Uncertainty-based market-clearing models: a comparative analysis based on the Dutch, French, and German systems

The deterministic day-ahead (DA) market does not accommodate the uncertainty from variable renewable energy sources (VRES), often leading to increased balancing costs. Alternatives based on stochastic programming (SP) and adaptive robust optimisation (ARO) have been proposed in the literature but have yet to be implemented in functioning DA markets. This paper compares the deterministic, SP, and ARO market-clearing approaches for the co-optimization of energy and reserves, highlighting their advantages and drawbacks. Three case studies inspired by the power systems of the Netherlands, France, and Germany are examined using in-sample and out-of-sample analyses. Sensitivity analyses explore the impacts of sample size and conservativeness parameters on model performance. The findings reveal that SP and ARO models enhance socio-economic welfare (SEW) and significantly reduce the need for balancing services after the DA compared to the deterministic approach. Notably, the deterministic model yields the lowest SEW on days with high forecasted VRES output, whereas uncertainty-based models demonstrate the greatest benefits under high VRES penetration. Paving the way towards real implementation, we show that the trade-offs between economic performance, integration of VRES, and balancing needs after the DA depend on both system characteristics and the uncertainty model applied.