Improved Performance of Hybrid Photovoltaic-Trigeneration Systems Over Photovoltaic-Cogen Systems Including Effects of Battery Storage
Bibliographic record
Abstract
Recent work has proposed that hybridization of residential-scale cogeneration with roof-mounted solar PV (photovoltaic) arrays can increase the PV penetration level in ideal situations by a factor of five. In regions where there is a significant cooling load PV-cogen hybrid systems could be coupled to an absorption chiller to utilize waste heat from the cogen unit. In order to investigate realistic (non-ideal) loads that such a hybrid system would need to service, a new numerical simulation called PVTOM (PV-trigeneration optimization model) was created and coupled to the results of the established CHREM (Canadian Hybrid Residential End-Use Energy and Emissions Model). In this paper, PVTOM is applied to representative houses in select Canadian regions, which experience cooling loads, to assess the fuel utilization efficiency and reduction in greenhouse gas emissions from hybrid PV-cogen and trigen systems in comparison with conventional systems. Results of the optimization runs are provided and the efficacy of PV-cogen and PV-trigen systems is discussed. Both PV-trigen and PV-cogen systems have demonstrated to be more effective at reducing emissions when compared to the current combination of centralized power plants and household heating technologies in some regions. © 2012 Elsevier Ltd.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".