The energy sector faces many complicated challenges, particularly now as demand for electricity continues to grow while decarbonization efforts increase. Modern power systems need to support multidirectional flows of electricity between various generators, users, and the grid. As the energy space’s intricacy evolves, so does the demand for AI in energy.
Defined as the “new power couple” by the International Energy Agency, AI and energy are increasingly working together to transform how we live. Some estimates show that artificial intelligence already serves over 50 different uses in the energy space.
However, the relationship between AI and energy is complicated. On the one hand, evolving AI solutions can improve energy distribution, support sustainability efforts, and minimize waste. On the other hand, AI consumes significant amounts of energy itself. AI systems already consume the same amount of electricity as a small country, and the consumption rates will only grow.
So, what do consumers and companies need to consider when it comes to AI in the energy sector? What are the challenges to overcome, and the opportunities to explore?
AI in Energy: The Consumption Conundrum
The use of AI in energy could be revolutionary for the future of human kind. However, before we discuss the use cases and opportunities in this sector, it’s worth noting that AI is something of an energy hog. We already know that machine learning models consume a lot of energy, although even the companies behind those solutions can’t say exactly how much they use.
Plenty of analysts have shared estimates though. Training GPT-3, the older language model offered by OpenAI, was estimated to require more than 1,300 megawatt hours of electricity – the same amount around 130 homes in the US consume each year.
Larger, more advanced language models, consume even more energy, in spite of many companies, like OpenAI themselves, looking for ways to make their systems less resource intensive. Simply asking ChatGPT a question consumes around 10 times the electricity it would take to enter the same query into Google. Goldman Sachs even estimates that the rise of AI models will lead to a 160% increase in data center power demand by 2030.
Although AI only makes up a fraction of the technology sector’s power consumption today, it’s easy to see why experts are concerned that AI transformation will lead us towards a new energy crisis. Undoubtedly, training, deploying, and fine-tuning AI will put increasing pressure on already strained electrical grids worldwide.
But it’s not all doom and gloom. Increasingly, energy sector leaders are finding ways to “fight fire with fire”, using AI to optimize the energy landscape.
The Evolving Use Cases for AI in Energy
Despite being extremely energy intensive, artificial intelligence is also a versatile, valuable solution for the energy landscape. The right AI systems have the potential to improve energy distribution strategies, assist businesses with capturing consumption data, and minimize emissions.
Some reports even predict AI could help mitigate 5-10% of all global greenhouse gas emissions by 2030. Let’s a take a closer look at the use cases for AI in energy.
1. Optimizing Demand Response Management
In the energy industry, demand response management (DRM), is a crucial tool. It helps companies balance electricity supply and demand, and maintain the stability of the electrical grid. With AI-powered DRM software, companies can adjust the electricity users of consumers (usually industrial and commercial entities), based on real-time signals from grid operators.
Instead of simply issuing the same amount of energy to everyone, energy leaders can turn energy availability up or down based on real-world conditions. This practice ensures everyone still accesses the energy they need, while helping to ensure electricity is distributed effectively.
According to the IEA, since demand response management sometimes includes delivering incentives to users, it can also help consumers embrace more sustainable practices. Companies can even offer consumers incentives to switch to sustainable energy solutions, like wind power, during periods when demand is lower.
2. Enhancing Energy Storage
Energy isn’t always created and immediately distributed to users. It can be stored for later consumption, and AI helps optimize this process. With algorithms to monitor and determine demand, supply, price, and grid conditions, AI can guide organizations. Software can offer insights into the best time to store energy, release it, and even how much to distribute.
This is particularly valuable as global resources diminish, and demand for renewable energy increases. Renewable energy sources like solar and wind aren’t easy to predict. AI solutions can use historical and real-time data about weather forecasts to help predict times when renewable energy sources may fall, giving organizations an opportunity to store energy in advance.
Already, solutions like VTT EnergyTeller is an AI-powered service that forecasts future energy needs and market developments to enhance renewable energy storage. With advanced energy storage solutions, organizations can improve sustainability, and minimize the potentially disastrous outcomes of disruptions. For instance, in hospitals, data centers, and emergency services locations, access to a backup power supply could be the difference between life or death.
3. AI in Energy for Smart Grid Development
In the past, energy management was pretty straightforward. Grids directed energy to users from centralized power stations. Now, grids need to be connected to a wide range of different systems and tools, to power an increasingly complex world. Smart grids, enhanced by artificial intelligence and machine learning, can help optimize global energy consumption.
Smart grids leverage digital communications technology, IoT, sensors, and software to detect and react to local changes in energy usage. In smart grids, AI algorithms can predict consumption patterns, using both real-time and historical data. This helps utilities allocate their resources more efficiently, and reduce waste.
During periods of sudden high demand, AI can ensure that power is directed where it’s needed most, reducing the risk of blackouts, and significant disruption. Smart grids equipped with AI can also detect disruptions and faults in the energy ecosystem, such as equipment failures. They can provide businesses with the insights they need to understand the cause of the issue and reroute power, reducing downtime and interruptions.
Plus, smart grids can be paired with smart meters, delivering real-time information to consumers and suppliers about energy usage. This can pave the way to more sustainable decision-making.
4. Nuclear Power Plant Monitoring
Currently, studies show that nuclear energy provides us with around 10% of the electricity we use worldwide. Managing, maintaining, and running nuclear power plants is far from simple. Sensor faults and issues with machinery don’t just cause energy disruptions, they can lead to explosions, and significant disasters. AI in energy plant monitoring can help.
