[agents] Job Opening: Research Associate (Data Scientist) in Power Distribution Network Optimisation

[Valentin Robu]

Job Opening: Research Associate (Data Scientist) in Power Distribution
Network Optimisation

Employer: Heriot-Watt University - School of Engineering and Physical
Sciences
Location: Based at Scottish Power Energy Networks, Blantyre (near Glasgow),
Scotland, UK
Salary: £26,000 to £30,000 (depending on degree and experience)
Closes: 29th May 2017

The Smart Systems Group at Heriot-Watt University and Scottish Power Energy
Networks (SPEN) are looking for a graduate researcher (ideally with a PhD)
in computer science or electrical engineering with a background in data
science to join an exciting knowledge transfer project in the area of using
AI, machine learning or big data techniques to research the Data Analytical
benefit of the distributed sensor network that will be created from the
Smart Meter Implementation Programme (SMIP) currently underway in the UK.

About the employer/research group:
The Smart Systems Group at Heriot-Watt University in Edinburgh is an
interdisciplinary research group that spans research ranging from
electrical engineering and energy systems to computer science and
artificial intelligence. The general engineering submission of Heriot-Watt
and the University of Edinburgh was ranked first in the UK in terms of
research power in the general engineering category in the most recent UK
REF assessment. At Heriot-Watt, the associate will work closely with the
project investigators, Dr. Valentin Robu and Dr. David Flynn.
SPEN is the licensed electrical network asset management company in the
South of Scotland, Liverpool and North Wales, where they provide the
electrical energy service requirements to 3.5million customers. Through our
immediate parent firm of ScottishPower SPEN is a part of the highly
respected multinational energy company Iberdrola S.A. which has business
interests in the UK, Spain, USA, Mexico and Brazil.

About the position:
The research collaborators are seeking a graduate with a strong background
and interest in data science and optimisation, and an affinity with network
analysis, energy systems, electrical engineering or smart grid
applications. Graduates from an AI, computer science and electrical
engineering with a strong data analysis component are encouraged to apply.
Graduates with a PhD are preferred (and would attract the higher salary),
but candidates with an advanced MSc degree will be considered.

In more detail, part of the overall Low Carbon Technology (LCT) agenda of
the UK Government involves the rollout of smart meters at all points of
energy delivery (individual customers).  One part of this major
infrastructure delivery has been specifically designed to allow
Distribution Network Operators (DNO?s) like SPEN to use these SMs as a
distributed sensor that can remotely monitor the increasingly dynamic
customer LCT energy requirements.  The overall aim of this project is to
allow SPEN to investigate this potential and maximise utilisation of SM
data to answer questions such as:
How could such data be used to model and provide early warnings of abnormal
network behaviour (such as voltage or power out of band fluctuations). This
can occur, for example, both due to excess of embedded solar generation
from rooftop panels or new loads such as EV charging.
How can this data be used to provide feedback for smart grid interventions,
such as sizing local storage or demand-side management programmes?
Final project output will be the creation of Proof of Concept demonstration
and input into the recommendation and specification of future enterprise
level process and system requirement in SPEN.

The associate researcher will be an employee of Heriot-Watt University and
will have full access to the university's facilities, but will mostly be
based at SPEN's head office in Blantyre (suburb of Glasgow).

How to apply:

Applications for this vacancy should be made online through the Heriot-Watt
University iRecruit system, by following the following link:

https://www.hw.ac.uk/about/work/jobs/job_SVJDOTc0OA.htm

Applications can be submitted up to midnight (UK time) on 29 May 2017.
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