Buildings are responsible for about 40% of carbon emissions and consume about 40% of all produced energy in the UK. Transforming how buildings use and produce energy is a fundamental steppingstone to achieving net-zero carbon emissions and sustainable economic growth. The abundance of data, flexible technologies and advanced control approaches open exciting opportunities to achieve cost-effective system decarbonisation and create places where people love to live for the increased comfort standards. A radical transformation of the building sector is possible using real-time monitoring, learning capabilities, advanced control strategies, distributed optimisation and coordination. Our research demonstrates that the energy consumption of buildings has a vast potential to be flexible and support an efficient grid operation. However, it is unclear how to design distributed control architectures and schemes managing millions of buildings in real-time to simultaneously achieve societal and individual consumer benefits. The proposed project seeks answers to critical open questions: How can we efficiently harness the adaptability of millions of diverse buildings to support the entire energy system while optimizing individual objectives concurrently? How can we harness data reliably to develop scalable, transferable control methods, bringing them closer to practical application? The aim of this research is to develop distributed solutions to reliably manage energy use across groups of buildings. We will consider for the first time the advantage of dynamically forming coalitions according to the environment's variability and individual real-time energy needs. To realise this, we have set the following objectives: 1. Extend the latest data-driven behavioural control and uncertainty modelling approaches, state-of-the-art distributed optimisation methods and reinforcement learning techniques. These methods should be scalable to bridge the gap between lab-scale demonstrations and real-world implementation. 2. Apply these innovative methods to models of building clusters. This will offer insights for shaping policies and driving innovation, bolstering their role in supporting the entire energy system. The close collaboration with UK Power Networks and SSE Energy Solutions will support the data-driven modelling and development of novel adaptive distributed control architectures to maximise the research output impact. A pressing question we will address is how to achieve both individual and societal benefits. Existing distributed solutions are focused on directly achieving a centralised objective. Such solutions do not fit the objectives of simultaneously achieving societal and individual objectives. Substantial performance limitations arise when pursuing exclusively conflicting objectives, since the buildings connected to the grid are strongly coupled.

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.

Deep changes are happening in the supply side of energy systems. The UK has halved carbon emissions from electricity system from over 150 million tonnes in 2012 to under 70 in 2018 and China is adding about 20 GW of wind generation capacity per year and has replaced all buses in the city of Shenzhen with electric busses. Very clearly there is much more to do: the remaining decarbonisation of electricity, the electrification of other sectors and sourcing alternative, zero-carbon fuels. Cities have traditionally been huge consumers of energy brought in from their hinterland and yet load growth in energy networks is inevitable as more services, notably transport, are decarbonised through electrification and building density increases through re-development of with taller buildings. The traditional response to this, adding more plant and equipment, is recognised as being an inefficient. An interesting trend is the emergence of Local Energy Systems (LES) and Multi-Energy Micro-Grids (MEMG). LES and MEMG are a means for raising self-consumption of local energy resources; tapping into sources of flexibility in how the services derived from energy; using local services for both local and national control and moving to a smart ways of ensuring resilience. The recent power outage in the UK (9/8/19) highlighted that transport systems and other urban infrastructure are particularly vulnerable. A re-imagining of how resilience is provided in the urban setting could hugely reduce that vulnerability. Despite the differences between the histories and geographies of cities in China and the UK, we find common challenges and a complementary set of research expertise. This project brings together experts in power electronics, optimisation, control and fault-management from UK and China. Existing energy networks, especially electricity networks, were designed assuming power enters a city from remote power stations and the network inside the city distributes this. This led to a radial set of lines spreading out from substations. This structure is unable to support the formation of flexible microgrids around local generation and storage resources. We propose to re-structure the legacy networks using power electronics devices that give controlled power flows between previously unconnected networks points. This opens up dynamically restructuring the power flow in urban areas to allow greater local self-consumption of energy, for instance moving solar power residential properties to work-place charging of electric vehicles. It also allows islands to be formed in reaction to power cuts that keep essential services running while placing non-essential services on hold. We also look at hardware and control issues. The hardware for electronic routing of power has been discussed in principle but it is too large and not efficient enough to be used in urban settings. We will work on new forms of modular power converter that raise efficiency, reduce physical volume and provide resilience to component failures. Control systems for energy networks are centralised: they gather data from across large areas, make decisions and then issue commands. The microgrid concept changes this to a decentralised approach. A key benefit of decentralisation is the ready access to information about flexibility in energy consumption, e.g which electrical vehicles could delay charging or might supply power to aid with a power cut. Local control also gives opportunities to run the heat/cooling of buildings, the transport energy system and the electricity system as an integrated whole. This can lead better integration of renewable energy and therefore deep decarbonisation but requires a major step forward in managing uncertainty over the local energy resources and demands. We will bring the techniques of stochastic optimisation and machine learning to bear on this problem and devise a control and operations framework for smart urban energy systems.

