What is locational marginal pricing?

Electricity feels like it should have a single price.

You plug something in, power flows, and you pay a rate per kilowatt-hour. It is easy to assume that electricity is like petrol: one product, one market, one price. At the power point or the switch it feels simple.

But electricity does not work like that.

Where it is produced, where it is consumed, and how it moves through the network all matter. And once the system becomes constrained — meaning the network cannot move all the electricity people want, where they want it — those differences start to show up as differences in cost.

Locational marginal pricing or LMP, also known as nodal pricing, is simply a way of making those differences visible.

Start with a simple idea

Imagine a very simple grid. There is a wind farm in one location, a city in another, and a transmission line connecting the two. When the wind blows and demand is moderate, the wind farm can supply the city easily. Prices are low, and everything looks simple.

Now imagine demand rises, or the transmission line reaches its limit. This is what is meant by a constraint: the physical network — the wires and transformers — can only carry so much electricity at a time. When that limit is reached, not all available power can flow where it is needed.

At that point, something changes. The wind farm may still be producing cheap electricity, but it cannot all reach the city. To keep the lights on, a more expensive generator closer to the city has to turn on.

The cost of supplying electricity is now different in the two places:

• at the wind farm, electricity is still cheap
• in the city, it has become more expensive

Nothing about the electrons has changed.

What has changed is the ability to move them.

One system, different prices

This is the core idea behind locational pricing.

Electricity does not have a single cost everywhere at the same time. It has a different cost depending on:

• how far it has to travel
• whether the network is constrained (physically limited)
• whether losses are high
• what generators are available nearby

LMP simply assigns a price to each location that reflects those conditions.

So instead of one regional price, you get many local prices.

The three parts of price

Under LMP, the price at any location is usually described as having three components.

You do not need to memorise them, but they are useful for intuition.

1. Energy

This is the base cost of producing electricity — what it would cost if there were no constraints in the system.

Think of this as the “underlying” price of electricity.

---

2. Congestion

This reflects the limits of the network.

If a line is full — that is, if it is constrained — cheaper electricity cannot reach you. More expensive local generation has to be used instead. The extra cost of doing that shows up as congestion.

In simple terms:

congestion is the cost of the network being full in the wrong place

---

3. Losses

Electricity is lost as heat as it travels through wires.

The further electricity moves, the more is lost. LMP accounts for this by adjusting prices based on location, so electricity delivered far away is slightly more expensive.

Put together, the price at a location becomes:

energy + congestion + losses

That is locational marginal pricing.

Why this matters

LMP is not just a pricing system. It is a way of sending signals.

If prices are higher in one location than another, that tells you something:

• the network is constrained there
• local supply is limited
• or demand is high

In other words, price becomes a map of where the system is under stress. It shows you where the system is struggling.

What happens without it

Many electricity markets, including Australia’s NEM, use regional pricing instead. That means large areas share a single price, even if conditions within that area are very different.

This works reasonably well when the system is simple and unconstrained — when electricity can flow freely. But as more renewable generation is added, and as flows become more uneven, constraints become more common.

When that happens:

• some parts of the grid have surplus energy
• other parts are short of energy
• and the network cannot fully connect the two

The system still experiences these differences — it just does not price them cleanly.

Instead, operators have to manage them through instructions, interventions, and workarounds.

A simple way to think about it

You can think of LMP like traffic.

If every road in a city had the same toll regardless of congestion, some roads would become overloaded while others sat empty. The system would still function, but inefficiently.

Now imagine pricing each road based on how busy it is.

Drivers would adjust:

• some would take different routes
• some would travel at different times
• some would avoid congested areas altogether

The system would become more efficient without building new roads.

LMP applies the same idea to electricity, pricing congestion instead of hiding it.

The trade-offs

LMP is not perfect.

It introduces more complexity. Prices become more local and can change more quickly. Participants need ways to manage that risk, usually through financial contracts.

It can also raise fairness questions. If some locations consistently face higher prices, that becomes a policy issue, not just a market one.

When you move from one price to many, you also spread the market out. In a single regional market, everyone is trading the same product. There are many buyers and sellers, and trades can happen easily without shifting the price. This is what is meant by liquidity — the ability to trade without moving the market.

Once prices become local, that depth is divided. Each location has fewer participants, fewer trades, and less activity. The market becomes more precise, but also thinner.

So there is a tension. More pricing points give you better signals, but less liquidity at each point. Prices become more accurate, but also more sensitive.

In practice, markets adapt by creating ways to trade across locations — using common reference points and financial contracts to rebuild depth where it is needed.

So LMP is not just a technical change. It is also a design choice about how the system should work.

Where is LMP used?

Locational marginal pricing can sound theoretical, but it is not an academic idea. It is already used in many of the world’s largest electricity systems.

In the United States, major power markets such as those in Texas, California, the eastern states, and parts of the Midwest all use locational pricing. In Texas, for example, the system moved from broad regional prices to detailed local pricing in 2010, and now publishes real-time prices that vary across the network.

California also uses locational pricing, with electricity prices broken into components that reflect the underlying cost of energy, the limits of the network, and the losses that occur as power flows across it.

Closer to Australia, New Zealand uses a similar approach. Prices are set at specific points on the grid rather than averaged across large regions, so they reflect local supply, demand, and network conditions.

These systems are not perfect. Texas, California and New Zealand all face their own challenges. But locational pricing itself is not unusual or experimental. It is a well-established way of running electricity systems where prices are used to reflect the physical limits of the grid.

Why it is coming back into focus

As electricity systems become more renewable and more dependent on transmission, constraints are becoming more important.

You can respond to that in three ways:

• build more network
• manage constraints manually
• or reflect them in prices

Most systems use a mix of all three.

LMP is the approach that leans most heavily on price signals.

The takeaway

Electricity does not have one cost. It has many, depending on where and when it is used. Locational marginal pricing does not create that reality. It reveals it. We are missing valuable price signals by not incorporating it in the NEM. For more about why we need LMP see Battling Entropy Issue 16.