Many current or projected future land-based renewable energy schemes are highly dependent on very localised climatic conditions, especially in regions of complex terrain. For example, mean wind speed, which is the determining factor in assessing the viability of wind farms, varies considerably over distances no greater than the size of a typical farm. Variations in the productivity of bio-energy crops also occur on similar spatial scales. This localised climatic variation will lead to significant differences in response of the landscape in hosting land-based renewables (LBR) and without better understanding could compromise our ability to deploy LBR to maximise environmental and energy gains. Currently climate prediction models operate at much coarser scales than are required for renewable energy applications. The required downscaling of climate data is achieved using a variety of empirical techniques, the reliability of which decreases as the complexity of the terrain increases. In this project, we will use newly emerging techniques of very high resolution nested numerical modelling, taken from the field of numerical weather prediction, to develop a micro-climate model, which will be able to make climate predictions locally down to scales of less than one kilometre. We will conduct validation experiments for the new model at wind farm and bio-energy crop sites. The model will be applied to the problems of (i) predicting the effect of a wind farm on soil carbon sequestration on an upland site, thus addressing the question of carbon payback time for wind farm schemes and (ii) for predicting local yield variations of bio-energy crops. Extremely high resolution numerical modelling of the effect of wind turbines on each other and on the air-land exchanges will be undertaken using a computational fluid dynamics model (CFD). The project will provide a new tool for climate impact prediction at the local scale and will provide new insight into the detailed physical, bio-physical and geochemical processes affecting the resilience and adaptation of sensitive (often upland) environments when hosting LBR.

Overview: We propose a Centre for Doctoral Training in Data Science. Data science is an emerging discipline that combines machine learning, databases, and other research areas in order to generate new knowledge from complex data. Interest in data science is exploding in industry and the public sector, both in the UK and internationally. Students from the Centre will be well prepared to work on tough problems involving large-scale unstructured and semistructured data, which are increasingly arising across a wide variety of application areas. Skills need: There is a significant industrial need for students who are well trained in data science. Skilled data scientists are in high demand. A report by McKinsey Global Institute cites a shortage of up to 190,000 qualified data scientists in the US; the situation in the UK is likely to be similar. A 2012 report in the Harvard Business Review concludes: "Indeed the shortage of data scientists is becoming a serious constraint in some sectors." A report on the Nature web site cited an astonishing 15,000% increase in job postings for data scientists in a single year, from 2011 to 2012. Many of our industrial partners (see letters of support) have expressed a pressing need to hire in data science. Training approach: We will train students using a rigorous and innovative four-year programme that is designed not only to train students in performing cutting-edge research but also to foster interdisciplinary interactions between students and to build students' practical expertise by interacting with a wide consortium of partners. The first year of the programme combines taught coursework and a sequence of small research projects. Taught coursework will include courses in machine learning, databases, and other research areas. Years 2-4 of the programme will consist primarily of an intensive PhD-level research project. The programme will provide students with breadth throughout the interdisciplinary scope of data science, depth in a specialist area, training in leadership and communication skills, and appreciation for practical issues in applied data science. All students will receive individual supervision from at least two members of Centre staff. The training programme will be especially characterized by opportunities for combining theory and practice, and for student-led and peer-to-peer learning.

