Jellyfish and Seaweed Surveillance (JASS), is designed to address the acute problem of the partner (EDF energy) regarding jellyfish and seaweed debris ingress into the water intakes of coastal nuclear power plants. These events have the potential to overload the water intake systems' capacity to filter out debris potentially leading to a temporary shut-down of the affected power plant. JaSS aims to deliver an early warning system based on two approaches. For the ingression of jellyfish we will develop a habitat model, a model that defines the type of environment conditions preferred by an organism, which can be combined with satellite monitoring to detect conditions conducive to jellyfish blooms. For seaweed ingression we will make use of novel, high resolution satellite products (Sentinel program) to detect and track clouds of seaweed detritus in the water surrounding the power plant. The outputs will be used by EDF energy to monitor the water and put preventive measures in place when the risk of marine debris ingress is high. This will reduce the need to shut-down a power plant, saving the company money and ensuring stable energy delivery across the UK power grid. It also has potential to be applied to other sectors of industry that are at risk from these kinds of events such as offshore energy, tourism, and desalination.

Have you noticed that ever more wind turbines appear in the countryside? And more and more solar panels are installed on the houses on your route from work to home? All these are signs of the increasing uptake of micro-generation, whereby individuals or organisations install their own small-scale, renewables-based energy generators to produce and use energy. Presently, in the UK, they must sell the excess of their production back to the grid at a set price. Perhaps you yourself have installed some PV panels and have to sell the excess of your energy production back to the grid at a set price? And perhaps you would much rather contribute your excess generation free of charge to the nearby homeless shelter instead? Or sell it to someone else at a better price? Such free trade between micro-generators could become possible through a peer-to-peer (P2P) energy market. Similar 'sharing' platforms are already in place in other markets, e.g., via Airbnb in the hotel industry, or Uber in taxi hire (though both of these still impose substantial intermediation charges). But what would such a market democratisation entail for the energy sector? Is the infrastructure for P2P energy trading technically feasible? Who would provide it? What will be the role of the current major power producers (like British Gas and EDF Energy) in such a market? Could supply continuity be ensured under the fluctuating generation imposed by the nature of these energy sources? What factors will encourage households/groups to join this market? What regulatory changes are necessary for this market to function? These are the questions that the HoSEM project sets out to address. The key aim of this project is to research the feasibility of such democratised P2P energy market. To enable such a P2P energy market, this project will: 1. Develop a novel technical platform to support P2P household-level energy trading. Here all market participants must have read and write access to the records for the production, sale, and purchase of energy at low cost per transaction; each transaction must be accurately recorded, verifiable, and encryption-secured to guarantee accurate assignment of rights and responsibilities for trades and billing, allowing equal access to all interested participants. The distributed ledger technology uniquely meets all these domain requirements, providing an ideal technical tool for such a platform. The ledgers will also be available to 3rd party businesses that wish to provide new value added services for the energy market. 2. Establish a scientific basis for factors that would foster trust in households and organisations to participate in this market. Since prospective market participants will be acting as individuals or groups (e.g., likeminded "greens" or "profit seekers"), factors for both kinds of such participants will be researched. For instance, individuals may act upon trust in information and its sources, while a group member may follow what other members trust. 3. Research various possible configurations of such a P2P trading (e.g., where a few large groups are formed and influence the energy price, or each individual trades independently) along with algorithms for trade optimisation under each configuration (e.g., how to optimise own income and cut emissions as an individual, or minimising external energy dependency when trading as a community group). 4. Study the social, and economic implications of such a market: what will such a change imply for the current market participants, its impact on the energy supply chain, and how would this market affect everyday individual/community life? The DLT-enabled P2P energy trading has a strong disruptive potential, which could enable new business models and processes in energy sector. This project will help the businesses, regulators, and households gain an understanding of this potential, and get ready to transition into and engage with this changing market.

