Today, the power sector is on the cusp of a technological revolution; AI is poised to transform how electricity is produced and consumed. However, as with any new technology, its use comes with hidden risks and challenges that must be addressed.
Power grids are becoming rapidly more complex due to the rising demand for electricity and the need for decarbonization. To manage this complexity, relevant data must be generated, exchanged, and analyzed at speeds and volumes beyond the capacity of human operators alone. Advanced analytical tools like AI will play a significant role in managing future power grids.
AI offers numerous opportunities in this regard. Overall, it could optimize energy consumption to reduce waste while improving efficiency and comfort levels. AI could also better forecast energy demand and supply, allowing energy providers to adjust their production and distribution to increase flexibility and reduce the risk of blackouts. AI tools could open new ways of interacting within the electricity grid, such as the dynamic charging and discharging of electric vehicle batteries.
Furthermore, AI could help integrate various renewable energy sources into the grid. Renewable energy sources such as solar and wind power are intermittent, meaning they are not always available when needed. However, AI can better predict when renewable energy sources will be available and adjust energy storage and consumption to optimize its use.
Still, the adoption of AI in the energy sector is not without its challenges. One of the biggest hurdles is the outdated power system infrastructure. Many of these systems were built decades ago and are not equipped to handle the demands of rapidly emerging technologies and changing consumer needs. Therefore, significant investments will be required to update the grid and realize the benefits of emerging technology.
It's important to note that upon deployment, AI applications could give rise to cybersecurity risks, supplier dependency, unethical decision-making, and jurisdictional and sovereignty challenges. One of the biggest risks associated with AI in the electricity system is the potential for cybersecurity breaches and intrusions into critical systems, which increases as electricity systems become more digitized. Additionally, relying on a few companies to develop and supply core infrastructure for AI poses significant risks to critical infrastructure that could lead to monopolization and create vulnerabilities that could affect service quality and price.
Moreover, when an AI application is designed to optimize efficiency, it can overlook ethical considerations. For example, imagine an AI system that manages power supply to homes. If a section of transmission and distribution experiences congestion, the AI model may prioritize overall efficiency over the needs of individual homes. In this scenario, the AI model may reduce the power supply to some homes to maintain overall efficiency, even if it means certain homes will be left without power. This could expose vulnerable populations to outages based on unfair or discriminatory criteria.
While each country needs to develop its own approach to AI in the electricity system, complications and vulnerabilities can arise due to the diversity of laws and regulations. The highly interconnected nature of the electrical systems means that policies in one country may have consequences in another. The potential impact of such regulatory divergence could be significant, and the recovery process could be slow and costly.
As the deployment of AI applications in the electricity system continues to gain momentum, policymakers around the world are grappling with the implications to ensure that they can reap the benefits of AI while limiting the risks associated with its deployment. The United States has taken steps towards developing AI guidance for the energy sector, such as creating the AI Risk Management Playbook and the DOE AI use case inventory. However, most federal policy documents on energy security do not explicitly mention AI, and there is still much work to be done to integrate AI into federal energy and infrastructure policies.
In comparison, the European Union (EU) is taking a proactive approach to AI policy with the development of the world's first comprehensive AI law, the AI Act, which takes a risk-based regulatory approach and aims to balance innovation with citizens' rights. The AI Act considers deploying AI applications in the management and operation of the electricity system to be high-risk, subjecting them to mandatory requirements and assessment. Additionally, the EU has adopted an Action Plan for the Digitalization of Energy to speed up the digitalization of the electricity system and strengthen its cybersecurity and resilience.
Despite the many general principles and guidelines for developing and deploying AI, there are currently no policies or principles explicitly tailored for the electric sector. Given the speed of AI development, many unknowns and emerging risks still present challenges to developing robust policy. The development of resilient and adaptable infrastructure is critical to ensure that the benefits of AI are realized while minimizing the risks and potential negative consequences. The future of the electricity system is dependent on the ability to strike the right balance between innovation and regulation.
Ismael Arciniegas Rueda is a senior economist at the nonprofit, nonpartisan RAND Corporation.
Henri van Soest is a senior analyst at RAND Europe.
Hye Min Park is a Ph.D. student at the Pardee RAND Graduate School and an assistant policy researcher at RAND.
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