22-26 April 2013
Organisers: Stan Zachary (Heriot-Watt), David Lenaghan (National Grid plc), Robert Leese (Industrial Mathematics KTN), Andrew Richards (National Grid plc), Sergey Foss (Heriot-Watt), James Cruise(Heriot-Watt), Chris Dent (Durham)
in association with the Newton Institute programme Stochastic Processes in Communication Sciences
(11 January – 2 July 2010)
This meeting is a one-week research and scoping workshop to discuss applications of mathematics to the management of complex energy systems. It will be held at the Isaac Newton Institute in Cambridge from 22 to 26 April this year. It is intended as a follow-up to the Spring 2010 Programme in Stochastic Processes in Communication Sciences, and the one-week Energy Systems workshop which formed part of that programme, and subsequent one-day events on the theme of mathematics in the management of energy (Energy Systems Day and Maths Underpinning Energy Workshop).
The aims of the meeting are:
It is intended that the meeting should focus on identifying where and how mathematics can contribute to the pressing system management problems now facing the energy industry. To that end there will be a relatively small number of talks, which are by invitation, together with substantial time for brainstorming and discussion.
Thus on the Monday and on the Tuesday morning there will be presentations from industry experts (not all of whom will be from industry itself), and we shall also take some time on the Friday to consider what we have learned during the week. The time in between(other than the Wednesday afternoon - see below) will have an appropriate balance of talks and brainstorming sessions.
On the Wednesday afternoon we shall have an an "Open for Business meeting", in which a number of senior people from industry and government will give high-level views of problems likely to present significant future challenges. One such is that of electricity market reform – a subject which raises longer term issues of design of efficient market mechanisms so as to provide optimal value to consumers.