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Artificial intelligence for energy management optimisation in smart grid

College of Science

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Dr S Jiang , Dr M Watkins , Prof C Bingham , Dr S Maleki No more applications being accepted Funded PhD Project (Students Worldwide)
Lincoln United Kingdom Applied Mathematics Artificial Intelligence Data Science Energy Technologies Internet Of Things Machine Learning Mathematical Modelling Operational Research

About the Project

Have you considered to optimise your household energy usage to reduce your bills and save the planet? Are you looking for approaches to schedule your energy consumption in response to energy pricing schemes imposed by energy providers? Smart grid technology represents an unprecedented opportunity to move the energy industry into a new era of reliability, availability, sustainability, and efficiency that will contribute to our economic and environmental health. This project aims to develop artificial intelligence approaches to efficient energy management that achieve demand-supply balance for stable, cost-effective operation of the smart grid system, maximising different stakeholders’ interests. In particular, the project will investigate AI approaches, including machine learning and/or bio-inspired optimisation, to optimise energy management from energy supply to energy consumption. It will look for novel solutions to trade-off the profit of energy suppliers and the bills of energy users while maintaining supply-demand balance of energy and lowering carbon emission.

The overall aim of the project is to develop a AI-based decision-making tool to improve energy management of smart grid that balances different stakeholders’ interests including energy suppliers’ profit, end users’ energy consumption and bill, governments’ net zero carbon goal, while ensuring demand-supply balance. In order to reach this aim, the project has the following key objectives:

  1. Investigate computational models of electricity markets of smart grid systems at different scales, including smart home, microgrid, or distributed resource systems.
  2. Develop machine learning algorithms for demand response, including dynamic pricing, from energy suppliers’ perspective.
  3. Develop machine learning algorithms to support smart scheduling of power consumption at peak time from energy users’ perspective.
  4. Multi-criteria optimisation of energy management from both energy suppliers’ and end users’ perspectives.
  5. Test and evaluate the proposed approaches in real-scenario experimental platforms.

You will be working with the Machine Learning group, School of Engineering, and School of Mathematics and Physics at the University of Lincoln. This is an exciting opportunity for developing a career in AI for smart energy and deliver world-leading research that impacts our economy and society.

Skills the candidate will learn:

Discipline specific knowledge, including machine learning, data analysis, modelling and optimisation, and smart energy technology. Ability to gather and interpret information, and ability to analyse data. Problem solving skills, project management skills, and oral and written communication skills. Ability to work independently and as a team member. The candidate will also receive a broad set of trainings on scientific research and transferable skills. 

Ideal candidates:

Interested applicants should carry, at a minimum, a 2.1 degree in AI, computer science, mathematics, engineering or any other relevant discipline and are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest. You must have the ability to engage in scientific research and to work collaboratively as part of a team. You must have a research interest and experience in at least one of the following areas: data analysis, artificial intelligence, machine learning, mathematical modelling, game theory, energy management, operational research and optimisation approaches (evolutionary computation, and multi-criteria decision making, etc). You are expected to have good communication skills in written and spoken English in order to work with both computer scientists and engineers, to present research findings in workshops/conferences and to publish papers in high-quality journals.

Who is eligible for funding?

Please make sure to check the eligibility criteria before you apply. Normally, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship.  UK students will be eligible for a full studentship, covering the costs of Home fees, and a stipend to support living costs for 3.5 years. 

Although most DTP students must be UK residents, we also have an opportunity for an international (EU and non-EU) student. The international studentship award will be subject to eligibility, and also the availability of complementary funding (to provide the differential to the international fee rate). You should get in touch with the lead supervisor before applying this award.  


To apply, please complete the application form and send it to [Email Address Removed]

Funding Notes

The University of Lincoln has received funding from the Engineering and Physical Sciences research Council to establish a Doctoral Training Partnership (DTP), which will provide skills training to foster the next generation of world-class research leadership in areas of strategic importance to both EPSRC and the University of Lincoln.


S. N. Fallah, R. C. Deo, M. Shojafar, M.Conti, S. Shamshirband, Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions, Energies 11 (2018) 596.
R. Lu, S. H. Hong, X. Zhang, A dynamic pricing demand response algorithm for smart grid: reinforcement learning approach, Applied Energy 220 (2018) 220 – 230
M. R. Z. Sabegh, C. Bingham, Model predictive control with binary quadratic programming for the scheduled operation of domestic refrigerators, Energies 12 (2019) 4649.
S. Jiang, S. Yang, A strength Pareto evolutionary algorithm based on reference direction for multiobective and many-objective optimization, IEEE Transactions on Evolutionary Computation 21 (2017) 329—346.
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