Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

The installation of photovoltaics today is largely evaluated in terms of quantity and the success of any market stimulation evaluated on the basis of how well the targets are met. This may cause significant problems for the national infrastructure and may lead to significant unnecessary costs for grid stabilisation. However, these factors are sometimes assessed too simplistically. When considering PV in a national context, it is also largely seen as a homogenous swarm of devices, i.e. all of them reacting rather similarly. This does not consider different orientations (system elevation determines the seasonal maximum, system orientation determines the daily maximum) or regional differences in the environmental conditions such as weather fronts passing in a matter of days over the country rather than instantaneously or the North experiencing a different weather front than the South; nationwide smoothing might very well limit the need for power control. Thus the overarching question in this proposal is 'How can we maximise the benefits and limit the costs for UK plc while having a vibrant PV market?'. The work is split into four topical areas (work-packages), which answer the four key questions: - How much PV are we likely to get with different policies and where is it likely to be installed? This will consider different socio-economic drivers, cost curves of PV and work on installation scenarios giving links to likely social background of installations, locations (as in regions) and quantities. - How much energy will this generate when and where? Based on current installations a model for the performance prediction of systems based on their post-code will be developed and validated against existing FIT data and other available monitoring data. A spin-off of this activity will be the widespread investigation of current installations, that will inform any further discussions on subsidy streams, and the potential for detailed condition monitoring with sparse data will be investigated. The model will be connected with the socio-economic drivers to stochastically locate future installations (using GIS and post-code classifiers), and estimate the energy yield for each system and aggregate to generation regions. This means that essentially for every system (which is today in the range of 400000 systems under the FIT) installed an hourly generation needs to be calculated, which will require very complex speed optimisation in the calculations. - How will it impact the infrastructure? Grid simulations will be carried out bottom up as well as top down to see if there are issues either locally or nationally with the proposed installations. This will allow the recommendation of further measures to strengthen infrastructure and will allow a cost-benefit analysis of PV technology to be undertaken. - What feedback will there be? Most policies will have effects on the questions above and thus it is foreseen that a feedback methodology will be created, calculating the costs/benefits for UK plc as well as evaluating likely responses of the policy makers and grid operators. The collaboration between the different groups will be tightly managed, so that the project outcomes interface well. Tools will be generated and made available with non-proprietary data for public use.

There are currently no techniques available to monitor the microstructural condition of power station steel components in-service (i.e. at elevated temperatures). This problem will become more acute as coal-fuelled power stations are being developed to operate at higher pressures and temperatures to provide greater efficiency; supercritical power stations could produce output efficiencies of 45 to 50 %, compared to subcritical power stations with efficiencies of 30 to 35 %. Operation at 620 deg C is now possible, with further temperature increases to 700 deg C planned by the year 2014. Supercritical power stations also emit up to 25 % less carbon dioxide into the environment (a one percent increase in efficiency gives a two percent drop in emissions such as carbon dioxide, and nitrogen and sulphur oxides). Currently the condition of power station components is monitored during shut down periods, when insulating lagging layers are removed and replicas from the component surface are made. These replicas are examined to determine the microstructural state (degree of degradation, e.g. through carbide population changes) and whether creep cavitation has initiated. Components are removed from service and replaced when end of predicted service life is reached or significant cavitation is detected. However, as the component condition can only be checked during a scheduled shut down period, sections are often replaced prematurely. If failure of a component occurs the economic impact is severe (an unplanned shutdown is estimated to cost approximately 1.5M per day per power station) and there is potentially significant risk to life and the environment. The proposed project is to investigate the potential of a multi-frequency electromagnetic (EM) sensor system for monitoring microstructural changes in power generation steels (e.g. boiler plate and pipe) due to high temperature exposure and creep for both in-service monitoring and evaluation during maintenance periods. The work will involve development of a sensor system for long term use at elevated temperatures, and analysis and modelling of sensor signals relative to microstructural changes in the steels.

Tides occur due to the changing gravitational movement of the Moon and Sun relative to the Earth. As astronomical movements are highly predictable the tides should also be predictable. This is one of the key advantages of tidal stream energy (a rapidly developing source of renewable energy). The existing methods which are used to predict tidal movements perform very well for predicting water levels and slow moving currents, but often perform very badly on fast flowing tidal streams of the type in which we areinteresting in placing tidal turbines. This project will address this by applying methods from the machine learning community to the analysis of fast flowing tidal streams. This will produce an algorithm which will allow users from the oceanographic and tidal energy community to greatly improve the prediction of tidal currents at any point indefinitely far into the future. Thus a robustprediction of the performance of tidal stream turbines can be obtained. In the rapidly growing area of tidal stream energy, accurate knowledge of the tidal currents is vital for: robust predictions of energy yield; for the calculation of loads and the design of the turbine; and to give confidence to investors.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.