Cover: The use of AI for improving energy security

The use of AI for improving energy security

Quantitative exploration of the opportunities of the deployment of AI applications in the electricity system

Published Jun 21, 2024

by Ismael Arciniegas Rueda, Hye Min Park, Joshua Steier, Henri van Soest, Harper Fine, Melusine Lebret

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Research Questions

  1. To what extent could AI applications at technology readiness level (TRL) 8 or 9 help improve energy security, if such applications were widely adopted in the short term (< 5 years)?
  2. Which AI applications perform better than others, and are there trade-offs between different energy security attributes (i.e. accessibility versus affordability) related to different AI applications?
  3. What are the policy implications that arise from this analysis?

Electricity systems around the world are under pressure due to aging infrastructure, rising demand for electricity and the need to decarbonise our energy supplies at pace. Artificial intelligence (AI) applications have potential to help address these pressures and increase overall energy security. For example, AI applications can reduce peak demand through demand response, improve the efficiency of wind farms and facilitate the integration of large numbers of electric vehicles into the power grid. However, the widespread deployment of AI applications could also come with heightened cybersecurity risks, the risk of unexplained or unexpected actions, or supplier dependency and vendor lock-in. The speed at which AI is developing means many of these opportunities and risks are not yet well understood.

The aim of the study was to provide insight into the state of the art of AI applications for the power grid and the associated risks and opportunities. We used a Python-based power system model called PyPSA to explore the extent to which different AI applications can improve energy security. This report contains the technical details of this approach and accompanies policy report RR-A2907-1.

Key Findings

  • Each of the AI scenarios leads to improvement in at least one of the energy security metrics, although the magnitude varies between scenarios' different metrics. Our study implies that some AI deployments generate trade-offs between energy security metrics.
  • The impact of AI applications across countries would likely depend on the energy market rules governing the European electricity system. Our research indicates AI's role in the grid should be part of the current policy discussions on market decentralisation that should govern the European electricity system. For instance, S2 showed different levels of impact on energy security metrics under centralisation and decentralisation.
  • When different AI technologies are implemented together, interactions between the applications may result in adverse effects on energy security. For instance, AI applications that reduce peak load may reduce the share of CCGT in the energy portfolio, as they are dispatched to cover peak load without changing the participation of dirtier coal-fired baseload power plants. Policymakers should recognise that the impact of AI on energy security is not necessarily additive and there could be trade-offs.
  • Our results indicate that AI applications aimed at load reducing behind the meter have significant positive impacts on all four energy security metrics (e.g. >10% on accessibility). Given the technological readiness of this approach, our results support policies that incentivise the use of AI HVAC control technology for buildings, smart metering and VPP and distributed energy resources.

Research conducted by

This research was conducted by RAND Europe.

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