Flexibility Characterization of Sustainable Power Systems in Demand Space: A Data-Driven Inverse Optimization Approach
Bibliographic record
Abstract
The deepening of the penetration of renewable energy is challenging how power system operators cope with their variability and uncertainty. The inherent flexibility of dispathchable assets present in power systems, which is often ill-characterized, is essential in addressing this challenge. Several proposals for explicit flexibility characterization focus on defining a feasible region that secures operations either in generation or uncertainty spaces. The main drawback of these approaches is the difficulty associated with relying solely on visualizing this feasibility region when there are multiple uncertain parameters. Moreover, these approaches focus on system operational constraints and often neglect the impact of inherent couplings (e.g., spatial correlation) of renewable generation and demand. To address these challenges, we propose a novel data-driven inverse optimization framework for flexibility characterization of power systems in the demand space along with its geometric intuition. The approach captures the spatial correlation of multi-site renewable generation and load using polyhedral uncertainty sets. Moreover, the framework projects the uncertainty on the feasibility region of power systems in the demand space, which are also calledloadability sets. The proposed inverse optimization scheme, recast as a linear optimization problem, is used to infer system flexibility adequacy from loadability sets as a scalar quantity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".