Overview

Every PJM location that carries a price, as three columns: location_id, name, and kind. 14,977 rows — load (12,159), generator (2,255), aggregate (293), bus (153), interface (82), zone (23), hub (12). This is the dimension the PJM price, and where applicable constraint, tables join to, and it is the one place a location's type is decided: resolve "which hubs exist", "is this a zone or a node", or "give me the readable name for this id" here rather than inferring a type from a name prefix. For PJM, location_id is the numeric pnode_id and name is the pnode name (e.g. WESTERN HUB). Join on location_id; report name. The same three columns exist for every market, so the equivalent question in another ISO is the same query against that ISO's table.

PJM market Ask the agent about this dataset

Use with MCP

This table is readable two ways, over the same guarded boundary. Over HTTP: POST /api/v1/datasets/pjm_locations/query, described in the API reference. Or from an MCP client: connect a market below, then use search_data or describe_data to load its query contract before calling query_data.

PJM MCP Server

Schema3

Column

Type

location_idLowCardinality(String)

nameLowCardinality(String)

kindLowCardinality(String)

Sample rows

location_id	name	kind
1	PJM-RTO	zone
101560795	HAYROAD 8CC	aggregate
1047909	TOSCOGIS	load
1047910	AMTRAK	load
1047911	AMTRAK	load
1047912	BETZWOOD	load
1047913	LLANERCH	load
1047914	LLANERCH	load
1047915	LLANERCH	load
1047916	UPPERPRO	load
1047919	EXETER	load
1047920	EXETER	load
1047921	HUNLOCK	load
1047925	POPETALB	load
1047926	COURTDAL	load
1047927	DALLAS	load
1047928	GLENVIEW	load
1047929	HANOVER	load
1047930	HANOVER	load
1047932	KING UGI	load
1047933	KING UGI	load
1047934	KOONSVIL	load
1047935	KOONSVIL	load
1047936	KUNKLE	load
1047937	LINC UGI	load

Coverage

No time-indexed data — coverage isn’t applicable to this dataset.

More data from PJM

7-Day Load ForecastPJM's seven-day-ahead hourly load forecast by forecast area, refreshed through the day. Rows are keyed by target time (event_time) with the issue time in evaluated_at_utc; take the latest vintage per target hour via an argMax over evaluated_at_utc. Forecast areas match the very-short-term feed (utility names, RTO_COMBINED for the system total). This is the table for tomorrow's peak and the week-ahead demand outlook. Source: PJM Data Miner 2.Day-Ahead Hourly LMPCleared day-ahead locational marginal prices for each PJM pricing node by delivery hour, with total LMP and its system-energy, congestion, and marginal-loss components in dollars per MWh. The table supports the posted next-day hourly curve, day-ahead-to-real-time basis, and node-level component attribution. A congestion component does not by itself identify which facility is expected to bind or establish a node-to-constraint relationship. Source: PJM Data Miner 2.Day-Ahead Transmission ConstraintsThe transmission constraints that bound in PJM's day-ahead market: the monitored facility, the contingency facility, the duration, and the congestion event. It names which elements are expected to limit flows for the next operating day. Note that PJM does not publish a real-time binding-constraint/shadow-price feed, so real-time congestion is read from the congestion component of the LMP tables rather than from a constraints table. Source: PJM Data Miner 2.Generation by Fuel TypePJM system generation broken out by fuel type every five minutes — gas, nuclear, coal, solar, wind, hydro, storage, and others — with each fuel's output in MW, its share of the total, and a renewable flag. This is the live fuel-mix signal for the PJM footprint: read it for current renewable output, the marginal fuel, and how the mix shifts across the day. Source: PJM Data Miner 2.