Optimal Planning of Nuclear-Renewable Micro-Hybrid Energy System by Particle Swarm Optimization
Bibliographic record
Abstract
To minimize the anticipated shocks to economic, environmental, and social systems for developing and least developed countries, the reduction of Greenhouse Gas (GHG) emissions is mandatory to a large extend. The nuclear-renewable integrated system is proficient in optimal energy distribution to multiple production schemes to reduce GHG emissions and maximize profit. This paper addresses the hybridization of the micronuclear reactor and Renewable Energy Sources (RESs) Energy Sources (RESs) to develop a flexible, cost-effective, sustainable, and resilient off-grid Hybrid Energy System (HES). The paper presents three types of hybridization methods, termed “Direct Coupling,” “Single Resource and Multiple products-based Coupling,” and “Multiple Resources and Multiple products-based Coupling.” The hybridization techniques are used to plan and identify the most efficient Nuclear-Renewable Micro-Hybrid Energy System (N-R MHES). The sizing, performance, and characterization of N-R MHES solely depend on the RES and load characteristics' availability. Based on proposed hybridization techniques, mathematical modeling of N-R MHES's economy is carried out in the MATLAB environment. An artificial intelligence optimization algorithm, namely Particle Swarm Optimization (PSO), is used to minimize the Net Present Cost (NPC) and achieve the optimal system configurations of different N-R MHESs. The simulation results determine that “Multiple Resources and Multiple Products-based N-R MHES” provides around 1.8 times and 1.3 times lower NPC than “Single Resource and Multiple products-based Coupling” and “Multiple Resources and Multiple products-based Coupling,” respectively, with an acceptable margin of reliability. A sensitivity analysis has also been conducted in this paper to strengthen the findings of the study.
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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) | 0.001 | 0.001 |
| 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".