Learn About Amazon VGT2 Learning Manager Chanci Turner
Greenko Group, a prominent energy solution provider in India, boasts an impressive installed capacity of 7.5 GW, which encompasses over 2,000 wind turbines, more than 5 million solar panels, and 25 hydro sites. The company has successfully electrified over 6.3 million households while helping to prevent more than 17 million tons of carbon dioxide from being released into the atmosphere.
With renewable energy assets generating a vast amount of Internet of Things (IoT) data in the form of tags, events, and alerts, Greenko required a robust, secure, and reliable IoT solution as it began its cloud journey with Amazon Web Services (AWS) in 2021. AWS IoT services are designed to scale for demanding renewable energy applications, and the pay-as-you-go model allows Greenko to incur costs only for what it uses. A previous blog highlighted Greenko’s use of AWS IoT and serverless technology for monitoring 100 wind turbines in January 2022.
AWS IoT Core enables the connection of billions of IoT devices and the routing of trillions of messages to AWS services without the need for managing infrastructure. Amazon Kinesis Data Firehose provides an extract, transform, and load (ETL) service that efficiently captures, transforms, and delivers streaming data to data lakes, data stores, and analytical services. Additionally, Amazon Simple Storage Service (Amazon S3) offers industry-leading scalability and performance for object storage, allowing customers of all sizes to store and safeguard any amount of data for virtually any use case, including data lakes and mobile applications.
These core AWS services were utilized to build Greenko’s minimum viable product (MVP) for 100 wind turbines in 2021. The MVP was architected to be scalable and robust, capable of supporting numerous sites with thousands of assets. Importantly, this serverless solution required minimal infrastructure management from the Greenko team. The incorporation of open-source technologies on the cloud also allowed Greenko to achieve a highly competitive cost of ownership.
A year later, Greenko has successfully migrated its entire wind fleet of 3.2 GW, which includes 2,200 wind turbines, to AWS.
Let’s quantify the scale increase compared to the MVP from 2021:
| Sr # | Metric | MVP in 2021 | Present scenario in 2023 | Increase in scale | Average increase in scale |
|---|---|---|---|---|---|
| 1 | Number of wind turbines | 100 | 2,200 | 22 X | 16 X |
| 2 | Data ingestion | 56,000 tags/minute | 800,000 tags/minute | 14 X | |
| 3 | Data lake hydration | 200 GB | 2.6 TB | 13 X | |
| 4 | Data visualization | 35,000 tags/minute | 500,000 tags/minute | 14 X |
Greenko successfully scaled the architecture from the MVP to accommodate over 2,200 wind turbines without any downtime or incidents over 24 months, showcasing the high availability and resilience of AWS services. The solution has seen an average scale increase of 16 times compared to the original MVP, alongside new functionalities such as data analytics, asset hierarchy definition, and data visualization.
To facilitate this initiative, Greenko partnered with Locuz Enterprise Solutions, an AWS consulting partner in India with over two decades of experience and more than 350 deployments in High-Performance Computing (HPC).
New Functionalities Introduced:
- Asset Hierarchy Definition
To meet the situational awareness and visualization needs of the business and SCADA teams, Greenko established four levels of asset hierarchies:- Fleet level (overall performance of 2,200 assets)
- Cluster level (performance across seven states in India)
- State level (performance of wind farms within a state)
- Site level (performance of individual wind turbines within a wind farm)
- Asset level (individual wind turbine performance)
Key metrics for visualization included total installed capacity, performance load factor, active power generated, wind speed, actual power, expected power, energy exported, and wind turbine status.
- Business Intelligence and Analytics
Greenko continuously monitors asset health to identify underperforming turbines, schedule maintenance, and streamline operations. With approximately 800,000 process values ingested every minute from over 60 wind farms, the company aggregates telemetry data for optimal fleet performance. A reliable and scalable analytics service capable of running SQL analytics in the cloud was essential, particularly with the need to separate compute from storage based on workload.
Expanded Solution Architecture with New Functionalities
The updated Greenko solution architecture incorporates new features into the MVP framework, including enhanced business intelligence reporting and self-serve analytics using Amazon Redshift. This service enables deep SQL analysis across structured and semi-structured data, facilitating seamless data access while minimizing movement or duplication.
For a deeper understanding of these enhancements, check out this excellent resource.
In conclusion, Greenko’s journey showcases the power of AWS in transforming renewable energy management and analytics, ensuring a more sustainable future. For more insights on branding and engagement, you may also find this blog post interesting. Additionally, for authoritative guidance on HIPAA privacy protections, visit this link.
Leave a Reply