Energy Management Platform includes Artificial Intelligence and Machine Learning

December 03, 2018 by Paul Shepard

The technology group Wärtsilä today announced the release of GEMS 6 (Greensmith Energy Management System, version 6), the latest generation of its award-winning software platform proven across grid-scale deployments globally. With this latest release, GEMS continues to be the most advanced energy management solution available today, deployed across more than 70 grid-scale systems in nine countries, integrated with a multitude of thermal and renewable generation assets as well as load and weather forecasting data.

Using software-based intelligence and machine learning to enhance grid systems and networks, GEMS 6 offers the widest library of algorithms and applications available in the industry.

“Greensmith Energy is proud to announce the latest innovation in our product portfolio with the release of GEMS 6,” said John Jung, President and CEO of Greensmith Energy. “With this industry-leading energy management platform, we will enable our utility and IPP customers to integrate and optimize all of their energy resources to improve resiliency and flexibility during a time of tremendous transition – paving a viable way towards a 100 percent renewable energy future.”

GEMS 6 offers a groundbreaking set of capabilities not before seen in the energy industry. The software platform has been used to integrate the broadest array of generation assets including solar, wind, hydro, storage and thermal. GEMS 6 is component-neutral to provide maximum flexible and has integrated 16 different batteries to date, each according to the application needs of each project. GEMS 6 can be used independent of energy storage and will be commercially available in 2019.

“The software release also means that regardless of hardware mix, GEMS 6 has the ability to deliver more reliability and value for any grid operator looking for a single platform to control all assets,” Jung added.

With GEMS, renewable energy sources and engine systems can now be equally modeled and simulated in a virtual environment using real-time data, which allows GEMS to be lab-tested, rather than on-site, and offers significant cost efficiencies in testing time, outages, and time-to-market. GEMS also integrates weather and load forecast data to optimize an entire grid system.

GEMS can operate autonomously and automatically, based on either a rule-based engine or machine-learning, to act and function according to different or specific conditions sensed within integrated systems such as solar and/or the grid itself. GEMS is capable of processing more than 100,000 points of data per second in default mode and uses data analytics to improve the way in which it optimizes energy storage and the overall grid system into which it is integrated.