Powering the Future: How ju:niz Energy Leveraged InfluxDB to Optimize Renewable Energy Systems

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Powering the Future: How ju:niz Energy Leveraged InfluxDB to Optimize Renewable Energy Systems

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ju:niz Energy is at the forefront of the decentralized energy transition in Germany. Why? Because ju:niz Energy has intelligent energy management systems that control battery storage and decentralized energy systems for optimal results. Their decentralized energy supplies include renewable energies, battery storage, hydrogen, and large-scale storage systems that operate in a grid-friendly, economical manner.

ju:niz Energy’s challenge

ju:niz Energy engineers need both fresh and historical data for decentralized production, storage, and conversion of renewable energy. For example, they use data to monitor the climate condition of each plant to ensure conditions remain at battery-safe levels. For battery optimization, engineers use frequency reserve and trading.

Eventually, ju:niz Energy’s legacy hardware and software systems couldn’t keep up with their data needs. Unreliable syncing between edge locations and ju:niz Energy’s centralized monitoring database caused data loss. The lack of storage space in edge database instances and the cloud led to tight retention policies. To continue growing, ju:niz needed new technology as innovative as their energy standards.

These challenges spurred ju:niz Energy engineers to upgrade InfluxDB OSS 1.x to InfluxDB 2.0 and add InfluxDB Cloud v1. Using InfluxDB OSS 2.0 and InfluxDB Cloud v1 together, ju:niz acquired reliable syncing between edge locations and the cloud, a highly available database, and increased storage space. But setbacks persisted. The remaining legacy hardware in ju:niz Energy’s older facilities was incompatible with Influx DB OSS 2.0’s tooling, rendering EDR inoperative. There were still data points that failed to reach the cloud. The Cloud v1 cluster bottlenecked regularly, and the CPU load hovered around 70-80%—not the result the engineering team hoped for.

Shortly after ju:niz Energy added Cloud v1, InfluxDB launched InfluxDB Cloud Dedicated, a fully-managed, single-tenant product in the InfluxDB 3.0 product suite. Cloud Dedicated was designed for larger workloads with inconsistent data, such as ju:niz Energy’s.

The solution

ju:niz Energy began sending data from all its edge locations to InfluxDB Cloud Dedicated. The Cloud Dedicated cluster keeps pace with data ingest, and the CPU and memory maintain normal levels while receiving fresh data. There are no spikes or bottlenecks. The process of migrating older data from Cloud 1 and OSS 1.x remains underway, and InfluxDB engineers are working on advanced migration tooling.

Architecture diagram from ju:niz Energy’s newer plants

The diagram below depicts the architecture and data flow from ju:niz Energy’s newer plants.

influxdb cloud dedicated

ju:niz Energy uses iEMS SPS to control its plants and hold all its data. Engineers connect to the iEMS SPS controller using the Modbus protocol. They then use open source Telegraf, the data collection agent for InfluxDB, to read data from the iEMS SPS and write to InfluxDB OSS. ju:niz Energy sends all data from the local InfluxDB OSS instances to the central, AWS-hosted, Cloud Dedicated cluster using EDR.

ju:niz Energy engineers no longer find it cost-prohibitive to store all their data in the cloud thanks to InfluxDB Cloud Dedicated’s high-ratio data compression. InfluxDB Cloud Dedicated queries perform well regardless of the size, time range, or number of included fields. Engineers can now view the results of several queries in a single Grafana dashboard and modify the query’s data points on demand. InfluxDB Cloud Dedicated’s performant queries enabled ju:niz to refine their Grafana alerting systems, streamlining their incident management workflow.

To learn more about ju:niz Energy, read the full case study here.

About the author

Jessica Wachtel

This article was written by Jessica Wachtel. She is a Developer Marketing Writer at InfluxData where she creates content that helps make the world of time series data more understandable and accessible. Jessica has a background in software development and technical journalism.