Queensland's state-owned energy giant
Stanwell Corporation is betting on artificial intelligence to navigate one of the most complex energy transitions in the Southern Hemisphere. The company, which supplies more than one-third of Queensland's electricity, has deployed the
Stanwell Modelling Platform (SMP) — an Azure-powered AI system that its CIO Kevin Lin describes as "our intelligent command centre."
From Coal Legacy to AI-Driven Renewables
Stanwell's portfolio has traditionally relied on two coal-fired power plants, but the company is rapidly expanding into wind, solar, and battery storage. The challenge is enormous: Australia's National Electricity Market operates on
five-minute pricing intervals, meaning every dispatch decision carries immediate financial consequences. The old approach — manual forecasting with spreadsheets and legacy models — simply cannot keep pace.
The SMP consolidates weather data, market pricing, usage patterns, and generation forecasts into a single AI-driven platform. The results speak for themselves:
- 200% improvement in battery asset performance optimization
- 30% improvement in forecast accuracy for key timeframes
- 15x faster simulation speeds compared to previous methods
- Scenario computation costs reduced to a fraction of former expense
Battery Storage: Where AI Meets the Grid
Stanwell's most visible AI success is in battery dispatch optimization. The company's
300MW/600MWh Tarong Battery Energy Storage System — which commenced commercial operations in early 2026 — relies on AI to determine precisely when to charge (capturing excess solar generation) and when to discharge (meeting peak demand).
This is not a trivial optimization problem. Battery degradation, weather variability, wholesale price volatility, and grid stability requirements all interact in ways that defeat traditional rule-based approaches. The SMP handles these variables simultaneously, making dispatch decisions that human analysts would need weeks to compute manually.
Additional storage projects in the pipeline include the
1,200MWh Stanwell Battery Storage System (grid connection targeted for 2027) and exclusive negotiations for Quinbrook's
6.24GWh hybrid energy storage facility.
From Trading Experiment to Enterprise Platform
The SMP's origin story is instructive for enterprise IT leaders. It began as an experiment within Stanwell's trading department, where a small team explored machine learning for market forecasting. Early wins in prediction accuracy caught executive attention, and the platform received enterprise-level investment.
Sophie Naughton, Executive General Manager of Business Services, emphasizes that the scaling process required more than just technology deployment. CIO Lin ensured that IT governance, compliance, and cybersecurity teams were involved from the early stages, connecting the platform to existing data pipelines, reporting tools, and access controls.
What This Means for the Energy Sector
Stanwell's approach offers a template for utilities worldwide facing similar transitions. The key insight is that
AI does not replace human decision-making — it accelerates it. Analysts still interpret results and make strategic calls, but they now work with forecasts that update in seconds rather than weeks.
The platform is designed for continuous learning. As more data sources come online and operational scenarios accumulate, the SMP's models improve automatically. For a company managing billions of dollars in energy assets across coal, gas, wind, solar, and battery storage, that adaptive capability is not optional — it is existential.
The broader lesson for Windows and enterprise IT professionals: cloud-native AI platforms built on Azure's infrastructure can transform even the most traditional industries, provided the implementation respects governance, security, and organizational change management.
Source: IT Brief Australia
https://itbrief.com.au/story/stanwell-s-ai-platform-powers-shift-to-renewable-energy/