The Impact of Geopolitics on Energy Markets: AI for Scenario Planning

The global energy market is intricately linked to geopolitical events. Wars, political conflicts, and international agreements can significantly impact energy supplies, demand, and prices¹. This inherent volatility poses significant challenges for stakeholders across the energy value chain, from producers and traders to consumers and policymakers. The ability to anticipate and respond to potential disruptions is crucial for mitigating risks and ensuring energy security².

Artificial intelligence (AI) is emerging as a powerful tool for scenario planning in the energy sector. By leveraging AI's ability to analyze vast amounts of data and model complex systems, energy market participants can gain a deeper understanding of the potential impacts of geopolitical events and develop proactive strategies to navigate uncertainty. This report explores how AI can be used to model and analyze the impact of geopolitical events on energy prices and supply chains, enabling energy traders to anticipate and respond to potential disruptions.

Geopolitical Risks to Energy Markets

Geopolitical events can disrupt energy markets in several ways:

• Oil Supply Disruptions: Many of the world's major oil reserves are located in politically volatile regions, making oil prices vulnerable to instability¹. Conflicts, political unrest, and sanctions can disrupt production and transportation, leading to price volatility and supply shortages. The Organization of the Petroleum Exporting Countries (OPEC) plays a significant role in influencing global oil supply. OPEC's decisions to increase or decrease production in response to political or economic circumstances can directly impact oil prices. For example, when OPEC decides to cut production, often in response to oversupply or falling prices, it can cause a rise in global energy costs¹. Maritime routes are essential for the transportation of oil, and many of these key paths are vulnerable to geopolitical tensions and disruptions due to war, piracy, or sanctions. This can restrict energy resources and lead to significant price volatility¹.
• Natural Gas Supply Disruptions: Natural gas markets are also susceptible to geopolitical risks, particularly in regions with high reliance on pipeline imports¹. Political tensions and conflicts can disrupt pipeline flows, leading to price fluctuations and supply uncertainties. For example, Europe largely depends on Russian gas supplied through pipelines that travel through Ukraine and Belarus. Political unrest between these countries has led to disruptions in supply, especially during winter when demand rises¹.
• Trade Wars and Tariffs: Trade disputes between major economies can impact energy markets by disrupting global supply chains and increasing costs for energy products and infrastructure¹. For example, during the US-China trade war, tariffs were imposed on various energy products, including liquefied natural gas. These tariffs caused a shift in trading patterns and forced US exporters to seek alternative buyers, leading to price fluctuations¹.
• Shifting Global Energy Demand: The shift in global energy demand from traditional OECD markets to developing countries has geopolitical implications for energy systems, market shares, and trade dependencies³. This shift can intensify competition for resources and influence energy security policies. It can also lead to friction between the Global South and North over the siting of material processing and climate finance³. The Global South may prioritize economic development and better living standards over renewable energy and climate concerns, potentially leading to a fossil fuel lock-in³.
• Critical Mineral Supply Chains: The rise of clean energy technologies has increased demand for critical minerals, many of which are concentrated in a handful of countries⁴. Geopolitical tensions and export restrictions could disrupt these supply chains, hindering the clean energy transition. The rising costs associated with the energy transition, termed "greenflation," have sparked concern as the prices of raw materials and renewable technologies soar. However, advancements in technology and regulatory mechanisms are beginning to mitigate these inflationary pressures⁴.
• Climate Change: Extreme weather events, intensified by climate change, are already posing challenges for the secure and reliable operation of energy systems². These events can disrupt energy production, damage infrastructure, and increase energy demand, leading to price volatility and supply shortages. Many power systems are currently vulnerable to an increase in extreme weather events, putting a premium on efforts to bolster their resilience and digital security².

AI for Scenario Planning

Scenario planning is a strategic method used to envision and prepare for possible future conditions. By considering different variables and their potential impacts, companies can develop flexible strategies that adapt to changing circumstances⁵. Traditionally, scenario planning involves identifying key uncertainties and developing a range of plausible scenarios⁵. These scenarios are then analyzed to understand their implications and to devise strategic responses.

AI can enhance scenario planning in several ways:

• Data-Driven Insights: AI algorithms can process and analyze vast datasets, providing insights that are grounded in empirical evidence rather than intuition⁵. This includes analyzing historical data, real-time news feeds, economic indicators, and social media to identify patterns and trends that might be missed by human analysts.
• Enhanced Accuracy: With machine learning models, forecasts become more precise, reducing the margin of error in scenario planning⁵. AI can identify subtle patterns and relationships between performance drivers, leading to more accurate predictions of potential opportunities and risks. For example, AI can uncover correlations between subtle distribution network changes and customer experience drivers, or issue warnings about connections between changes in planned maintenance schedules and equipment downtime that affect output⁶.
• Scalability: AI systems can handle complex and large-scale scenarios, making it possible to consider a wider range of potential futures⁵. This allows for more comprehensive scenario planning and risk analysis.
• Real-Time Updates: AI allows for continuous monitoring and updating of scenarios based on new data, ensuring that plans remain relevant and responsive⁵. This adaptability is crucial in the dynamic energy market, where geopolitical events can rapidly shift the landscape.

