PRIMAVERA – high-resolution global climate modelling for energy applications
Paula Gonzalez and David Brayshaw
The Horizon 2020 PRIMAVERA project is a major international research programme developing a new generation of advanced high-resolution global climate models, capable of simulating and predicting regional climate with unprecedented fidelity for the benefit of governments, business and society. As part of this international research team (involving 19 institutions across Europe), the energy-meterology group are developing process-based understanding of how climate variability and change will impact on the energy sector.
S2S4E – Subseasonal to seasonal forecasting for energy
Hannah Bloomfield, Emma Suckling, Paula Gonzalez, Andrew Charlton-Perez and David Brayshaw
The S2S4E project is developing an operational climate service that to enable renewable energy producers and providers, electricity network managers and policy makers to design better-informed strategies at sub-seasonal to seasonal timescales. As part of this international effort, the energy-meterology group are developing process-based understanding of how the European power system responds to weather and climate variability on timescales of weeks to seasons, and the extent to which it can be predicted in state of the art S2S forecast systems.
Solar PV Forecasting
Daniel Drew, Janet Barlow and Phil Coker
A recent, dramatic increase in installed photovoltaic generation is now impacting the electricity demand profile. This influence has been challenging to predict and is currently leading to significant demand forecast errors. The total solar capacity in Great Britain is now in excess of 9.3 GW, and is forecast to rise to 15.7 GW by 2020. Owing to the size of the individual installations (the largest solar farm in the UK is just 48 MW) all of this capacity is embedded within the distribution networks.
The purpose of this project is to
- derive datasets and specific knowledge of characteristics of solar PV generation in terms of variability, ramping and persistence, and the joint characteistics of how the solar resource interacts with the wind resource.
- develop new models for converting solar irradiance into generated solar PV power.
- improve short term solar generation forecasts
This project is funded by National Grid.
Understanding climate uncertainty in power systems planning
Adriaan Hilbers (MPE – Reading and Imperial), David Brayshaw and Axel Gandy (Imperial)
Policymakers, industry and academia frequently employ power systems models (PSMs) to aid in their decision-making. Recently, such models have been criticised for relying on short samples of weather data, shown to be inadequate in determining optimal strategy under weather and climate variability. For example, the same model may recommend investing in dierent technologies depending on which year of weather data is used, at most one of which can be optimal in the long run. However, due to their computational complexity, PSM runs using longer data samples are typically infeasible. This PhD project, conducted through the Mathematics for Planet Earth CDT, seeks to develop techniques to ensure power system planning models determine the truly optimal strategy under climate-based uncertainty while maintaining computational feasibility.
Understanding forecast value in complex decision-making systems
James Fallon (SCENARIO DTP Reading), David Brayshaw and John Methven
The application of forecasts to produce socio-economic value has long been a goal of weather and climate research. The Climate Services sector in particular – the provision of user-relevant predictions spanning weeks to decades ahead – has seen rapid growth in recent years. However, despite the apparent “end use” focus of these activities, scientific understanding of how meteorological forecasts interact with decisions in complex human- and environmental- systems is limited. This PhD project, conducted through the SCENARIO DTP, seeks to address this important research gap and to lead a step-change in how climate-impact forecasts are assessed.
Colin McKinnon, Jon Blower, Alan Yates, Ben Lloyd-Hughes, Neil Parley, Barbara Percy, Maria Noguer, David Brayshaw
RE-SAT: Renewable Energy Spatial Tool. Informing the deployment of renewable energy in Small Islands Development States using space data.
Climate changing renewable power outputs
Daniel Hdidouan (Imperial College London), Iain Staffell (Imperial College London), David Brayshaw, Rob Gross (Imperial College London).
A NERC funded PhD project (via the Science and Solutions for a Changing Planet, SSCP, DTP) based at Imperial College. The research aims to develop a framework to value the potential impact climate change may have on the European and North African power system. The framework focuses on two key aspects of climate-energy interaction at the power system scale: energy generation from wind and solar resources, and power system performance due to temperature (hot and cold extremes). Meteorological reanalyses datasets will be used with CMIP5 simulations and simulations (see Hdidouan and Staffell, 2017) and then coupled with a virtual wind/solar farm model to calculate capacity factors. These then form the inputs to a techno-economic model which analyses the impact of climate forcing on the levelised cost of energy from renewables.
Subseasonal predictability for energy
David Livings, Andrew Charlton-Perez, Steve Woolnough, Nick Klingaman, David Brayshaw
This project explores new opportunities to exploit subseasonal predictability and forecasting in the energy sector.
Forecasting weather impacts on the UK telecommunication network
Alan Halford, David Brayshaw, Stefan Smith
An EngD project joint funded by the EPSRC and the BT group with the aim of understanding the effects of weather on the UK telecommunication network to minimise weather related impacts. The telecommunication network as with other infrastructure are exposed to the weather and can be damaged by adverse conditions. Establishing statistical relationships between weather and fault numbers allows future fault numbers to be predicted using numerical weather prediction techniques. With the knowledge of future fault numbers, network management decisions can be optimised.
Climate and energy balancing: variability, mechanisms, predictability and impacts
Hazel Thornton (Met Office), Brian Hoskins, David Brayshaw and Adam Scaife (Met Office)
A PhD project, funded by the Met Office to investigate the influence of weather and atmospheric circulation on energy extremes and their predictability. The weather is known to play an important role in the management of the energy system, as demand and wind power generation are strongly influenced by atmospheric conditions. This PhD aims to quantify how both temperature and the driving weather pattern affect demand and wind power generation, to help understand the climatological risk of extreme demand periods and the availability of wind power during peak demand conditions. The predictability of key weather types on the monthly to seasonal timescale will also be explored.
