Integrated transmission and storage systems investment planning hosting wind power generation: continuous‐time hybrid stochastic/robust optimisation
Bibliographic record
Abstract
In this study, a continuous‐time hybrid stochastic/robust optimisation is proposed for the integrated investment in transmission lines (TLs) and energy storage systems (ESSs) with high penetration of uncertain wind power generation (WPG) sources from a central planner viewpoint. The main objective of the problem is to achieve a simultaneous expansion of transmission assets, TLs and ESSs, whereas minimising the investment cost while taking the operational aspects of a power system into account to accommodate higher shares of uncertain and intermittent WPGs. However, the integrated expansion planning of joint TL and ESS to integrate WPGs via conventional hourly discrete time model can increase the operation cost and result in a non‐optimal sizing and siting of TLs and ESSs, hence, can impose an opposite effect on the favourite. Accordingly, a continuous‐time model is proposed to coordinate the expansion planning of both TL and ESS to deal with sub‐hourly uncertainty of WPGs. Also, the WPG uncertainty in expansion planning problem is characterised using a hybrid stochastic/robust optimisation framework. Numerical tests are implemented on a modified IEEE RTS 24‐bus system and the achieved results confirm the efficiency of the proposed model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".