AI Techniques for Scenario Planning

Several AI techniques are employed in scenario planning, each with its own strengths and applications:

• Machine Learning: This involves training algorithms on large datasets to identify patterns and make predictions. In energy scenario planning, machine learning can be used to forecast energy demand, predict equipment failures, and optimize energy trading strategies.
• Deep Learning: A subset of machine learning, deep learning uses artificial neural networks with multiple layers to analyze data and extract complex features. Deep learning models can be used for tasks such as image recognition, natural language processing, and time-series analysis, which can be applied to analyze satellite imagery, news articles, and energy market data for scenario planning.
• Natural Language Processing (NLP): NLP focuses on enabling computers to understand and process human language. In energy scenario planning, NLP can be used to analyze news reports, social media sentiment, and policy documents to identify geopolitical trends and assess their potential impact on energy markets.

Applications of AI in Energy Scenario Planning

AI can be applied to various aspects of energy scenario planning:

• Identifying Baseline Scenarios: AI tools can analyze historical data and recognize patterns to identify baseline scenarios, providing a solid foundation for the planning process⁷. For example, AI can analyze past energy consumption data, weather patterns, and economic indicators to establish a baseline scenario for future energy demand.
• Formulating Scenarios based on Trends: By utilizing the vast knowledge and predictive capabilities of large language models (LLMs), organizations can formulate scenarios that align with emerging trends, allowing for more accurate and forward-looking planning⁷. For instance, LLMs can analyze news articles, research papers, and social media discussions to identify emerging geopolitical trends and their potential impact on energy markets.
• Generating Innovative Ideas: LLMs can spark creativity by generating innovative ideas and potential courses of action that might not have been considered otherwise, enriching the organization's strategic decision-making⁷. For example, LLMs can be used to brainstorm new energy storage solutions, demand response strategies, or grid management techniques to enhance energy security in the face of geopolitical risks.
• Combining Scenarios: AI can efficiently combine scenarios and assess their potential implications, enabling more comprehensive scenario planning and risk analysis⁷. For instance, AI can combine scenarios of oil supply disruptions with scenarios of increased renewable energy generation to assess the overall impact on energy prices and grid stability.
• Evaluating Ideas: AI tools can aid in evaluating various ideas and actions generated for different scenarios, providing valuable insights into the most promising strategies for enhancing survivability⁷. For example, AI can simulate the impact of different energy efficiency measures under various geopolitical scenarios to identify the most effective strategies for reducing energy consumption and mitigating risks.
• AI for Optimizing Renewable Energy: AI can be used to optimize wind and solar power by analyzing historical data and weather forecasting patterns⁸. This can help energy companies maximize the output of renewable energy projects, reduce reliance on fossil fuels, and enhance energy security. For example, AI algorithms can predict wind power output based on weather patterns and adjust turbine operations to maximize energy generation.
• AI for Nuclear Energy: AI can be used in nuclear power plants to enhance safety and efficiency⁹. AI systems can monitor plant operations, detect anomalies, and predict potential failures, improving safety and reducing downtime. For instance, AI can analyze sensor data to identify potential issues with reactor cooling systems or predict maintenance needs for critical components.

By integrating these diverse applications, AI empowers energy companies to proactively address geopolitical challenges. It enables them to anticipate potential risks, optimize resource allocation, and adapt to changing circumstances, ultimately enhancing their resilience and ensuring a more secure energy future.

Case Studies of AI in Geopolitical Analysis

While AI is still an emerging tool in geopolitical analysis, several case studies demonstrate its potential:

• Predictive AI in Information Warfare: AI-powered language models are being used to create compelling fake content for disinformation and propaganda campaigns¹⁰. This raises concerns about the potential for AI to undermine democratic processes and escalate geopolitical tensions. For example, a suspected influence campaign revealed by Google's Mandiant identified a rising trend of AI utilization in information campaigns by entities associated with various national governments¹⁰. This includes the use of AI-generated content, such as counterfeit profile pictures, in politically motivated campaigns to manipulate public opinion.
• AI for Semiconductor Supply Chain Analysis: AI can be used to model and analyze the geopolitical risks to the semiconductor supply chain, which is crucial for AI development itself¹¹. This includes assessing the impact of potential disruptions on technological advancement and global power dynamics. For instance, AI can be used to simulate the effects of geopolitical events, such as conflicts or trade wars, on the production and distribution of semiconductors, helping to identify potential vulnerabilities and develop mitigation strategies.
• AI for Energy Market Analysis: AI-powered data intelligence platforms are being used to analyze real-time news feeds, economic indicators, and political analysis to understand and anticipate market trends¹². This enables market participants to gain a more comprehensive understanding of geopolitical risks and their implications for investments. For example, during the escalation of trade tensions between the United States and China, data intelligence providers monitored various sources to assess the impact on financial markets, helping investors navigate the volatility and adjust their strategies accordingly¹².
• AI for Military Applications: AI is being explored for various military purposes, including the development of autonomous weapons systems¹³. This raises concerns about the potential for AI to escalate conflicts and increase the risk of unintended consequences. However, AI can also be used for defensive purposes, such as analyzing intelligence data to identify threats and predict potential attacks.
• AI and Malign Objectives: Rogue nations like Iran and Russia, which may not have the capacity for global leadership in AI, could use new AI tools to advance their malign objectives, including through misinformation campaigns and the development of AI-enabled autonomous weapons systems¹³. This highlights the need for international cooperation and responsible AI governance to mitigate the risks of AI being used for harmful purposes.

These case studies highlight the diverse ways in which AI is being used to analyze and respond to geopolitical events. They also underscore the potential for AI to both exacerbate and mitigate risks in the energy sector. As AI technology continues to evolve, it will be crucial to monitor its applications in geopolitics and develop strategies to ensure its responsible and ethical use.

Data Sources for Geopolitical and Energy Market Analysis

Effective AI-driven scenario planning requires access to reliable and up-to-date data. Several data sources can be used for geopolitical and energy market analysis:

AI algorithms can be used to analyze data from these various sources to generate more comprehensive and accurate scenarios. For example, combining data on geopolitical events with energy market prices can help to identify correlations between political instability and oil price volatility. By integrating data from multiple sources, AI can provide a more holistic view of the energy market and its vulnerability to geopolitical risks.

Open-Source AI Tools for Scenario Planning

Several open-source AI tools and libraries can be used for scenario planning:

Companies and Organizations Using AI for Scenario Planning in the Energy Sector

Several companies and organizations are at the forefront of using AI for scenario planning in the energy sector:

Experts in Geopolitics and Energy Markets

Several experts have been identified who can provide valuable insights into the impact of geopolitics on energy markets and the use of AI for scenario planning:

These experts can contribute to further research and analysis on the impact of geopolitics on energy markets and the use of AI for scenario planning by providing their specialized knowledge and insights on various aspects of the topic. They can help to identify key trends, assess potential risks, and develop effective strategies for navigating the complex geopolitical landscape.

Risks and Limitations of Using AI for Scenario Planning in the Energy Sector

While AI offers significant potential for scenario planning in the energy sector, it is essential to consider the associated risks and limitations:

• Cybersecurity Risks: AI systems can be vulnerable to cyberattacks, which could compromise data integrity and disrupt energy operations³⁵. For example, attackers could manipulate AI algorithms to cause blackouts or disrupt energy trading platforms. Protecting AI systems from cyberattacks is crucial for ensuring the reliable and secure operation of energy infrastructure.
• Data Bias and Accuracy: AI models are only as good as the data they are trained on. Biased or incomplete data can lead to inaccurate predictions and flawed scenarios³⁶. For example, if an AI model is trained on data that does not accurately reflect the diversity of energy sources or geopolitical risks, it may produce inaccurate scenarios that could mislead decision-making. Ensuring data quality and addressing potential biases are essential for building reliable AI models for energy scenario planning.

Limitations of AI for Scenario Planning in the Energy Sector

In addition to the risks, there are inherent limitations to using AI for scenario planning in the energy sector:

• Lack of Transparency and Explainability: Some AI models, particularly deep learning models, can be "black boxes," making it difficult to understand how they arrive at their predictions³⁷. This lack of transparency can hinder trust and adoption. For example, if an AI model predicts a sudden surge in energy prices, it may be difficult to understand the underlying factors that contributed to this prediction, making it challenging to assess the reliability of the forecast.
• Ethical Considerations: The use of AI in energy scenario planning raises ethical considerations, such as data privacy, algorithmic bias, and the potential for job displacement³⁷. For example, AI algorithms used to optimize energy consumption could potentially discriminate against certain communities or lead to job losses in the energy sector. Addressing these ethical considerations is crucial for ensuring that AI is used responsibly and equitably.
• Energy Consumption of AI: AI technologies, particularly those involving machine learning and LLMs, are energy-intensive³⁸. This raises concerns about the environmental impact of AI and the need for sustainable energy sources to power AI systems. The energy consumption of AI is largely driven by the two phases of an AI search: training and inference. Training involves feeding massive amounts of data to AI models, requiring significant computational power. Inference refers to the process of using the trained model to make predictions or generate outputs, which also consumes energy. To mitigate this, strategies such as energy-efficient hardware, AI-optimized cooling, and smarter data centre design and operations are being explored³⁹.
• Uncertainty in AI's Energy Impact: While AI is expected to increase energy demand, the magnitude of this growth remains uncertain³⁹. This uncertainty can make it challenging to plan for future energy needs and grid capacity. For example, it is unclear how much energy will be required to power the growing number of AI-driven applications in the energy sector, making it difficult to predict the impact on electricity demand and grid stability.

