Key takeaway: Marginal Loss Factors are forecast directly from the nodal network model, reflecting how losses change as power flows to and from each connection point. Forecasts are produced separately by generation technology, and batteries get distinct import and export values, calibrated against AEMO's published determinations.
Marginal Loss Factors (MLFs) quantify the impact of electrical losses on energy transfer between a generator or load and the Regional Reference Node (RRN) within the NEM. They are crucial for accurately modelling revenue and dispatch outcomes, especially for battery energy storage systems (BESS).
1. Forecasting losses from the nodal network
The nodal fundamentals model solves a transmission network with over 200 nodes, resolving how electricity flows across every line in the network for each dispatch interval. MLFs are derived directly from those flows, rather than from a statistical regression.
- Losses from power flows: Every network line loses some energy as power flows across it, and losses grow the harder a line is worked. The model calculates how much total losses change when an extra unit of power is generated or consumed at each node.
- Per-region reference: Loss factors are measured against each region’s RRN, the point where energy is settled, so they are always expressed relative to the correct settlement location.
- Evolving with the network: As new transmission lines and circuits are added over the forecast horizon, the resistance between a node and its RRN falls. MLFs at affected nodes improve over time as a result, reflecting the network augmentation built into the nodal fundamentals model.
2. Technology-specific and BESS import/export MLFs
A node’s loss sensitivity varies through the day as flows on the network change. Two assets at the same node can therefore have different effective MLFs if they generate, or dispatch, at different times.
- Technology-weighted MLFs: The model weights each node’s loss sensitivity by the typical output profile of a given technology (for example, solar output concentrated around the middle of the day, wind more evenly spread), producing separate MLF forecasts for solar, wind, thermal and hydro generation at every node.
- BESS import and export MLFs: Batteries are treated separately from generators. A battery importing (charging) is typically weighted to the middle of the day, when prices are lowest, while a battery exporting (discharging) is weighted to the evening peak. This produces two distinct loss factors, an import MLF and an export MLF, for every node.
3. Calibration against AEMO’s published determinations
The nodal network is a simplified representation of the real NEM, so the model’s raw loss estimates are calibrated to align with AEMO’s actual published MLFs before being used in the forecast.
- Anchored to published MLFs: The calibration ensures that both the level and the spread of MLFs across nodes align with AEMO’s published MLF determinations, for each region and technology.
- Bounded forecast range: Forecast MLFs are constrained within a realistic range, and each RRN is always fixed at exactly 1.0, by definition of the reference point.
4. Integration with dispatch modelling
Nodal prices produced by the fundamentals model capture network congestion only; electrical losses are not built into those prices. The battery dispatch model applies each asset’s forecast MLF separately, using the import MLF when the battery is charging and the export MLF when it is discharging, so that the modelled revenue reflects both the congestion and loss components of the asset’s actual price signal.