Every day, energy prices fluctuate based on supply and demand. In nodal power markets, system operators use locational marginal prices to set the cost for electricity across the grid.
Continuing our energy basics series, let’s dive into what locational marginal prices are and how you calculate them.
A node is a point on the power grid, where an entity removes or injects electricity. These nodes are spread across the system, and each node has a locational marginal price (LMP). Nodal markets allow system operators, such an Independent System Operator, to send pricing signals to specific locations to ensure reliability and competitive costs.
The graphic below provides a visual representation of the LMPs in North America. Each dot on the map represents the locational marginal price of electricity at a generator node, load node, hub (aggregation of nodes), or a zone/interface node. In the graphic below, the nodes are colored by the real time price of electricity at that moment in time (purple=negative price, red=high price).
The LMP is the system marginal component, plus the marginal congestion component, plus the marginal loss component.
The system marginal energy component is the cost to produce the electricity. The fuel type used to generate the electricity mostly controls this. It is often called the energy cost component.
This graph from the EIA shows the economic dispatch curve, determining the system marginal component.
Generally, system operators first use electricity from the lowest-cost sources to meet consumer demand. The lowest-cost sources are usually renewable sources due to their low fuel cost, but these sources depend on the weather, e.g. you only get solar power when the sun shines. Given these resources’ unpredictability, system operators typically rely on the next lowest-cost power plants to provide a constant base to meet demand, adding in renewables when available.
As demand increases, operators have to turn on more and more expensive generators to meet it. The most expensive sources are often oil and gas-powered generators, as you can see in the graph above.
Then there’s the congestion component. When a line or piece of equipment has too much electricity flowing through it’s called grid congestion – similar to when there are too many cars on a road and we call that traffic congestion.
Congestion occurs before a piece of equipment on the system exceeds its capacity. Capacity is the amount of electricity that can safely flow through a transmission line or other equipment. System operators have to monitor a line's capacity limit because if more electricity flows through a line or piece of equipment than it can handle, it could damage equipment, cause a power outage, or start a fire.
System operators add this congestion cost to the LMP as a signal to the market that a piece of equipment is approaching its capacity, and the cost will incentivize the generators and load centers to respond to relieve that congestion. When this occurs, a piece of equipment on the grid has a constraint placed on it by system operators.
Given the geographical nature of congestion, the cost associated isn’t distributed equally across all nodes. The congestion cost is calculated using two elements: the shadow price and shift factors.
System operators assign a shadow price to a constraint. How much it would cost to alleviate congestion by one MW determines this price. The ISOs distribute the total shadow price for a constraint across the nodes.
Every node on the system has a unique shift factor when a constraint hits, and this shift factor is what is used to determine how that cost is distributed across the nodes. This shift factor is multiplied by the shadow price to determine how much the congestion component will be for that node. Shift factors range from -1 to +1, depending on how much that node impacts the congestion. A zero would mean no impact and therefore no congestion cost.
Calculating the congestion component for a node can be complicated because there often is more than one constraint put on the system at a time, meaning that the different shadow prices and shift factors for each constraint impact the congestion cost of a node.
Finally, the marginal loss component is the small amount of electricity lost when electricity travels through the transmission line.
This varies depending on how close or distant the power demand is from the power supply. (This is similar to when you water your garden with a hose and some water remains in the hose instead of pouring onto the garden.)
The example below shows the buildup of the real-time price at the LENOX 115 KV 1 TX price node in PJM on August 30, 2024. The real-time price in PJM is a summation of the energy component of the LMP, the congestion component of the LMP, and the loss component of the LMP.
As shown below, the real-time price at the LENOX 115 KV 1 TX node was predominantly driven by congestion, which drove up the price at the node.
Source: Yes Energy’s PowerSignals product
Using the constraint tools in Yes Energy’s PowerSignals® product, we can identify the constraints that drove up the price at LENOX 115 KV 1 TX on August 30. Nodes with a high positive shift factor drove up the congestion component of the LMP at LENOX 115 KV 1 TX on August 30.
As shown below, we can see that the LENOX 115 KV LENOX-NMESHOPP NML 1090 constraint (first row in the table) was the constraint that drove up the congestion component of the LMP at the LENOX 115 KV 1 TX node on August 30.
Source: Yes Energy’s PowerSignals
Pulling this all together in a map, the screenshot below overlays the real-time LMP at LENOX 115 KV 1 TX with the transmission constraints (yellow circles and lines) occurring in the real-time market in PJM on August 30. The shift factors in the table above multiplied by the shadow prices in the screenshots below tell you the contribution of each constraint to the congestion component of the LMP for LENOX 115 KV 1 TX.
For example, the LENOX 115 KV LENOX-NMESHOPP NML 1090 constraint contributed $1,222 of the $1,237 LMP at the LENOX 115 KV 1 TX node at 5:10 am on August 30 ($2,000 shadow price x 0.611 shift factor = $1,222/MWh).
The LMP price spike sends a message to surrounding generators that they could make money by ramping up their production, which in turn would help alleviate the congestion that exists elsewhere on the system.
Source: Yes Energy’s PowerSignals
In nodal power markets, each node has an LMP. You can calculate the LMP by adding the congestion, energy, and loss component together (there are exceptions for other ISOs, such as ERCOT).
System operators use LMPs as a critical tool to send signals across the grid to keep equipment safe and functioning.
Our example shows how the congestion component is impacted when a transmission element is constrained, signaling to the market that electricity flow needs to change to avoid a piece of equipment exceeding its transmission capacity.
Want to learn more about nodal power markets? Review our Power Markets 101 series.
Have a question about nodal power markets? Ask our team.
About the author: Whether it’s through her work at Yes Energy or through her previous roles as a teacher, Sarah Hatch finds purpose in helping people grow and push toward the unknown boundaries of their potential. She holds a B.S. in natural resources and a Masters of Education – both of which have come in handy here at Yes Energy. In her free time, Sarah enjoys reading, spending time with her family, and being near the ocean.
About the author: Emily Merchant is a director of product at Yes Energy in charge of setting the vision and strategy for Yes Energy's PowerSignals, QuickSignals, and Trading Regions (public data) products. Emily has over 12 years of experience working in the energy industry. Prior to Yes Energy, Emily worked at Navigant Consulting (now Guidehouse) for seven years where she helped utilities assess the impact of their energy efficiency programs. She has also worked at E Source, Energy Trust of Oregon, and GDS Associates. A career highlight was being on the team that brought on S&P Global on as a new partner to Yes Energy in 2022. Outside of work Emily loves traveling (London is her favorite city), biking, reading, and spending time with friends and family.