Posters

Abstract

Accurate validation of wind farm yield predictions including, turbine interaction effects, requires accurate production data and realistic representations of time-varying availability and grid curtailment. While high-resolution SCADA data would provide the most direct insight into these operational effects, such data are proprietary and not publicly available, significantly limiting wider access to validation data. Publicly available data from NESO’s Balancing Mechanism (BM) therefore represent a valuable alternative, offering insight into real-world production data and grid-driven operational constraints across a range of GB offshore wind farms [1]. This study presents a novel methodology for inferring high-resolution wind farm production, availability and grid curtailment using the publicly available Elexon BM datasets [2]. The BM data includes Actual Generation Output (B1610), Bid-Offer Acceptance Levels (BOALs), Final Physical Notifications (FPN) and Maximum Export Limit (MEL). The BOAL instructions are real-time formal instructions to wind farm operators to deviate from their FPN either by increasing generation or curtailing it. Thus, using BOAL and FPN data it is possible to when curtailment has been implemented from the grid instructions. Furthermore, the wind farm operators submit MEL which represents the maximum power a wind farm operator believe they can produce during any period. A reduction of MEL over time may infer downtime within a wind farm, which can be used to calculate availability.  By combining FPN, BOAL and MEL this study demonstrates how grid curtailment and wind farm availability time series can be generated. This information is integrated into wind farm energy yield assessment (EYA) model chains using a turbine-level availability/curtailment matrix. This study will set out experience and learnings from processing and working with the BM data; including aligning with wind farm meta data, inconsistencies in the data and differences in operation strategies. It explains and demonstrates that BM data can robustly capture grid curtailment and wind farm availability and provide a pragmatic, though imperfect, method when validating wind farm production estimates in the absence of SCADA data. References: 1.  National Grid ESO, The Balancing Mechanism Reporting Service (BMRS) (National Grid, London, 2023). 2.  Elexon, Guidance Note: Balancing Mechanism Reporting Agent (BMRA) Data (Elexon Limited, London, 2022). 3.  Ofgem, Renewable Energy Guarantees of Origin (REGO): Guidance for Generators (Office of Gas and Electricity Markets, London, 2021).