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Control of Smart Grid Systems to Address the Energy Trilemma

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Ensuring a secure and affordable supply of electrical energy, whilst also reducing emissions, is a key challenge faced by governments all over the world. An upward trend in energy demand combined with the potential for the mass adoption of new technologies such as electrical vehicles, heat pumps, and micro-generation will likely push our existing infrastructure beyond its limits. An upgraded, smarter grid is therefore considered essential and governments around the world are investing heavily in the research and development of new technology in this area. A key part of this future smart grid is electrical energy storage. However, the smart control of distribution generation, demand response services and electric vehicles is also essential.

The successful applicant will develop innovative modelling and control approaches to support the use of distributed energy storage and active demand management in the grid. Although the research work will have a largely technical focus, effective strategies for energy storage and other smart systems must take into account financial and policy-driven constraints and consideration of these aspects will be an important part of the research work. Specifically, this PhD research work will include the modelling, control and simulation of energy systems using software such as Matlab and OpenDSS. Of particular interest is the collaboration between multiple energy storage systems to work together to achieve both technical, social and economic goals.

Funding Notes

First-class or 2.1 (Hons) degree, or Masters degree (or equivalent) in an appropriate subject. Prior experience with energy systems is desirable.

How good is research at University of Reading in Architecture, Built Environment and Planning?

FTE Category A staff submitted: 34.90

Research output data provided by the Research Excellence Framework (REF)

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