Constraints

How constraint equations work

Each constraint equation has a left-hand side (LHS) and a right-hand side (RHS). The LHS is a linear combination of decision variables—typically unit outputs, interconnector flows, or demand—weighted by coefficients. The RHS is a constant limit. For inequality constraints, the LHS must be less than or equal to (≤) or greater than or equal to (≥) the RHS. Equality constraints require the LHS to equal the RHS exactly.

NEMDE incorporates all active constraints into a linear program and finds the dispatch that minimises total cost while satisfying every constraint. When a constraint binds, its shadow price (marginal value) reflects the cost of relaxing that limit by one unit. Binding constraints with high shadow prices are often the primary drivers of regional price separation.

AEMO constraint naming conventions

AEMO uses a structured naming scheme for constraint equations. Names typically encode the type of limit, the affected equipment or region, and the direction of the constraint. Common patterns include:

• Region and flow: e.g. N>>NIL_33_34— thermal limit on northern NSW 330 kV lines
• Contingency and direction: e.g. Q^^N_NIL_SRAR—voltage collapse limit for loss of Sapphire–Armidale (8E)
• Interconnector and direction: e.g. NQTE_ROC,QNTE_ROC—rate of change limits on Terranora

AEMO publishes the full set of active constraints in the MMS Data Model and in the Monthly and Annual Constraint Reports. Constraint names and RHS values can change with network conditions, outages, and seasonal ratings.

Binding vs non-binding constraints

Only constraints that bind in a given interval directly affect dispatch and prices. A constraint binds when the optimal solution sits exactly on its limit—the LHS equals the RHS (or the limit). Non-binding constraints have slack: the solution could move further before hitting the limit, so they impose no active restriction.

In practice, only a small fraction of the thousands of potential constraints bind in any interval. Identifying which constraints bound the solution is crucial for understanding why a particular unit was dispatched or why prices diverged between regions. supagrid and AEMO data sources allow analysis of binding constraints and their marginal values.

Types of constraint impacts

Constraints fall into broad categories by function. The table below summarises the main types and how they manifest in the market.

How constraints affect prices

Binding constraints restrict the flow of electricity or the output of generators. When transmission limits bind, cheaper generation in one region cannot reach demand in another; the constrained region must rely on more expensive local generation, raising the regional price. The shadow price of the binding constraint reflects the value of an extra megawatt of transfer capability.

Local network constraints can reduce output from individual units or stations even when they would otherwise be economic. Security constraints can force dispatch of additional generation or limit interconnector flow to maintain stability after contingencies. Analysing which constraints bound the solution helps explain price spikes, regional separation, and unexpected dispatch patterns.

AEMO resources

AEMO publishes constraint data through the MMS Data Model (including Constraint equations and Constraint data), the Monthly Constraint Report, and the Annual NEM Constraint Report. These reports list active constraints, RHS values, binding incidents, and root causes. For real-time and historical binding constraints, the NEMWEB publication sets provide constraint data aligned to settlement intervals.

Related

Market & Network Constraints introduces constraints at a conceptual level. Interconnectors describes how interconnector constraints shape flows and prices. For AEMO variable definitions, see AEMO variables.