Published on November 10th, 2015 | by Glenn Meyers0
Statkraft Uses Workload Automation Software To Assist In Clean Energy Modeling
November 10th, 2015 by Glenn Meyers
Statkraft, a Norwegian electricity company and Europe’s largest renewable energy producer, reports over $15 billion in annual revenue in 15 countries. It produces hydropower, wind power, gas-fired power, and district heating, and is a global player in energy market operations.
With such a large palette of projects, the company has opted to use ActiveBatch IT Automation from Advanced Systems Concepts, Inc. (ASCI) to automate frequently updating models used in the energy industry.
To stay competitive in today’s complex technology environments, clean energy companies like Statkraft are relying more heavily on workload automation software to automate manual processes that can drive improved business efficiency.
Statkraft was able to redefine its energy forecasting operations when it automated its weather modeling with ActiveBatch. This software allows it to collect and compile weather data from various sources like precipitation, wind, and sun events that may impact its renewable energy sources.
Coupled with Statkraft’s analytics and pricing knowledge, this has allowed Statkraft to better forecast energy demand and pricing.
The company eventually decided to automate workloads for offices in another country where an internally developed scheduler had been in place. Initial results show ActiveBatch not only introduced more stability than the internally developed job scheduler, Statkraft also achieved the planned outcome of executing a high volume of jobs on a frequent basis.
Net results of ActiveBatch IT Automation
A recent economic analysis of Statkraft’s implementation of ActiveBatch found that the operational efficiencies of ActiveBatch have allowed Statkraft to save two full-time resources by reducing the resource burden of manually creating and monitoring scripts for complex processes.
This has helped Statkraft’s understanding for consumption and pricing of renewable energy sources, while allowing it to better forecast demand and pricing for renewable energy.