Project Overview
A UK renewable energy operator with 28 onshore wind farms, 840 turbines, and £420M annual generation revenue n...
Technology Stack
Compliance & Standards
The Challenge
A UK renewable energy operator with 28 onshore wind farms, 840 turbines, and £420M annual generation revenue needed a unified digital asset management platform — replacing spreadsheet-based asset tracking, manual SCADA exports, and paper-based maintenance scheduling. Ofgem Balancing Mechanism reporting, REGO (Renewable Energy Guarantees of Origin) certificate management, UK Grid Code compliance, HSE (Health and Safety Executive) wind farm safety regulations, SECR, and UK GDPR for staff data were mandatory. Budget: £140,000.
Our Approach
Wind farm SCADA
each turbine has a SCADA system (GE, Siemens Gamesa, Vestas, Enercon — each with different data protocols).
Data collection
OPC-UA server at each wind farm site (standardises SCADA protocols), AWS IoT Greengrass (edge compute — pre-processes turbine data before cloud upload), AWS IoT Core (MQTT ingestion — all 840 turbines reporting at 10-second intervals), Kinesis Data Streams (real-time turbine data stream), S3 (time-series data lake — 10-second resolution retained 7 years).
Turbine metrics
active power output (kW), wind speed (m/s), rotor RPM, nacelle temperature, vibration, pitch angle, yaw position.
REGO Certificate Management
REGO (Renewable Energy Guarantees of Origin): Ofgem issues 1 REGO certificate per MWh of eligible renewable generation.
REGO lifecycle
- 1monthly meter read submission to Ofgem REGO Registry via Ofgem e-serve API,
- 2REGO certificates issued by Ofgem (1 per MWh),
- 3certificates sold to energy suppliers (for green tariff products),
- 4certificates redeemed by suppliers (prove renewable generation to Ofgem).
Platform
- automated REGO application (monthly generation data → Ofgem e-serve API), REGO registry integration (certificate portfolio management), and REGO trading workflow (certificate sale to counterparties with ISDA confirmation).
- Predictive Maintenance and O&
Wind turbine maintenance
- 1OEM scheduled maintenance (Siemens Gamesa, Vestas — annual service per turbine),
- 2condition-based monitoring (vibration analysis — bearing wear detection before failure),
- 3corrective maintenance (turbine fault — technician dispatch).
Predictive maintenance ML
- turbine vibration time-series → AWS SageMaker (anomaly detection model — trained on historical failure data) → maintenance alert (bearing replacement recommended 6 weeks before predicted failure vs reactive replacement saving £28,000 per turbine).
- O&
M scheduling
maintenance calendar (HSE Working at Height regulations — safe weather window for turbine climb), technician dispatch (nearest engineer to site), spare parts inventory.
Ofgem Balancing Mechanism Reporting
UK Balancing Mechanism (BM): National Grid ESO dispatches generation to balance supply and demand.
Wind farm BM reporting
- 1physical notifications (PN) submitted to Elexon API (expected generation for next 30 minutes — 30-minute settlement periods),
- 2bid/offer pairs (willingness to increase/decrease generation at a price — wind farms offer to curtail for constraint payments),
- 3final physical notifications (FPN — actual generation),
- 4metered volume data (half-hourly actual generation from settlement metering).
Imbalance cost
- inaccurate PNs lead to imbalance costs (Elexon charges for deviation from submitted PN).
- ClickMasters automates PN submission from SCADA forecast data.
The Results
Platform live at 22 weeks, £128,000 — under budget.
SCADA integration: 840 turbines live (10-second data).
REGO: 100% automated (previously 2 days/month manual).
Imbalance cost: 28% reduction (more accurate PN submissions from SCADA forecast).
Predictive maintenance: 18 bearing replacements predicted in first year (vs 4 reactive failures — £504,000 avoided maintenance cost at £28,000/turbine).
O&M scheduling efficiency: 22% improvement in technician utilisation.
SECR automated.
Ofgem audit: zero findings.
“840 turbines on 10-second SCADA data. REGO automated. Imbalance cost 28% down. £504,000 avoided maintenance cost from predictive maintenance in year one. O&M utilisation 22% better. SECR automated. Ofgem zero findings. The predictive maintenance ML was the business case anchor — 18 bearing predictions vs 4 reactive failures. The platform paid for itself in the first six months from bearing replacements alone." — Head of Asset Management, UK Renewables Operator (name withheld)”
Project Details
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