National Carbon Intensity Forecast

This dataset contains national carbon intensity forecast for the GB electricity system. The carbon intensity of electricity is a measure of how much CO2 emissions are produced per kilowatt hour of electricity consumed. --- Publicly Available Carbon Intensity Forecast Methodology Authors: Dr Alasdair R. W. Bruce, Lyndon Ruffa, James Kelloway, Fraser MacMillan, Prof Alex Rogersb a St. Catherine’s Lodge, Wokingham, NESO, b Department of Computer Science, University of Oxford Issue: May 2024 National Energy System Operator (NESO), in partnership with Environmental Defense Fund Europe and WWF, has developed a series of Regional Carbon Intensity forecasts for the GB electricity system, with weather data provided by the Met Office. Introduction NESO’s Carbon Intensity API provides an indicative trend of carbon intensity for the electrical grid of Great Britain up to 48 hours ahead of real- time [1]. It provides programmatic and timely access to forecast carbon intensity. This report details the methodology behind the regional carbon intensity estimates. For more information about the Carbon Intensity API see here. What’s included in the forecast The Regional Carbon Intensity forecasts include CO2 emissions related to electricity generation only. The forecasts include CO2 emissions from all large metered power stations, interconnector imports, transmission and distribution losses, and accounts for national electricity demand, and both regional embedded wind and solar generation. While we recognise upstream emissions and indirect land use change impacts and other GHG emissions are important, it is only CO2 emissions related to electricity generation that are included in the forecast. This work does not consider the CO2 emissions of unmetered and embedded generators for which NESO does not have visibility of. Methodology wind and solar generation, as this impacts the amount of dispatchable generation that is required to meet demand. The forecast also makes use of historic generation data to make predictions about future generation, which invariably changes per system conditions. It is therefore important to note that these forecasts are likely to be less accurate than forecasts such as electricity demand, since it includes the confluence of uncertainties from demand, wind, solar, and CO2 emissions by fuel type. Estimated carbon intensity data is provided at the end of each half hour settlement period. Forecast carbon intensity is provided 48 hours ahead of real-time for each half hour settlement period and uses NESO’s latest forecasts for national demand, wind and solar generation. The GB carbon intensity 𝐶𝑡 at time 𝑡 is found by weighting the carbon intensity 𝑐𝑔 for fuel type 𝑔 by the generation 𝑃𝑔,𝑡 of that fuel type. This is then divided by national demand 𝐷𝑡 to give the carbon intensity for GB: ∑ 𝐺 𝑔=1 𝐶𝑡 = 𝑃𝑔,𝑡 × 𝐶𝑔 𝐷𝑡 The Carbon Intensity forecast is particularly sensitive to short-term forecast errors in demand, The carbon intensity is then corrected to account for transmission losses to give the intensity of 1 Publicly Available consumption [3]. Table 1 shows the peer-reviewed carbon intensity factors of GB fuel types used in this methodology. Carbon intensity factors are based on the output-weight average efficiency of generation in GB and DUKES CO2 emission factors for fuels [4]. Interconnector carbon intensity factors Daily at 6am, the average generation mix of each network the GB grid is connected to through interconnectors is collected for the previous 24 hours through the ENTSO-E Transparency Platform API [6]. The factors from Table 1 are applied to each technology type for each import generation mix to calculate the import carbon intensity factors. If the ENTSO-E API is down, the import carbon factors default to those listed in Table 1. Fuel Type Biomassi Coal Carbon Intensity gCO2/kWh 120 937 Gas (Combined Cycle) 394 Gas (Open Cycle) 651 Hydro Nuclear Oil Other Solar Wind Pumped Storage French Imports Dutch Imports Belgium Imports Irish Imports 0 0 935 300 0 0 0 ~ 53 ~ 474 ~ 179 ~ 458 wind, nuclear, combined cycle gas turbines, coal etc. Estimated data is used for embedded wind and solar generation. Weather data, such as wind speeds and solar radiation, are procured separately by NESO and so are not publicly available. A rolling-window linear regression for each fuel type is performed and used with forecast demand, wind, and solar data to estimate forecast carbon intensity. An index for carbon intensity has been developed to illustrate times when the carbon intensity of GB system is high/low. Table 2 (overleaf) shows the numerical bands for the Carbon Intensity index. Table 2: Numerical bands for the Carbon Intensity Index. Carbon Intensity values are given in gCO2/kWh: Limitations There are several limitations with this methodology. This approach does not use Physical Notifications (PNs) of Balancing Mechanism (BM) units in the forecast. This is to ensure that the commercial sensitivities surrounding the balancing market are maintained. This means that only historic data is used in the analysis, limiting forecast accuracy. Finally, this work does not consider the emissions of embedded generation of which NESO does not have visibility. Future work will look at estimating these contributions to GB carbon intensity. Contact For any suggestions, comments or queries please contact: lyndon.ruff@nationalgrideso.com The estimated carbon intensity uses metered data for each fuel type, which is also available from ELEXON via the Balancing Mechanism Reporting Service, and includes fuel types such as metered References [1] Carbon Intensity API (2017): www.carbonintensity.org.uk 2 [6] ENTSO-E Transparency Platform: https://transparency.entsoe.eu/ Publicly Available [2] GridCarbon (2017): www.gridcarbon.uk [3] Staffell, Iain (2017) “Measuring the progress and impacts of decarbonising British electricity”. In Energy Policy 102, pp. 463-475, DOI: 10.1016/j.enpol.2016.12.037 [4] DUKES (2017): www.gov.uk/government/collections/digest-of-uk- energy-statistics-dukes [5] BM Reports (2017): https://www.bmreports.com/bmrs/?q=generation/ i Using ‘consumption-based’ accounting, the carbon intensity attributable to biomass electricity is reported to be 120 ± 120 gCO2/kWh [2]. The large uncertainty relates to the complex nature of biomass supply chains and the difficulty in quantifying non-biogenic emissions. 3