The programme of research that constitutes AMIDiNe will devise analytics that link point measurement to whole system to address the increasingly problematic management of electrical load on distribution networks as the UK transitions to a low carbon energy system. Traditionally, distribution networks had no observability and power flowed from large generation plant to be consumed by customers in this 'last mile'. Now, and even more so in future, those customers are generators themselves and the large generators that once supplied them have been supplanted by intermittent renewables. This scenario has left the GB energy system in position where it is servicing smaller demands at a regional or national level but faces abrupt changes in the face of weather and group changes in load behaviour, therefore it needs to be more informed on the behaviour of distribution networks. The UK government's initiative to roll out Smart Meters across the UK by 2020 has the potential to illuminate the true nature of electricity demand at the distribution and below levels which could be used to inform network operation and planning. Increasing availability of Smart Meter data through the Data Communications Company has the potential to address this but only when placed within the context of analytical and physical models of the wider power system - unlike many recent 'Big Data' applications of machine learning, power systems applications encounter lower coverage of exemplars, feature well understood system relations but poorly understood behaviour in the face of uncertainty in established power system models. AMIDiNe sets out its analytics objectives in 3 interrelated areas, those of understanding how to incorporate analytics into existing network modelling strategies, how go from individual to group demand behavioural anticipation and the inverse problem: how to understand the constituent elements of demand aggregated to a common measurement point. Current research broadly involving Smart Metering focuses on speculative developments of future energy delivery networks and energy management strategies. Whether the objective is to provide customer analytics or automate domestic load control, the primary issue lies with understanding then acting on these data streams. Challenges that are presented by customer meter advance data include forecasting and prediction of consumption, classification or segmentation by customer behaviour group, disambiguating deferrable from non-deferrable loads and identifying changes in end use behaviour. Moving from a distribution network with enhanced visibility to augmenting an already 'smart' transmission system will need understanding of how lower resolution and possibly incomplete representations of the distribution network(s) can inform more efficient operation and planning for the transmission network in terms of control and generation capacity within the context of their existing models. Improving various distribution network functions such as distribution system state estimation, condition monitoring and service restoration is envisaged to utilise analytics to extrapolate from the current frequency of data, building on successful machine learning techniques already used in other domains. Strategic investment decisions for network infrastructure components can be made on the back of this improved information availability. These decisions could be deferred or brought forward in accordance with perceived threats to resilience posed by overloaded legacy plant in rural communities or in highly urbanised environments; similarly, operational challenges presented by renewable penetrations could be re-assessed according to their actual behaviour and its relation to network voltage and emergent protection configuration constraints.

Rapid transformation of Power Networks is only possible if industry can recruit highly trained individuals with the skills to engage in R&D that will drive innovation. The EPSRC CDT in Power Networks at the University of Manchester will educate and train high quality PhD students with the technical, scientific, managerial and personal skills needed by the Power Networks sector. Prof. Peter Crossley, whose experience includes leadership of the Joule Centre, will lead the CDT. This CDT is multidisciplinary with PhD students located in the Faculties of Engineering & Physical Science and Humanities. All students will first register on a "Power Networks" Postgraduate Diploma; when successfully completed, students will transfer to a PhD degree and their research will be undertaken in one or more Schools within these Faculties. During their PhD studies, students will also be required to expand their knowledge in topics related to the management, design and operation of power networks. Using the support of our industrial partners, students will engage in policy debates, deliver research presentations, undertake outreach activities and further their career development via internships. The CDT will deliver world class research and training, focused on the UK's need to transform conventional power networks into flexible smart grids that reliably, efficiently and economically transport low-carbon electrical energy from generators to consumers. Specific areas of research are: - Electrical power network design, operation and management The rapidly increasing need to integrate renewable energy into power networks poses numerous challenges, particularly cyclical and stochastic intermittency. This is further complicated by future proof buildings, decarbonisation of heat and transport, and other innovations that will change electrical demand. Existing Power Networks include a mixture of old and new plant, some of which is beyond design life. This may not be a problem at historical loading levels, but future visions involve increased power densities