By 2015, the UK is expected to face an electrical power shortage of over 20GW, based on projected economic growth and projected life expectancy of a number of existing power plants. There is currently an exceptionally wide variety of new generation technologies being considered. Nuclear power generation will take a long time from build to generation; in fact, the earliest estimated time of generation from new nuclear power stations would be 2018. Renewable energy alone is not capable of generating enough electricity to fill this gap. Around 40% of the current electricity is generated by gas/oil in the UK, but the price of gas/oil faces a huge fluctuations and uncertainty. So gas/oil is not the suitable choice to fill the big electricity generation capacity gap. To meet the various requirements in electricity demand, environment, finance and performance, coal fired power generation is really in need, actually the realistic choice, for compensating the generation gap. Plans have been made for new coal-fired power stations to be built in the UK in the near future. In China, more than 70% of electricity is currently generated by Coal. New coal fired power stations bring into generation almost every month in China. In American, 335,830MW electricity is generated by coal. It is likely that coal remains a dominant fuel for electricity generation from many years to come. Coal is, no doubt, playing an important role in electrical power generation but we must make it cleaner. Supercritical coal fired plant technology is one of the leading options with improved efficiency and hence reduced CO2 emissions per unit of electrical energy generated. Indeed, power plants using supercritical generation have energy efficiency up to 46%, around 10% above current coal fired power plants. On the other hand, this technology costs less than other clean coal technologies and can be fully integrated with appropriate CO2 capture technology in a timely manner. In addition to higher energy efficiency, lower emission levels for supercritical plants are achieved by using well-proven emission control technologies. However, power plants adopting supercritical boilers face great challenges from the UK National Grid Code (NGC) compliance. The UK grid code is far more demanding than in other European countries due to the relatively small scale of the UK electricity network. The most significant issue for a supercritical steam plant is the absence of the stored energy provided by the drum of a conventional plant. As a result the plant would struggle to produce the 10% frequency response requirement in the Grid Code quickly enough Ensuring NGC compliance for supercritical boiler power generation is an important pre-requisite for gaining acceptance in the UK for this highly promising cleaner coal technology. The generation companies have already proposed the Grid Code review request to NGC for the possibility of grid code change to accept supercritical plant There is an urgent demand to conduct the whole process modelling and simulation study to get a clearer picture of the dynamic responses of the supercritical coal fired power plant and to study the feasible strategy to improve the dynamic responses. Also, it is essential to establish the university based research capacity in the UK to provide research solutions in response to the challenges arising from adopting supercritical technology in electrical power generation and also to provide the training needed for future electrical power engineers. Currently, no supercritical or ultra-supercritical boilers operate in the UK, which make it difficult for UK researchers alone to conduct the above proposed study. There are more than 400 such units worldwide, with China operating 24 of them and more to be built. So this proposal is proposed to collaborate with Chinese top universities for this challenging research.

Heating indoor spaces by burning natural gas accounts for ~30% of the UK's total CO2 emissions. Around 23 million properties are connected to the gas network. Each 1kg of gas burned delivers ~12kWh of heat and releases ~4kg of CO2. That cannot continue in a future net-zero UK and capturing CO2 at individual buildings is completely implausible using any known technology. Many consider that hydrogen should replace natural gas in the gas network. Technically, this is feasible. Hydrogen can be produced from electrolysis or from natural gas. In case of the latter, 'carbon-capture' methods can collect most of the resulting CO2 and pump that underground. However, distributing hydrogen through the gas network might not necessarily be the most sensible course of action in all cases. This project will answer the question about how best to use different parts of existing gas network in a future net-zero UK. Even with carbon-capture, producing hydrogen from natural gas does cause some CO2 emissions. Typically >5% escapes. Using renewable electricity to make 'green' hydrogen via electrolysis and then burning that in boilers delivers less than 7kWh of heat into homes for every 10kWh of electricity used. By contrast, using electrically driven heat pumps can deliver 40kWh of heat for every 10kWh of electricity consumed. Although there are other advantages to producing hydrogen for heating, it remains questionable whether this is optimal in many parts of the UK. It is very likely that a large fraction of the existing infrastructure will be used for distributing hydrogen across the country. However, some specific parts of the network could be better exploited in a different way. This project will explore the different possible uses for those parts of the gas network. All of these potential uses are motivated mainly by solving problems that would arise if heat pumping were deployed very extensively in the UK as the primary heating mechanism. One possible future use for parts of the gas