Fuzzy AHP-based Siting of Small Modular Reactors for Power Generation in the Smart Grid
Bibliographic record
Abstract
Distributed Generation (DG) resources could help in mitigating the increase in the electricity demand and the high burden on the central grid by reducing the transmission and distribution power losses. As the name implies, DG resources are in general located in a distributed manner near the load centers. Therefore, choosing a proper site for a new DG is a critical step for its long term efficient power generation. In addition, DG siting involves many factors such as economic, social, environment, geographic, availability of electrical infrastructure, etc. Examples of DGs include, solar panels, micro wind turbines, small hydropower units, fuel cells and Small Modular Reactors (SMRs). In this paper, we introduce a model using the Analytical Hierarchy Process (AHP) and the Fuzzy AHP (FAHP) algorithms to develop a ranking system to choose proper sites for SMR power generation units. We consider electrical and non-electrical loads, existing and retiring generation, transmission lines, switching stations as the location-dependant scenarios for determining suitable locations. We produce more precise results by implementing fuzzy logic based AHP algorithm which deals with the linguistic vagueness and uncertainty of the siting data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".