Flexible Distributed Networked Control and Data Analytics for Stochastic Renewable Energy Driven Futuristic Smart Grids - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2930

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Dr Saptarshi Das, University of Exeter
Professor Stuart Townley
, University of Exeter

University of Exeter Penryn Campus, Cornwall

Project Description
The futuristic vision of smart grid technology aims to integrate more renewable energy resources in traditional power networks in place of fossil fuels. This creates immense challenge for interoperability in wide-area monitoring, state estimation, control and optimisation of smart power grids, not only at the component level, but also from the aspects of communication, information, functional and business objectives. For increased flexibility and robustness of present day grid frequency/voltage control methods, there needs to be a dramatic change in the underlying mathematical principles to model and characterise its dynamical behaviour. This will allow moving towards a smarter power network where information and communication technologies will play a central role in the conventional active/reactive power flow control. This includes integration of generation/load forecast data in the control algorithms to frame an adaptive yet robust strategy, efficient management of grid/network connected devices or the “internet of things”, tackling cybersecurity and extreme event challenges, damping the stochastic fluctuations due to uncertain renewable energy sources as well as load profiles.

There were recent attempts to develop conceptual models and architectural standards e.g. smart grid architecture model (SGAM), National Institute of Standards and Technology (NIST) and IEEE 2030-2011 standard model. However, a mathematical analogue for such conceptual models in the form of large interconnected complex cyber-physical systems, with hybrid/switched dynamics, needs to be developed first to analyse the grid stability, efficient power flow management, optimization, control and assess other risk factors.

This PhD project will make use of existing mathematical concepts from distributed multiagent intelligent control, state estimation, stochastic, adaptive, robust fault tolerant, networked control and systems theories and find a right combination of these for greater grid resilience in fluctuating environments due to high penetration of renewable energies and non-stationary load profiles. The first supervisors earlier works [1, 2, 3, 4] in the area of smart grid controls have used interconnected system of systems dynamic models for studying grid frequency oscillation damping which will be extended in this project by including the potential effects of arbitrary number of plug-in electric vehicles, distributed generation and storage elements with different generation patterns and finally developing joint grid frequency and voltage control schemes. The PhD student will contribute to the mathematical systems and control theory for component level modelling of a hybrid power systems or smart grids, considering the communication, information flow, functional and business objectives in the control algorithms. The models will be validated through mathematical analysis for robust stability and achievable control performances as well as through cyber-physical system simulation case studies to better understand the broader statistical characteristics of such complex smart grids while making use of existing benchmark power system simulation models for coupled voltage/frequency control.

This PhD project will contribute to strengthen the collaboration between the Mathematics and Renewable Energy Departments at the Exeter Penryn Campus while combining mathematical models and experimental datasets to facilitate future funding opportunities in this area. The conflicts amongst these control objectives [5], role of selecting observable and choice of control algorithm will also be investigated in this PhD project.

1] I. Pan and S. Das, “Fractional order AGC for distributed energy resources using robust optimization", IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2175-2186, Sep. 2016.
[2] I. Pan and S. Das, “Kriging based surrogate modeling for fractional order control of microgrids”, IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 36-44, Jan. 2015.
[3] I. Pan and S. Das, “Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO”, ISA Transactions (Elsevier), vol. 62, pp. 19-29, May. 2016.

[4] I. Pan and S. Das, “Fractional order load-frequency control of interconnected power systems using chaotic multi-objective optimization”, Applied Soft Computing (Elsevier), vol. 29, pp. 328-344, Apr. 2015.
[5] S. Das and I. Pan, “On the mixed H2/H∞ loop shaping trade-offs in fractional order control of the AVR system”, IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 1982-1991, Nov. 2014

Entry Requirements
You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in any of the disciplines – Mathematics/Statistics, Computer Science, Electronic/ Electrical/ Mechanical/ Chemical/ Control/ Energy/ Power Engineering. Good analytical, computational skills and prior experience in Matlab/Simulink and Python/R programming is necessary. Some prior research experience in data analytics, mathematical/statistical computing is also desirable.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes. If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.


Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral Collegepgrenquiries@exeter.ac.uk

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email: pgrenquiries@exeter.ac.uk.
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.