Due to climate change, extreme meteorological phenomena such as heavy precipitation, extreme temperature, strong winds and sea level rise, seem to be growing more severe and frequent, but the actual estimation of this evolution in extreme weather events remains subject to large uncertainty. For example, in December 2015 when storm Desmond hit the UK, several communities were badly affected by water level rises. Rainfall in this storm crept up to new record levels and provided us with critical lessons on how we can better prepare to withstand similar hazards. However, these lessons learned are in hindsight. When looking at the occurrence probability of such an extreme event that out-spans the range of previously recorded data, Extreme Value Theory (EVT) is the most appropriate branch of probability theory to be implemented as risk assessment and forecasting have a strong probabilistic foundation. In many operational settings, risk mitigation measures are required to balance costs with safety. For example, in insuring systems and infrastructure against extreme events, it might not be enough to sift through extreme record events that emerge from historical data, but it would also be nonsensical to channel most resources into a safety system so robust that it would spectacularly exceed the actual risk being protected against. EVT offers an appropriate statistical toolkit for forecasting extreme outcomes to a high degree of accuracy, thus providing critical evidence for assessing risk more accurately in preparing a proportionate response. There are varying layers of complexity in EVT enveloped in the recently introduced class of multivariate max-stable processes. These are promising models for the structural components that capture how extremes from multiple phenomena (hence the prefix multivariate) are likely to manifest themselves jointly across a certain region over time (hence the so-called space-time processes, also termed random fields). Real life applications abound in the multivariate infinite-dimensional max-stable processes frameworks. For example, the Fukushima nuclear disaster in 2011 was ignited by the combination of a huge earthquake followed by a tsunami. The main goal of this research proposal is to develop a general theory for multivariate infinite-dimensional extremes (extremes of two or more random fields) that will culminate in the development of statistical methodology for modelling interactions of two or more related extreme events. Recent studies have found that there exists significant long-term impact of climate change on storms that combine wind speed and precipitation, deeming it critically important that any fragility analysis be conducted in such a way as to ensure probabilistic safety levels of a nuclear power plant for extreme weather events. For example, the sting jet phenomena often unleashes very extreme local wind speeds, heavy rainfall and extreme temperatures on a nuclear plant. This is therefore the first application area of the developed statistical methodology. It is intended that this research programme will not only lead to improvement in safety standards and operational reliability of the nuclear energy fleet but also carries with it the potential of reducing costs in expensive overprotection measures that could run into millions of pounds. In addition to the nuclear energy sector other application areas will be explored. Energy supply and renewables power systems are so unwieldy that people are still trying to unravel some intriguing aspects of time dependent peak demands. The statistical methodology developed as part of this research programme will enable a better understanding to be gained of the characteristic features in smart-meter data, which will ultimately give people access to more affordable energy, providing more interaction and safety and thus more choice.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Currently the accepted technology for large wind turbines is the doubly-fed induction generator (DFIG). This technology is popular primarily due to the reduced cost of the partially rated power electronic converter. On the negative side is the fact that the generator requires brushes and slip rings which require regular maintainance.An alternative scheme is based on the brushless doubly-fed reluctance machine (BDFRM) which also has the cost benefit of a partially rated power converter but as its name implies does not require brushes and slip rings.The BDFRM has not been used for a wind power application. This project will experimentally examine its performance for a wind power application. There are a number of different approaches to the control of a BDFRM. The project will examine the use of Direct Power Control (DPC). This control approach will include sensorless operation and machine parameter independence. With the proliferation of wind power generation the issue of power system stabilty is of great concern. It is important to examine the fault-ride-through (FRT) capabilty of any generation system. This project will examine the FRT capability of the BDFRM and compare this to that of the DFIG. This will require that special grid fault emulation equipment is included in the laboratory test rig.

