AI-based Two-stage Stochastic Electricity Market Model for the Nordic Region (AIM4NORD)

The project addresses the need to manage uncertainty in Nordic electricity markets, where renewable penetration is among the highest globally. Driven by the rapid expansion of wind, solar, and hydropower, the region faces operational challenges such as frequency instability and higher balancing costs. Traditional deterministic models fail to capture the full scope of these uncertainties, leaving decision-makers with limited foresight in planning and operation. To bridge this gap, the project proposes an AI-based Two-Stage Stochastic Electricity Market Model that combines advanced optimization with machine learning-driven scenario generation.

First step is the AI Scenario Generation Module, which leverages historical and real-time data on renewable generation, electricity demand, and weather conditions to produce dynamic scenarios that better reflect real-world uncertainties, including rare but impactful events such as generator outages or sudden renewable energy drops. These scenarios provide more reliable inputs for market modeling compared to conventional forecasting.

The second step is the Two-Stage Stochastic Market Model. In the first stage (“here-and-now”), strategic decisions such as unit commitment, reserve allocation, and start-up/shut-down scheduling are optimized before uncertainties are realized. In the second stage (“wait-and-see”), the model adapts to revealed uncertainties, handling tasks like generation dispatch, reserve adjustments, and risk management. Conditional Value at Risk (CVaR) is incorporated to assess and mitigate financial risks, ensuring resilient operations under volatile market conditions.

This project provides a forward-looking, risk-aware framework that strengthens decision-making, enhances market stability, and supports a cost-efficient transition towards a sustainable Nordic energy system.