network is to feed non-potable water into properties. This water could serve as the source of low-temperature heat to support heat pumps. A new variety of heat pump turns incoming water into an ice slurry and discards the slurry to melt again later. This 'Latent Heat Pump' (LHP) can extract a lot of heat out of cold water (12L of water provides ~1kWh of heat). That heat emerges from the water at about 0C and as a consequence, the LHP can have a coefficient-of-performance (COP) >4 even when the outside air is very cold. For most air-source heat pumps, the COP falls sharply in very cold weather and, for obvious reasons, the COP matters most in very cold weather. A second possible future use for the gas network is to serve as a return (collection) network rather than as a delivery (distribution) network. Here, the fluid returning through the gas network would be an aqueous solution of a chemical that was hydrated (mixed with water) at the property to release heat. This measure would be taken only in very cold weather. Calcium Chloride and Magnesium Sulphate are two very cheap salts that release heat when dissolved in water. There are other inexpensive substances that release large quantities of heat upon reacting with water. Finally, if water was being conveyed in the low-pressure tiers of the gas network, the high-pressure tiers of the gas network would be free for another use. A very attractive possibility here would be to use those parts as the pressure vessel for a compressed air energy storage system. That system would simultaneously be able to assist the electricity transmission system by doing a parallel transmission from North to South at times of high North-South power traffic. How acceptable each of these propositions is to key social stakeholders (including policy makers, prospective business, and public end-users) will be integral to their real-world viability, and so will be examined here also.

The UK has challenging GHG reduction targets. It is believed that carbon capture and storage (CCS) will play a critical role in the energy systems of the future, in part to support the decarbonisation objective and in part to provide grid flexibility in a future system including a large fraction of less responsive low carbon energy systems (e.g. nuclear baseload and intermittent wind). The whole systems modelling and analysis programme proposed here is designed to support wider UK initiatives by reducing technological risk and identifying performance bottlenecks. CCS will require substantial capital investment in capture and transport systems and storage complex management. Although elements of the whole chain have been studied through modelling and experimentation, there is little work on whole system assessment. For complex systems such as CCS, whole system assessment is vital ahead of large scale deployment as it identifies critical integration and interaction issues between the components and evaluates whole system performance as a function of component design parameters. Thus the whole system may be optimised; simply optimising the design of individual components is likely to result in a sub-optimal system design. The proposed research methodology is based on multiscale modelling. This involves the development of fit-for-purpose models of the individual components which describe phenomena that operate over different length and time scales and which support integration and data exchange across scales. The reason for this is that relatively localised phenomena (e.g. mass transfer in an amine scrubber) might affect the overall system transient response by limiting the rate at which the power plant flue gas flowrate can be turned up or down. Similarly, the important performance trade-offs in individual component designs must be characterised and used for overall system design. There are a number of important issues to be resolved regarding future CCS systems; the applicants believe that multiscale systems modelling approach is ideal to develop relevant insights and guidance. Examples of the issues to be addressed through whole systems modelling, analysis and optimisation include: - The development and application of a methodology to optimise the time-phased evolution of the whole CCS system design (incorporating its important individual components), including sources to recruit and location of storage sites, balancing long-term and short-term investment imperatives. - Performing integrated assessments of alternative CCS systems, through the application of fit-for-purpose models (e.g. those able to quantify trace emissions of harmful substances) and rigorous life-cycle based analyses. - Characterising the transient performance of the integrated system (how will it perform in actual operation?), understanding whether or not it affects the flexibility of the wider energy system with which it is interfaced, what the safety critical components are and the network's dynamic stability and operability bottlenecks - Understand issues of systems integration - how do the different phenomena associated with the different components in the system cause effects to propagate through the network (e.g. the effect of impurities in captured CO2, the transport network and the storage complex). What are the important considerations that must be taken into consideration when designing and operating the whole system? The outcome of the programme will be relevant to a very wide range of stakeholders interested in CCS, including industry, regulatory and policy agencies and academia. The most important contributions of the project will be: - making available methodologies to design and analyse future CCS systems - generating insights into the most important interactions involved in system design and operation - quantifying (economics, environmental impact, safety & operability) the performance of UK CCS systems