Due to climate change, extreme meteorological phenomena such as heavy precipitation, extreme temperature, strong winds and sea level rise, seem to be growing more severe and frequent, but the actual estimation of this evolution in extreme weather events remains subject to large uncertainty. For example, in December 2015 when storm Desmond hit the UK, several communities were badly affected by water level rises. Rainfall in this storm crept up to new record levels and provided us with critical lessons on how we can better prepare to withstand similar hazards. However, these lessons learned are in hindsight. When looking at the occurrence probability of such an extreme event that out-spans the range of previously recorded data, Extreme Value Theory (EVT) is the most appropriate branch of probability theory to be implemented as risk assessment and forecasting have a strong probabilistic foundation. In many operational settings, risk mitigation measures are required to balance costs with safety. For example, in insuring systems and infrastructure against extreme events, it might not be enough to sift through extreme record events that emerge from historical data, but it would also be nonsensical to channel most resources into a safety system so robust that it would spectacularly exceed the actual risk being protected against. EVT offers an appropriate statistical toolkit for forecasting extreme outcomes to a high degree of accuracy, thus providing critical evidence for assessing risk more accurately in preparing a proportionate response. There are varying layers of complexity in EVT enveloped in the recently introduced class of multivariate max-stable processes. These are promising models for the structural components that capture how extremes from multiple phenomena (hence the prefix multivariate) are likely to manifest themselves jointly across a certain region over time (hence the so-called space-time processes, also termed random fields). Real life applications abound in the multivariate infinite-dimensional max-stable processes frameworks. For example, the Fukushima nuclear disaster in 2011 was ignited by the combination of a huge earthquake followed by a tsunami. The main goal of this research proposal is to develop a general theory for multivariate infinite-dimensional extremes (extremes of two or more random fields) that will culminate in the development of statistical methodology for modelling interactions of two or more related extreme events. Recent studies have found that there exists significant long-term impact of climate change on storms that combine wind speed and precipitation, deeming it critically important that any fragility analysis be conducted in such a way as to ensure probabilistic safety levels of a nuclear power plant for extreme weather events. For example, the sting jet phenomena often unleashes very extreme local wind speeds, heavy rainfall and extreme temperatures on a nuclear plant. This is therefore the first application area of the developed statistical methodology. It is intended that this research programme will not only lead to improvement in safety standards and operational reliability of the nuclear energy fleet but also carries with it the potential of reducing costs in expensive overprotection measures that could run into millions of pounds. In addition to the nuclear energy sector other application areas will be explored. Energy supply and renewables power systems are so unwieldy that people are still trying to unravel some intriguing aspects of time dependent peak demands. The statistical methodology developed as part of this research programme will enable a better understanding to be gained of the characteristic features in smart-meter data, which will ultimately give people access to more affordable energy, providing more interaction and safety and thus more choice.

Electricity and natural gas networks, as two major energy transport infrastructure, have traditionally been planned and operated independently from each other. Electricity generation was dominated by coal, oil and nuclear power stations prior to 1990, when the "dash for gas" brought a significant number of gas-fired power stations into the generation mix. These geographically dispersed large power stations have created a loose link between natural gas and electricity networks at the energy source. As the pace of decarbonising our electricity section accelerates, the two energy networks will progressively become more closely linked by end users, driven by the electrification of heat and major efficiency improvements. This presents critical new challenges to the traditional network modelling, operation and optimisations, in particular as they were developed independently for natural gas and electricity networks. The traditional methods do not take into account of the substantial rise in the interaction between the two networks, i.e. how a change in gas demand/resource might impact the demand/generation of the electrical system and vice versa. The vision of this research is to develop a statistical model for combined gas and electricity systems at the distribution level that can efficiently simulate the interactions across the energy vector under severe uncertainties. The developed model will then be fed to the novel optimal operation strategies to manage the two systems for encouraging increased use of renewables and infrastructure and promoting customer interaction with the systems. This fellowship will address this vision by developing highly efficient network sampling methodologies, multi-vector probabilistic energy flow and optimisation tools that will transform the modelling and analysis of highly integrated systems. These new developments will: i) enable detailed real-time analyses of energy flows and capacity bottlenecks of the highly integrated energy systems with high accuracy and in reasonable time scale, ii) assist network operators to optimise the performance of the existing energy systems to minimise the cost of integrating low carbon generation and demand, and iii) assist policy makers to design effective policies and regulations for economic and sustainable energy network development.