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.

The emission of carbon dioxide into the atmosphere has caused huge concerns around the world, in particular because it is widely believed that the increase in its concentration in the atmosphere is a key driver of climate change. If the current trend in the release of carbon dioxide continues, global temperatures are predicted to increase by more than 4 degrees centigrade, which would be disastrous for the world. With the increase in world population, the energy demand is also increasing. Coal-fired and gas-fired power plants still play a central role in meeting this energy demand for the foreseeable future, even though the share of renewable energy is increasing. These power plants are the largest stationary sources of carbon dioxide. Carbon capture is a technique to capture the carbon dioxide that is emitted in the flue gas from these power plants. This proposal seeks to make a significant improvement in the methods used for carbon capture in order to reduce the total costs. Post-combustion CO2 capture by chemical absorption using solvents (for example, monoethanolamine - MEA) is one of the most mature technologies. The conventional technology uses large packed columns. The cost to build and run the capture plants for power plants is currently very high because: (1) the packed columns are very large in size; (2) the amount of steam consumed to regenerate solvents for recirculation is significant. If we can manage to reduce the size of packed columns and the steam consumption, then the cost of carbon capture will be reduced correspondingly. From our previous studies, we found that mass transfer in the conventional packed columns used for carbon capture is very poor. This proposed research is expected to make very significant improvements in mass transfer. The key idea is to rotate the packed column so that it spins at hundreds of times per minute - a so-called rotating packed bed (RPB). A better mass transfer will be generated inside the RPB due to higher contact area. With an intensified capture process, a higher concentration of solvent can be used (for example 70 wt% MEA) and the quantity of recirculating solvent between intensified absorber and stripper will be reduced to around 40%. Our initial analysis has been published in an international leading journal and it indicates that the packing volume in an RPB will be less than 10% of an equivalent conventional packed column. This proposal will investigate how to design and operate the RPB in order to separate carbon dioxide most efficiently from flue gas. The work will include design of new experimental rigs, experimental study, process modelling and simulation, system integration, scale-up of intensified absorber and stripper, process optimisation, comparison between intensified capture process and conventional capture process from technical, economical and environmental points of view. The research will include an investigation into the optimum flow directions for the solvent and flue gas stream (parallel flow or counter-current) for intensified absorber and the optimum design of packing inside the RPB. The proposal will also compare the whole system performance using process intensification vs using conventional packed column for a CCGT power plant. Based on this, an economic analysis will be carried out to quantify the savings provided by this new process intensification technology.

In many cases failure mechanisms initiate and propagate from the surface, including failure under corrosion, fatigue and wear. Critical to this is the surface finish (SF) and the surface integrity (SI). While surface finish has received much attention, surface integrity, a term used to describe the localised sub-surface region that differs from the bulk (residual stresses, plastic deformation, chemical changes, hardness, etc) has received much less attention. Traditionally people have used simple cross sections to examine the surface microstructure.In this project we will apply a suite of state-of-the-art methods to characterise as fully as possible the local microstructure in 3D across a range of scales. These include serial sectioning using a focused ion beam (FIB), mechanical sectioning and X-ray tomography. In the latter X-rays are used to obtain a 3D picture without mechanically sectioning the sample. Critical to the former methods are the means of removing material quickly and efficiently without introducing damage. Emerging methods to remove the damaged layer will be developed such that we can obtain EBSD, texture, chemical mapping, residual stress and insights into plastic deformation near-surface. This will lead to one of the best surface integrity assessment facilities in the world to support industry. In addition we will develop micromechanical methods to assess mechanical properties and corrosion and wear performance. In this way we will relate surface integrity to surface durability. This is critical if we are to predict and engineer surface performance. In addition to developing these metrology tools we will apply them to a set of industrial case studies including corrosion of stainless steel for the energy sector, the performance of thermal barrier coatings for the turbine engine sector, the wear performances of WC-Co coatings and nanostructured coatings. Further case studies will be identified by our industrial steering group.

