Optimisation of Energy Forecasting - analysis of datasets of metered embedded wind and PV generation

Completed

Renewable Energy Sources(Wind Energy)
Renewable Energy Sources(Solar Energy)

Applied Research and Development

PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics)
ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences)

Not Cross-cutting

Project Contact
National Grid Electricity Transmission

Network Innovation Allowance

Ofgem

01 June 2017

01 February 2018

8 months

£34,150

Power

London

Network Innovation Allowance

Project Contact, National Grid Electricity Transmission

The project plans to deliver:

• Power curves for PV and embedded wind Generation, refined by location, season and/or any other factors found to be relevant.
• Methodologies for estimating installed capacity of PV and embedded wind generation, including review of the feasibility of using satellite imagery to identify solar panels
• Methodology for forecasting dispersed PV and embedded wind generation, including definition of appropriate levels of granularity for modelling, and making use of satellite imagery if this proves practicable.
• Analysis of generation volatility that can be used as an input to response and reserve holding policy.
• Anomaly detection to identify (for example) partial outages of wind farms in the dataset.
• Improved models for wind speed cut-out.
• An analysis of the reduction in demand forecasting error that can be achieved by the implementation of the new models
• A plan for implementation of the new models, and a methodology for demonstrating the ongoing reduction in demand forecast error as a consequence of this project

Delivery of objectives listed above, leading to a reduction in mean demand forecast error in the three months following completion of the project compared to the previous three months of at least 20 MW. The benchmarking analysis will be based on the 365 day average error. It is expected that a 20 MW step change would show as a 5 MW reduction after 3 months on a rolling 365 day average.

Forecasting renewable generation output is a key challenge for National Grid as GB System Operator; effective forecasting is vital to keeping the lights on and minimising the costs of operation that fall onto industry and consumers. Two new datasets of embedded generation output have recently been acquired by National Grid: Several years of historic output from around 20,000 domestic PV installations, at 30 minute or better resolution. To go with this data, we have hourly weather forecasts and outturns for around 60 weather stations for the last 18 months, and 80 for the last 6 months. Embedded wind generation output by Grid Supply Point by half hour for the last 4 years. To go with this, we have wind speed forecasts and outturns for 80 - 100 weather station locations, and metered output from directly connected wind generators - both 10 second spot data and 30 minute integrated data. We want to analyse this data to optimise the correlations used to convert forecast weather variables into power generation forecasts for both PV and embedded wind power generation. The proposed work will be supported by three PhD research students from The Alan Turing Institute (ATI), under the supervision of academics from the universities of Oxford, Warwick, Edinburgh and Sussex to work on the identified datasets for three months this summer. The ATI, as a leading UK institute for data science, specialises in dealing with Big Data, such as the data sets now available to us at National Grid, and have the analytical tools, expertise and access to resources necessary to analyse this data. The students will work with support from Senior Energy Forecasters from the Energy Forecasting Team at Electricity National Control Centre (ENCC) in order to analyse the large volume of data available and identify innovative ways of improving our demand forecasts. Key deliverables from the project would be new power correlations for wind and solar generation which could be implemented immediately into National Grid’s forecasting process (after suitable testing to ensure that there is no risk to system security)

Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above

14/09/18