Addressing these limitations is crucial for ensuring the responsible and effective use of AI in energy scenario planning. This includes developing more transparent and explainable AI models, addressing ethical considerations, and promoting sustainable energy use to power AI systems.

Conclusion

Geopolitical events have a profound impact on energy markets, creating volatility and uncertainty. AI offers a powerful tool for scenario planning, enabling energy market participants to anticipate and respond to potential disruptions. By leveraging AI's ability to analyze vast amounts of data, model complex systems, and generate predictive insights, energy traders can develop proactive strategies to mitigate risks and ensure energy security.

However, it is crucial to acknowledge the risks and limitations associated with AI in energy scenario planning. Addressing data bias, ensuring cybersecurity, promoting transparency, considering ethical implications, and managing energy consumption are essential for responsible AI development and deployment.

The future of AI in energy scenario planning holds immense potential. AI has the capacity to transform the energy sector by enabling more efficient and resilient energy systems, optimizing renewable energy integration, and enhancing grid stability. Continued research and development in this area, along with collaboration between stakeholders across the energy value chain, will be crucial for realizing the full benefits of AI while mitigating its risks. By proactively monitoring the evolving intersection of AI and energy, stakeholders can clarify challenges, uncover opportunities, and guide transformative solutions that ensure a secure and sustainable energy future in a rapidly changing geopolitical landscape.

Works cited

1. How Geopolitical Events Affect the Energy Market and Prices - Direct Business Solutions
2. Geopolitical tensions are laying bare fragilities in the global energy system, reinforcing need for faster expansion of clean energy - News - IEA
3. The geopolitics of the global energy demand shift - Elcano Royal Institute
4. How geopolitics will both hinder and accelerate the global energy transition
5. QueBIT Blog: The Future of Scenario Planning: Integrating AI and Forecasting
6. 3 Ways AI Is Reinventing Scenario Planning - Paro
7. Contingency Scenario Planning using Generative AI - California Management Review
8. Artificial Intelligence Energy Companies: Powering a New Era in Energy - STX Next
9. Top 10 applications of AI in the energy sector | FDM Group UK
10. The Digital Cold War: Predictive AI's Disruptive Role in Geopolitics - APCO Worldwide
11. How artificial intelligence is shaping global power dynamics - the geopolitics of the ai-relevant semiconductor supply chain - Graduate Institute
12. Understanding Geopolitical Risk: How Data Intelligence Empowers Market Understanding
13. The generative world order: AI, geopolitics, and power | Goldman Sachs
14. Understanding Geopolitical News and Information Sources
15. The GDELT Project
16. Geopolitical Risk (GPR) Index - Matteo Iacoviello
17. ACLED (Armed Conflict Location and Event Data)
18. Wholesale Electricity and Natural Gas Market Data - U.S. Energy Information Administration
19. energy-prices - Dataset - Catalog - Data.gov
20. Energy Prices - Data product - IEA
21. Today in Energy Daily Prices - U.S. Energy Information Administration (EIA)
22. Prices & Trends - Department of Energy
23. Top 10 Open Source AI tools in 2024 - Pesto Tech
24. 8 open-source tools to build your next AI SaaS app - DEV Community
25. 12 AI Energy Companies For A Brighter Future 2025 review - ClimateSort
26. The Future of AI in Energy Sector: Discover Top 10 Applications - StartUs Insights
27. Top AI Companies Revolutionizing the Energy Sector
28. Anaplan: AI-infused Scenario Planning, Analysis, and Reporting
29. Sustainable Energy for All and IBM Launch New AI Solutions for Energy and Urban Development - IBM Newsroom
30. Artificial Intelligence: A Catalyst to Transform Energy Sector for Sustainable Future - CSIS
31. Geopolitics Experts Available - ExpertFile
32. Geopolitics and International Security: Research & Analysis | CSIS
33. Geopolitics and Foreign Policy - CSIS
34. Geopolitical - Brunswick Group
35. Artificial intelligence can help manage the grid but creates risks if deployed 'naïvely,' DOE warns | Utility Dive
36. Potential Benefits and Risks of Artificial Intelligence for Critical Energy Infrastructure
37. AI brings huge opportunities and new but manageable risks for the energy industry - DNV
38. AI's Impact on Energy: Challenges and Opportunities
39. Artificial Intelligence's Energy Paradox: Balancing Challenges and Opportunities - Publications - The World Economic Forum