Micro Nuclear Reactors: Potential Replacements for Diesel Gensets within Micro Energy Grids
Bibliographic record
Abstract
Resilient operation of medium/large scale off-grid energy systems, which is a key challenge for energy crisis solutions, requires continuous and sustainable energy resources. Conventionally, micro energy grids (MEGs) are adopted to supply electricity and thermal energy simultaneously. Fossil-fired gensets, such as diesel generators, are indispensable components for off-grid MEGs due to the intermittent nature of renewable energy sources (RESs). However, fossil-fired gensets emit a significant amount of greenhouse gases (GHGs). Therefore, this study investigates an alternative source as an economical and environmental replacement for diesel gensets that can reduce GHG emissions and ensure system reliability. A MEG is developed in this paper to support a considerably large-scale electric and thermal demand at Ontario Tech University (UOIT). Different sizes of diesel gensets and RESs, such as solar, wind, hydro, and biomass, are combined in the MEG for off-grid applications. To evaluate diesel gensets’ competency, the diesel genset is substituted by an emission-free generation source named microreactor (MR). The fossil-fired MEG and MR-based MEG are optimized by an intelligent optimization technique, namely particle swarm optimization (PSO). The objective of the PSO is to minimize the net present cost (NPC). The simulation results show that MR-based MEG could be an excellent replacement for a diesel genset in terms of NPC and selected key performance indicators (KPIs). A comprehensive sensitivity analysis is also carried out to validate the simulation results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".