Exploring Synergy Among New Generation Technologies—Small Modular Reactor, Energy Storage, and Distributed Generation: A Strong Case for Remote Communities
Bibliographic record
Abstract
Abstract With a steady rise in power demand in the remote communities in Canada, utilities are looking for new options to provide a reliable supply of electricity. While distributed generation is a promising option, scaling and firming up the capacity of distributed generators is essential. Alternatively, small modular reactors (SMRs) can be used as a prime local source of electricity for remote feeders provided they are flexible enough to respond to the fluctuations in demand. Electrical energy storage (EES) can be used as a buffer to absorb fluctuations in demand and generation, and as a critical back-up for the SMR on-site power supply system by replacing the diesel-generator sets. The synergy of SMR-EES-distributed generation can be an all-inclusive alternative with win-win situation for both the utility and remote communities. This paper discusses the technical feasibility of the proposed synergy using an example of an existing remote feeder in Saskatchewan, Canada. The integral pressurized water reactor is considered along with the photovoltaic (PV) generation in an existing remote feeder in Northwest Saskatchewan to estimate the plant load factor (LF) of the SMR with and without the PV generation and EES. The results quantify the benefit of having EES to support the SMR in hosting more PV generation in remote communities. EES when used in support of the SMR to host 60% PV penetration, the plant load factor improves by as much as 5%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".