What question did this study set out to answer?

To compare deterministic, stochastic programming, and adaptive robust optimization market-clearing models for the co-optimization of energy and reserves under renewable generation uncertainty.

August 19, 2026Energy SystemsOpen Access

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

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Authors

JLJoran van der Loo LSLina Silva-Rodriguez ASAnibal Sanjab

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Overview

Simulation study reveals that uncertainty-based market clearing improves economic welfare and reduces balancing needs in power grids, highlighting benefits for renewable energy integration.

Key Points

To compare deterministic, stochastic programming, and adaptive robust optimization market-clearing models for the co-optimization of energy and reserves under renewable generation uncertainty.
Evaluated deterministic, stochastic programming (SP), and adaptive robust optimization (ARO) market-clearing formulations using in-sample and out-of-sample analyses.
Conducted comparative case studies parameterized on the national power systems of the Netherlands, France, and Germany.
Carried out sensitivity analyses to examine the effects of sample size and optimization conservativeness parameters on clearing performance.
SP and ARO models enhanced socio-economic welfare (SEW) and substantially decreased the volume of balancing services required after day-ahead clearing compared to deterministic models.
Deterministic clearing resulted in the lowest SEW during periods of high forecasted variable renewable energy (VRES), whereas uncertainty-aware models yielded the greatest performance gains under high VRES penetration.
The trade-offs between socio-economic welfare, VRES utilization, and post-day-ahead balancing requirements depended on the specific power system structure and the chosen uncertainty framework.

Cite This Study

Loo et al. (2026) studied this question.

synapsesocial.com/papers/6a8563ae03308d306e2d7009 https://doi.org/10.1007/s12667-026-00829-9

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August 19, 2026Energy SystemsOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

JLJoran van der Loo LSLina Silva-Rodriguez ASAnibal Sanjab

Discussion

Member takes

Overview

Simulation study reveals that uncertainty-based market clearing improves economic welfare and reduces balancing needs in power grids, highlighting benefits for renewable energy integration.

Key Points

To compare deterministic, stochastic programming, and adaptive robust optimization market-clearing models for the co-optimization of energy and reserves under renewable generation uncertainty.
Evaluated deterministic, stochastic programming (SP), and adaptive robust optimization (ARO) market-clearing formulations using in-sample and out-of-sample analyses.
Conducted comparative case studies parameterized on the national power systems of the Netherlands, France, and Germany.
Carried out sensitivity analyses to examine the effects of sample size and optimization conservativeness parameters on clearing performance.
SP and ARO models enhanced socio-economic welfare (SEW) and substantially decreased the volume of balancing services required after day-ahead clearing compared to deterministic models.
Deterministic clearing resulted in the lowest SEW during periods of high forecasted variable renewable energy (VRES), whereas uncertainty-aware models yielded the greatest performance gains under high VRES penetration.
The trade-offs between socio-economic welfare, VRES utilization, and post-day-ahead balancing requirements depended on the specific power system structure and the chosen uncertainty framework.

Cite This Study

Loo et al. (2026) studied this question.

synapsesocial.com/papers/6a8563ae03308d306e2d7009 https://doi.org/10.1007/s12667-026-00829-9

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