Joint PEV Charging Network and Distributed PV Generation Planning Based on Accelerated Generalized Benders Decomposition
Why this work is in the frame
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Bibliographic record
Abstract
Integration of plug-in electric vehicles (PEVs) with distributed renewable power sources will reduce PEVs' well-to-wheels greenhouse gas emissions, promote renewable power adoption, and defer power system investments. This paper proposes a multidisciplinary approach to jointly plan PEV charging stations and distributed photovoltaic (PV) power plants on a coupled transportation and power network. We formulate a two-stage stochastic programming model to determine the sites and sizes of: 1) PEV charging stations and 2) PV power plants. This proposed method incorporates comprehensive models of: 1) transportation networks with explicit PEV driving range constraints; 2) PEV charging stations with probabilistic quality of service constraints; 3) PV power generation with reactive power control; and 4) alternating current distribution power flow. The formulation results in a mixed-integer second-order cone program. We then design a generalized Benders decomposition algorithm to efficiently solve it. Numerical experiments show that investing in distributed PV power plants with PEV charging stations has multiple benefits, e.g., reducing social costs, promoting renewable power integration, and alleviating power congestion. The benefits become more prominent when utilizing PV generation with reactive power control, which can also help to enhance power supply quality.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it