Start: October 2016
The Smart Grid represents a vision for digital upgrade of electrical power systems. The deployment of Smart Grid concepts into existing power systems is leading to the optimisation of grid operations, enhancing grid security, and opening new markets for the utilization of sustainable energy. This project will develop optimisation tools for the Smart Grid.
There is a significant drive to increase the amount of electricity generated from renewable sources. This drive is mostly influenced by the UK’s environmental commitments to reduce greenhouse gas emissions. The aforementioned drive is leading to an increase in demand for renewable generation connections at the distribution network level, and driving the distribution network operators to include more active approaches in in the operation of such networks. In the UK, this is reflected in the development of Active Network Management approaches, which use communications equipment and centralised control systems to limit generation for short periods, when output exceeds the operating limits of the network.
Technologies already exist which are capable of providing inter-temporal flexibility, of which two particular examples are energy storage and demand response. The term inter-temporal refers to devices whose operation needs to be planned across time, with decisions on how to operate at one point in time affecting the ability to operate at other points in time. Energy storage devices allow to convert electrical energy to another form of energy which is stored, before being changed back to electricity when required. Electricity consumers participating in demand response schemes adjust their usage to help manage a constraint on the network.
The project will develop tools for the optimisation of next generation Active Network Management schemes including inter-temporal technologies. The work will focus on the development of Dynamic Optimal Power Flow algorithms for management of energy storage and flexible demand, with consideration of dynamic transmission line ratings and of uncertainty in wind and demand forecasts.
The project will require the development and implementation of network, demand and generation models, as well as programming dynamic optimal power flow algorithms in Matlab, along with the use of various computational optimisation tools for the generation of results.
There is a critical need to develop a new generation of power engineers to design, analyse, build and operate a Smart Grid that incorporates diverse renewable energy sources, advances in control systems, communications, signal processing, and cybersecurity.
Applicants should have (or be expected to have by October 2016) at least an upper second class UK honours degree (or its overseas equivalent) in electrical/electronic engineering, renewable energy, automatic control, mathematics, physics, or a closely related area, as well as a strong interest in electrical power systems, renewable energy, and smart grids. Any previous experience in these areas would be an advantage. Prior knowledge of Matlab programming and numerical optimisation tools is highly desirable.
Dr Ahmed Zobaa from the Brunel Institute of Power Systems will act as an external adviser in this project.
The University of Portsmouth is ranked within the top two per cent of universities in the world in the most recent Higher Education World University rankings. We have a thriving research community of more than 800 research students across five faculties. The University of Portsmouth Graduate School provides excellent support and guidance for the university-wide community of research students. Our comprehensive research degree training and development programme offers over 150 workshops on a wide range of topics to help you develop your research, professional and transferable skills. The University is one of the top ten modern universities in the UK according to The Times and Sunday Times Good University Guide. Our students have access to state-of-the-art experimental and computational facilities, as well as expert technical support staff. These facilities include a well-equipped Power Systems Laboratory located in the School of Engineering.
Research profile of main supervisor: https://researchportal.port.ac.uk/portal/en/persons/victor-becerra%28f7631daf-4d4d-4da8-a7c1-33256c2e802c%29.html
Research profile of external collaborator: http://www.brunel.ac.uk/people/ahmed-zobaa
Name and email contact details of the main supervisor:
Professor Victor Becerra
email: [email protected]
telephone: +44 (0) 23 9284 2393,