Reliability, Availability and Resilience Assessment of Heating Systems Using Sequential Monte-Carlo Simulation and Critical Load Analysis. Masters thesis, Concordia University
Bibliographic record
Abstract
Ambitious sustainable development goals lead cities to invest in renewable energy infrastructure as the primary energy source. With an increasing number of people moving to urban areas, providing reliable heating energy, especially in cold regions needs more investigation. A resilient and reliable energy system is able to provide the intended demand in day-to-day operation under a wide range of failure modes, as well as extreme situations. This research aims to provide a framework to investigate reliability indices, availability, and resilience of electric heating systems. \nThe availability and reliability indices such as Energy Not Served (ENS) and Loss Of Load Expectation (LOLE) are evaluated using sequential Monte Carlo (SMC) simulation. SMC is a probabilistic approach that is able to capture the random failures and behaviour of the systems over a defined sequence of time. To evaluate the energy system resilience under a major power outage, a method considering the resilience in terms of system robustness, ENS, and Average Energy Not Served (AENS) is proposed. During the power outage, resilience metrics are analyzed considering critical loads instead of business-as-usual demand. The critical load is the minimum demand that needs to be provided to customers and it is defined based on the ranking and assigning weights to user types. \nThe method is applied to a district to compare the performance of centralized and decentralized ground source heat pump systems. In terms of system resilience, the two energy systems have similar performance, however, results of the reliability simulation indicate that the centralized scenario is more reliable than the decentralized design in terms of the number of hours where energy is available to the area, and the amount of energy served to the consumers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".