Electric utilities are taking a course in machine learning to create smarter grids for tough challenges ahead.
The winter 2021 megastorm in Texas left millions without power. Grid failures the past two summers sparked devastating wildfires amid California's record drought.
Extreme weather events of 2021 highlighted the risks climate change is introducing, and the importance of investing in more resilient electricity grids, said a May 2021 report from the International Energy Agency, a group with members from more than 30 countries. It called for a net-zero carbon grid by 2050, fueled by hundreds more gigawatts in renewable sources.
The goal demands a transformation. Yesterday's hundred-year-old grid - a one-way system from a few big power plants to many users - must morph into a two-way, flexible, distributed network connected to homes and buildings that sport solar panels, batteries and electric vehicles.
Given the changes ahead, experts say the grid must expand autonomous control systems that gather data at every node and use it to respond in real time.
An Essential Ingredient AI will play a crucial role maintaining stability for an electric grid that's becoming exponentially more complex with large numbers of low-capacity, variable generation sources like wind and solar coming online and two-way power flowing into and out of houses, said Jeremy Renshaw, a senior program manager at the Electric Power Research Institute (EPRI), an independent, non-profit that collaborates with more than 450 companies in 45 countries on energy R&D.
AI can support grid operators already stretched to their limits by automating repetitive or time-consuming tasks, said Renshaw, who manages EPRI's AI initiative.
Rick Perez, a principal at Deloitte Consulting LLP with more than 16 years working with utilities and data analytics, agrees.
The future energy grid will be distributed and fueled by thousands of intermittent power sources including wind farms and various storage technologies. Managing it requires advanced AI methods and high performance computing, he said.
Real Projects, Real Results Work is already underway at power plants and substations, on distribution lines and inside homes and businesses.
Some of the largest utilities in the U.S. are taking the first steps of creating a data engineering platform and an edge-computing practice, using sensor arrays and real-time analysis, said Perez.
For example, a utility in a large U.S. city recently got traction with AI on NVIDIA GPUs, determining in less than 30 minutes the best truck routes for responding to a storm. Past efforts on CPU-based systems took up to 36 hours, too long to be useful.
To show utilities what's possible, Deloitte runs jobs on NVIDIA DGX A100 systems in its Center for AI Computing. One effort combines data on the state of the electric grid with local weather conditions to identify - in time to dispatch a repair crew - distribution lines caked with ice and in danger of failing.
Because it's an open system, we could use our existing IT staff and, with NVIDIA's support, do supercomputing-class work for our client, Perez said.
Building AI Models, Datasets At EPRI, Renshaw reports progress on several fronts.
For example, more than 300 organizations have joined its L2RPN challenge to build AI models with reinforcement learning. Some are capable of controlling as many as five tasks at once to prevent an outage.
We want to automate 80 percent of the mundane tasks for operators, so they can do a better job focusing on the 20 percent of the most complex challenges, said Renshaw.
A 2021 report on how AI can address climate change cited as an important use case the L2RPN work which is expanding this year to include more complex models.
Separately, EPRI is curating 10 sets of anonymous data utilities can use to train AI models for their most critical jobs. One is a database that already sports 150,000 images taken by drones of aging equipment on powerlines.
EPRI also leads a startup incubator where utilities can collaborate with AI startups like Noteworthy AI, a member of NVIDIA Inception, to work on innovative projects. To keep shared data private, it can use NVIDIA FLARE software to train AI models.
Power Plants Get Digital Twins Both EPRI and Deloitte are helping create industrial digital twins to optimize operations and training at power plants. For example, a power plant in one southern U.S. state is acting as a demo facility in an EPRI project that's gathered broad interest.
Separately, Deloitte plans to use NVIDIA Omniverse Enterprise to develop a physically accurate digital twin of a nuclear power plant for worker training scenarios.
Regulators are providing multiple grants for building digital twins of power plants to increase safety and reduce the high costs of shutting systems down for tests, Perez said.
Truly Smart Meters Debut This Year Similarly, both EPRI and Deloitte are helping define the next generation of smart meters.
We call today's systems smart meters, but in reality they send maybe one data point every 15 minutes which is very slow by today's standards, said Renshaw.
By contrast, software-defined smart grid chips and meters in development by Utilidata, a member of NVIDIA Inception, a free program for cutting-edge startups, and Anuranet use the next generation of the NVIDIA Jetson edge AI platform to process more than 30,000 data points per second. They seek insights that save energy and cost while increasing the grid's resilience.
If we can get sub-second data, it opens up a wealth of opportunities - we've identified 81 use cases for data from the next generation of smart meters, he said.
AI using data from one of these new meters could have predicted his home HVAC system needed repair before it failed last year, costing him more tha
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Electric utilities are taking a course in machine learning to create smarter grids for tough challenges ahead.
