The Australian Energy Market Operator (AEMO) has been publishing increasingly urgent assessments of the grid’s transformation. Coal plants retiring, renewables increasing, demand patterns shifting—the system that’s powered Australian manufacturing for decades is changing fundamentally.

What does this mean for industrial energy users? And how does AI fit into the picture?

What’s actually changing

Generation mix shift

Coal generation is declining as plants age and close. Renewables (solar, wind) are growing but produce intermittently. Gas provides flexible backup but at higher cost.

For manufacturers, this means:

• Higher and more volatile wholesale electricity prices
• More price variation between peak and off-peak periods
• Greater value in flexible load

Grid stability concerns

Renewables don’t provide the same grid stability services (inertia, voltage support) as traditional generators. AEMO is implementing various mechanisms to maintain stability, some of which affect large users.

Demand response opportunities

As grid conditions become more variable, the value of demand response—reducing or shifting load in response to grid needs—increases. Programs pay industrial users to curtail when the grid is stressed.

Behind-the-meter generation

Many manufacturers are installing solar, batteries, or gas generators on site. This changes the relationship with the grid and creates new optimisation opportunities.

Where AI helps

These changes create complexity that AI is well-suited to manage.

Energy cost optimisation

AI can optimise energy consumption based on:

• Time-of-use tariffs (when is power cheapest)
• Wholesale market exposure (if you’re on market-linked pricing)
• Demand charges (avoiding peak demand)
• Behind-the-meter generation and storage

The decisions are complex—thousands of possibilities depending on production schedule, equipment flexibility, and energy prices. AI handles this complexity better than manual approaches.

A food processor I worked with reduced energy costs by 17% through AI-optimised scheduling, without any change to equipment or total production volume.

Demand response participation

Demand response programs require:

• Knowing how much load you can shed
• Responding to dispatch signals quickly
• Minimising production impact
• Verifying performance for payment

AI can manage participation—identifying dispatchable load, optimising which operations to curtail, automating response, and maximising payments while minimising disruption.

Battery and storage optimisation

If you have battery storage, AI optimises:

• When to charge (low prices, excess solar)
• When to discharge (high prices, peak demand)
• Grid services provision (frequency response, etc.)
• Backup power reservation for reliability

The economics of industrial batteries often depend on intelligent optimisation. Without AI, batteries may not pay back. With AI, they often do.

Solar and generation integration

Behind-the-meter generation creates scheduling opportunities. AI can:

• Shift flexible load to periods when solar is generating
• Coordinate with battery charging/discharging
• Decide when to export versus use onsite
• Predict generation based on weather forecasts

Power quality management

Grid changes can affect power quality—voltage fluctuations, frequency variations. AI can:

• Monitor power quality parameters
• Identify patterns and predict issues
• Optimise power factor correction
• Coordinate with backup systems

Practical steps for manufacturers

Understand your energy profile

Before optimising, understand your current situation:

• What’s your total consumption and cost?
• How variable is consumption by time of day/week/year?
• What’s your peak demand (and associated charges)?
• Which operations are flexible? Which are fixed?
• What’s your exposure to wholesale market prices?

This baseline enables identifying opportunities.

Assess flexibility

What can you actually shift or reduce?

• Batch processes that can move timing
• Thermal loads with storage capacity (heating/cooling)
• Equipment that can be deferred
• Non-production loads (charging, HVAC)

More flexibility means more optimisation potential.

Evaluate on-site generation

Does behind-the-meter generation make sense?

• Solar economics depend on roof space, consumption profile, export value
• Battery economics depend on demand charges, flexibility value, backup requirements
• Gas generation provides reliability but has carbon implications

Get specific analysis for your situation. Generic rules don’t capture the complexity.

Implement energy monitoring

You can’t optimise what you can’t see. Granular energy monitoring (by major equipment and process) enables both manual and AI-based optimisation.

Explore demand response

Several programs in the National Electricity Market pay for load flexibility:

• Wholesale demand response mechanism
• Reliability and Emergency Reserve Trader (RERT)
• Network support services
• Retailer-specific programs

Understand what’s available and whether you can participate.

Consider AI optimisation

For manufacturers with:

• Significant energy costs (>$500K/year is where it often becomes worthwhile)
• Operational flexibility
• Variable pricing exposure
• Complex energy systems (generation, storage, multiple meters)

AI-based energy optimisation can deliver substantial savings. The technology is mature and proven.

The broader context

Australia’s energy transition is happening whether manufacturers like it or not. Coal plants are closing, not because of policy alone, but because they’re old and uneconomic.

This creates challenges (higher costs, more volatility) but also opportunities (demand response payments, renewable energy, storage economics).

Manufacturers who adapt—understanding the new energy landscape and optimising within it—will have advantages over those who simply complain about rising prices.

AI is a tool for adaptation. It enables managing complexity that’s now part of the industrial energy landscape.

Working with energy expertise

Energy optimisation touches areas many manufacturers aren’t expert in:

• Electricity market structures and pricing
• Behind-the-meter generation economics
• Demand response program rules
• Regulatory compliance

Consider engaging energy specialists alongside AI/technology expertise. The best outcomes combine energy market knowledge with technical implementation capability.

AI consultants Sydney and similar firms can help with the technology side, but energy strategy often benefits from energy-specific expertise as well.

Looking ahead

The pace of grid change is accelerating. What works today may not work in five years. Planning for flexibility—both operational and technical—positions you to adapt as conditions evolve.

Energy is increasingly an area where operational capability (the ability to respond and optimise) matters as much as capital investment. AI enables that capability.

Australian manufacturers can’t control the grid transition. But they can control how they respond to it. Proactive engagement with the new energy landscape is the path to managing costs and maintaining competitiveness.