Predictive Control of an Integrated Energy Storage Facility

Level PhD
Supervisory Team Dr Matteo Rubagotti and Prof Matthew Turner, University of Leicester
Expected Duration 3 years full time
Expected Start Date 25th September 2017
Application Deadline Monday 10th April 2017 12pm (midday)
Interviews Expected 2nd-5th May 2017


    Designing and testing of advanced control methods for the real-time management of an Integrated Energy Storage Test and Verification (IESTV) facility within the ERA framework

    Demonstration of improved operational behaviour compared to current approaches, in particular thanks to the ability of Model Predictive Control to explicitly take operational constraints into account

    Opportunity to test advanced control algorithms on a state-of-the-art experimental facility

Overview and Methodology

The research activity of this PhD proposal is aimed at designing and testing advanced control methods for the real-time management of the above-mentioned IESTV facility, that is being set up as a part of the University of Leicester participation to ERA, with a total equipment budget of £395k. The facility will include state-of-the art energy storage devices, and give the opportunity to design and test advanced algorithms that can have an impact on real-life implementations. The combined use of multiple storage systems [1] (more precisely: electrochemical batteries, supercapacitors, compressed air energy storage, and pumped hydro storage) can simultaneously exploit all of their advantages, but requires the ability to manage their complex interaction in real time. Storage systems have been studied in recent years, mainly in the fields of automotive engineering (electric and hybrid-electric vehicles) and microgrids [2-3]. However, the number of experimental implementations of innovative management strategies is still limited, due to the lack of availability of advanced testing facilities. The proposed PhD activity will be aimed at designing a strategy that takes into account the practical characteristics of the IESTV.

The PhD activity will consist of three main steps, each corresponding to one year of activity:

  • Year 1: study of the theoretical aspects of MPC and of modelling of storage elements. Derivation of a mathematical model of the IESTV suitable for control purposes. The derivation of this model will be based on running experiments (e.g., measuring the variation of the battery state of charge in different charging/discharging scenarios), collecting data from them, and using data analysis techniques to obtain suitable dynamical models. This is a crucial step, which will benefit from the experience of Dr. Lefley in modelling and control of energy storage devices.
  • Year 2: definition of a computer simulation of the IESTV, and experimental validation of the derived model. Testing of a simple MPC control law in simulation. This phase will require the use of numerical optimisation algorithms for defining, in real time, the best way to manage the storage facility.
  • Year 3: Experimental implementation of the MPC control law on the IESTV, and refinements. This phase will require controller tuning, plus possibly a refinement of the models identified during Year 1, if necessary.

IESTV schematic
Simplified schematic of the energy flow in the IESTV, to be managed by the MPC controller

The energy flow to/from the IESTV will depend on the power demand, and the power produced by photovoltaic renewable sources. The designed management system will carry out the tasks of (a) deciding in real time how much power to be drawn from the grid, or possibly “selling” electricity to the grid itself in case of excess storage, and (b) how to split the storage between the elements taking operative constraints into account (e.g., limiting the charge/discharge cycles on batteries, or the depth of charge/discharge of the different elements). The management problem will be solved by continuously predicting the consumer demand and the future power flow from renewable energy sources (via weather forecast), and re-planning the optimal strategy in order to minimise the total energy consumption, or the overall expense for buying electricity from the grid.

This approach is known as Model Predictive Control (MPC), a control technique that has been applied to many practical problems in industry, and is now, for instance, a standard in petrochemical plants. MPC is the main research interest of Dr Rubagotti, who has already experimentally applied this technique for managing a different storage facility within his previous assistant professor position, as PI for grant “Integration, Automation and Control of Renewable Power Sources” (274k USD, 3 years project). In this project he coordinated a group of 5 researchers and 3 academics: this experience (which led to the publication of [4]) will be a valuable asset for the considered PhD activity.

Further Reading

  1. Basak, P., Chowdhury, S., and Chowdhury, S.P. “A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid.” Ren. Sustain. Energy Rev., 16(8), pp.5545-5556, 2012.
  2. Liegmann, E., and Majumder, R. "An efficient method of multiple storage control in microgrids." IEEE Trans. on Power Systems 30(6) (2015), pp.3437-3444, 2015.
  3. Morstyn, T., Hredzak, B. and Agelidis, V.G. “Control Strategies for Microgrids with Distributed Energy Storage Systems: An Overview.” IEEE Trans. on Smart Grid, in press.
  4. Khakimova, A., Kusatayeva, A., Shamshimova, A., Sharipova, D., Bemporad, A., Familiant, Y., Shintemirov, A., Ten, V. and Rubagotti, M. “Optimal energy management of a small-size building via hybrid model predictive control.” Energy and Buildings, 140, pp.1-8, 2017.

About the Supervisors

Dr. Rubagotti is an expert in the fields of model predictive control and sliding mode control. His PhD and post-doc studies in Italy were done under the direction of two of the world-leaders in these fields. He has published frequently in the premier journals in the control/mechatronics field.

Prof. Turner is an international authority on constrained control. He is an Associate Editor for the International Journal of Control and serves on the UK Automatic Control Executive Committee. One of his papers, published in the flagship journal IEEE Transactions on Automatic Control, with over 400 citations, is a standard reference in the field of anti-windup control.

Entry Requirements

  • Applicants for a PhD should normally have a good Masters level degree in a relevant field or a very good first degree (equivalent to a First Class or Upper Second Class Bachelor of Science Degree)
  • Standard English language requirements
  • Available for full-time registration only
  • Applicants must be able to start on 25th September 2017


For UK Students

Fully funded College of Science and Engineering studentship available, 3 years duration.

For EU Students

Fully funded College of Science and Engineering studentship available, 3 years duration.

For International (Non-EU) students

Stipend and Home/EU level fee waiver available, 3 years duration. International students will need to provide additional funds for remainder of tuition fees.

About ERA (Energy Research Accelerator)

The Energy Research Accelerator (ERA) is a cross-disciplinary energy innovation hub which brings together capital assets, data and intellectual leadership to foster collaboration between academia and business to accelerate the development of solutions to the global energy challenge. It will provide new buildings and cutting-edge demonstrators, develop highly skilled people and jobs, as well as new products and services to ultimately transform the UK’s energy sector. Building on existing programmes and academic expertise across the partnership, universities within ERA have committed over £2m for doctoral students as a critical part of the ERA skills agenda.

Delivered through Innovate UK, the government has committed an initial capital investment of £60m, and ERA has secured private sector co-investment of £120m. ERA’s initial priorities of Geo-Energy Systems, Integrated Energy Systems and Thermal Energy will help deliver the new technologies and behaviours that will open the avenues for its future development and demonstrate the transformative effect ERA can have across the energy spectrum.

Through the Midlands Energy Consortium (MEC), Midlands’ universities have already worked closely to deliver essential research and postgraduate skills – clustering energy research and development to deliver technologies capable of enabling the UK’s transition to a low-carbon economy. ERA is the next step along that journey to become a major hub for energy talent.

ERA is a key programme within Midlands Innovation – a consortium of research intensive universities which has the overall aim of harnessing the Midlands’ combined research excellence and industry expertise to play a critical role in tackling some of the biggest challenges facing the UK.

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College of Science and Engineering
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