The techniques of Control Theory, Artificial Intelligence and Statistical Data Analysis have been applied to engineering systems with great success, and the resulting is better energy efficiency using storage. There are multiple research PhD projects in this field and in particular forecasting energy demand and mathematical modelling for control of energy storage. In this context there are three projects proposed below and candidates can apply to one of the research interest:
Price forecasting for control of energy storage
The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. A probabilistic price forecasting model will be required for these local energy markets to better describe future price uncertainty. The PhD Project considers the application of probabilistic electricity price forecasting models to the control of energy storage and compares the models for a better understanding of their capabilities and limits in driving demand.
The PhD Study will compare point forecasts and probabilistic forecasts, and Machine learning forecast on the control efficiency of the energy storage
2 Modelling of Smart Grid Systems
Countries all around the world are seeking to make systems more energy efficient and create a Smart Grid that integrates renewable energy sources. The worldwide action towards low carbon emissions and renewable energy sources has led to an urgent need for efficient electric vehicles and wind, wave and solar power generation systems.
This PhD project will develop advanced modelling techniques for Smart Grid Systems – which are key to integrating renewable energy sources into the grid because of their time-varying nature. This will include new mathematical models and investigation of demand-side management techniques and power electronic control and their impact on network stability.
3 Analysing the fractal properties of energy demand series and practical implications for storage control algorithms.
The primary aim will be to transfer learning and knowledge from fractal geometry and their application within other domains (in particular finance and other sciences) to the demand data of energy demand, especially at the low voltage level. At this level demand data has largely been unexplored but the tools from fractal geometry seem well suited to better understanding and optimising the control of such systems as they are very rough and self-similar.
The aims of the project will be to try and understand
· How the “roughness” (fractal dimension) of the system changes with application, level of aggregation and temporal effects such as time of day and seasonality.
· How does the fractal dimension relate to important features of the time series and demand behaviour? What are the primary drivers behind this?
· How can we recreate such time series with limited monitoring data to better estimate the peaks and demand and other important features? Can fractal interpolation
· How does information regarding the fractal dimension help us understand how controllable the demands are with storage devices? When limited information is available, can fractal interpolation help us to make optimal decisions due to their ability to estimate realistic time series generation?
· How does these new control methodologies compare to more traditional implementations of control?
The research will include exploring tools and techniques for calculating fractal dimension, fractal interpolation and fractal controls. Further it will require other time series analysis tools, statistics and optimization.
The successful applicant should have been awarded, or expect to achieve, a Bachelors or a Masters degree in a relevant subject with a 60% or higher weighted average, and/or a First or Upper Second Class Honours degree (or an equivalent qualification from an overseas institution) in Electrical Engineering; Computer Science, Control Engineering, Mathematics. Preferred skill requirements include knowledge/experience of Data Science, Control Systems; Mathematical Modelling, Artificial Intelligence and Machine Learning.