The UK has a commitment to reduce its greenhouse gas emissions by at least 80% by 2050 relative to 1990 levels. DECC's 2050 Pathway Analysis shows the various ways through which we can achieve this target. All feature a high penetration level of renewable generation and a very substantial uptake of electrification of heat and transport, particularly from 2030 onwards. This will place unprecedented demand and distributed generation on electricity supply infrastructure, particularly the distribution systems due to their size. If a business as usual model is to apply, then the costs of de-carbonisation will be very high. Being equally confronted by the pressure of global climate change and sustainable development, the Chinese government has declared that by 2020 the carbon emission per-unit GDP will reduce to 40-45% of that in 2008. However China also needs to meet a 10% annual demand increase which has been on-going for the past 20 years, and this rate of growth is expected to continue for at least another 10 years. Therefore reinforcement of current distribution networks in an economic and sustainable way while meeting customers' rising expectation of supply quality and reliability is one of the basic requirements of Smart Grid development in China. It is a matter of urgency to investigate how to develop and adapt the current distribution network using Smart Grid interventions in order to facilitate timely connection of low carbon and sustainable technologies in a cost-effective manner. This is a global challenge faced by UK, China and many other countries. Our consortium brings together leading researchers from the UK and China to jointly investigate the integrated operation and planning for smart distribution networks to address two key research challenges: (1) Conventional network operational and planning approaches do not address the emerging opportunities offered by increased measurement and control and do not deal with the inevitable uncertainties of smart distribution networks. (2) A general understanding of how national or regional electricity distribution infrastructure should be developed and operated using Smart Grid interventions is required urgently by those making policy within Distribution companies and in Government/Regulators. Such an understanding cannot be gained from running conventional power system analysis tools and then manually assessing the results. New techniques and approaches will be investigated to address these important questions (1) Distribution state estimation and probabilistic predictive control approaches will be used to determine the location and control policies of smart grid interventions including Soft Open Points and electronic embedded hybrid on-load tap changers. (2) Novel dynamic pricing techniques will be proposed to resolve conflicts between energy markets and network operation and find synergies where these exist. (3) A very fast network assessment tool and a rolling planning tool that will bridge the gap between planning and operation will be developed. (4) New visualisation and reporting techniques will be developed to give network planners, operators as well policy makers clear insights as to how Smart Grid interventions can be used most effectively. Complementary, cross-country expertise will allow us to undertake the challenging research with substantially reduced cost, time and effort. The research will build upon the long-time well established collaborations between partner institutions of the two countries. Our ambition is to provide a strategic direction for the future of smart electricity distribution networks in the 2030-2050 time frame and deliver methodologies and technologies of alternative network operation and planning strategies in order to facilitate a cost effective evolution to a low carbon future.

Electrical power and energy systems represent critical national infrastructure that interoperates with wider economic sectors including transport, communications, agriculture, manufacturing, construction and education. Reliable power system operation is the key in ensuring the electricity generated from various energy sources can travel in a secure way to meet the electrical loads of those sectors. Reliable power system operation will greatly impact the social comfort of people's lives as well as the healthy growth of the UK economy. Electrical power and energy systems will be key to meet legally binding 100% decarbonisation of overall energy usage in the UK. As a consequence, the large scale of low-carbon and renewable energy sources (RES) such as wind and solar, together with electric vehicles, energy storage and smart grids are integrated into the top and tail of future power systems. These new technologies are fundamentally changing the way power systems operate with complex infrastructure and highly dispersed RES. The increasing size and complexity of electrical power and energy systems require advanced monitoring, modelling and control techniques to handle major disturbances such as blackouts. In August 2019, around 1 million homes lost power due to a series of events on the GB power system with high penetration of RES under extreme weather impacts. The UK has rich RES and ambitious net zero targets, but its power systems are geographically islanded from the mainland with limited undersea cable connections, reduced system "strength" due to many small RES replacing large centrally controlled power plants, making the GB power systems more "brittle" and "volatile" under the major system events. The Fellow will take an industry focused approach to investigate the fundamental modelling methods and advanced simulation tools to address the challenges in real-time power system operation with up to 100% low-carbon and RES. A key novelty of this Fellowship is to understand the fundamental interdependency and interoperability of various energy systems and entities, and across transmission, distribution and renewable system operators. This will include novel development of whole system modelling approaches and applications of digital simulation techniques into the future power system operation. The Fellowship development will be based on the Fellow's highly relevant industry experience in Electricity National Control as well as research expertise in renewable power system operation. The Fellow will work with electricity system operators at the whole system level (transmission, distribution, renewables) and is supported by market-leading digital technology industry partners. Prototype applications will be demonstrated in power systems and computing facilities at Brunel University London, with further deployment to energy management systems at industrial scales. The investigation, development, demonstration and deployment of advanced power system models, tools and techniques will provide cost-effective and scalable simulation platforms to wider industry and academic communities. The research and innovation outcomes of the Fellowship will be expected to better monitoring and managing of power systems as well as maintaining the control and optimisation of power grid reliability. This will assist in achieving the UK net zero emissions target through the energy transition of digitalisation, decarbonisation and decentralisation. With support from UKRI and host institution the Fellow will develop into a Future Leader in the field of power and energy systems digitalisation, by working with eight industrial partners in power systems engineering and digital technologies, three internationally respected research and engineering societies, and accessing mentors from academia and industry. The Fellowship will plan intensive training activities across academia and industry, as well as building Fellow's research and innovation networks.