Integrating Wildfires Propagation Prediction Into Early Warning of Electrical Transmission Line Outages

Wildfires could pose a significant danger to electrical transmission lines and cause considerable losses to the power grids and residents nearby. Previous studies of preventing wildfire damages to electrical transmission lines mostly analyze wildfire and power system security independently due to their differences in disciplines and cannot satisfy the requirement of the power grid for active and timely responses. In this paper, we have designed an integrated wildfire early warning system framework for power grids, taking prediction of wildfires and early warning of line outage probability together. First, the proposed model simulates the spatiotemporal process of wildfires via a geography cellular automata model and predicts when and where wildfires initially get into the security buffer of an electrical transmission line. It is developed in the context of electrical transmission line operating with various situations of topography, vegetation, wind and, especially, multiple ignition points. Second, we have proposed a line outage model (LOM), based on wildfire prediction and breakdown mechanisms of the air gap, to predict the breakdown probability varying with time and the most vulnerable poles at the holistic line scale. Finally, to illustrate the validation and rationality of our proposed system, a case study for a 500-kV transmission line near Miyi county, China, is presented, and the results under various wildfire situations are studied and compared. By integrating wildfire prediction into the LOM and alarming the holistic line breakdown probability along time, this paper makes a significant contribution in the early warning system to prevent transmission lines to be damaged by wildfires, illustrating the related breakdown mechanisms at the line operation level rather than laboratory experiments only. Meanwhile, the implementation of cellular automata model under comprehensive environmental conditions and simulation of the breakdown probability for the 500-kV transmission line could serve as references for other studies in the community

Impact of wildfires on power systems

Power systems, particularly those located near forests, are exposed to wildfires during summer seasons especially in regions with high temperatures. This issue may impact on operation of power grids and thus their reliability and resilience. However, the traditional reliability and resilience models may not be effective anymore as such events occurs more frequently and their impacts are broader than the ones were previously considered in system reliability assessments. In this study, the byproducts of wildfires are identified, and their impacts are modeled to provide a better picture for reliability and resilience assessments. These models will help analyze the effect of wildfires on different components in a power system, for instance, the impact of heat, smoke, or ash on the power lines and renewable energy resources. Studying the effects of wildfires on electrical power systems, analyzing and formulating these impacts, and determining their intensity on electrical components and energy production are contributions of this thesis. In particular, this work provides an overview of efforts in evaluating the impact of wildfires due to heat, smoke, and ash on different power grid components

Quantification and mitigation of the impacts of extreme weather on power system resilience and reliability

Modelling the impact of extreme weather on power systems is a computationally expensive, challenging area of study due to the diversity of threats, complicatedness of modelling, and data and simulation requirements to perform the relevant studies. The impacts of extreme weather – specifically wind – are considered. Factors such as the distribution of outage probability on lines and the potential correlation with wind power generation during storms are investigated; so too is sensitivity of security assessments involving extreme wind to the relationships used between failures and the natural hazard being studied, specifically wind speed. A large scale simulation ensemble is developed and demonstrated to investigate what are deemed the most significant features of power system simulation during extreme weather events. The challenges associated with modelling high impact low probability (HILP) events are studied and demonstrate that the results of security assessments are significantly affected by the granularity of incident weather data being used and the corrections or interpolation being applied to the source data. A generalizable simulation framework is formulated and deployed to investigate the significance of the relationship between incident natural hazards, in this case wind, and its corresponding impact on system resilience. Based on this, a large-scale simulation model is developed and demonstrated to take consideration of a wide variety of factors which can affect power systems during extreme weather events including, but not limited to, under frequency load shedding, line overloads, and high wind speed shutdown and its impact on wind generation. A methodology for quantifying and visualising distributed overhead line failure risk is also demonstrated in tandem with straightforward methods for making wind power projections over transmission systems for security studies. The potential correlation between overhead line risk and wind power generation risk is illustrated visually on representations of GB power networks based on real world data.Open Acces

Energy Systems (Chapter 6)

Warming cannot be limited to well below 2°C without rapid and deep reductions in energy system carbon dioxide (CO2) and greenhouse gas (GHG) emissions. In scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot (2°C (>67%) with action starting in 2020), net energy system CO2 emissions (interquartile range) fall by 87–97% (60–79%) in 2050. In 2030, in scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot, net CO2 and GHG emissions fall by 35–51% and 38–52% respectively. In scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot (2°C (>67%)), net electricity sector CO2 emissions reach zero globally between 2045 and 2055 (2050 and 2080). (high confidence)