The Gray Wolf Optimization-Based Transmission Expansion Planning in Renewable-Rich Power Systems
Bibliographic record
Abstract
With increasing penetration of renewable energy sources and electric vehicles (EVs) in power grids, developing appropriate models for power system planning is of great importance. This article proposes a novel gray wolf optimization (GWO) algorithm-based transmission expansion planning (TEP) method considering renewable energy sources and EVs and evaluates the performance of alternating current power flow (ACPF) and direct current power flow (DCPF) models in the TEP problem in terms of accuracy, run-time, and objective functions. The uncertainty associated with renewable energy sources, EVs, and loads are explicitly modeled in this study using probability density functions (pdfs) and the Copula approach. The proposed GWO-based TEP method is validated by case studies using the IEEE 24-bus reliability test system (RTS) and IEEE 118-bus test system. The ACPF model leads to fewer transmission lines and lower costs than the DCPF model in most scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".