Together with a large European energy company we set up a central data hub to efficiently buy and sell electricity and other commodities.
One important feature distinguishes energy trading from buying and selling traditional financial products such as stocks: the diversity and complexity of factors that determine the price of a kilowatt-hour at a certain point in time. Those factors range from the political situation in the Middle East to the spare grid capacity of international networks to weather forecasts for certain regions.
Therefore, several energy trading companies have undertaken to setting up systems that would integrate all this information into one central platform. Here, all the disparate data can then be accessed and coherently analysed.
Forecast for 40 Years
Amid the increasingly central role played by data analytics, we collaborated with a leading European energy company to set up such a platform. Specifically, the system was to automatically gather all necessary data from exchanges, weather stations, network operators and other sources through specific interfaces. Then it had to integrate the data into a central data hub.
There it would be made available for real time analysis and price forecasts for a period of up to 40 years, a particularly long-term outlook necessary for planning issues such as investments in power stations. The establishment of such a comprehensive system is a mammoth task, on which three other companies had previously already failed to deliver.
For this purpose, we distributed a team of up to 20experts across our offices in Hamburg, Munich and Wroclaw as well as three European offices of our client. Together they developed a data hub with three building blocks: a central database; a service layer with individual access rights determining who can retrieve and edit which data when; and an analytics kernel using Matlab software to run previously developed models. One of the experts responsible for the models was our data analyst Paulina Florczak.
“For me, the project was not only exciting because I got to know three working environments and challenges during my assignment between Wroclaw, Hamburg and Stockholm. The development of the analytics framework was also the kind of tricky undertaking that I love as a mathematician.”
Paulina Florczak, Data Analyst
To this technological basis we added three further elements: a feed framework that automatically integrates information from newly connected sources. Second, a data catalogue allowing all stakeholders to collectively gather, organise and enrich data, which facilitates discovery, governance and collaborative model development. Finally, we jointly developed a quantitative model library. It provides specifically adapted algorithms for our client’s analysis and trading business.
Gathering Data and Models
External data feeds established
Matlab models integrated into the Model Library
Our steps to automate the integration and processing of derived market data as well as valuation and risk analytics have brought several advantages. They have simplified, stabilised and accelerated essential daily processes of our client.
Good Data Travels Fast
Pricing process from quotes to final forward curves
< 45 mins
Even more importantly, our work guided our client in its transformation into a data-enabled organisation. Specifically, the creation of a coherent data platform for the entire enterprise has boosted collaboration between different units.
Furthermore, it has generated new insights into changes and interdependencies in the market. That has allowed our client to strategically position its trading business for future challenges.
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