ECEM – Climate Services for Energy
Emma Suckling and David Brayshaw
The European Climatic Energy Mixes (ECEM) project is an EU Copernicus Climate Change Services Project (C3S) seeking to develop a proof-of-concept model – or demonstrator – for the provision of climate services for energy applications. Working closely with sectoral stakeholders, its purpose is to enable the energy industry and policy makers to assess how well energy supply will meet demand in Europe over different time horizons, focusing on the role climate has on energy supply and demand. The energy-meteorology group is developing a series ‘case studies’ to demonstrate the value and use of this service. The demonstrator can be found here.
Future climate, future energy
Hannah Bloomfield, David Brayshaw, Len Shaffrey, Phil Coker, Hazel Thornton (UK Met Office), Jason Lowe (UK Met Office)
A NERC funded PhD project (CASE partnered with the UK Met Office) which aims to understand the effects of increased weather dependent renewable generation on national and international power systems. A key goal is to understand the multiple sources of meteorological sensitivity of the power system (e.g., demand response to temperature, wind-power, solar) and how weather events effect the power system as a whole through these pathways. Meteorological reanalyses (e.g., NASA MERRA) and state-of-the-art climate model data will be used to create a long baseline to simulate the impacts of weather on the UK and European power systems both now and into the future. Power system behavior will be understood using both a mixture of models from a classical “load duration curve” approach to simplified integrated power system “dispatch” models. An introduction to the impact of inter-annual variability on the GB power system is available here, and along with selected publications here and here.
Subseasonal weather forecasting for the energy sector
Kieran Lynch, David Brayshaw, Andrew Charlton-Perez
This PhD investigated the potential for subseasonal weather forecasts to provide useful information for the energy sector. The project quantified the level of predictability in wind/temperature forecasts and demonstrated that they can be utilised in a decision making process to add economic value. The ECMWF monthly forecasting system was used to produce probabilistic wind and temperature forecasts, and empirical models developed to convert these variables into wind power and electricity demand. These form two of the fundamental inputs to a stack model, which is used to forecast the power price.
Industrial supervisor and NERC PURE project
This PhD was in collaboration with the energy company Centrica. In addition to this, a NERC grant was awarded as part of the PURE project to implement probabilistic subseasonal wind power forecasts operationally at Centrica. Further details of this 4 month project can be found here: Extending Wind Power Forecast Horizons
Understanding and predicting extreme wind power generation events
Dirk Cannon, David Brayshaw, John Methven, Phil Coker
This project investigated the climatological behaviour of extreme wind power generation events in Great Britain, as well as their predictability at the medium range (1-10 days). This work has been used to inform new strategies for system reserve and security in an age where wind power accounts for a significant and growing proportion of electricity production.
A model was developed to construct multi-decadal, nationally aggregated, wind power time series from atmospheric reanalysis data (this model, along with a 33 year wind power time series for Great Britain is freely available here). Using this model, we investigated the climatological behaviour of extreme wind power generation events in Great Britain, focusing on prolonged low and high generation events, and rapid swings in generation. Read our paper here.
The second stage of the project focussed on the predictability of extreme wind power generation events in state-of-the-art global forecast models, at forecast lead times of 1-10 days ahead. A multi-model approach was adopted using 7 years of forecast data from the THORPEX Interactive Grand Global Ensemble (TIGGE). The skill of these forecasts was assessed on a statistical basis and compared to a climatological approach, after which the most extreme and worst forecast events were examined in more detail. This work was completed during two projects funded by National Grid through the Network Innovation Allowance (NIA_NGET0016 and NIA_NGET0085).
Investigating the wind speed variability across a single offshore wind farm
Daniel Drew, Janet Barlow, Omduth Coceal, Phil Coker and Ben Potter
This project used a canopy modelling approach, similar to that developed for flow through fine-scale roughness elements (such as vegetation canopies and urban areas), to estimate the wind speed variability across large offshore wind farms caused by the wake effects of the individual turbines. The model was then used to derive directionally dependent power curves for all of the offshore wind farms in Great Britain. This work was funded by National Grid (Network Innovation Allowance NIA_NGET0028).
Clustering effects of major offshore wind developments
Daniel Drew, Janet Barlow, Omduth Coceal, Phil Coker and David Brayshaw
The expansion in offshore wind generation coming with the round 3 projects is bringing particular uncertainty for strategic and operational planning of the power system. Wind farms of the scale now planned influence the lower atmosphere sufficiently to impact the performance of adjacent farms, therefore the power generation characteristics of a cluster of wind farms (such as that planned for Dogger Bank) are largely unknown. This project aims to determine the power characteristics of a cluster of large offshore wind farms for a range of meteorological conditions, taking into account the wake effects of the individual turbines and the shadow effect of neighbouring farms. This project is funded by National Grid.
Subseasonal predictability for energy trading across Europe
Emma Suckling, David Brayshaw and Andrew Charlton-Perez
Power market prices across Europe are often highly affected by variability in meteorological drivers, for example wind speeds over Germany, Spain and the UK, as well as temperatures in France and solar radiation across Europe. Having skilful forecasts of these variables from days to weeks ahead is therefore of great value to the power market sector, for example in predicting renewable energy trading properties. In collaboration with Rubykon AG, we have performed a probabilistic calibration and skill evaluation of operational ensemble forecasts from ECMWF’s monthly forecast system for several weekly-averaged meteorological variables over a number of nations and regions in Europe. This project was funded by Rubykon AG.
Reanalysis data for wind resources in capacity assessment
Marc Stringer and David Brayshaw
Tools provided to enable state-of-the-art reanalysis data to be used to estimate wind resource in OfGEM’s capacity assessment reports (2012-2014).