Artificial intelligence systems capable of monitoring huge volumes of data in real-time can maintain a clear view over plant operations. They consistently analyze data from various instruments and sensors, detecting even the slightest deviations from crucial safety standards. This can help organizations stay one step ahead of potential disasters.
Plus, through advanced models for predictive maintenance, AI can go beyond simply identifying issues as they happen in nuclear power plants. They can anticipate potential failures and issues based on performance trends and operational stresses. This allows operators to take proactive measures to address problems before they escalate.
5. Enabling Advanced Oil and Gas Exploration
Though reliance on fossil fuels is diminishing, as governments worldwide embrace more sustainable strategies for energy production, these fuels are still critical to the energy sector. AI’s application in the oil and gas landscape can be astronomical. Intelligent systems can analyze large amounts of geological data with incredible precision, identifying reserves human beings have missed.
AI tools can also examine the viability of those reserves, determining issues with drilling and anomalies before organizations begin extracting resources. This leads to a significant improvement in efficiency, as well as an opportunity to massively improve the success rate of exploration activities.
AI also plays a significant roll in optimizing drilling operations. Predictive models can assess all kinds of factors, from equipment performance to geological formations and environmental conditions. This helps teams proactively enhance safety measures and improve drilling processes.
6. AI in Energy for Predictive Maintenance
As mentioned above, predictive maintenance is an extremely important use case for AI in energy management and optimization. It’s not just a crucial tool for keeping nuclear power plants and other facilities safe. Predictive maintenance gives organizations a way to effectively optimize resource allocation, and minimize downtime.
AI software with built-in machine learning capabilities can help energy companies to predict when their equipment may need maintenance, or be likely to fail. The right tools can analyze huge amounts of information linked to usage statistics, and historical records, to essentially prevent problems before they occur. AI tools can even be more effective at predicting issues than humans.
The ability to compare and contrast large amounts of data, AI systems can detect even subtle patterns and correlations that might indicate the arrival of impending issues.
7. Optimizing Carbon Capture, Utilization and Storage
CCUS, or Carbon Capture, Utilization and Storage, has become a critical focus for many organizations in the energy sector, due to the increasing focus on sustainability. CCUS processes enable companies to capture carbon dioxide and use it, or store it underground to reduce its impact on climate change. AI can enhance the efficiency of CCUS processes.
The right technologies can identify effective methods for using captured carbon in industrial processes, as well as strategies for storing carbon safely. Case studies have shown that AI networks are incredibly effective at accurately screening CCUS sites. AI tools can easily predict potential faults in a process, and minimize risks with CCUS initiatives.
Many experts believe CCUS optimization will be one of the major AI in energy use cases worth exploring in the years ahead, as we continue to fight against climate change.
8. Transforming Energy Trading
Energy trading is a complex process critical to the continued profitability and growth of the energy sector. Artificial intelligence can help businesses, operators, and grid leaders make intelligent decisions, adapt to market conditions, and improve forecasts.
AI systems can process real-time data on pricing, supply trends, and demand, helping energy leaders to make informed, lucrative decisions. Many AI-powered tools can also enhance risk management, proactively examining market volatility and potential issues. Plus, AI solutions power algorithmic trading strategies at incredible speed.
Intelligent automated tools can execute countless trades in seconds, optimizing energy portfolios, and continually adapting to market conditions. The right tools can even help businesses simulate market scenarios during data-driven decision-making processes.
9. Empowering Sustainability Decisions
Finally, AI in the energy sector can empower consumers to make more sustainable decisions. AI is excellent at democratizing and processing data. Already, consumers have smart meters and IoT devices that work alongside AI to help them understand how to reduce energy consumption and costs. They can also use AI to automatically switch off devices to reduce energy wastage.
In the business landscape, AI-powered tools can assist organizations in the same way, allowing them to reduce energy consumption with automated processes. Plus, AI tools can help businesses understand how their various facilities, physical assets, and resources impact their energy consumption.
On a broader scale, AI helps companies and energy operators on a large scale to accurately gather data about their ESG initiatives. This will become increasingly crucial in the years ahead, as government mandates require more businesses to share transparent insights into consumption rates, and the efforts they’re taking to protect the planet.
The Future of AI in Energy
The opportunities for AI in energy will only continue to grow in the years ahead. Increasingly sophisticated solutions are appearing throughout the landscape. The most advanced AI algorithms can now help energy leaders to increase grid stability, reduce emissions, and optimize energy generation, consumption, and distribution.
As we move forward into the new age of sustainable energy, AI will assist organizations in leveraging, storing, and using sustainable resources more effectively. It will also ensure that companies can continue to take advanced measures to minimize disruptions and faults.
However, there are challenges to overcome too. It’s not just the significant energy consumption of AI that leaders need to consider. There are significant upfront costs associated with implementing AI systems into existing infrastructure, causing barriers for some energy companies.
Additionally, since the energy sector deals with large volumes of sensitive data, such as customer information and grid data, ensuring the security of that data will be critical. Energy companies will need to ensure they’re implementing robust strategies to enhance AI governance, and minimize data security risks in the years ahead.
Still, even with these challenges to overcome, the future of AI in energy could be extremely bright. Intelligent tools could pave the way to not only a better-powered world, but a more sustainable, resilient, and efficient one too.
