Severe storms lead to the uprooting and breakage of trees and this, in turn, can cause considerable damage to electricity supply networks. For example, over the Christmas period 2013/2014 over 500,000 customers were off supply for over five days in the East of England and this was mainly due to damage to overhead power lines caused by catastrophic tree failure in storms. This type of disruption is likely to become more common in the UK as climate change causes storms to become more frequent and severe. It is possible to reduce the impacts of storm damage on electricity networks by felling those trees that are close to power lines and are at more risk of failing in a severe storm. However, assessment of the likelihood of a tree failing is currently done by a surveyor who makes a subjective and qualitative personal judgement based on a field observation of the tree. This approach lacks consistency and scientific rigour and the number of trees that it is feasible to assess and the frequency of repeat surveys is restricted by logistical and financial constraints. This project will address these limitations by developing a new approach to evaluating the risk of failure of individual trees in severe weather. This will begin by developing computer software which can prioritise trees at greatest risk of failure across a landscape which may contain thousands of trees that are close proximity to power lines. Those trees at greatest risk will then be targeted for more detailed measurements, particularly using laser scanners, which will allow us to provide more realistic and objective assessments of the risk of failure of individual trees. We will work with our project partners Scottish Power to demonstrate how this new approach is able to help them target their resources for managing trees at greatest risk of failure, in order to increase the resilience of electricity supply networks in severe weather and minimise disruption to customers. The techniques developed in this research will be valuable for improving the management of trees that are in close proximity to other infrastructure such as roads, railways and buildings, thereby helping to reduce the likelihood that storms will cause financial losses, disruption to services and harm to humans.

In line with the UK's target to reach net zero by 2050, Electrical Vehicles (EV) charged by renewable energy are one of the solutions towards carbon-neutral road transport, which is the 2nd largest carbon emission both nationally in the UK and locally in Newcastle city (it contributed about 33% of total emission in 2020). The electrification of business fleets (either commercial or for public service) has recently emerged as one the key factors in reducing transportation related CO2 emissions. However, according to the Global Covenant of Mayors's (GCoM) guidance the electrification of fleets leads to the reduction of direction emission, it does not imply reduction of overall emission nationally or globally if the electricity charged for EV is still sourced from the fossil fuels (see also the NU et al.'s recent policy report: https://www.seev4-city.eu/wp-content/uploads/2020/09/SEEV4-City-Policy-Recommendations-and-Roadmap-1.pdf). A recent trend in Renewable Energy Sources (RES) is an increasing amount of small-scale RES installed on-site , referred to as ORES. For instance, in March 2021, Newcastle City Council announced a £27M plan to install solar panels, energy storage etc. at schools, leisure centres, cultural venues, depots and offices to decarbonise public buildings and transport. Likewise, Gateshead Council has approved in Nov. 2020 plans to develop two significant-scale urban solar farms, and furthermore installing solar PV canopies above car parking bays in sites like Gateshead Civic Centre, and furthermore are including rooftop solar PV on new developments such as the Gateshead Quay Arena and the proposed Gateshead Quays multi-storey carpark (construction of both commencing in 2021). This provides good opportunities for EV to use more on-site generation renewable electricity to actually reduce the overall emission for road transport. The key issue is the efficient use of ORES. Using battery as a electricity storage can alleviate this, but at significant investment and operation cost. V2G is proposed to reduce static battery storage, but causes battery degradation. And smart charging is needed to avoid or reduce the operation cost of battery degradation. Most existing EV smart charging studies focus on the EV charging only to reduce charging cost and/or peak-shaving, under the assumption of EVs' electracy demand are given and non-adjustable (either constant or statistical model, e.g. Poisson distribution). This is reasonable for non-collaborative individual EVs. However, for a electric fleet (EF) consisting of collaborative EVs, in addition to the optimal EV charging, the electricity demand can be optimized by EF dispatching, i.e. adjusting EF's travel plan by assigning the right EV to the right service to maintain the right state of charge of the battery, and allocating to the right charging station at a right time window, such that a better marginal benefits can be achieved in terms of better efficiency and utilization of on-site renewable energy. However, the power generation of ORES is highly variable - resulting in an undesired fluctuation at the supply side. On the demand side, EVs' charging demand also comes with uncertainties, to meet various tasks with dynamic travelling and charging demands. In shifting EV energy from less variable fossil electricity (imported from the grid) to high variable on-site ORES, the main challenge is the charging strategy of maximizing self-consumption of own ORES under uncertainties, whilst meeting the variable EV demands, at minimized cost in energy storage and less impact on grid's peak load. This project is to investigate the possibility to intelligently integrate the dynamic charging demand of electric fleets with the high variable on-site renewable energy by developing a data-driven reinforcement learning (RL) decision support tool.

