Electricity is so deeply ingrained in everyday life that when it is not available many things, essential and simply pleasurable, cease working. Finding ways to better manage faults in electricity distribution systems is a key way of improving the quality of supply offered to customers. Society also faces choices about its sources of energy and we need to find ways to remove technical barriers to the connection of small scale renewable generation without large cost penalties. The reasons that both of these tasks are difficult are (i) that the low voltage part of the distribution system was designed for simple operation without active control and (ii) the distribution system is very large and overall central control is not realistic. Solutions are also constrained by the large amount of existing equipment that is only part way through a long service life and is too expensive to replace prematurely. This project will explore a means to gradually devolving control authority from the existing central control room (which is semi-automated and semi-manual) and use a peer-to-peer network of controllers/decision-makers placed at each substation. The controllers can open and close remotely controlled switches to reallocate loads to different parts of the network and take various voltage correction actions. There is a strong need for communication to obtain feedback information and to allow a controller with only a partial view of the system to cooperate in finding an optimal set of actions to take in the event of a fault, an out-of-tolerance voltage or a generator whose output is being limited by network constraints. The project is challenging because it requires integration of research in distributed control, decision making, network analysis and communications. For this reason we have assembled a team drawn from 7 universities and 3 major international companies in the power industry.

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.

This Prosperity Partnership (PP) is a direct response to the growing local, national and international consensus that climate change should be treated as an emergency and that as a key part of this, the energy system must be transformed. Fundamental change is needed if energy system transformation is to be achieved. The UK will only be successful if the many parties involved - government at all levels, regulators, innovators, industry, investors, consumers and citizens - are engaged, empowered and equipped to make properly informed decisions in a timely manner. These decisions span the whole energy system: across the energy value chain - from generation to delivery to use; across the vectors and sectors - heat, transport, power, industry; and across technology, business models, markets, policy and regulation. Critically needed decisions must account for the complex interactions, interdependencies and trade-offs between these aspects of the whole energy system. The complexity and uncertainty of the environment in which these decisions must be made require new approaches. Digital Twins are much talked about as playing a role. This PP will take "whole system thinking" to "whole system action"; it will move Digital Twins from possibility into reality where they are being used by a network operator to deliver transformation and realise good climate, economic and social outcomes across the stakeholder community. The novelty of this work can be expressed in several ways: The creation of a Digital Twin that spans and INTEGRATES multiple vectors - electricity, heat, hydrogen and industry - to enable system level insights to be gained, questions to be answered and decision making to be supported The creation of an architecture that facilitates the integration of multiple energy vectors in a consistent and coherent way so that a whole system view is obtained, not separate views of separate "siloes". The development of new techniques using sophisticated modelling, Artificial Intelligence and Machine Learning in innovative ways; this will generate Intellectual Property that progresses use of Digital Twins closer to "Business As Usual". Application of the principle of openness so that enhancement, extension and integration with other Digital Twins is made easier thereby strengthening the ability to transform, and to do so at greater pace and with lower risk. An iterative development approach which enables adaptation to respond to learnings gained in the course of the PP and from advancements in the art and science of Digital Twins and from the process of transformation happening in the sector. Specific goals and commitments are in front of us and with little time to deliver on them. We have committed to a Net Zero Power system in 2035 subject to security of supply. We have declared a British Energy Security Strategy which depends upon a multi-vector approach. How do we know a Net Zero Power System in 2035 is possible and that it will work? How can we have comfort that security of supply can be reasonably assured? How do we know what trade-offs between vectors will be needed to deliver the best possible outcomes? How can we align the efforts of all players in the sector to deliver what is needed? An integrated Digital Twin - the key focus of this PP - will help do this. It will enable people to form a shared view of the future system and its operation. It will provide a way for stakeholders from different vectors to discuss and solve problems using a common reference point and language. It will help innovators see the context for their good ideas. It will help investors see opportunities and price risk. It will support people make decisions that are too complex for the human mind to make without the assistance of sophisticated capabilities to support them. This PP delivers that capability and importantly, does so in the context of the industry - connecting research, innovation and real-world applications.

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.

Electrical power systems are undergoing unprecedented and ever-increasing change that will increase the levels of complexity and uncertainty to unprecedented levels, particularly in GB. Ensuring secure, reliable and stable power system operation is clearly paramount; not only for "traditional" electrical loads, but to power telecommunications, water supply and sanitation, natural gas production and delivery, and for transportation. Social discomfort, economic disruption and loss of life can arise in cases of partial or full blackouts. Uncertainty and complexity will arise due to the prevalence of Renewable Energy Sources (RES). In GB, millions of intermittent small energy sources (not under the control of the system operator) may be connected to the electricity distribution system in future, as opposed to historical arrangements, where a much smaller number (100 or so) of large-scale generators, under the control of the system operator, were connected to the transmission system. Furthermore, energy storage, electric vehicles, heat pumps, HVDC interconnectors, "smart grids" and associated control systems, will all act to increase the complexity and unpredictability of, and possibly introduce chaos to, the system. Extreme weather events are on the increase empirically and with reliance on renewable sources (mostly from solar and wind), this could also increase risks associated with uncertainty, complexity and system operability. Internationally respected organisations such as the IEEE and CIGRE emphasise the increasing complexity of power systems and highlight problems with unpredictable and changing power system dynamics as challenges that might compromise security and could increase the risk of blackouts. They also highlight potential improvements in reducing these risks through enhanced monitoring, control, automation and special protection schemes. Prevention and mitigation of the risk of blackouts is essential and the focus of this proposal. Understanding the changing nature of system dynamics is fundamental to addressing this risk. This Fellowship is focused on investigating, understanding, defining and representing previously un-encountered dynamic phenomena that will be manifest in future power systems due to the aforementioned increases in complexity and uncertainty. Novel modelling, prediction and control tools and methodologies will be developed to ensure an accelerated path to stable, secure, reliable and cost-effective operation and enhance understanding. This research will lead to prototype applications and demonstration in the world-leading facilities available at the host institution. Ultimately, the main impact will be maximisation of the secure use of renewables and effective decarbonisation of the electricity system, through creating models and tools to enhance "operability" of electrical power systems and reduce blackout risk. The Fellowship will enable the candidate and his institution to be international leaders in this field, which impacts both society and the economy.