Distributed Solar Systems at EDF Renewables and AWS IoT: A Natural Fit (PUT304) - AWS re:Invent 2018
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The AWS suite of managed services for IoT enables companies to quickly and easily deploy devices to the edge and synchronize their industrial time-series data from multiple sites to the AWS Cloud, where advanced analytics and machine learning can generate valuable insights about their business. In this session, learn how EDF Renewables used AWS Greengrass, AWS IoT Core, AWS IoT Analytics, and AWS Lambda to facilitate the collection, aggregation, and quality assurance of operational data from solar installations. Hear how working with AWS Professional Services transformed its approach to product development, and learn what challenges and solutions came with choosing leading-edge services form AWS.
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Distributed Solar Systems at EDF Renewables and AWS IoT: A Natural Fit (PUT304) - AWS re:Invent 2018
- 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Distributed Solar Systems at EDF Renewables and AWS IoT: A Natural Fit Amy Lindsay Product Owner: Solar & IoT EDF Renewables P U T 3 0 4 Matthieu Fuzellier IoT Architect AWS Professional Services
- 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda • Introduction • Problem definition • Technical solution • Lessons learned • Business results
- 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. After this breakout session • A few days after the event… • The presentation will be shared as a PDF on SlideShare • The recording will be available on YouTube • Visit the Power & Utilities web site at: aws.amazon.com/power-and-utilities/
- 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. EDF Renewables
- 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. EDF renewables • We are in the process of building a common cloud-based digital platform to address industry-wide challenges • Decentralization and “prosumers” of energy • Need for more agility, increased speed to test new services • Breaking the silos of data and knowledge-base by providing transparency at the group level on performance and processes • Adopted a flavor of scaled agile framework
- 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data challenges on solar assets • Geographically distributed assets • Unreliable connectivity • Diverse portfolio for owned and third party assets, leading to a variety of • Physical devices • Asset layouts • Data sources & data stores • Communication protocols • Data quality is critical since underperformance detection relies on cross-data- source analysis
- 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data challenges on solar assets An example: Inverters Inverters Inverters DC combiner boxes DC combiner boxes Panel temperature sensors MET station Substation Meter Real Time Automation Controller (RTAC) Supervisory Control And Data Acquisition (SCADA) Data historian Modbus or proprietary communications over TCP/IP or RS485 Modbus / DNP3 Utility DNP3 Modbus / OPC DA / DNP3 Modbus / OPC DA / DNP3 Trackers Video / camera
- 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Methodology • Working backwards: delivering a strong business use case before addressing the technical solution • Accelerated time to delivery of an end-to-end solution • Ramp up internal skill sets while benefiting from expertise of AWS teams • Interviews of key business users • PR / FAQ 2 weeks • Workshop with AWS ProServe • Target architecture 3 days • Feature refinement: Proof of Concept vs long-term • Prioritized backlog 2 days • Agile delivery framework • Demonstration of end-to-end ingestion (Proof of Concept) 5 weeks
- 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Business outcomes “No more need to remote into multiple SCADA systems that all differ in the way they collect and store the data - you can now see how your portfolio of plants is performing from a central location and troubleshoot issues quickly. The data pipelines include OPC DA, OPC UA, Modbus and DNP3, covering all popular communication protocols for solar sites.” - extract from mock PR / FAQ Sample user stories from workshop: “As a Site Manager, I want to be able to export a clean data set with data quality indicators so that reporting is accurate and shows the level of confidence in the data” “As a Performance Analytics Engineer, I want to have normalized data tags so that multiple sites can be analyzed ” “As a SCADA Engineer, I want to have a gateway that is easy to onboard so that I can spend more time on higher value-added tasks” “As a Site Manager, I want to see the data of my multiple site refreshing every 10 minutes so that I can troubleshoot issues quickly”
- 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data acquisition from solar sites
- 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Solution overview AWS Cloud Solar site assets Inverters, panel sensors, meter, met station, camera… GridEdge Gateway Reporting, Analytics, Machine Learning, Monitoring/Alerting Solar Site (Edge) AWS Cloud Data Consumers Operations control center, third parties, asset owners, utilities…
