Power and Renewables

Finding the Edge: How FTR Traders Can Leverage Weather Intelligence in Renewable-Dominated Markets

byTianze Peng
April 24, 2025

In today’s FTR auctions, it’s not just about bidding skill — it’s about outsmarting uncertainty. As renewables grow their footprint across U.S. power markets, the influence of weather on congestion patterns, volatility and forward price curves is more critical than ever — and full of opportunity for traders who know where to look. That’s where advanced weather analytics can provide traders with an edge.

Behind the Weather: More Than Just Temperature Swings

Most traders know weather matters, but few go beyond the surface. The real value lies in understanding how ocean-atmosphere systems shape seasonal patterns that impact generation and load across ISO regions.

Events like El Niño and La Niña don’t just affect hurricanes — they ripple through the grid. A warm patch in the Pacific today can lead to increased wind volatility in SPP or raise the likelihood of a severe heatwave in ERCOT months later. Recognizing these upstream signals is the key to anticipating shifts before they show up in the day-ahead or real-time markets.

Here’s how different regions respond to large-scale oceanic patterns:

  • CAISO: Highly sensitive to El Niño-Southern Oscillation (ENSO), affecting precipitation and drought risk.
  • NYISO & ISONE: Influenced by the North Atlantic Oscillation (NAO), shaping winter storm patterns.
  • ERCOT & MISO: React to a mix of Atlantic, Pacific and Arctic signals, driving wind and temperature variability.
Get ahead of seasonal risk with expert-led insights from our Summer and Winter Outlook Series. Each session breaks down weather patterns, generation trends and congestion risks across all major ISOs, helping traders sharpen their FTR strategy.

Harnessing Weather: Introducing Mid-Term Nodal Renewable Forecast

To translate complex weather dynamics into actionable market intelligence, Enverus developed Mid-Term Nodal Renewable Forecast, transforming seasonal weather signals into high-resolution trading signals.

Built on physics-based modeling and a continuously updated farm-to-node mapping system, Mid-Term Nodal Renewable Forecast delivers detailed, hourly forecasts of wind, solar, load and temperature up to 150 days in advance across all major U.S. ISOs. Traders also gain access to an ensemble of 100 scenarios, historical data for rigorous backtesting and coupled load-renewable outlooks — all available via API or the Panorama platform.

We show MISO wind generation forecast for May 2025. In the top panels we select the wind nodes of interest. Selected nodes are populated in the time series, showing mean generation at each node from 2017 – Aug 2025. We select MEC.ORIENT1 node for further analysis, including hourly percentiles in the bottom time series. The right map shows MISO zonal generation forecasts and the green dots show wind generation nodes.

Whether you’re identifying congestion risk, refining auction strategies or preparing for grid stress events, Mid-Term Nodal Renewable Forecast provides the granularity, flexibility and foresight FTR traders need to stay ahead. It helps uncover localized renewable signals, quantify congestion risks and adapt to a grid that’s constantly evolving. Below are some of the ways FTR traders can leverage the forecast to make more winning trades.

Drilling Down: From ISO to Farm-Level Precision

Mid-term Nodal Renewable Forecast provides ISO and regional insights all the way down to individual renewable assets. Using 100 weather-driven simulations, the forecast delivers localized, farm-level forecasts that allow traders to assess renewable output variability with even more precision.

We select a MEC.ORIENT1 in the drill down feature. The time series shows monthly generation for historical and forecast values. The solid line is mean generation, the narrow band is hourly p25-p75 values, and the broad band is p5-p95 values. Hourly generation values are available via API.

At this scale, users can analyze both time series trends and monthly generation distributions. Traders can go further by comparing on-peak vs. off-peak generation distributions and tracking how these patterns evolve across months.

This level of detail uncovers where renewable generation is expanding rapidly and where volatility may introduce new congestion risks. Whether you’re managing long-term congestion exposure or fine-tuning near-term trades, Mid-Term Nodal Renewable Forecast gives you a deeper understanding of renewable uncertainty, giving FTR traders the ability to confidently identify which nodes and regions are likely to be stressed and position accordingly.

From Global Clues to Local Bids: Weather as a Trading Signal

By comparing upcoming weather scenarios to 30-year climate baselines, Mid-Term Nodal Renewable Forecast enables traders to anticipate deviations in renewable generation and load that can drive congestion.

Mid-Term Nodal Renewable Temperature forecasts by PJM Transmission Zone are compared with climate means.

Monthly and zonal-level forecasts help traders hone in on specific nodes at risk. For instance, our January 2025 MISO outlook flagged significant forecasted departures from climatology — a crucial early signal that allowed traders to reevaluate positions ahead of auction deadlines.

This capability turns large-scale climate patterns into local, tradable intelligence.

Constraint Forecasting: Going Beyond the Forecast

While weather is often the catalyst, it’s grid conditions that determine whether congestion actually materializes. That’s why the Mid-Term Nodal Renewable Forecast includes the Constraint Forecast Model, extending forecast’s power by integrating high-resolution grid data, generation/load imbalance signals and oceanic pattern inputs to forecast congestion down to the hourly level, up to 150 days out.

The constraint forecasting tool provides probabilities that a constraint binds due to a weather driven event. Hourly probabilities for the forecast horizon are available via API.

This first-of-its-kind model lets traders anticipate where the grid will be stressed — before the market reacts.

Smarter Strategy Starts With Smarter Data

As renewables reshape congestion dynamics, traders need more than instinct — they need integrated, forward-looking intelligence.

Enverus and Mid-Term Nodal Renewable Forecast gives FTR traders the tools to confidently navigate market complexity, manage risk, and act decisively. Whether you’re planning long-term auction strategy or adjusting near-term positions, our solutions help you find an edge while others are flying blind.

Want to Learn the Considerations Behind Building a Winning FTR Trading Strategy?

FTR auctions can be challenging due to various factors, such as new builds, planned outages and increased demand during summer months. These complexities mean that FTR traders need to confidently manage their data and analytical tools to make successful bids and protect themselves against financial losses.

Our e-book, “Preparing for FTR Auctions,” will dive into crucial considerations and important factors that FTR traders need to review to prepare for auctions. Learn how to leverage weather analytics and data, predict demand spikes, analyze constraints, navigate outages, adapt to infrastructure changes, stay updated on project delays and align with regulatory shifts. 

About Enverus Power and Renewables

With a 15-year head start in renewables and grid intelligence, real-time grid optimization to the node, and unparalleled expertise in load forecasting that has outperformed the ISO forecasts, Enverus Power and Renewables is uniquely positioned to support all power insight needs and data driven decision making. More than 6,000 businesses, including 1,000+ in electric power markets, rely on our solutions daily.  

Picture of Tianze Peng

Tianze Peng

Tianze Peng is a Staff Data Scientist in Power and Renewables who joined Enverus in 2023. He specializes in developing quantitative algorithms and has extensive modeling experience in air-sea interactions, renewable energy, and geographic information systems. Currently, he focuses on weather-driven renewable energy and power grid forecasting across multiple time scopes. Tianze holds a Ph.D. in Environmental Fluid Dynamics from the University of Notre Dame and an M.S. in Energy and Transport from Arizona State University.

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