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FastLMP

Harnessing the power of advanced analytics to provide consistent LMP (Locational Marginal Price) basis forecasts across thousands of nodes in the ERCOT and PJM power markets.

When evaluating power assets for development, investment, or acquisition, one of the most important considerations for site selection is the electricity price at any given location.

Typical early- and mid-stage project evaluation or valuation is done with zonal price forecasts or using a simple nodal basis assumption. But these approaches are crude approximations; prices may vary widely between different nodes within the market. If the price at the particular node you are evaluating varies significantly from the zonal price in magnitude or volatility, your preliminary financial projections may be highly inaccurate. Full nodal production cost modeling will provide you with the answers you’re looking for, but early in the project scoping process you may need to assess hundreds of different prospective sites—full nodal modeling at this stage would be cost prohibitive. Similarly, in early stage M&A processes, you likely have neither the time nor the budget to commission a full nodal production cost simulation. 

Your solution is FastLMP.

The advanced data analytics methods in our one-of-a-kind FastLMP product provide a consistent and high-quality view of LMP forecasts by combining hourly LMP basis forecasts for two critical wholesale power markets, PJM and ERCOT, with IHS Markit zonal power price forecasts out to 2050. Using terabytes of data in our simple point-and-click mapping interface, we bridge the gap between crude heuristic methods and full nodal production cost modeling, helping customers gain access to better pricing and nodal risk information much earlier in their project lifecycle at an affordable price. 

There is no other product in the marketplace that provides you this quantity of accurate and consistent LMP forecast data, with such a user-friendly interface at an affordable price point.

FastLMP Overview

Our advanced data analytics solution can help you find the perfect site for your next power generation project.

Key Features:

Hourly LMP basis forecasts

High quality hourly LMP basis forecasts are available for more than 22,000 nodes across ERCOT and PJM.

Convenient mapping interface

Our mapping interface features dynamic heatmaps and filters that enable easy scoping of early stage deals.

API interface

Our full API interface lets you plug hundreds of pricing forecasts directly into your valuation models to get you the insight you need.

Vetted, accurate data and forecasts

You can trust our rigorously vetted data and forecasts:

  • Mixed model approach: We select the best forecast from four models that layer machine learning and advanced statistics on top of IHS Markit fundamentals forecasts.
  • Training period optimization: Keeps our data accurate by intelligently scrapping outdated or unrepresentative historical data
  • Dynamic model selection: Our algorithm chooses the most accurate forecasting tool using a twelve-month validation period which is then used to retrain the best model.
  • Stochastic outlier generation: We accurately replicate and generate outage behaviors and unusual pricing events using five layers of stochastic draws.

Not a “black box”

We offer total transparency on our forecast validation. We provide hourly model validation dashboards, model decompositions, and performance metrics so you can feel confident about the forecasts we deliver.


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Hourly model validation dashboards, model decompositions, and performance metrics



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Our algorithm chooses the most accurate forecasting tool for every node from a basket of machine learning and statistical models.

FastLMP Video Tutorials

Video 1: FastLMP Map Tutorial


Video 2: FastLMP Dashboard Introduction

Video 3: FastLMP Forecast Tab Tutorial

Experts

Barclay Gibbs

Barclay Gibbs, Senior Director, leads North American power analytics for IHS Markit. He advises on power economics, due diligence, wholesale power price forecasting, and environmental/climate policy. Mr. Gibbs has advised electric utilities, independent power producers, fuels producers, investment banks, private equity firms, manufacturers of power generation equipment, construction/engineering firms, and industrial users of power.Recent/ongoing consulting projects include investment advisory and market benefits assessments for offshore wind projects in the Northeast US, an in-depth market study supporting a US nuclear power plant portfolio transaction, a buy-side market assessment for a portfolio of combined-cycle gas turbine plants (northeast US), and a property tax assessment in the WECC for a gas-fired power plant. Mr. Gibbs recently played a critical role on a team advising an African government regarding its electric sector growth strategy. He also oversaw the market simulations to support a gas-fired power and desalination plant transaction in Oman.Educated in chemical/environmental engineering, applied economics, and technology policy, Mr. Gibbs holds a BS from Bucknell University, an MA from Johns Hopkins University, and MS degrees from both Clemson University and the Massachusetts Institute of Technology (MIT).

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Duncan Anderson

Mr. Anderson has worked in electricity market modeling for almost a decade; developing, calibrating, and tailoring commercial and bespoke simulation products for consulting and retainer work focused on long-term power sector outlooks. At IHS Markit, he focuses primarily on using his broad technical skill set to develop new ways to process large volumes of power market data to produce actionable insights for clients and develop new products for the North American market.Mr. Anderson is the lead analyst responsible for developing the FastLMP locational price forecasting tool for the North American market. He is also responsible for developing and maintaining the North American Power Market dashboards on Connect.He holds a Bachelor of Science degree in Applied Economics from Cornell University and is currently completing a thesis in pursuit of a Masters of Science in Engineering degree in Industrial Engineering and Operations Research from UT Austin. His academic research focuses on the efficacy of clustering algorithms for infrastructure placement in simulated networks.

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Rajkumar Narasimhan

Raj is a senior Data Scientist and Executive Director at IHS Markit. He has over 17 years of data science experience. He has led product management and strategy for data science in companies across US and India. He has an MS in Industrial Engineering from Purdue University, West Lafayette US and a BS in Mechanical Engineering from CEG Anna University, India. At IHS Markit, he has led development of many data science products across Automobile, Finance, Energy and Maritimine & Trade businesses.

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