Integrating Mobile Thermal Energy Storage (M-TES) in the City of Surrey’s District Energy Network: A Techno-Economic Analysis
Bibliographic record
Abstract
The City of Surrey in British Columbia, Canada has recently launched a district energy network (DEN) to supply residential and commercial buildings in the Surrey Centre area with hot water for space and domestic hot water heating. The network runs on natural gas boilers and geothermal exchange. However, the City plans to transition to low-carbon energy sources and envisions the DEN as a key development in reaching its greenhouse gas emissions (GHG) reduction targets in the building sector. Harvesting and utilizing waste heat from industrial sites using a mobile thermal energy storage (M-TES) is one of the attractive alternative energy sources that Surrey is considering. In this study, a techno-economic analysis (TEA) was conducted to determine the energy storage density (ESD) of the proposed M-TES technology, costs, and the emission reduction potential of integrating waste heat into Surrey’s DEN. Three transportation methods were considered to determine the most cost-effective and low-carbon option(s) to transfer heat from industrial waste heat locations at various distances (15 km, 30 km, 45 km) to district energy networks, including: (i) a diesel truck; (ii) a renewable natural gas-powered (RNG) truck, and (iii) an electric truck. To evaluate the effectiveness of M-TES, the cost of emission reduction ($/tCO2e avoided) is compared with business as usual (BAU), which is using a natural gas boiler only. Another comparison was made with other low carbon energy sources that the city is considering, such as RNG/biomass boiler, sewer heat recovery, electric boiler, and solar thermal. The minimum system-level ESD required to makes M-TES competitive when compared to other low carbon energy sources was 0.4 MJ/kg.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".