European Companies Search Engine
UK funding (£214,717): Optimal Prediction in Local Electricity Markets Ukri1 Sept 2015 UK Research and Innovation, United Kingdom
Overview
Text
Optimal Prediction in Local Electricity Markets
| Abstract | Over the next four years this Fellowship aims to build an internationally leading research team in stochastic modelling of local energy markets. The Fellow and researcher will develop theory and numerical methods to solve emerging mathematical problems in UK power systems, with a focus on modelling the community scale, in order to achieve the maximum welfare benefit from the design of these markets. The research programme will be carried out in the context of the Fellow's existing Energy group, which includes three PhD students working in power systems and energy storage. This work will involve fruitful interactions with other probabilists in the UK, power systems engineers in the UK, US and Canada, and UK industry experts working on such problems. It will have significant impact through the creation of algorithms and software, enabling the efficient numerical solution of planning and operational problems for local electricity markets. A longer term impact will be to further establish Manchester (and the UK) as a centre for talented researchers in cross-disciplinary applications of probability theory to power systems. |
| Category | Fellowship |
| Reference | EP/K00557X/2 |
| Status | Closed |
| Funded period start | 01/09/2015 |
| Funded period end | 25/04/2017 |
| Funded value | £214,717.00 |
| Source | https://gtr.ukri.org/projects?ref=EP%2FK00557X%2F2 |
Participating Organisations
| Queen Mary University of London | |
| Energy Systems Catapult Ltd | |
| FUTURE DECISIONS LTD |
The filing refers to a past date, and does not necessarily reflect the current state. The current state is available on the following page: Queen Mary University of London, London.
The visualizations for "Queen Mary University of London - UK funding (£214,717): Optimal Prediction in Local Electricity Markets"
are provided by
North Data
and may be reused under the terms of the
Creative Commons CC-BY license.
