Proposed Project Title: Load Forecasting with Increased Embedded Renewable Generation

Principal Supervisor: Prof Seán McLoone, Dr. Xueqin (Amy) Liu
Industrial Collaborator: System Operator Northern Ireland, Northern Ireland Electricity

Project Description:

In Northern Ireland, due to the relatively small size of the power system coupled with the ambitious targets for incorporating renewable generation (40% energy generation from renewable sources by 2020), SONI/NIE in conjunction with Eirgrid is at the forefront of identifying, and solving, many technical challenges.

Power System Operators must supply enough electricity generation to meet demand at all times. Consequently, load forecasts are used to guide planning and scheduling of the optimum generation on a day ahead basis. Predicting system load as accurately as possible is crucial to ensure system stability is not at risk. This is becoming increasingly challenging due to the distortion in load forecasting caused by small-scale wind & photovoltaic generation, the contribution of which is intermittent and often invisible to the Power System Operators.

Queen’s University Belfast, in conjunction with SONI/NIE, is seeking a highly motivated individual for a collaborative PhD research project on ‘Electric Load Forecasting with Increased Embedded Renewable Generation’.

The successful applicant will spend at least 3 months in SONI, located in Belfast. This is a unique opportunity for a student to gain access to the highly technical & restricted environment of the Control Centre of a Transmission System Operator. The student will have the opportunity to train with SONI Engineers in their control room and gain hands-on experience of the various methodologies employed to forecast system demand. This will provide valuable context for the student’s research project, and will enable them to gain privileged access to sensitive data and specialist domain knowledge.

Objectives:

• Examine the impact of the growth in small-scale distributed generation on load forecasting accuracy;
• Investigate models that predict the invisible distributed generation component;
• Develop new load forecasting tools that take account of this generation.  
• Develop advanced data analytical methods for large and complex data

GENERAL INFORMATION

The PhD studentship, jointly funded by EPSRC, SONI & NIE, commences on 1 October 2015.

Eligibility for both enhanced EPSRC stipend (approximately £18,000 per year tax free for up to 3.5 years) and fees depends on the applicants being either an ordinary UK resident or an EU resident who has lived permanently in the UK for the 3 years immediately preceding the start of the studentship. Non UK residents who hold EU residency may also apply, but if successful may be eligible for university fees and a maintenance grant of approximately £6,000 per year for 3 years. Overseas students are NOT eligible for funding.

Further information on eligibility about EPSRC CASE Award is available at:

https://www.epsrc.ac.uk/skills/students/help/eligibility/

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

Further information available at:  http://www.qub.ac.uk/schools/eeecs/StudyattheSchool/PhDProgrammes

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required. Candidates with strong mathematical, programming, and communication skills are highly desirable.

Contact details

Supervisor Name:
Prof. Seán McLoone, Dr Amy Liu
Tel: +44 (0)28 9097 4125

QUB Address:

Queens University of Belfast
School of EEECS
Ashby Building,
Stranmillis Road,
Belfast
BT9 5AH
Email: s.mcloone<στο>qub.ac.uk; x.liu<στο>qub.ac.uk

Deadline for submission of applications is: 26 May, 2015

For further information on Research Area click on link below: http://www.qub.ac.uk/research-centres/EPIC/