How AI strengthens energy systems amid climate and geopolitical pressures

Cybersecurity, grid management and stability, and infrastructure and resource planning all appeared less prominently in the data, particularly when measured using trigram frequency methods. These roles are often articulated through varied or diffuse language, making them harder to detect in abstract-level analysis despite their strategic importance.

Artificial intelligence (AI) is rapidly embedding itself into the operational fabric of global energy security, with new research offering the most detailed functional breakdown to date. The analysis, published in Energies, identifies how AI is being used to forecast, optimise, protect and manage energy systems in an era defined by climate change, geopolitical instability and decentralised infrastructure.

Titled “Operational Roles of Artificial Intelligence in Energy Security: A Triangulated Review of Abstracts (2021–2025)”, the study examined 165 peer-reviewed journal abstracts published over the past five years. It applied a triangulated methodology, combining trigram frequency analysis, manual qualitative coding and semantic clustering, to produce a structured typology of AI’s operational roles in energy security. The review reveals eight core functions, with predictive, optimisation and market-oriented applications dominating the scientific discourse, while strategic planning and cybersecurity remain underrepresented.

Forecasting, optimisation and market operations dominate AI functions

The study’s findings show a strong focus on operational performance and real-time system management. Forecasting and prediction emerged as the most prevalent role, covering energy demand forecasting, price prediction and renewable generation forecasting. These applications allow operators to anticipate system behaviours, reduce uncertainties and align production with fluctuating consumption patterns.

Optimisation of energy systems ranked alongside forecasting in prominence. AI is being deployed to enhance generation efficiency, fine-tune distribution networks, improve storage integration and reduce operational costs. Such optimisation extends from controlling microgrids and managing gasification processes to adjusting system outputs in real time.

Energy market operations and trading form another key pillar. AI systems are used to predict market trends, manage supply-demand balancing and optimise trading strategies in electricity markets. The research indicates that these market-focused functions are particularly visible in lexical analysis due to standardised terminology in academic discourse, though they are often elaborated less extensively in contextual interpretations.

While renewable energy integration is not as frequent in abstract-level keyword analysis, it is a significant operational role when examined semantically. AI supports the intelligent coupling of solar, wind and other renewable sources with grids and storage systems, enabling smoother transitions to low-carbon energy portfolios.

Strategic and resilience roles underrepresented in literature

The review identified important but less frequently discussed roles that are critical for long-term energy security. Cybersecurity, grid management and stability, and infrastructure and resource planning all appeared less prominently in the data, particularly when measured using trigram frequency methods. These roles are often articulated through varied or diffuse language, making them harder to detect in abstract-level analysis despite their strategic importance.

Cybersecurity applications include AI-based intrusion detection, layered defence strategies for smart grids and mechanisms to counter non-technical losses. Grid management and stability functions cover operational resilience, theft detection mitigation and protection from disruptive events. Infrastructure and resource planning, detected only through semantic clustering, involves using AI to inform long-term energy development strategies, such as capacity planning, resource allocation and infrastructure resilience upgrades.

The research highlights a critical gap between AI’s documented operational use in immediate performance optimisation and its underrepresentation in long-term resilience and strategic planning. The author notes that these roles may require more extensive textual exposition, often found in full-text studies, white papers or regulatory documents rather than in brief academic abstracts.

Methodological triangulation confirms robust typology

To ensure reliability, the study employed a triangulated approach. Trigram frequency analysis identified recurring three-word phrases linked to operational functions. Manual coding enabled context-driven classification of 219 excerpted segments, focusing on the functional meaning of AI applications rather than specific wording. Semantic clustering used machine learning to group conceptually similar excerpts, revealing latent patterns not captured by the other methods.

The integration of these methods confirmed eight operational roles: forecasting and prediction; optimisation of energy systems; renewable energy integration; monitoring and anomaly detection; grid management and stability; energy market operations and trading; cybersecurity; and infrastructure and resource planning. Statistical testing showed strong agreement between manual coding and semantic clustering, indicating that context-aware approaches capture similar functional structures. Trigram analysis, while useful for roles with standardised terminology, aligned less closely with these methods, underscoring the need for multiple analytical lenses.

The research also tested its typology against grey literature by reviewing 39 relevant arXiv preprints. This supplementary analysis found no additional roles beyond those identified in peer-reviewed abstracts, reinforcing the framework’s stability. Roles related to forecasting, optimisation and market operations appeared consistently in both formal and informal scientific sources.

Implications for policy and future research

The findings underline the growing integration of AI in securing energy systems, but also point to blind spots in academic coverage. The dominance of forecasting, optimisation and market operations reflects the sector’s focus on operational efficiency and immediate returns. However, the limited representation of cybersecurity, grid stability and infrastructure planning suggests a need for broader strategic engagement.

In practical terms, the typology can serve as a reference for policymakers and system operators seeking to align AI investments with real-world energy security needs. By mapping AI functions against policy objectives such as the EU’s REPowerEU plan, the NIS2 Directive or the International Energy Agency’s resilience indicators, decision-makers can ensure that AI deployment addresses not just operational goals but also resilience, reliability and long-term sustainability.

Future research, the author notes, would benefit from analysing full-text studies and incorporating non-academic sources, including industry reports, regulatory filings and policy strategies. Such an expanded scope could capture more detailed accounts of underrepresented roles, particularly those tied to strategic foresight, cross-sectoral coordination and complex threat mitigation.

The study also opens the door for cross-sector comparisons. AI’s operational roles in energy security may share similarities with its deployment in other safety-critical sectors such as transport and manufacturing, where forecasting, optimisation, monitoring and protection functions must also be tightly integrated with regulatory and resilience frameworks.