 |
 |
Job Opening: 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 Closes: 29th May 2017 The Smart Systems Group at Heriot-Watt University and Scottish Power Energy Networks (SPEN) are looking for an enthusiastic graduate researcher 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. Iberdrola S.A is keen to promote cross business synergy and offers many opportunities across all divisions. 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 both computer science and electrical engineering with a strong data analysis component are encouraged to apply. 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 SM?s 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 can higher resolution of data such as voltage and power be useful to manage distribution networks? 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 scale systems 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 but will be located full time in SPEN?s head office in Blantyre (near Glasgow) at the centre of the companies? Smart Grid innovation investigation. The associate will though have access to all HWU university facilities (computing, office space, training programmes etc.) available to regular university employees, as required. Though this is an initial 2 year project opportunity there is considered likelihood of enduring requirement for data analytical investigation and generation of new data analytical departments for managements of the increasingly complex Smart Grid requirements. For a view of these future `vision? requirements it is advisable to read SPEN?s latest Distribution System Operator (DSO) vision document. https://www.spenergynetworks.co.uk/pages/dso_vision_consultation.aspx Candidates with a background in Geographical Information Systems (GIS) are also strongly encouraged to apply. Key responsibilities: Over the 2 years duration of the project, the post holder will be asked to: Review the state of the art in data-driven approaches to distribution network monitoring Build a classification for different types of ?at risk? based on their topology (radial, mesh) and the type of embedded generation sources (PVs, small scale wind) and new loads (EV charging) Work closely with a highly respected energy network consultant who will provide the underlying data analytical LV network model that can be utilised for this research into distributed sensor data possibility Design algorithms for early warning systems of voltage or power out of band fluctuations, using distributed smart meter data. Such a system could use a variety of machine learning or data-driven approaches (depending on the experience/interest of the candidate), as well as the network features identified above Evaluate these strategies under different conditions and scenarios Design methodologies and protocols for capturing smart meter data at a resolution that is actually feasible and useful for decision making Understand and define integration of the results within the current and future direction vision of SPEN Process?s & IT Systems. Document the above, assist the project team in disseminate of the results internally and externally Drive further interest in the benefit of big data knowledge to different stakeholders and user groups within SPEN and the general electricity sector Disseminate results in scientific venues, including relevant journals and conferences Candidate profile: Essential Criteria: Have a good MEng or MSc degree (with PhD strongly preferred) in either Computer Science or Electrical Engineering programme Experience of planning, and working through projects involving simulations, data visualisation and testing Significant previous exposure to programming (although no knowledge of a specific programming language is required) Capability to be self-directed and think innovatively. Demonstrate enthusiasm in the topic of the project, be a team player Capability to present own research results Desirable Criteria: A PhD in data science, artificial intelligence or another programme with a strong computational component is strongly preferred Graduates with significant relevant experience in industry, in particular in the power systems distribution area can be considered as relevant experience instead of PhD Prior knowledge and familiarity with data analysis or visualisation techniques relevant to the project, and/or multi-agent/stochastic optimization techniques is a definite plus Affinity or interest in power systems, or distribution networks is a clear advantage Specific prior knowledge or project-based experience with distribution networks or big data analysis related to power systems is a highly desirable skill Prior publications in the topic areas of the project is a plus Candidates with a background in Geographical Information Systems (GIS) are also strongly encouraged to apply. 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. -- Dr. Valentin Robu Assistant Professor in Smart Grids School of Engineering & Physical Sciences Heriot-Watt University Riccarton Campus, EM3.15, Edinburgh, Scotland, EH14 4AS Phone: +44 (0)131 451 3438 Email: [log in to unmask]<mailto:[log in to unmask]> ________________________________ Founded in 1821, Heriot-Watt is a leader in ideas and solutions. With campuses and students across the entire globe we span the world, delivering innovation and educational excellence in business, engineering, design and the physical, social and life sciences. This email is generated from the Heriot-Watt University Group, which includes: 1. Heriot-Watt University, a Scottish charity registered under number SC000278 2. Edinburgh Business School a Charity Registered in Scotland, SC026900. Edinburgh Business School is a company limited by guarantee, registered in Scotland with registered number SC173556 and registered office at Heriot-Watt University Finance Office, Riccarton, Currie, Midlothian, EH14 4AS 3. Heriot- Watt Services Limited (Oriam), Scotland's national performance centre for sport. Heriot-Watt Services Limited is a private limited company registered is Scotland with registered number SC271030 and registered office at Research & Enterprise Services Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS. The contents (including any attachments) are confidential. If you are not the intended recipient of this e-mail, any disclosure, copying, distribution or use of its contents is strictly prohibited, and you should please notify the sender immediately and then delete it (including any attachments) from your system. To unsubscribe or modify your gis-jobs settings, please visit: http://www.jiscmail.ac.uk/gis-jobs