- 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. MQTT High-level architecture Solar site assets Inverters, panel sensors, meter, met station, camera… GridEdge Gateway AWS CloudSolar Site (Edge) Notifications Raw and processed data
- 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Edge detailed architecture MQTT Topic Structure Shadow Sync AWS Cloud Shadow Device Registry Greengrass Group Certificate and Policy Device Provisioning Site Configuration Solar Site (Edge) Solar site assets Inverters, panel sensors, meter, met station, camera… GridEdge Gateway Greengrass Lambda function Certificate Shadow Modbus over TCP
- 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Edge configuration and IEC61850 mappings Site layout configuration, stored in the Cloud: { "siteID" : "mySite01" "points" : [ { "modbus_start_register":"25", "modbus_end_register":"26", "iec_name":"INV1MMXU1.AvWPhs.mag.f", "explanation":"active power for inverter 1", "type":"Input Register", "rtac_poll_frequency_ms":"1000” } ... ] } Config File Solar Site (Edge) Solar site assets Inverters, panel sensors, meter, met station, camera… GridEdge Gateway AWS Greengrass Lambda function Certificate Modbus over TCP
- 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Edge data sampling and stacking Payload: { "timestamp": "20181015 09:01:00", "someField": 11 } Time Value 09:00:10 10 09:00:20 12 09:00:30 11 09:00:40 14 09:00:50 9 09:01:00 10Config File Solar Site (Edge) Solar site assets Inverters, panel sensors, meter, met station, camera… GridEdge Gateway AWS Greengrass Lambda function Certificate Modbus over TCP
- 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Solar Site (Edge) Edge data cleaning and tagging Payload: { "timestamp": 20181015 09:01:00, "someField ":11, "dataQuality": { "max": 14, "min": 9, "sample_count": 4, "confidence": 0.66, } } AWS Cloud Raw Data Raw samples Solar site assets GridEdge Gateway AWS Greengrass Lambda function Certificate Modbus over TCP Time Value 09:00:10 10 09:00:20 NULL 09:00:30 11 Time Value 09:00:40 14 09:00:50 9 09:01:00 9999
- 21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Edge summary { "timestamp": "2018-10-11 11:39:29", "gridedgeid": "000001", "payload": [{ "type": "SOLAR", "name": "device.name", "values": { "INV6MMXU1.AvWPhs.mag.f": 809.8, ... }, "dq": { "INV6MMXU1.AvWPhs.mag.f": { "max": 810.3, "min": 809.1, "std_deviation": 0.1678, "sample_count": 22 }, ... } }] }
- 22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Cloud - Ingestion Solar site assets Inverters, panel sensors, meter, met station, camera… GridEdge Gateway Solar Site (Edge) Notification AWS Cloud MQTT Shadow Rules Engine Reporting EDF/3rd party integration Datasets Templates and scripts Automation
- 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Upcoming Solar Site (Edge) Solar site assets Inverters, panel sensors, meter, met station, camera… GridEdge Gateway Greengrass Certificate Shadow Modbus / DNP3 Raw samples Feed into ML and complex analytics Notification AWS Cloud Data Lake Automation Configuration Operations Control Center Operators Management and Security Remote management Lambda InsightsAlerts Detect stale data Auto backfill data
- 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lessons learned • Infrastructure automation cannot always keep up with fast-pace changes to AWS services • Not all services support AWS CloudFormation e.g. IoT Analytics • Permission boundaries for AWS Identity and Access Management (IAM) not yet in AWS CloudFormation • Configuring hardware outside of the cloud platform to run cloud- native services such as AWS Greengrass requires orchestration tools to perform necessary actions on the device and in the cloud • As expected, the proof of concept does not answer all questions for a deployment at scale but sets out a clear path to achieve that goal
- 26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. • Start small, keep the big picture in mind • How to reuse / comply with existing OT / IT infrastructure • Data architecture and data normalization are key pieces of the solution • Digital disruption is real: focus on change management Lessons learned THINK BIG Start small
- 27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Business Results • Demonstrated the suitability of AWS managed services around IoT for our business requirements, within 5 weeks • Deployed 1 test device to 1 test site • Identified cybersecurity best practices and recommendations when extending the cloud platform out to the edge Proof of Concept = small: - Deployment - Feature set Roll-out plan = big: - Deployment - Feature set
- 29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Business results Next steps • Deployment at scale • Skill sets & partners • Architecture validation 4 weeks • Hardware design • Multi-device management • Industrialization 12-16 weeks • Production-grade • Roll-out • Full-scale deployment Steady-state
- 30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Business results Four GW+ in AWS cloud and counting Solution could address 100 MWs today and many more in the pipeline
- 31. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amy Lindsay Solar & IoT EDF Renewables amy.lindsay@edf-re.com www.linkedin.com/in/amy-lindsay Matthieu Fuzellier Solution Architect AWS ProServe mattfuz@amazon.fr www.linkedin.com/in/mfuzellier
- 32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.