and changes in primary and secondary substation topology. Research on asset management and life-time extension is required to provide economical and reliable solutions to these issues. Integration of DC interties and Power Electronics within networks has been identified as key enabling technologies. Therefore projects on HVDC, power electronics, intermittent generation, energy storage, dynamic demand, intelligent protection and control and the use of data provided by smart meters and local/wide-area monitoring systems are required. - Power Network Operation, Planning and Governance Transmission and Distribution Operating Companies need projects on planning processes that co-ordinates land-use with other infrastructures. Projects include planning uncertainty and complexity, integration of modelling with geographical information systems, stakeholder behaviour, decision modelling and the impact of resource allocation and operating lifecycles. Projects on smart operational control strategies can simplify network planning and reduce the cost of implementing: demand response; combined heat and power; and district heating. - Changes to the pattern of energy demands and their effect on the power network Climate change will have an adverse effect on network reliability and projects are required to help network companies economically manage the electrification of heating, cooling and transport. Projects are also required on the interaction between energy vectors and network infrastructure with multiple uncertainties. - Cross cutting technologies Research in Mathematics and Management on stochastic dynamic optimisation techniques can be used to underpin projects on heat and electrical energy storage under uncertain price and supply conditions. Projects using a cognitive lens to uncover how large infrastructure projects can be delivered through meta-organisations are also required.

National infrastructure (NI) systems (energy, transport, water, waste and ICT) in the UK and in advanced economies globally face serious challenges. The 2009 Council for Science and Technology (CST) report on NI in the UK identified significant vulnerabilities, capacity limitations and a number of NI components nearing the end of their useful life. It also highlighted serious fragmentation in the arrangements for infrastructure provision in the UK. There is an urgent need to reduce carbon emissions from infrastructure, to respond to future demographic, social and lifestyle changes and to build resilience to intensifying impacts of climate change. If this process of transforming NI is to take place efficiently, whilst also minimising the associated risks, it will need to be underpinned by a long-term, cross-sectoral approach to understanding NI performance under a range of possible futures. The 'systems of systems' analysis that must form the basis for such a strategic approach does not yet exist - this inter-disciplinary research programme will provide it.The aim of the UK Infrastructure Transitions Research Consortium is to develop and demonstrate a new generation of system simulation models and tools to inform analysis, planning and design of NI. The research will deal with energy, transport, water, waste and ICT systems at a national scale, developing new methods for analysing their performance, risks and interdependencies. It will provide a virtual environment in which we will test strategies for long term investment in NI and understand how alternative strategies perform with respect to policy constraints such as reliability and security of supply, cost, carbon emissions, and adaptability to demographic and climate change.The research programme is structured around four major challenges:1. How can infrastructure capacity and demand be balanced in an uncertain future? We will develop methods for modelling capacity, demand and interdependence in NI systems in a compatible way under a wide range of technological, socio-economic and climate futures. We will thereby provide the tools needed to identify robust strategies for sustainably balancing capacity and demand.2. What are the risks of infrastructure failure and how can we adapt NI to make it more resilient?We will analyse the risks of interdependent infrastructure failure by establishing network models of NI and analysing the consequences of failure for people and the economy. Information on key vulnerabilities and risks will be used to identify ways of adapting infrastructure systems to reduce risks in future.3. How do infrastructure systems evolve and interact with society and the economy? Starting with idealised simulations and working up to the national scale, we will develop new models of how infrastructure, society and the economy evolve in the long term. We will use the simulation models to demonstrate alternative long term futures for infrastructure provision and how they might be reached.4. What should the UK's strategy be for integrated provision of NI in the long term? Working with a remarkable group of project partners in government and industry, we will use our new methods to develop and test alternative strategies for Britain's NI, building an evidence-based case for a transition to sustainability. We will analyse the governance arrangements necessary to ensure that this transition is realisable in practice.A Programme Grant provides the opportunity to work flexibly with key partners in government and industry to address research challenges of national importance in a sustained way over five years. Our ambition is that through development of a new generation of tools, in concert with our government and industry partners, we will enable a revolution in the strategic analysis of NI provision in the UK, whilst at the same time becoming an international landmark programme recognised for novelty, research excellence and impact.

