Variability and Uncertainty in Energy Systems Chris Dent Turing Gateway workshop: Maths and Public Policy - Cities & Infrastructure 11 March 2015

∂ Contents Motivations ‒ Integration of variable/uncertain generation ‒ Capital planning – 10s of £billions of investment ‒ Efficient asset renewal ‒ Greater scale ‒ (Smartgrids) Examples, and areas of mathematics required Institutional issues ‒ Bringing right people together ‒ Technology transfer

∂ EXAMPLES OF VARIABILITY AND UNCERTAINTY

∂ Short term forecasting Diagrams from National Grid, INI OfB, 2012 Uncertainty in forecasts ‒ Non-stationary Use in reserve setting ‒ Extremes most important ‒ Limited data

∂ Optimal scheduling of generators Diagrams from A. Tuohy et al, IEEE TPS, 2009 Some conventional generators have large startup costs, min up/down times, etc ‒ Optimise schedule for next 1-2 days under uncertainty over wind power forecast (and demand and reliability) Three aspects ‒ Write down structure of problem ‒ Scenario tree (need to have simple representation of uncertainty) ‒ Solve optimisation problem (which is large and hard)

∂ Network capital planning Left diagram from ENSG, 2014 “Right” amount of congestion ‒ Uncertainty in wind resource, plant location, demand growth, mechanical reliability, etc etc

∂ Adequacy of supply Top left from CA study – risk of shortfall Current modelling issues ‒ Wind-demand relationship, interconnectors, costs of shortfalls, capacity market decision making

∂ Generation investment (e.g. DDM) How to project investment in generating plant ‒ Design of markets, prices in capacity market ‒ Need to imagine being market designer/operator, and make that entity’s assessment of judgments of gencos ‒ How to draw conclusions about real world?

∂ Interconnection – greater scale GB network will look less like an island ‒ Larger scope of modelling required ‒ May have lesser quality of data across wide interconnection

∂ Efficient asset renewal Diagram source ScottishPower ‒ Assessment of asset base condition ‒ Plan renewal programme balancing risk and capital costs

∂ Smartgrids – greater complexity Large increase in number of entities interacting with system ‒ Centralised control not tractable ‒ New decentralised approaches required

∂ INSTITUTIONAL ISSUES

∂ UK skills in mathematics of energy systems e.g. EPSRC call on “Maths underpinning energy research”, 2010, ‒ Mathematical foundations for energy networks: buffering, storage and transmission (Cambridge, Heriot-Watt, Durham): storage, forecasting, decentralised control ‒ Mathematical tools for improving the understanding of uncertainty in offshore turbine operation and maintenance (Strathclyde): strategic asset management in absence of operational experience ‒ Locally stationary Energy Time Series (Bristol/Lancaster): non- stationarity is a natural framework in many energy applications (e.g. weather systems) Well linked to industry, to each other, and to some engineering research - but to mainstream of RCUK Energy Programme? ‒ Also workshops at Newton Institute, with Energy Storage Network 1-2 OU, Lancaster, Durham Risk Day, PMAPS, etc.

∂ Institutional issues Many areas of current energy research require skills from mathematical sciences as much as from the application communities ‒ How to bring right people together for academic research projects? ‒ How to bring together industry with mathematicians and statisticians who have the skills to work on their challenges ‒ Right team will not always consist of people with long experience in energy applications ‒ Need combination of methodological and application knowledge Challenges in technology transfer ‒ Greater uncertainty and complexity requires new mathematical and statistical technologies to be applied in energy systems ‒ These skills are not universal in the industry ‒ How to take into field application useful techniques developed in universities?

∂ Any questions?