Chapter 09 · Sustainability & AISustainability & AI

AI grid optimisation

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Definition

Using AI to operate electricity systems more efficiently: balancing supply and demand, dispatching storage, managing congestion and integrating variable renewables.

References

IEADigitalisation and Energy (2017)

This source provides part of the technical or institutional basis for the definition.

MIT PressSutton & Barto — Reinforcement Learning: An Introduction, 2nd ed. (2018)

This source supports the explanation of how the term is applied, measured or governed in practice.

Overview

What it means

Power systems are becoming harder to operate as generation decentralises and electrifies. AI assists operators with forecasting, state estimation and control recommendations; reinforcement-learning approaches are being tested for dispatch and flexibility coordination. The IEA frames digitalisation as essential infrastructure for secure transitions.

How it is used

System operators and utilities deploy AI-based tools for forecasting, outage prediction and flexibility markets; research pilots test autonomous control of microgrids and storage fleets under strict safety constraints.

Why it matters

Grids are the bottleneck of electrification. Faster, smarter operation via AI can defer reinforcement and absorb more renewables — but ceding control of critical infrastructure to learned models demands conservatism, testing and human oversight.