The UK has binding targets to reduce carbon emission by 80% from 1990 levels by 2050. To achieve this, our energy systems are changing rapidly with a growing portion of electricity coming from renewable energy sources, and electrification of heating and transport. The result of this transition is an electricity system that is increasingly dependent on the weather: as well as having to manage variable amounts of power available from wind and solar resources, demand for electricity is becoming increasingly weather-dependent. Electricity network operators, generators and suppliers must rely on weather forecasts to plan their operations and ensure that supply meets demand, and they must do so in the knowledge that weather forecasts are imperfect, and therefore that future generation and demand uncertain. This research will develop new energy forecasting methodologies to address the needs of the energy industry in this new paradigm. Energy forecasts are required for all weather-dependent elements of the electricity system, and their uncertainty must be quantified. Critically, there is a high degree of interdependence between uncertainty across the electricity system which must be captured to correctly characterise overall uncertainty. Furthermore, the precise nature of that interdependence will vary depending on specific weather conditions. The methodologies developed here will provide a framework for system-wide energy forecasting considering large-scale meteorological conditions, and provide decision-makers with valuable information about forecast uncertainty. In addition, specific decision-support tools will be derived to condense voluminous and complex probabilistic forecast information into actionable analytical support. Tools to aid operational decision for power system operators, such as deciding how much back-up power to have available and how to manage constrains on the gird will be developed. Similarly, tools for generators and suppliers will be produced to enable more efficient participation in electricity markets. The overall objective of this work is to reduce the cost, and increase the reliability, of power systems with a high penetration of renewables.

Even within a single population, many organisms show marked variation between individuals in the timing of events such as the ages at which they metamorphose, reach sexual maturity and/or first reproduce. This variation can be linked to genetic differences among individuals and/or can be a consequence of differences in early environmental conditions that shape the rate of juvenile growth and development. While different rates of juvenile development can give rise to adults that appear, superficially at least, morphologically indistinguishable, the associated variation in the age at which they reach particular developmental stages can influence both the number and form of offspring they produce. Thus individuals with a relatively fast juvenile development may produce a different kind of offspring from those that developed more slowly. Such links between parental developmental rate and type of offspring they produce may or may not be adaptive. They could enable parents to adjust or 'program' their offspring to suit the conditions that the offspring are most likely to encounter during growth, as informed by the parents' own early life experience. Alternatively, parents that grew and developed slowly (because they were either in a low quality environment or poorly adapted to it) may be low quality adults, that then produce low quality offspring. However, as yet there has been little rigorous investigation of how offspring produced by parents with different early life trajectories actually perform in different environmental circumstances. Whether or not the offspring of particular parents are 'tailored' to particular environmental conditions is, in addition to being of great interest to evolutionary biologists, also of considerable importance in an applied context. Managers responsible for animal reintroduction and supplementation programmes need to know whether offspring from particular kinds of parents would perform better in some environments than in others. In wild fish management programmes, for example, variation in the previous life history of the parental broodstock is not currently taken into account when deciding where to release eggs or fry. This project, which is an application under NERC's Knowledge Transfer Partnership Scheme, involves a collaboration between fish biologists and managers to address this issue. It will be one of the first to test experimentally in the natural environment whether parental development rate in salmon influences the viability of their offspring under different environmental conditions - and, importantly, whether taking this into account can directly improve the success of re-stocking programmes. By using a series of experiments in which the fate of eggs from different kinds of female are evaluated in natural rivers, it will test whether female salmon that took longer when juveniles to reach the stage when they migrate to sea produce offspring that are best suited to less productive parts of a river catchment, such as areas of lower nutrient supply or higher altitude. It will then test whether use of this information can improve the methods that fishery managers use to supplement salmon populations by stocking out of eggs.