How Artificial Intelligence (AI) and Machine Learning (ML) Streamlines Renewable Energy Trading

The stakes are high, with fast-paced wholesale electricity markets fundamentally shifting due to the energy transition.  Participating in only the real time energy market as a renewable operator often leads to curtailment, reduced operating revenues, and missed opportunities. Leveraging AI/ML informed recommendations enables enhanced project revenues through day ahead participation which hedges low real time price risk while still allowing for real time price spike value capture. 

Accurate Predictions Drive Increased Revenue 

Staying competitive grows increasingly more complex as renewable energy assets disrupt traditional models — those built for predictable thermal generation — that energy traders and asset managers have historically relied on for forecasts and insights. Traditional or outdated models are not able to handle the uncertainty renewables bring to the table for two primary reasons:  

1. Unable to consolidate the enormous amounts of data for power producers to make informed decisions. 
2. Unable to factor in the three essential elements for trusted recommendations:  

• Asset performance predictions. 
• Price predictions. 
• Risk-aware recommendations. 

With increasingly competitive and volatile markets, one way to stay ahead is by investing in advanced analytics and machine learning technology to improve Day Ahead (DA) offers. As McKinsey states in "A new age for energy and commodity trading," “using advanced analytics, especially in volatile short-term markets such as intraday power trading, can make the difference between profitability and risk to exposure of significant income shortfalls.” 

Unlike traditional power sources, such as coal or natural gas, renewable energy generation is highly dependent on the region, local weather patterns, cloud cover, wind speed and other factors that can vary significantly over time and space, making it difficult to predict the amount of energy that will be generated.  

Recommendations with accurate predictions are critical for informed decision-making and avoiding missed opportunities. Traditional models typically unable to handle the complexity and risk when estimating renewable energy generation output on a continuous basis.   

Volatility without visibility is risky 

Energy market data are more diverse and decentralized than ever. An overwhelming amount of information bombards energy traders daily in today’s market, with the sheer volume and complexity making uniting and analyzing the information quickly enough to anticipate market movements in time to capture value challenging.   

Given the shortcomings of traditional models and static spreadsheets in estimating the impact of higher renewable generation on energy prices, many renewable energy traders are hesitant to use the DA market to help offset low real-time prices. When price volatility negatively impacts your operations, a lack of accuracy or timeliness in generation and price forecasts can result in significant financial losses, curtailing and missed opportunities.  

A handful of merchant renewable operators participate in the DA market at the nodal level.  Doing so requires either a favorable outlook on the DA vs. real-time price for passive participation or a reliable prediction of plant generation and the DA/real-time price spread for active participation.  

DA trading, however, is nearly impossible without precise renewable generation and price predictions.  

Enter GE Digital’s Alpha Trader for Merchant Renewable Operators

The unique solution leverages advanced AI/ML to produce generation and price predictions then combines them with a risk management approach matched to the customer’s risk profile. By utilizing cutting-edge tools like Alpha Trader to better predict wind and solar generation, energy traders and asset managers can optimize their investments’ economic value and offer into the DA market with greater confidence. 

Below is an example examples of how a 250MW merchant wind farm in the U.S. could profit from active DA market participation.   

How GE Digital's Alpha Trader leverages AI/ML Models for Accurate Generation, Prediction and Risk-Adjusted Recommendations

Considering renewable energy generation variability and price spread volatility, effective risk management requires answers to critical questions every hour of every day:  

1. How much energy will the solar/wind farm generate tomorrow?  

2. How much will that energy be worth?  

3. What is the right offer strategy to match my risk profile?  

Alpha Trader answers those questions by utilizing digital twin and AI/ML models to provide enhanced insights into tomorrow's generation and price movements then combining them with an understanding of your risk profile to recommend improved strategies regarding when and how much to offer into the DA market. The solution advises the MW/hour to offer for each plant and aggregating multiple sites into a single view to provide an overview of the portfolio. Users can easily access daily recommendations via their user interface, email or API. 

Advantages of using Alpha Trader for Merchant Renewable Power Plants

1. Hedge Risk: Minimize exposure to intraday price variance by confidently participating in DA markets with Alpha Trader.  
    
2. Create Process Efficiency: Alpha Trader’s data capture and analysis automation ensures accurate, reliable renewable generation and market price forecasting.  
    
3. Increase Productivity: Save time and improve accuracy by switching from manual, Excel-based modeling to Alpha Trader, increasing your capacity factor by 30% or more.  
    
4. Unlock Value: Risk-adjusted recommendations for DA market participation delivered $2.5 million in additional annual revenue for a 250-MW wind farm over 9 months, additional trials across six wind farms in the U.S. resulted in $20 million of opportunity.  

 
 
The energy market is constantly evolving, and the challenges of predicting renewable energy generation and real-world performance are not going away anytime soon. Energy traders and asset managers can harness the power of AI/ML to optimize their energy market trading strategies and achieve new levels of success in an increasingly complex and competitive marketplace.