Introduction 

Artificial intelligence (A.I.) is arguably the most hotly contested technological advancement of our time, and it is easy to see why. It boasts capabilities far beyond anything a human could accomplish, and even in its early stages of implementation, A.I. offers unimaginable possibilities. Yet many fear the technology for its potential to eliminate jobs or even to take control over its creators. The technology is so new and has so much room to develop that it is impossible to predict its trajectory, but now that this “Pandora’s Box” has been opened, A.I. will continue to play a significant role in future years.

A.I. has a massive range of applications, one of which is the energy industry; however, it also poses a threat to the electric grid if left unchecked. Since data centers run on copious amounts of electricity, A.I. and energy are permanently intertwined — for good and for bad. It is crucial to adapt the energy sector to the onset of A.I. to ensure both the power grid and the technology function as needed. Therefore, this article considers the problems and benefits of A.I. from an energy perspective. 

Energy Consumption Problems

A major problem with A.I. that has been gaining global attention recently is the technology’s alarming levels of energy consumption. An example from current headlines is Google. The tech giant’s emissions have increased by 48% in the past five years due to electricity consumption by data centers and supply chain emissions. Moreover, A.I. conversation uses 10 times more energy than a Google search due to the highly advanced software required to run A.I. Such data centers are used to train and operate A.I. models such as the ChatGPT chatbot, and training a single model uses more electricity than it takes to power 100 US homes for a year. Meanwhile, supply chain emissions are associated with manufacturing and transporting the computer parts used in A.I. 

Due to these factors, A.I. alone is projected to result in data centers using 4.5% of global energy generation by 2030. Even tech giants like Google admit the problem; the corporation’s 2024 environmental report concedes that “reducing emissions may be challenging due to increasing energy demands from the greater intensity of A.I. [computing].” Aside from substantially increasing emissions, A.I. growth is likely to begin to put serious strain on the power grid. In many areas of the United States, such strain could be catastrophic if the grid cannot meet these new demands. For example, in Texas, the ERCOT grid has already struggled to meet demand thanks to rapid population growth, aging infrastructure, and extreme weather. Data centers will only worsen matters. A.I.’s immense energy requirements are a threat both to the power grid and the climate and could quickly reach an unsustainable level if left unchecked. 

Grid Implementation Problems 

In addition to the conundrum of supplying the energy needed to power A.I., some notable problems arise when contemplating implementation within the energy sector. Firstly, the increased energy demand alongside the increased costs for utilities to service A.I. will likely force utilities to raise their rates. This will result in unhappy customers and would make it difficult for some to pay their bills. Secondly, A.I. can fail or be misused, which leads to negative outcomes like decreased reliability or malfunction. This can happen through bias, extrapolation, or misalignment, all of which are currently issues common to A.I. In the energy sector, these problems could manifest as a skewed replication of energy infrastructure behavior, poor responses to extreme weather events, prioritizing economics over grid reliability, and other undesirable scenarios. 

Thirdly, A.I. in the grid could lead to the degradation of human skills and expertise in the industry, which creates a dangerous reliance on technology. In a scenario where A.I. fails, reliance could cripple the power grid to the point where humans could not easily fix it. This reliance could also potentially eliminate energy jobs, but it is more likely to simply change the tasks required for many jobs. Lastly, using A.I. as part of the grid creates a cybersecurity threat, as much of A.I.’s data is open-access or can be hacked fairly easily. Such issues will need to be overcome if A.I. is to be utilized in one of the most essential components of life in developed societies. 

Benefits of A.I. Use in Energy 

Given the magnitude of these concerns, A.I. would never have gained so much traction had it not been for the multitude of benefits, especially within the energy industry. For example, A.I. can be incredibly helpful for managing the power grid. Its capability to perform demand forecasting means it can assist with load shifting, peak shaving, and anticipating grid issues. This means A.I. can be crucial for managing energy supply and demand during key hours to maximize efficiency while reducing intermittency, variability, and capacity concerns. Google exemplifies this, stating that A.I. has increased the financial value of its wind power by 20%. This is because A.I. demand forecasting can plan around inconsistent energy sources such as wind. So, while A.I. drives up electricity demand, it also helps meet existing demand. 

