Demand

Demand modelling for the NEM captures both established consumption patterns and the growing influence of distributed energy resources (DERs). Total demand is calculated by state and then allocated to individual network nodes based on where load actually connects, so demand is represented at the same locational resolution as the model’s transmission network of over 200 nodes.

Data Sources and Forecasting Method

For backtesting, the model uses historical operational demand data sourced from AEMO. For forecasting, demand profiles from a representative median reference year are applied, scaled to align with annual consumption projections from AEMO’s Electricity Statement of Opportunities (ESOO). This approach preserves realistic temporal variability while anchoring forecasts in long-term energy trends.

Operational vs. Underlying Demand

To reflect the impact of DERs on grid behaviour, the model distinguishes between two key types of demand:

• Operational Demand: Electricity demand to be supplied directly by the supply stack.
• Underlying Demand: Total electricity demand, including generation from behind-the-meter resources.

This disaggregation enables the model to isolate the influence of growth of technologies such as rooftop solar and behind-the-meter storage on operational demand, and therefore forecast future demand more accurately.

Modelling Consumer Energy Resources (CER)

The model also accounts for other consumer-side technologies that reduce operational demand, including:

• Rooftop PV: Distributed solar generation that directly offsets underlying demand at the point of consumption.
• Household battery storage: Total household battery capacity comes from Modo Energy’s own household battery storage forecast (Conservative case), assuming a duration of 1.7 hours. The forecast projects 28.7 GWh NEM-wide by FY2030, rising to 47.2 GWh by FY2050. The proportion of household batteries that respond to grid signals (coordinated) rather than reducing demand passively is set using the ratio from AEMO’s 2026 Integrated System Plan (ISP), Step Change scenario.

Data centre demand

Data centre demand is modelled as a flat, round-the-clock load rather than following the daily shape used for other consumption categories, reflecting the continuous power draw of data centre operations. This load is based on Modo Energy’s own data centre demand forecast, rather than AEMO’s in-scenario demand projections.

The Central scenario forecasts NEM-wide data centre demand growing from 5.5 TWh in FY2026 to 14.2 TWh in FY2030 and 36.8 TWh by FY2050. Growth is concentrated in New South Wales and Victoria, which together account for the large majority of the national total by FY2050. The High scenario assumes a materially larger build, reaching around 54 TWh NEM-wide by FY2050.

Below is an example of how demand is modelled across a single day in 2035:

1. Operational demand starts from the reference year.
2. Rooftop PV generation from the reference year is added to give underlying demand.
3. Underlying demand is scaled to TWh demand projections from AEMO’s 2026 Integrated System Plan (ISP), using Step Change for most demand types and Slower Growth for hydrogen demand.
4. EV charging demand and Modo Energy’s own flat data centre demand are added separately.
5. CER generation (rooftop solar and passive CER storage) is modelled and added to give modelled operational demand.
6. Subtracting unconstrained renewable generation gives an example residual demand profile.

Use the step buttons on the chart below to walk through each stage of the demand modelling process.

Demand composition

Total operational demand is projected to grow around 29% by 2050 in the Central scenario, from around 184TWh in 2026 to 238TWh in 2050. This is primarily due to electrification (of transport, heat, industrial processes), as well as data centre buildout, and is offset by large growth in rooftop solar.

Ongoing forecasted demand

The shape of operational demand will change over time with the buildout of more renewable capacity, particularly solar. As we move into the later years in the forecast horizon, we expect demand to be lower in the middle of the day relative to higher and higher morning and evening peaks, and that the “duck curve” will therefore become more pronounced over time.