Colin Parris

Senior Vice President and Chief Technology Officer
GE Digital

When world leaders met in Glasgow, Scotland, for the 26th United Nations Climate Change Conference in November 2021, there was one conspicuous attendee that all participants acknowledged: global energy demand. Right now, nearly all of the participating nations are experiencing what is being referred to as an energy “crisis” — rising costs and tight supplies driven largely by increasing demand.

According to research results from the Energy Information Administration (EIA), we are projected to see a 50% increase in global energy usage by 2050. Meanwhile, findings of a recent study from the International Energy Agency (IEA) show that “70% of the projected increase in global energy demand is in emerging markets and developing economies, where demand is set to rise to 3.4% above 2019 levels.”

Coping With the Rising Global Energy Demand

The challenge we are facing is three-fold. First, we need to continue providing reliable energy to homes, hospitals, schools and businesses to meet increasing societal demand and boost productivity and prosperity. Second, we need to decarbonize the energy sector and bring more clean power sources onto the electric grid because, simply put, the future of our planet depends on it. Third, we need to increase the reliability and resiliency of the grid network, which is becoming progressively dynamic with increasing penetration of intermittent centralized-bulk and distributed renewable assets. When combined, the energy transition is one of the largest, most complex puzzles humanity has ever had to solve.

For this, we should consider a new approach that uses the latest technologies. This includes software and artificial intelligence (AI) solutions that can increase grid capacity, accelerate renewables integration and reduce outage time. At the same time, these solutions can help transmission and distribution energy providers optimize energy flow to meet demand and carbon targets. We need to embrace digital in the future of energy.

Initially, the grid was a one-way flow of electricity from predictable generation sources to static consumption endpoints. Today, endpoints such as solar rooftop panels in homes and wind farms in industrial businesses can both consume and generate electricity. This adds a layer of complexity given the intermittent nature of renewable sources. And the complexity increases as more distributed renewables — electric vehicles, solar panels, batteries and wind farms — are added to the network, as well as when energy demands and carbon restrictions increase and weather events magnify.

Digital Technologies to Consider

The challenge for utilities begins with forecasting this varying and complex electricity demand to generate and purchase the electricity needed a day ahead — or even minutes ahead — while orchestrating this flow to maintain the operation of the grid. By using digital technologies, utilities could transform the enormous amount of data coming from these distributed energy resources (DERs) into situational intelligence.

For example, advanced distribution management solutions (ADMS) aim to provide tools for the safe and secure management and orchestration of the generated electricity while maintaining stability and responding quickly to changes that can occur. An advanced energy management system (AEMS) works to process the flow of electricity generated by bulk energy generation sources (e.g., combined cycle gas and steam power plants, large wind and solar farms or nuclear reactors). This aims to securely and stably transmit electricity over long distances to the distribution networks while managing the unique problems that occur in these long-haul electric lines.

To stabilize the influx of renewable power onto these complex, dynamic networks that comprise the grid, digital twins could be used. These living, learning physics models or AI-based software representations of physical assets or systems allow utilities to detect current problems, prevent escalation, predict future situations and optimize electric flow. In this way, they can help solve the demands for more electricity with less carbon output and at a more affordable cost. In addition, accurate digital data from sensors and other monitoring equipment, as well as information about weather forecasts, is important in managing the growth of DERs and providing the best data input for digital twins.

Accounting for predicted weather conditions gives operators using digital twins and other sophisticated analytics the ability to look days ahead so they can be proactive in planning for distribution grid impacts and the response. At the same time, utilities need to mitigate the threats to the network that are brought on by vegetation-caused outages and damage to assets triggered by extreme weather events brought on by climate change. Software solutions that include visual intelligence through satellite and drone technology can help address these challenges by determining what vegetation should be grown near these lines, thereby helping to prevent the problem before it starts.

The Bottom Line

The bottom line is this: The key to providing reliable power to the world and ensuring a secure energy future in every corner of the globe starts by using data, analytics and software solutions to help solve this global imperative. Through the use of digital technology, the world can meet this challenge and secure a more promising energy future.

This article was originally published on and has been reprinted with permission.

Colin Parris is Senior Vice President and Chief Technology Officer of GE Digital, a global leader in digital technology and empowering digital transformation efforts at scale. A recent recipient of the 2023 Black Engineer of the Year award by US Black Engineer & Information Technology magazine, he has more than three decades of experience developing new technologies and driving digital transformation for his companies and their clients. He has written more than 300 articles and technical papers on digital twin, industrial AI, artificial intelligence, power systems, machine learning and more.

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