Design–operation optimisation of run-of-river power plants
Bibliographic record
Abstract
This paper addresses a strategy for the optimal design, control and operation of small hydropower (run-of-river (RoR) power) plants with the honey bee mating optimisation (HBMO) algorithm, while taking into account optimal design of the associated penstock as well as the turbines' number, type and their operation in the system. Civil engineering and electromechanical cost-effectiveness and constraints in an expected stream flow are also considered. The optimisation is driven by an objective function that includes the annual difference between generated energy, operating costs and depreciation costs for both initial investment and operation costs, considering various performance and hydraulic constraints. The HBMO algorithm specifies the annual benefit of generated energy and simultaneously determines the annualised operating cost. The solution includes selection of turbine types, number of turbines, penstock diameter, as well as scheduling the operation of an RoR power plant that results in maximum annualised benefit for a given set of river inflow histograms. The results of the proposed algorithm, which are compared with those of an analytical approach using Lagrange multipliers (LM), highlight the advantages in design, effective operation, ease of application and capability of the proposed HBMO algorithm for solving complex problems of this type.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".