How can AI improve energy trading strategies?

What challenges do energy traders face today?

Energy traders must contend with the volatility of renewable energy, which is influenced by unpredictable weather patterns that affect output and lead to outages, as well as continuously shifting policies to navigate.

This volatility can create data fragmentation issues across the automated data acquisition systems of SCADA, as well as commercial decision-making tools such as ETRM and market feeds. This can be addressed by standardising data collection and integrating data gathering into plants using strategies such as the Internet of Things (IoT). 

Manual processes only compound the issue: slower, manual processes aren’t suitable for the fast-paced, changing environment associated with renewable energy. These sluggish, disconnected methods create latency in processes such as bidding, as well as financial strategies like hedges, and affect elements like imbalance management.

Integrating renewables with traditional models typically associated with the energy industry can also pose challenges: we may observe these existing models struggling with the non-linear regimes and rapid intraday fluctuations that characterise AI-led strategies.

How can AI improve forecasting accuracy?

Using hybrid models that combine AI-led machine learning with more traditional data analytical techniques, such as statistics, can leverage the strengths of both model types for more reliable data analytics.

Forecasting helps you prepare for the inevitable. Applying probabilistic forecasts can capture implementable information about uncertainty and extreme events, such as large drop-offs in production due to unpredictable weather, also known as tails. AI also enables energy traders to blend meteorological features, such as solar irradiance or wind speed, which allows traders to more accurately forecast market movement, predict and prepare for outages, and determine when to hold or sell in relation to intraday liquidity.

We can calibrate predictive models by type for accuracy, for example, by asset type, which would consider how specific renewable factors might affect energy trading, down to the granular specifics of the effect of wind turbine variations. Other considerations might be timing and its effect on trading, for example, day-ahead vs intraday, which occurs on the day of trade. 

AI can also be used to predict pricing. Some of the primary KPIs used by AI to predict pricing are MAE and RMSE: the latter can help to forecast the effect that prediction errors may have on the market. Pinball losses also analyse the effect of errors in prediction on the trading market, with calibration curves helping to represent how trading might behave in a real-world market.

Where does AI help with bid and dispatch optimisation?

AI can also help to model how energy generators might approach the volatility of energy pricing. AI can generate bid stacks from data, including operation, fuel, and system cost implications and calculate bids or offers for a portfolio of energy prices to take to market.

AI evolves by constantly being exposed to information, and AI acting in the energy market is no different. Reinforcement learning exposes AI to market behaviours until it's able to fine-tune intraday re-bids under fluctuating liquidity limits: a volatility that human operators often find difficult to apply decision-making.  

AI can automate some of these processes to a certain extent, utilising auto-submit within guardrails, with the ability for human override in exceptional circumstances when it is most appropriate. 

Portfolio optimisation can also benefit from AI input: machine learning can help to reduce the risk associated with managing diverse (and often very volatile) renewable energy sources. It can do this by optimising storage systems, monitoring fluctuations in pricing and determining when energy should be dispatched from storage systems when the price of energy is higher or lower.

Can AI reduce balancing and imbalance costs?

The use of AI in balancing the cost of renewable energy generation can be extremely successful by identifying energy drifts, leaks or waste using sensors connected up by the IoT. Real-time detection on meters and telemetry helps identify anomalies such as unexpected downtime due to energy theft, machine health issues, or pressure drops, preventing them from occurring and reducing costs. AI can create alerts when a forecast error exceeds predefined thresholds, utilising triggers to automate corrective trades and prevent price spikes. Short-horizon nowcasts, ranging from five minutes to half an hour, can enable predictive financial strategies to take effect, for example, in relation to renewable assets and pre-positioned financial hedges to help mitigate risk.

How does AI strengthen risk management?

AI can also help businesses become more risk-averse. Scenario engines utilise hypothetical models to stress-test the levels of profit and loss your business can withstand by simulating weather and price paths. While unexpected circumstances can always throw a curveball in real-case scenarios, running these models and estimating how your business might cope can help to prepare businesses for future challenges. Machine learning can monitor the market and pricing behaviour to issue counterparty early-warning signals, using historical and real-time payment behaviour as a benchmark.

What data and platform foundations are required?

AI is only as effective as the data it’s learning from, and therefore, it’s essential to maintain a clean data pipeline. This includes accurate market price data, order books, weather behaviour, tracked outages and telemetry that closely monitors assets. It’s not just the quality of the data that’s important, but also the depth. Near-real-time streaming, ideally within a parameter of 1–5 min and a historical backfill of like-for-like data, will ensure adequate training for machine learning, leading to more accurate models and, in turn, actionable insights.

What pitfalls should energy firms avoid?

Accuracy should be at the heart of any AI-first model, with a focus on achieving model accuracy without compromising latency or interpretability. This should be implemented from the beginning of any data analysis processes: you can do this by challenging the accuracy of data collected from the onset and avoiding underestimation of data quality and missing-ness.