This project contributes substantially to enhancing the UK's resilience to climate variability and change through working with key stakeholders to ensure research is fit for purpose. This advance will be achieved by embedding a high qualified researcher in EDF to apply new modelling techniques that examine the vulnerability of their power stations to flooding and erosion from extreme rainfall. Embedded researchers play an important role in connecting businesses and environmental managers to current state-of-the-art in research approaches and techniques. By being embedded in EDF, the researcher will establish a good understanding of day-to-day working, drivers, decision-making contexts, and knowledge and information needs as well as the regulatory requirements for implementation. The researcher will establish an excellent understanding of both operational and planning requirements of flood risk assessment, and vulnerability thresholds for the associated hazard of erosion from surface water flows. The researcher will also identify the organizational mechanisms whereby this improved assessment is put into practice through planning, management and the implementation of the necessary mitigation measures. The researcher will, therefore, develop an intrinsic understanding of the problems due to flooding and erosion from extreme rainfall events, and then bring appropriate knowledge and innovative tools to bear on how these climate-related hazards are best predicted and communicated. Working with both EDF colleagues and University of Liverpool academics, the researcher will undertake assessments of flood and erosion risk that provide useful and usable information, "working collaboratively to generate new knowledge, synthesize and communicate findings to promote learning across relevant science and business domains." The risk modelling comprises models of flood and erosion hazard (probability of impact and extent) and damage (economic loss), the product of which are probability maps of buildings and structures at risk. A hydro-erosion model will be used to produce these maps, allowing the risk to nuclear power generation and decommissioning from extreme rainfall events to be assessed. This model predicts how much rainfall becomes runoff, how runoff is routed according to slope and relief, and how the resulting flows are then able to erode, transport and deposit sediment. Model outputs are fine scale maps of flooding, erosion and deposition, updated slope and relief, and runoff through time. To provide an assessment of erosion hazard from changing event intensity and frequency, UK Climate Change Projections will be used to generate rainfall depth duration frequency curves and river discharge time series for the next 60 years. To quantify the business impact of extreme rainfall and the vulnerability of assets, the project will forecast the economic loss caused by physical damage, judging the cost of mitigation measures against the associated economic benefits. Project outputs will be disseminated to energy sector stakeholders through workshops, conferences and webinars, showcasing how decision-relevant risk data can optimise the deployment of resources and reduce operational costs. This dissemination provides an opportunity to deliver a 'common language' for the communication of storm-related risk, and set an example of best practice in using risk-based analysis to inform operational decision-making. The outputs will include: - Scientific insights into the changing flood and erosion risk to nuclear power stations as a result of climate projections - How erosion hazards influence the vulnerability and resilience of these safety-critical assets to a changing climate - An integrated quantitative predictive modelling framework and decision-support tool that provides the much needed strong evidence base for sustainable, resilient decision making - Deepened engagement between scientists and stakeholders in the energy sector

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.

The complexity of the present UK energy system (including numerous generators ranging from nuclear plants to individual households, transmitters, distributors, storage providers, regulators, and consumers) is ever growing. While once only a few major power producers delivered energy to the whole country, today the energy system is drastically changing. To give a few examples: every household can supply energy into the grid, the environmentally-concerned consumers wish to purchase energy from specific sources, and communities and businesses may wish to ascertain energy self-sufficiency, but also expect to rely on the main grid as provider of the last resource. Transmission capacity must grow to meet increased consumption needs. Intermittence of new energy types (e.g., wind and solar), require larger and longer-term storage. As the technical and participant variety in the energy system grows, the system's architecture can no longer remain uniform, for instance, some communities could rely on wind energy, others on biofuels; the level of participation of smaller suppliers would vary per locality, as will priorities of communities. Thus, there is no longer one optimal energy system architecture for the whole country. Instead, each community should be able to identify the best way that its energy system could be structured and take planned steps towards achieving and maintaining this optimal structure. Thus, this fellowship aims to transform how the energy system is viewed, managed and evolved: moving away from the current perception of a single, uniform system across the whole of the UK, to that of localised, adaptive, largely self-reliant system-of-systems. In this new setting, the local systems will each be individually optimised, yet globally connected. To enable this locally optimised and globally connected energy system, the fellowship will deliver a set of system refactoring patterns, tools, and techniques. Refactoring is a disciplined approach to gradually changing the internal structure of an existing system without changing its externally useful services. The fellowship will: 1. Collect and integrate data sources and models that would allow each community to monitor the current state of their local energy system, identify emerging problems, and address these problems through refactoring patterns. The models will also help to observe the expected effects of a refactoring application both locally and on the larger, interconnected system-of-systems. 2. Set up an open, commonly accessible technical infrastructure for data recording and model evaluation. A simple (non-specialist focused) user interface will be set up to enable all interested stakeholders to choose, evaluate and interpret models. 3. Deliver innovative methods, tools, a pattern catalogue, and good practice guidelines for energy systems refactoring. 4. Engage individuals, communities, businesses, regulators, and NGOs with the localised, renewables-based energy generation activities.