Inaccurate representation of grid assets makes the system less reliable and more expensive to operate, according to Queued Up: Characteristics of Power Plants Seeking Transmission Interconnection as of the End of 2020, a study from Lawrence Berkeley National Laboratory. Berkeley Lab released a series of notes in 2023 analyzing interconnection cost trends in five U.S. wholesale electricity markets, which found that incomplete modeling of grid load capacity resulted in under -network utilization and network upgrade costs reflecting 20-40% of capital expenditure for next generation projects.
Network digitization also enables the application of a system of protocols and standards to help ensure network reliability and control, much like the United States provides predictable and coordinated Internet service. Beyond reliability benefits, digitalization can help identify optimal locations for energy storage on the grid, so owners and operators can create electron warehouses – or energy buffers – for access to customer demand in a reliable, resilient and affordable manner. This buffer is also valuable because it can help address the increasing number and complexity of resources in the distribution system as well as respond responsively and flexibly to the dynamic needs of the transportation system.
Virtual power plants
AI/ML is driving the transition to a digital grid. Data collected, modeled and analyzed using AI/ML creates a virtuous cycle of continuous refinement and improvement.
By enabling trained AI to engage in problem-solving conversations with engineers, the tedious and manual problem of network modeling for planning and interconnection can be significantly improved. The planning process can be further improved through high-quality data feeds from digital assets and advances in IT efficiency, thereby speeding up modeling and optimizing the incorporation of new assets. These advances in AI support, high-quality data and compute help bring T&D together into a holistic, fully integrated network planning process with an interconnected workforce that now has bandwidth and data-driven insights to identify and address future networks, customers and decarbonization needs in a coordinated manner across all levels of the network.
A workforce so equipped can leverage AI/ML to optimize the active participation of distributed energy resources (DERs), including virtual power plants (VPPs), to meet local grid needs in a manner that more delegated and distributed. By aggregating a collection of smaller energy resources (e.g., smart thermostats, electric vehicles, batteries, distributed solar, and other smart devices), VPPs can provide generation resources, reliability, and economic value to the grid, just like traditional power plants. When these assets are intelligently optimized and coordinated, they deliver cost-effective electrification, locally resilient energy supply and grid services while reducing T&D bottlenecks.
As a next-generation utility, AES is at the forefront of exploring new technologies to build a digital grid that is not only capable of greater flexibility and reliability, but optimized to manage and integrate renewable assets that will be necessary to decarbonize the energy system. .
Where to go from here
By adopting intelligent digitalization, exploring innovative solutions and using data – the value of which increases over time – network owners, operators and energy producers can build a more dynamic and resilient network that delivers reliably clean, affordable energy. AES already leverages AI to help with vegetation management, weather forecasting and asset optimization, and has begun using AI to analyze transmission system data to enable dynamic line assessment . Embracing a digital future powered, in part, by AI/ML will enable utilities to achieve their clean energy goals more quickly. And a digital system could be closer than the industry thinks.