It is widely acknowledged that the power industry faces a number of serious challenges including infrastructure, capacity constraints and the need to reduce greenhouse gas and other, but more complex issues have arisen from deregulation in many countries. This has resulted in a form of balkanisation that tends to cause additional stress to the legacy electricity grid, which has a structure based on centralised command and management of large scale generating plant, long-range high voltage transmission and local low voltage distribution networks. A number of interrelated problems on varying scales and at different levels need to be addressed, including the need for expensive standby capacity to meet peak loads, high capital cost and long lead-times for new plant, vulnerability to energy security threats of various kinds, and non-technical barriers to distributed energy resources (DERs) and more flexible and sophisticated energy services that might lead to greater energy efficiency.There are signs that a new paradigm for the modern electricity industry is being defined with a decentralised model based on recent and expected advances in DERs and electricity storage technology and, in particular, rapid developments in information and communication technology that will enable the wide scale deployment of smart devices. Particularly in the USA, this new concept - known as the smart grid - is attracting large scale investment and policy recognition, with some commentators comparing its development to that of the Internet and predicting change on a scale that could represent a paradigm shift of a similar kind for the electricity industry and its end-users. If this indeed occurs, then centralist theories, laws and techniques will at some point cease to be valid as the means of control.As well as being a new paradigm for business, the Internet has been considered to be a paradigm case for complexity theory and the parallel with the smart grid concept indicates the appropriateness of this new science as the means of articulating and answering the challenges it sets. The existing structure and organisation of the power industry provides the essential starting point and context for meaningful research into the mechanisms underlying the envisioned evolution, which may represent an example of a punctuated equilibrium. Complex systems thinking and modelling is all about the occurrence of such major, structural changes and the possible ways that the system may evolve under different policies and interventions. These factors combine to offer a unique opportunity to gain important insights into the emergence of self organisation and the evolution of complex adaptive systems in scenarios with extremely high relevance for a range of vital policy issues affecting energy security, carbon reduction and fuel poverty. Complexity science offers both a synergistic conceptual framework for the research questions raised and provides a set of tools and approaches particularly suited to their solution. This research will be based primarily on agent-based modelling, which enables simulation of the complexity arising from many non-linear, dynamic, history-dependent, multi-scale interactions with feedback effects that would defeat traditional equation-based and statistical modelling. Techniques not typical of previous modelling and simulation of this kind will be developed to reflect the special features of the problem domain, in particular the close coupling of socio-economic and technical systems, in which human and artificial intelligent agents are modelled and simulated together, and the need to find appropriate levels and forms of cognitive representation. The models will be based on evidence from the wealth of previous research into energy usage and supply issues and in particular from recent examples of small scale deployment of the technologies and mechanisms identified as key to the evolution of the smart grid as a complex adaptive system.

Peak electricity demand is becoming an increasingly significant problem for UK networks as it causes imbalances between demand and supply with negative impacts on system costs and the environment. The residential sector is responsible for about one third of overall electricity demand (DECC, 2013). During peak demand, electricity prices in wholesale markets could fluctuate from less than 0.04 Euros/kWh to as much as 0.35 Euros/kWh (Torriti, 2015). In the future the peak problem is expected to worsen due to the integration of intermittent renewables in the supply mix as well as high penetration of electric vehicles and electric heat pumps. Understanding what constitutes peaks and identifying areas of effective load shifting intervention becomes vital to the balancing of demand and supply of electricity. Whilst there is information about the aggregate level of consumption of electricity, little is known about residential peak demand and what levels of flexibility might be available. REDPeak will fill this gap. The overall aim of REDPeak is to analyse the variation in sequences of activities taking place at times of peak electricity demand with a view to identify clusters of users which might provide flexibility for peak shifting intervention. The project will analyse 10-minute resolution time use activity data from the UK Office for National Statistics Time Use Survey with a view to derive information about occupancy and synchronisation of activities. Markov chains will be used to model load profiles in combination with appliance-specific parameter data. Since Markov chains have proven effective at generating electricity load profiles except for peak times, REDPeak will develop Hybrid Monte Carlo modelling to account for demand moving in larger steps during peak periods. Sequence analysis will be used to mine activities at periods of peak electricity demand. REDPeak will cluster respondents according to sequences of activities and analyse to what extent appliance-specific control variables explain activities at specific times of the day. Three datasets will be used for direct validation between metered data and time use data. Findings on sequence analysis will feed into algorithms for automated demand management or Demand Side Response.