Enhancing grid stability: A weather-adaptive robust optimization to mitigating renewables curtailment
Bibliographic record
Abstract
This study presents a novel framework to maximize renewable energy penetration and enhance grid reliability within interconnected energy networks. The primary objective is to address the challenges posed by the inherent variability of renewable energy generation and the complexities of managing energy storage and power-to-x (P2X) conversion technologies. A dynamic two-stage optimization model is developed to achieve this objective, enabling power grids to operate with foresight and adaptability by making strategic day-ahead decisions and real-time adjustments based on unfolding uncertainties. This study combines dynamic thermal rating with an energy degradation model , offering an integrated approach to managing thermal capacity and storage decay under real-time conditions. A hybrid Benders decomposition algorithm is integrated with robust optimization techniques to efficiently manage the computational complexity arising from the stochastic nature of renewable generation and demand fluctuations. Additionally, a weather-adaptive deteriorating inventory model is introduced to realistically manage storage units by accounting for the decay or deterioration of stored energy over time. This study also investigates the impact of ambient weather conditions on thermal capacity to improve the utilization of transmission infrastructure, reduce congestion, and facilitate the integration of renewable energy sources . The proposed model demonstrated a reduction in renewable energy curtailment by 1.37–1.58 % and a 12.4 % decrease in operational costs, with increased revenues from P2X synthesis by 8.7 %, using real data. The framework's novelty lies in its combination of adaptive thermal rating, dynamic energy deterioration modeling, and an efficient optimization structure, ensuring practical and scalable results for real-world applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".