Dear all,
Please see an announcement and further details about the upcoming energy system study group with industry.
I would be very pleased to answer any questions – I hope that members of the list will find topics of interest, and will be interested in helping address these industry and government challenges.
Chris
--
Second Energy Systems Study Group with Industry,
ICMS, 05-07 December 2018.
Dear colleague,
We have finalised our problems for the Second Energy Systems Study Group with Industry being held at ICMS<http://www.icms.org.uk/> on the 5th - 7th December 2018. We have five challenges confirmed with two recent entries from Edinburgh City Council<http://www.edinburgh.gov.uk> and Upside Energy.<https://upsideenergy.co.uk> Please find below the problem summaries:
Edinburgh City Council: Planning Edinburgh City Infrastructure for Uptake of Electric Vehicles
Useful Expertise: Uncertainty, Statistics, Operational Research, Decision Support
Challenge Statement: The city of Edinburgh has ambitious plans for increasing uptake of electric vehicles, in order to improve air quality and reduce carbon emissions. This requires infrastructure developments, including provision of charging points and electricity network capacity, and also taking advantage of synergies with local renewable generation. Availability of energy supply in turn influences the delivery of residential and commercial development. Planning background uncertainties include rate of uptake of vehicles, technology growth, and the spatial development of the city itself. The group will scope approaches to these issues, and council analysts will provide data on planning background projections and be available for discussion of uncertainty in these.
Upside Energy: Mathematical Language and Sequential Bundling of Demand-Response Contracts Under Uncertainty
Useful Expertise: Uncertainty, Statistics, Operational Research, Decision Support
Challenge Statement: Upside Energy Ltd. Provides balancing services to the UK National Grid using a diverse portfolio of electricity assets. These assets are owned by partners and are controlled by Upside’s platform according to service agreements, such as response times, power magnitudes, rates of change, cost structure etc. These conditions are stated into a Demand-Response (DR), which is interpreted as an option for Upside to use for balancing services.
However, these contracts (or offers) might expire frequently and the set of conditions might vary often depending on the asset owner circumstances. For instance, a DR offer may be expressed as “use 1MWh of battery X for charging and discharging operations from 00:00 to 05:00 hrs for the next 2 days at 10GBP / MWh”, Since these DR opportunities might become available at short notice, the first part of this challenge is to develop a language in which partners (asset owners) can write their own contracts which allows mathematical reasoning (e.g. formal verification). Reliability of DR contracts might vary and they are usually bundled to satisfy a given service level. The second part of this challenge is find an optimal bundling of DR offers, similar to a multistage stochastic unit commitment problem.
DuoDrive: A Marine Propulsion Control System: Holy Grail or Maths?
Useful Expertise: Applied Mathematics, Control
Challenge Statement: Duodrive is interested in novel marine propulsion systems which will provide variable speed drive and allow optimised matching of the engine output. The design will allow diesels to operate at a fixed speed in their optimum zone, independent of the demanded propulsion speed. Duodrive is aware that key to this will be the control system, and without a robust methodology it will be impossible to figure if empirical design will result in the ideal result. Duodrive wishes to explore the optimum control system by creating a virtual model of the drive-train.
Mocean Energy: A Curious Fluid Resonance in Sloped Channel on a Wave Energy Converter.
Useful Expertise: Fluid Mechanics, Applied Mathematics
Challenge Statement: Mocean Energy is an Edinburgh-based company developing a wave energy converter (WEC), which converters energy in ocean waves into electricity. The dynamics of the system is complex, including multiple, coupled degrees-of-freedom and frequency-dependent forces. We believe that the optimisation has stumbled upon a phenomenon caused by the wave channels that creates a wave resonance, which may (or may not) be trapped mode. We would like a linear mathematical model that is relevant to our conditions.
V.Ships: Increasing Maritime Efficiency Through the Application of Analytically Enhanced Predictive Planned Maintenance
Useful Expertise: Statistics, Operational Research Data Science
Challenge Statement: V.Group is the leading global provider of Maritime Support Services. Our database covers all aspects of vessel operational and technical management, such as routes sailed, crew information, purchasing spend and suppliers, maintenance schedules, observations and history - stretching back decades.
The Maritime industry has for many years carried out the majority of its maintenance activities based on a traditional combination of manufacturer’s guidelines and time based maintenance interventions. More recently, Condition Based Maintenance (CBM) has been introduced; however, this has not been as widely or extensively adopted as originally envisaged. There are a considerable number of barriers to wider adoption of CBM within the Maritime industry, not least the limited understanding and articulation of the opportunity that CBM represents. Leaders in the industry are already looking beyond CBM towards Predicative Planned Maintenance (PPM). We believe that there are significant and real tangible advantages for British Industry to be at the forefront of Maritime PPM creating a value chain around equipment quality rather than low initial cost with hidden high in service life time costs. Arguably however the real value is in efficiency, be it the cost of operation, time lost during unnecessary interventions or cost of unexpected failure.
The aim of this Study Group challenge is to objectively determine the effectiveness of current pump maintenance routines and based upon these experiences; seek to both predict future interventions (maintenance) and map meantime between failures – in essence the basis of a predictive maintenance algorithm.
More information on these problems and registration details can be found on the Study Group website. <http://www.icms.org.uk/KTN_Energy_SG.php>
Study Group Programme
Wednesday 05 December
From 09:30am: Registration ICMS, Bayes Centre, Edinburgh
Followed by Welcome, Introductions and Problem Presentations
11.30am – 5.00pm: Work on Problems
6.00pm: Dinner
Thursday 06 December
9.00am – 5.00pm: Work on Problems
6.30pm: Workshop Dinner
Friday 07 December
9.00 – 2.00pm: Work on Problems
2.00pm: Presentations
General Information
All participants will be provided with lunch, dinner and coffee from Wednesday morning until the workshop finishes on Friday afternoon. There is a nominal charge of £35 to take part in this Study Group.
Accommodation
Accommodation will be provided close by the ICMS which will consist of individual rooms with Breakfast.
Best wishes,
Matt Butchers and Chris Dent
Dr Matt Butchers
Knowledge Transfer Manager. Industrial Mathematics
Knowledge Transfer Network
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