conditions, a methodology based on scenarios has been selected. The methodology allows the modeler to reproduce the complexity of the problem while still encouraging the learning process. The core of the methodology is founded on a scenario of electric supply systems and a scenario of extreme weather events. The first scenario includes three models: electric, geographic, and reliability. The electric model comprises components of the network compatible with load flow calculations. The geographic model contains a representation of each power line in a geographic information system, and each of these lines is divided into segments that are associated with a reliability model. The reliability model evaluates failure rates related to exposure to extreme weather conditions. Scenarios of extreme weather events are built on data from weather stations or by numerical simulations implemented in a geographic information system. A vulnerability level index is calculated on the basis of probability and severity indices of a priori possible CMCs. These probabilities are evaluated by a simulation of the interaction between the scenario of transmission network and scenario of extreme weather event. They are a subjective and temporal measure of plausibility of CMCs stemming from interactions in space and time of the two systems previously mentioned. The severity index of CMCs is calculated by a contingency analysis that involves the evaluation of security limit violations. A matrix of vulnerability is constructed as a projection of the vulnerability level in two dimensions, including the lines involved in overload and those being overloaded. This matrix allows the identification of the infrastructures involved in the vulnerability and the determination of major zones of vulnerability. Countermeasures are then proposed to reduce the vulnerability of these zones, and these measures may include additions or retirement of lines or other network topology changes. This methodology is applied to the Swiss transmission network in 2018 subject to summer thunderstorms. A reference case from 2006 is compared in terms of vulnerability to a plausible scenario of transmission network 2018. The latter includes an increased consumption of 20%, around 3 GW of pumped-storage power plants, and network changes proposed by the electricity sector plan supported by the Swiss Federal Office of Energy. On another level, two scenarios of extreme weather events were constructed. The first is an intense thunderstorm occurring in the past, where lightning strikes were recorded by a tracking system. The second case stems from a simulation of a thunderstorm event composed of six cells passing over the Swiss territory. The vulnerability of both scenarios of electric supply system impacted by both scenarios of extreme weather events was evaluated. Two major zones of vulnerability were detected both in the reference case 2006 and the plausible scenario 2018. They are in mountainous regions near major centers of production. The network changes between 2006 and 2018 did not decrease the vulnerability. This is due to the installation of large generation capacity and the difficulty of building lines as a result of the topography in these regions. To reduce vulnerability, the addition of a 380 kV overhead line is proposed for the plausible scenario 2018. This line allows draining off the new hydro generation by offering a new route to major consumption centers and drastically reduces the vulnerability of the two zones mentioned. This measure illustrates the methodology's ability to identify areas of vulnerability and propose actions to increase network resilience. One of the major contributions and innovative points of this research is the consideration of the spatiotemporal correlations of extreme weather conditions for the geographical distribution and structural resistance of transmission lines. In the application of the Swiss 2018 scenario of transmission network, the concept of major zones of vulnerability was useful in identifying the weakest zones and in finding a measure capable of increasing network resilience.

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