PJM Markets & Operations — LMP, Load, Fuel Mix and Reserves Data

It is the operations-and-markets face of PJM Interconnection, the regional transmission organization that runs both the wires and the wholesale market across all or parts of thirteen states plus the District of Columbia - the largest such footprint in the country. The landing surface carries a live “Today’s Outlook” panel reading current RTO load in megawatts, forecasted peak, real-time RTO price, generation totals, renewables output and zonal prices side by side; behind it sit dozens of individually documented report families consolidated under one catalog entry.

Prices come first: day-ahead hourly locational marginal prices and real-time prices at both hourly and five-minute resolution, every total broken into system energy, congestion and marginal loss components, quoted per bus, zone, hub and external interface, and restated through a version flag rather than overwritten. Load follows - hourly metered values with an estimated variant and a preliminary cut, tagged by NERC region and market region simultaneously, plus seven-day and very-short-term forecasts. Supply is generation by fuel type with megawatts, percentage-of-total and an explicit renewable marker, with wind and solar output published in dedicated feeds. Reliability products round it out: Day-Ahead Scheduling Reserve clearing prices and cleared quantities, sync and non-sync reserve and regulation results, transfer limits against actual flows, actual versus scheduled interchange, forecasted generator outages and uplift by zone.

Two properties make this catalog entry unusual. First, scale: a single day of five-minute real-time prices across every node runs to millions of rows, which is why scoped pulls beat whole-archive drags. Second, honesty about revision: because price rows carry their version metadata, a backtest can reconstruct what traders knew when, not just how events settled.

Basis and spread modeling. Take day-ahead and real-time prices decomposed into energy, congestion and loss at your specific nodes and you can price a merchant asset's basis against its hub, backtest how often congestion ate the spread, and stress the result against the real-time series instead of assuming the day-ahead settle held.

Scarcity and reserve research. Day-Ahead Scheduling Reserve clearing prices arrive next to the quantities that cleared, and sync-reserve, non-sync reserve and regulation clear separately - enough structure to study how scarcity propagates from reserves into energy prices without improvising proxies.

Load forecasting with honest error bars. Metered hourly load by NERC region, market region and load area sits in the same schema as the seven-day and very-short-term forecasts, so forecast error becomes a measurable, region-specific quantity rather than a story.

Renewables penetration and curtailment pressure. Fuel-mix percentages over time, with wind and solar output tracked separately and flagged renewable, chart exactly how fast gas displaced coal and where renewables now set the margin.

Congestion attribution. The congestion component at each node lines up against transfer limits and flows on the interfaces, turning “the wires were tight” into a dated, quantified event series.

East-west benchmarking. Normalized node-and-interval tables pair directly against the western tape in CAISO OASIS for cross-market studies.

Traders and quant researchers build basis, spark-spread and storage models directly on the nodal and hub price families via investors quants use cases. Data scientists train price and load models on versioned observations and labeled forecasts via data scientists use cases.

IPP analysts and originators benchmark candidate sites against realized hub and zonal prices, then read the reserve and outage feeds for the reliability backdrop their revenue depends on. Market researchers and consultants chart fuel mix, load growth and reserve trends into regional energy reports via market researchers use cases. Developers and product builders power eastern-interconnect applications on keyed deliveries; see developers builders use cases. Journalists, academics and students fact-check grid-stress and price-spike stories against official operational records rather than dashboard screenshots.

The common thread: all of them need the decomposed, versioned rows. Averaged zone charts hide both the congestion and the restatements that move the money.