Markov chain optimisation for energy systems (Ext.)

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This is an extension of the Fellowship: 'Optimal Prediction in Local Electricity Markets'. In this project we will develop novel approaches to the optimisation of energy systems under uncertainty. Our approach, based on methods of computationally intensive statistics, offers significant advances on multiple fronts relative to the state of the art. Firstly more detailed and appropriate representations of random variations will be made possible, to address the increasingly important question of the integration of renewable power generation. Secondly we will apply cutting-edge approaches in computationally intensive statistics to reduce the computational time required for the optimisation of energy systems under detailed models of uncertainty, and to develop methods capable of scaling up to large power systems. We will work together with both established and start-up energy companies in the UK to maximise the potential impact of our work. The developed methods will be general in their applicability across energy systems and this research will also support the technical development in the UK of heat networks, a potentially efficient method of delivering water and space heating to multiple buildings. Our research therefore offers multiple contributions to the 'Energy trilemma' of delivering affordable, clean and reliable energy and to the COP21 agenda.