Data-Driven robust and optimal smart grid control

Reference number: SCEBE/21S/016/IK

Aim and Scope

Smart grids refer to electricity networks that enable a two-way flow of electricity and data with digital communication links to help detect, react and pro-act to changes in demand and other issues. The transition from traditional power grids to smart grids is driven by several factors including the deregulation of energy markets, rise of micro-electricity generation and microgrids, renewable energy mandates and rising purposes for which electricity is required e.g. charging points for electric vehicles. Smart grids are recognized as one of the key technologies to implement future low carbon and sustainable energy systems which is key in the drive to create sustainable cities and communities.

With recent advances in battery technology, widespread installation of battery storage in residential buildings to complement rooftop photovoltaic cells is expected to occur over the next decade. This deployment presents several challenges to existing electricity networks such as the possibility of large demand-supply swings in power if battery charging and discharging is poorly scheduled. Thus, in the perspective of a grid operator, the main aim is to minimize the fluctuations in power demand which is generally achieve via peak shaving. 

Despite the intense research efforts that have been put into optimal scheduling of batteries storage at the residential level in order to minimize energy supply and demand fluctuations, several challenges in terms of smart grids control still abound in this area. With respect to existing challenges, this PhD research aims to address the following: (i) investigation of suitable prediction method for net energy consumption since most researchers assume that this is available to them. (ii) design of data-driven optimal control algorithm that offers higher reliability, minimum communication link requirement in the network and peak shaving. An important consideration should be the scalability of the method since energy sources enter or leave the network over time (iii) investigation of methods to improve the controller’s robustness to measurement noise since the method do not require a model of the dynamic system (iv) interpreting the grid operator’s goal - minimising variations in the power demand - as a minimisation of electricity cost so as to investigate the benefit of the peak shaving to individual residential energy systems.

  - Candidates are requested to submit a more detailed proposal (of a maximum of 2000 words) on the project area   as part of the application. 

 - highlight your membership of a particular research community or research group with url.

Preferred experience:

(i) Bachelor’s (UK 2:1 or better) or Master’s degree (Merit or Distinction) with major/specialization in Electrical and Electronics, Mechatronics, Systems and Controls or any other relevant engineering/science discipline.

(ii) Very good programming skills.

(iii) Excellent background in control systems and engineering is desired.

(iv) Previous publication is preferred but not required.

(v) Previous experience with microgrids operation is desirable.

-Indicative range of Bench Fees tied to the project 

How to Apply

This project is available as a 3 years full-time or 6 years part-time PhD study programme. 

Candidates are encouraged to contact the research supervisors for the project before applying. 

Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using our Application Process page, choosing Engineering and their preferred intake date.  

Please send any other enquires regarding your application to: researchapplications@gcu.ac.uk