Standard Profiles UK Power Networks Uses for Electricity Generation

## Introduction The dataset captures yearly generation profiles for different generation technology types, used by UK Power Networks to run export curtailment assessment studies. UK Power Networks has been running curtailment studies since 2014 in the three licence areas and have been using standards technology specific profiles to model the accepted not yet connected generation capacity. Generation specific profile include the following generation types: solar photovoltaic, wind, battery and non-variable generation. The profiles have been developed using actual generation data from connected sites within UK Power Networks licence areas falling into each of the generation categories. The output is a yearly profile with half hourly granularity. The values are expressed as load factors (percentages) i.e., at each half hour the value can range from 0% to 100% of the maximum export capacity. The profiles are revised on a regular basis to ensure that they represent as closely as possible the operational behaviour of unconnected sites. The following change have taken place since UK Power Networks has started issuing curtailment reports: * The solar profile was updated in 2019/2020 and capacity factor was increased from 13.2% to 14.2%; * The electricity storage profiles were updated in April 2024. new profiles available:“Storage_export_enhanced” and “Storage_import_enhanced”; * Gas profiles were updated in September 2024 differentiating between small and large gas generators. ## Methodological Approach This section outlines the methodology for generating annual half-hourly demand profiles. Connected metered generators falling in each of the categories have been used to create the representative profiles. Historical data from each of these connected generation sites are retrieved from UK Power Networks’ Remote Terminal Unit (RTU) and consist of annual half-hourly meter readings. The profiles are averages of power/capacity at every time period i.e.: Paverage,t = average(P1,tc1 + P2,tc2 + … + Pn,tcn) where * t is time, 30 minutes resolution for one year; * P is the export in MW from sites 1, 2, ..., n; and * c is capacity of site 1, 2, ..., n. If there was bad/missing data then [PVSYST] and local meteorological data were used. ## Electricity Storage profiles For export/generation studies, storage is modelled as constantly exporting with a varying profile throughout the day, whereas for demand/import studies, storage is modelled as constantly importing with a varying import profile throughout the day. This represent a conservative view, which is used due to the unpredictable pattern of Electricity Storage sites The solar and storage combined profile is calculated as the maximum of the storage and solar profiles during each 30-minute timestamp. Storage profiles are detailed in our design standard Engineering Design Standard (EDS 08-5010). Storage profiles were updated in April 2024 based following a piece of work delivered by Regen to UK Power Networks on the operational behaviour of battery storage, with the purpose to model reflect battery storage more realistically in curtailment studies . the revised profile are “Storage_export_enhanced” and “Storage_import_enhanced”. Curtailment reports issued prior to 22 April 2024 were produced using the legacy storage profiles (“Storage”). ## Gas Profiles Gas profiles were updated in September 2024 to provide a more representative view on how gas generator operate and to enable more representative curtailment results. The legacy profile used to model unconnected gas generators until September 2024 was the “non-variable” profiles. From September 2024, unconnected gas generators are modelled using new “Gas_large” and “Gas_small” profile. The two profiles are meant to capture the differences between smaller genset, quickly ramping up from 0 to maximum capacity, and larger gas generators that rather have a more stable behaviour. The profile have been created with the same equation described above, taking a percentile between 95 and 98, rather than the average. ## Quality Control Statement Quality Control Measures include: * Manual review and correct of data inconsistencies * Use of additional verification steps to ensure accuracy in the methodology ## Assurance Statement The Open Data Team and DSO Data Science worked together to ensure data accuracy and consistency. ## Other Download dataset information: [Metadata (JSON)] Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: [https://ukpowernetworks.opendatasoft.com/pages/glossary/]