Extended LMP 

In a market, producers and consumers react to prices by adjusting output and consumption.  In wholesale energy markets, the market clearing price represents the price of energy that will provide the greatest benefit to the consumer while maximizing the greatest efficiencies of production for producers.


Limitation of Locational Marginal Pricing

Wholesale energy markets in the United States traditionally have used bid-based, day-ahead schedules that use security-constrained day-ahead unit commitments and economic dispatch, also called Locational Marginal Pricing. Price point locations on the grid, or LMPs, are determined based on the value of the next megawatt of energy needed to satisfy demand, given system congestion. In other words, system congestion costs cause location-based differences in prices. 

However, LMPs may not cover a resource's operating costs at the dispatch point, which might not reflect a true market clearing price. As a result, we use uplift payments to incent participants to follow dispatch.


Filling the Gap with ELMP

Extended Locational Marginal Pricing, or ELMP, promises to reduce or even eliminate uplift charges by incorporating all offer costs into the market clearing price of energy. ELMP more accurately reflects the gap between the real costs of generating electricity and the current congestion pricing methodology. In mathematical terms, that gap is called the convex hull. By determining the mathematical relationship at the hull, the gap narrows, resulting in more accurate market clearing prices. 


Achieving Market Objectives with ELMP

Extended Locational Marginal Pricing provides an analytically grounded and internally consistent methodology for achieving several energy market objectives:

  • Minimize uplift charges.
  • Allow gas turbines and other units operating at their economic minimum or maximum to affect the energy price when appropriate.
  • Allow emergency demand response that is called in blocks to affect prices when appropriate.
  • Reduce deviations in day-ahead prices.
  • Relieve spot price spikes that can result from forecasting errors and/or commitment errors.
  • Better align prices with cost causation, eliminating spikes in LMPs.

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