Energy Trading with Apache Kafka and Flink

Energy trading and data streaming are connected because real-time data helps traders make better decisions in the fast-moving energy markets. This data includes things like price changes, supply and demand, smart IoT meters and sensors, and weather, which help traders react quickly and plan effectively. As a result, data streaming with Apache Kafka and Apache Flink makes the market clearer, speeds up information sharing, and improves forecasting and risk management. This blog post explores the use cases and architectures for scalable and reliable real-time energy trading, including real-world deployments from Uniper, re.alto and Powerledger.

(Originally posted on Kai Waehner’s blog: “Energy Trading with Apache Kafka and Flink”… Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter)

What is Energy Trading?

Energy trading is the process of buying and selling energy commodities in order to manage risk, optimize costs, and ensure the efficient distribution of energy. Commodities traded include:

• Electricity: Traded in wholesale markets to balance supply and demand.
• Natural Gas: Bought and sold for heating, electricity generation, and industrial use.
• Oil: Crude oil and refined products…