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FastLMP

Learn more about 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 S&P Global 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.

Try out FastLMP for yourself Get access to our demo hub
REQUEST DEMO
FastLMP: A mixed model approach for battery storage project site evaluation in ERCOT See our case study
DOWNLOAD

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.

Who Benefits From This Product?
  • Renewable energy project developers
  • Financial sector
  • Global energy companies
  • Independent power producers
What Key Questions Does This Product Address?

Renewable energy project developers:

  • How do I easily evaluate siting options for my development projects?
  • How can I compare nodal pricing at different sites within my project pipeline?

Financial sector:

  • How can I get more complete nodal and plant data early in the process of deal assessment?
  • How can I make M&A analysis more efficient and accurate?

Global energy companies:

  • How do I get a “big picture” view of nodal pricing relationships when I’m looking to diversify investments?
  • How can I get a clear picture of contract and basis risk early in my assessment of big power projects?

Independent power producers:

  • How can I get long-term nodal pricing to support my power portfolio valuation?
  • How can I better support preliminary asset M&A?
How Do Clients Use This Product? A Sample of Use Cases
  • A battery storage developer needs to assess to profitability of prospective project sites. As part of this process, she needs to find the battery arbitrage margins at many sites in North America. She connects the data supplied by the S&P Global long-term LMP forecast API in the FastLMP product to an energy arbitrage optimization algorithm, and this enables her to see quickly see the battery arbitrage margins at hundreds of locations across ERCOT and narrow down the suitable locations for her development project.
  • A wind developer is looking for a suitable site to build a series of turbines in West Texas. As part of the early scoping process, he needs to rank the potential land lease agreements. Using FastLMP, he evaluates nodes and interconnection points for numerous potential sites, assessing the LMP basis risk relative to the traded hubs where the contracts would settle for each.
  • A senior analyst at a private equity firm wants to acquire assets in the power industry that are likely to be the most profitable. With FastLMP, she can quickly perform evaluations on multiple power plants using S&P Global plant characteristics, LMP basis forecast, and basis variation statistics for the node.
  • A renewables portfolio owner identifies an opportunity to acquire a power project acquisition and needs to assess its risk. He uses FastLMP to assess the merchant tail risk of the target, gaining a quick view of both zonal and nodal price movements in the distant future.
What Makes Us Rise Above the Competition?
  • Consistent LMP basis forecasts across an entire power market: While nodal engineering dispatch models typically lack the data to produce a fully consistent LMP basis forecast across an entire power market, the FastLMP forecast is consistent—it is deeply rooted in historical relationships and a set of key drivers.
  • LMP basis forecasts are aligned with the S&P Global comprehensive power outlook: The zonal prices to which the LMP basis forecasts are referenced and the drivers of future LMP basis are consistent with the S&P Global comprehensive power outlook out to 2050.
  • Bringing all nodal prices to customers’ fingertips: Through simple point, click, visualize, and data export steps, FastLMP users can quickly identify, obtain, and use the historical and forecast LMP (and LMP basis) data of interest to them.

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 S&P Global 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



enlarge to view

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

Events

Risky prospects: Forecasting nodal basis risk for wind project siting (AMER/EMEA)

November 19 2020 | North America This webinar explores the pitfalls of various (commonly used) heuristic methods for assessing basis risk and showcases FastLMP, a new S&P Global tool...
Learn more

Risky prospects: Forecasting nodal basis risk for wind project siting (APAC)

November 19 2020 | Asia Pacific This webinar explores the pitfalls of various (commonly used) heuristic methods for assessing basis risk and showcases FastLMP, a new S&P Global tool...
Learn more
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Experts

Barclay Gibbs

Barclay advises on power economics, due diligence, wholesale power price forecasting, and environmental/climate policy. He 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, market scenarios and strategic advisory services for a wind/solar developer, preparation of a report to support the refinancing of a solar portfolio in SERC, 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.Educated in chemical/environmental engineering, applied economics, and technology policy, Barclay 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).

  • Climate and Sustainability
  • Solar Power
  • Wind Power
  • Electric Generation Commercial Due Diligence
  • Electric Power Markets
  • Electricity Market Modeling and Analysis
  • Electricity Policy and Regulation
  • Energy Environment Policies and Practice
View Profile

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 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.

  • Climate and Sustainability
  • Clean Technology & Renewables
  • Data Science
  • Quantitative Research
  • Research and Development (R&D)
View Profile

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