Imperial College London Featured PhD Programmes
University of Sheffield Featured PhD Programmes
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Design of Scalable Control Methods for Microgrids

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Microrgrids are small electrical networks heterogeneously composed of distributed generations (DGs), loads, and energy storage systems. One of the main challenging problems associated with the control of microgrids is plug-and-play (PnP) functionality of DGs as well as microgrid topology change. DGs frequently join and leave the power generation system due to availability and intermittency of renewable energies, such as solar power and wind, an increase in energy demand, faults, maintenance, etc.

Scalability of the control design is central in microgrid systems, where DGs enter and/or leave over time. Classical control theory does not scale well for large-scale systems such as microgrids. This PhD project focuses on the development of scalable control approaches for large-scale and interconnected systems with applications to AC/DC microgrids. The proposed control solutions are evaluated on the experimental setups of microgrids available in the Control and Power Laboratory.

Funding Notes

Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive. it will be possible to make Scholarship applications from the Autumn with a strict deadline in late January 2020. Specific information is available at:
View Website


We require applicants to have a minimum undergraduate Honours degree (UK 2:1 or better) or MSc (Merit or Distinction) in a relevant Science or Engineering subject from a reputable institution.
Full entry requirements can be found at the following link:

How good is research at University of Sheffield in General Engineering?

FTE Category A staff submitted: 21.80

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

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