Green Gas Access will define tools to improve how green gas is managed across UK distribution networks, supporting net-zero goals. With fossil fuels still expected to dominate the energy mix by 2050, we must ensure resilient supply and avoid capacity loss as we integrate decentralised sources like biomethane. The solution is to enable real-time network operation, including dynamic supply modelling, scenario planning, and technology deployment. Key outcomes include: improved green gas injection control, better asset use, onboarding new suppliers efficiently, and supporting the transition to low-carbon systems through coordinated green gas, storage, and power-to-gas operation

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Our project aims to develop a web-based tool - Climate Searchlight - which allows investors, asset managers, asset operators and other stakeholders to make informed decisions regarding the climate risks that may affect asset portfolios or individual assets in the future. The tool will provide relevant information that will help the user to identify and assess the impacts of physical and transition risks relevant to their specific assets under different potential future climate change scenarios (e.g. RCP 4.5 (1.5 oC) and RCP 8.5 (2.3 oC)): \*Physical risks -- higher global warming will result in increased levels of physical risks (e.g. flooding, extreme heat, high winds, drought, etc.) which will have significant impacts on physical infrastructure, productivity levels and global supply chains. \*Transition risks -- reducing the future extent of climate change will require policy measures, technology uptake and social changes which will disrupt business and their underlying business models and revenue streams. DNV has two comprehensive datasets/algorithms which are used for modelling physical risks and transition risks, C-GEAR and the Energy Transition Outlook (ETO) Model respectively. Both of these are large, data intensive and complex models which are continually updated. The project will therefore provide an opportunity for DNV to bring to market a user-friendly climate risk analytics software tool, Climate Searchlight, which will allow users to screen both the physical and transition risks which their assets face so that they can make informed investment decisions regarding the most appropriate climate adaptation measures and decarbonisation pathway options. The tool also aims to raise awareness of climate related risks within client organisations and ignite business change through recommended actions and next steps. DNV will run this project with support from the Beazley Group, an international provider of insurance and underwriting services. This will provide us with the opportunity to thoroughly test and prototype our product with a member of our target user-base to ensure that its specifications and outputs meet the needs of the users. In addition, DNV will develop a white paper to guide stakeholders on how to systematically model climate risks in their decision-making frameworks.

Wind energy is expected to be a major contributor to the global energy supply in the coming decades, and in many areas it is already the cheapest form of electricity generation available. Offshore wind energy is now playing a significant and rapidly increasing role in areas with suitable shallow seas. A further step to floating offshore wind turbines, which are just beginning to be installed commercially, could massively increase the potential offshore wind resource by allowing deeper sea areas to be used. Research over many years has led to the very large, efficient and cost-effective wind turbine designs seen in today's wind farms. Much recent research has focussed on more efficient and cost-effective installation and operation of wind farms. There is serious interest in novel wind farm control strategies which can improve the operation of the wind farm as a whole, rather than just controlling each wind turbine as if it was operating in isolation from its neighbours. One strategy for example, known as 'wake steering', attempts to deflect each turbine's wake away from downstream turbines, allowing increased overall power production, and longer lifetime through reduced fatigue damage. This study will focus on the use of wake steering in a floating offshore wind farm context. More research is required to get a deeper understanding of the wake effects, particularly in wind conditions typical of offshore environments, so that the most effective control strategies can be devised. _DNV GL, Durham University_ and _Ocean Flow Energy_ have partnered to apply to this R&D programme in order to investigate the feasibility of this innovative control technology on floating-offshore wind farms. The main objectives of this research proposal include: * increasing confidence in the use of wake steering, * identifying technical challenges and advantages of using wake steering on floating offshore wind farms, * analysing the effects of the use of wake steering using the Starfloat floating platform design as a benchmark, * analysing the effects of the application of wake steering on the economic performance of a floating offshore wind farm. This research will be carried out in cooperation with a US consortium, led by NREL, which has been formed to investigate similar control strategies for fixed offshore wind farms. Sharing of expertise and wind farm data will lead to improved wake modelling techniques which will help bring the technical and economic benefits of wake steering to the growing US and UK offshore wind farm markets.

Visual inspection of large infrastructure assets in extreme environments, such as offshore wind turbines, is essential for maintaining performance and ensuring safety. However, it is expensive, labour intensive, and hazardous, with the majority of inspections being carried out manually by inspectors using rope access or boom lift. Currently tens of millions of man hours per annum are spent looking for defects in safety critical infrastructure. Automatic visual inspections using robots equipped with cameras offers a potential solution, significantly reducing costs, increasing quality assurance and reducing safety concerns. Major progress has been made in capturing images for inspection, notably using drones, and in some cases, these have advanced to the point of being technically viable alternatives to traditional manual inspections. However, although the capture of images has been automated, the processing of images in order to identify defects remains a semi-automated process, with reliance on visual inspections of image data by trained experts. This means that although the visual data can be collected automatically and quickly, inspections still take a significant amount of time. Hence there is a need for fully automating the data processing component if these systems are to fully realise their potential. Alongside this there is also a need to garner wider acceptance of automatic processing techniques across the industry. They need to be commercially viable and demonstrably reliable - adoption of new approaches to inspection relies on a full understanding of their limitations and the criteria by which they can be assessed and categorised. This is necessary if such methods are to be acceptable to current and future regulatory requirements. Addressing this issue is therefore of equal importance to technical development.This project aims to address both of these issues. It is a collaboration between Garrad Hassan (DNV-GL), world leaders in risk management and quality assurance, Perceptual Robotics (PR), an SME specialising in visual inspection of wind turbines using drones, and the University of Bristol (UoB), experts in computer vision and AI. An automated processing pipeline will be developed and demonstrated, and incorporated within PRs Dhalian system, a world leading semi-automated drone inspection system for wind turbines. Alongside this, a general framework for assessing, characterising and comparing such systems will be validated and verified, with the aim of generating broader acceptance across the industry and informing future regulation. The project will provide both competitive advantage to PR and contribute to growth of the UK automated inspection industry.

To develop state-of-the-art approaches and tools for fire and explosion assessment of offshore facilities with particular emphasis on the concept and pre-FEED stages of development.