Like many other industries, the energy sector is currently grappling with the best ways to use artificial intelligence (AI) to improve operations and drive progress.
Photo by Biel Moro via Unsplash
One intriguing opportunity for bringing AI into the energy industry lies in finding solutions to challenges involved in energy storage. AI may offer numerous opportunities to optimize and enhance energy storage systems, making them more efficient, reliable, and economically viable. The opportunities made available by AI will also be essential in furthering the transition to renewable energy.
Using Patterns to Prepare for the Future
AI programs, if properly trained and implemented, can serve as excellent tools for recognizing patterns and pairing those patterns with automated responses.
In the context of the energy industry, this skill can be brought into play for the benefit of, for example, a public utility looking to monitor changes in energy demand at different times of the day or year to get an accurate picture of when and how energy will be needed. This is especially essential for firms looking to utilize renewable energy resources, as technologies such as solar panels and wind turbines only produce energy under the right conditions. These firms will need to store energy when possible in order to meet the needs of high-demand periods, and AI can help make storage strategies more adaptive.
AI programs can also use their pattern-recognition capabilities to quickly identify and respond to faults or abnormalities in energy storage systems, including issues like damaged or dysfunctional infrastructure. This proactive approach helps prevent catastrophic failures and ensures the continued operation of storage systems by warning energy companies of potential issues before they become critical.
Battery management offers another opportunity to integrate AI into an energy firm’s operations, according to a recent analysis for Energy Storage News by Carlos Nieto, Global Product Line Manager at the energy technology company ABB.
“As many operatives will know, energy storage operations can be complex. They typically involve constant monitoring of everything, from the BESS [Battery Energy Storage System] status, solar and wind outputs through to weather conditions and seasonality. Add to that the need to make decisions about when to charge and discharge the BESS in real-time, and the result can be challenging for human operators,” Nieto wrote.
“By introducing state-of-the art AI, we can now achieve all of this in real-time, around-the-clock for a much more effective and efficient energy storage operation.”
Nieto argues for an approach to using AI that takes advantage of the tech’s ability to tirelessly generate and respond to data. He also suggests that using AI to run simulations of different storage-related scenarios can help ensure the plans firms have in place for handling adverse events will be likely to work when the real thing hits.
AI, Energy Storage, and Renewable Energy
The transition away from traditional energy sources to renewables is one of the biggest challenges the energy sector must face at this time. The success of this transition is crucial to the reduction of greenhouse gas emissions and the worst effects of climate change. The transition is also a source of opportunity, presenting economies across the globe with new jobs and investment options while offering a chance at a healthier world, according to the United Nations.
One significant hurdle to implementing renewable energy sources is the unpredictability of those sources, according to Greg Jackson, founder and CEO of U.K.-based Octopus Energy.
“To create a clean-energy system in a way that is good for consumers and good for businesses, we need to build the technology that lets us capitalize on the times when the sun is shining, the wind is blowing, and you’ve got this endless, abundant, zero-marginal-cost energy,” he told consulting firm McKinsey in a recent report.
Jackson isn’t alone in recognizing this challenge and the role technology can play in overcoming it. A 2022 article published in the journal Energy and AI, for example, offered a more robust overview of the potential successes and shortcomings of using AI in the energy transition.
According to the article’s authors, “numerous studies have shown that the renewable energy generation will make the grid highly volatile due to the massive application of intermittent and fluctuated renewable energy (such as wind and solar energy). Therefore, reasonable operation methods of renewable energy generation equipment are required to achieve automated system control and improve the automation with grid intelligence.”
Future developments in AI technology for the energy industry will likely go in a similar direction, the authors conclude, stating that researchers will need to continue targeting other renewable energy-related weak points such as connectivity for electric vehicles and and the revitalization of energy communities.