In the net-zero transition of the UK by 2050, electricity demand will increase and more renewable power generation will be installed in industrial plants. The bulk electricity system also faces the challenges of increased total and peak demand, increased difficulty in balancing supply and demand, and increased network issues. The flexibility of industrial plants, i.e., the ability to change the normal electricity generation/consumption patterns, can be utilised to address these challenges, through improving the utilisation of renewable power generation onsite and providing balancing and network services to the bulk electricity system. However, the scheduling and control for tapping this flexibility are subject to great difficulty due to significant uncertainties and computational complexity. Digital twins are systems of advanced sensing, communication, simulation, optimisation and control technologies, and can provide updating system states and prediction, based on which data-driven approaches can be developed to tackling the uncertainties and computational complexity in scheduling and control. Specifically, a kernel-learning based method is proposed to characterise the uncertainty sets, and an artificial neutral network based method is proposed for predictive control of industrial plants in real-time operation. A test digital twin platform is established in the lab to demonstrate and assess the proposed data-driven solutions. The platform adopts a two-level structure, with the upper-level global digital twin for whole-plant level predictive control and lower-level local digital twins representing industrial processes, renewable power generation and energy storage systems. The measurements are taken from sensors or a data generator which produces mimic data flow. Two industrial case studies with real data are tested on the platform. One case is an industrial site with a number of bitumen tanks and PV panels, and the other is a paper mill with onsite wind turbines and battery storage.

The cooling sector currently consumes around 14% of the UK's electricity and emits around 10% of the UK's greenhouse gases. Global electricity demand for space cooling alone is forecast to triple by 2050. Moreover, as air temperature increases, the cooling demand increases, but a refrigerator's Coefficient of Performance decreases. This results in a time mismatch between a refrigerator's efficient operation and peak cooling demand over a day. Clearly, this problem will deteriorate over the coming decades. Indeed, research by UKERC recently reported that cooling sector will cause a 7 GW peak power demand to the grid by 2050 in the UK. A solution is to employ cold thermal energy storage, which allows much more flexible refrigeration operation, thereby resulting in improved refrigeration efficiency and reduced peak power demand. Large-scale deployment of cold thermal energy storage could dramatically reduce this peak demand, mitigating its impact to the grid. Moreover, the UK curtails large amounts of wind power due to network constraints. For example, over 3.6TWh of wind energy in total was curtailed on 75% of days in 2020. Therefore, through flattening energy demand, cold thermal energy storage technology provides a means to use off-peak wind power to charge cold thermal energy storage for peak daytime cooling demand. This project, based on the proposed novel adsorption-compression thermodynamic cycle, aims to develop an innovative hybrid technology for both refrigeration and cold thermal energy storage at sub-zero temperatures. The resultant cold thermal energy storage system is fully integrated within the refrigerator and potentially has significantly higher power density and energy density than current technologies, providing a disruptive new solution for large scale cold thermal energy storage. The developed technology can utilise off-peak or curtailed electricity to shave the peak power demand of large refrigeration plants and district cooling networks, and thus mitigates the impacts of the cooling sector on the grid and also reduces operational costs.

Two of the most critical global challenges currently being faced are energy security and climate change. In the UK, more than £100 bn of investment in new UK power stations and grid infrastructure is projected within the next decade, both to replace ageing plant and to allow for the incorporation of renewable sources. Such changes will involve a paradigm shift in the ways in which we generate and transmit electricity. Since a central element of all items of power plant is electrical insulation, meeting our future challenges through the deployment of new innovative plant, this will require the development and exploitation of new high performance insulation material systems. Polymer nanocomposites have demonstrated clear potential, but the lack of detailed understanding of the underlying physics and chemistry is a major impediment to the technological realisation of this potential. In certain laboratory studies, nanodielectrics materials have out-performed unfilled and traditional micro-composite insulating materials. However, entirely contrary results have also been elsewhere. Undoubtedly, this variability in macroscopic behaviour comes about as a consequence of our inability to define and control the key factors that dictate the dielectric behaviour of nanocomposites. The overarching aim of this project is to resolve this issue such that the potential of dielectric nanocomposites - nanodielectrics - can be fully exploited. As such, the project is totally aligned with the EPSRC Materials for Energy theme in which it is accepted that "in the field of advanced materials it will be necessary to strengthen approaches to the rational design and characterisation of advanced materials and their integration into structures and systems". It also aligns with the Advanced Materials theme of the "Eight Great Technologies", it which it is accepted that "these materials are essential to 21st century manufacturing in a UK market worth £170 billion per annum and representing 15 per cent of GDP". Our research hypothesis is that the macroscopic properties of nanodielectrics cannot be reliably controlled without understanding the processes that occur at the interfaces between the matrix material and the nanoparticles, because these regions directly affect two critical issues. First, interfacial interactions will affect the nanoparticle dispersion, which has a major bearing on many physical properties and, second, the nature of the interface determines the local density of states in the system, and thereby the material's overall electrical characteristics. To understand such local processes is challenging and we propose to do this through a combination of computation simulation and experiment, where both aspects are closely aligned, thereby allowing the simulation to direct experiment and the experimental result to refine the simulation. The work programme has been divided in 3 distinct themes, which will progressively move the work from fundamentals to exploitation. Theme 1 will therefore concentrate on model systems, where simulation and experiment can be most closely aligned. Theme 2 will then seek to deploy the key messages to the development of technologically relevant systems and processes. Throughout, Theme 3 will engage with a range of stakeholders that will range from key industry players (equipment manufacturer s, energy utilities, standards bodies) to the general public t maximise the reach and significance of its ultimate impact (economic, environmental, societal). We see the involvement of our Industrial Users Group as being particularly important, both in helping to guide the project and in terms of ensuring acceptance of the technologies that will ultimately arise.