Furthermore, A.I. can be used for predictive maintenance, where energy asset performance is monitored to find potential current faults ahead of time. European utility E.ON has done this, using the results to estimate grid outages could be reduced by up to 30% with A.I. implementation. This can save utilities a lot of time and resources while preserving power for citizens, so A.I. can therefore improve grid stability, even as it strains the grid. 

Finally, A.I. can be used to find energy options that minimize emissions. The Global e-Sustainability Initiative predicts that by 2030, A.I.-run smart homes could reduce household carbon dioxide emissions by up to 40%. Similarly, A.I. can use atmospheric data to map out airplane flight paths with the fewest contrails. Because contrails make up over ⅓ of commercial aviation’s emissions, this discovery alone would reduce more emissions than the emissions from all A.I. in 2020. Lastly, A.I. can be used for electrical system planning. These applications can optimize energy usage to curb emissions. 

For the above reasons, the Midwestern Independent System Operator, which runs the Midwestern section of the US power grid, has been testing an A.I. model for potential integration into its system. This specific model is meant for optimizing daily planning. Grid operators run equations every day predicting how much electricity will be needed the next day to try to find the most cost-effective way to dispatch the energy, and the MISO model showed that this calculation can be done 12 times faster with the help of A.I. MISO’s success demonstrates that A.I. can be an invaluable tool for optimizing electrical applications in the status quo. 

Solutions to Emerging Issues

These benefits offer a glimpse into why proponents are so determined to solve the problems with the technology. Regarding energy consumption, one argument A.I. supporters put forth is that technology’s software and hardware are becoming more energy efficient. For example, more efficient cooling methods are starting to be implemented. Additionally, many argue using A.I. to improve the energy sector can help counterbalance the energy strain that it creates. Moreover, experts believe A.I. can help significantly with renewable research. This technology can test large numbers of designs for renewables and select the most promising candidates for future development; it can also search for necessary materials such as rare earth minerals or uranium. Such capabilities can at least partially counteract the exorbitant energy consumption. 

Solutions exist for problems with grid implementation as well. For starters, improving cybersecurity for an organization can minimize risk of cyberattack. As hackers have begun using generative A.I. themselves to become more sophisticated, current cyberattacks are already formidable, so increased cybersecurity is necessary for the electrical industry independent of their decision to implement A.I. In this way, A.I. is beneficial because it gives companies a reason to boost cyberdefenses. 

On a different note, staff training on how to harness A.I. would reduce misuse and reliance concerns. This involves setting up systems so that company staff can make better decisions, not so the A.I. can make decisions itself. Training can prevent A.I. disruption in the workplace and can set clear guidelines so everyone in the organization understands the technology’s purpose and pitfalls. If all employees fully grasp how to properly use A.I. without becoming completely dependent, companies can eliminate disruption concerns. Solutions such as these show that the issues surrounding A.I. can be mitigated. 

Conclusion 

While A.I. has some serious problems that cannot be ignored, it is also capable of radically changing the energy sector for the better. Energy consumption concerns and other grid implementation difficulties must be addressed to successfully harness A.I. in the energy industry. Since the technology is seemingly here to stay, the industry should begin looking for solutions that mitigate A.I.’s energy consumption across sectors. At the same time, however, the energy sector should not overlook the benefits A.I. offers. Though A.I. grid management can seem like a difficult and frightening change from traditional industry methods, it could truly revolutionize the grid. Thus, industry leaders should keep an open mind and explore the technology while proactively taking steps to avoid other pitfalls of A.I. If used cautiously, A.I. can be an invaluable tool for managing and optimizing the power grid, improving the way the energy industry functions.