ECO-TECHNO-ECONOMIC ANALYSIS OF DECENTRALIZED NATURAL GAS-BASED COGENERATION ENERGY MANAGEMENT CENTER
Bibliographic record
Abstract
Decentralized cogeneration systems offer a promising solution for meeting high demand for heat and electricity in urban environments, especially with the inclusion of technologies like seasonal thermal storage. In Canada, reducing greenhouse gas emissions while ensuring reliable energy supply is a top priority. Energy management centers (EMCs) present a cost-effective and environmentally friendly approach to addressing this challenge. This thesis aims to bridge the gap in Canadian literature by designing and optimizing EMC systems with and without seasonal thermal storage. Economic and environmental analyses are conducted to compare the proposed systems with business-as-usual scenarios in Ontario and Alberta. A superstructure framework is employed to design the EMCs, which are then optimized using a multi-objective particle swarm optimization algorithm. Detailed life cycle analyses and dynamic LCA methods (ReCiPe 2016 and TRACI 2.1 US-Canada 2008) are used to investigate the environmental impact of the designs. An economic analysis considering a 20-year lifetime is conducted to estimate the levelized cost of the proposed EMCs. The results of the study are compared with business-as-usual scenarios in Ontario and Alberta, and the cost of carbon avoided (CCA) is calculated to evaluate the economic viability of each proposed EMC design. Further, we include a scenario in which all coal-based electricity generation sources are replaced by natural gas. If the CCA of a given design is below the carbon tax, then it is both environmentally and economically advantageous to implement that design. Overall, this thesis provides a comprehensive analysis of the cradle-to-gate impact of decentralized cogeneration systems in the Canadian context and demonstrates the economic feasibility of incorporating EMCs into energy management strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".