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Holistic non-linear optimization of the layout, sizing, and operation of a district heating plant

2024· article· en· W4390925694 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Conversion and Management · 2024
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSizingThermal energy storageBoiler (water heating)Process engineeringDecoupling (probability)EngineeringEnergy storageSimulationEnvironmental scienceComputer scienceWaste managementControl engineeringPower (physics)

Abstract

fetched live from OpenAlex

A large fraction of district heating networks in Europe are still operating without thermal energy storage. While offering operational flexibility, the presence of energy storage complicates the problems of optimal sizing and control of the heat production plant. This necessitates a novel approach to system sizing based on non-linear programming in order to capture the system’s thermal response and inertia. Therefore, the present study focuses on such a typical plant in France and describes a holistic approach to optimize the capacities/parameters of system components, positions of the energy sources, as well as hourly flow rates. Such a simultaneous tuning of the system parameters and operating variables is essential to maximize the cost savings over the system's lifetime. The proposed framework here maintains the original non-linear form of the objective function and the constraints for more accuracy while allowing precise modeling of the thermally stratified storage tank by decoupling its calculations at a lower simulation level. Hence, the computation time in MATLAB® R2020A is mostly below 20 min on a typical personal computer. The lowest levelized cost of heat (57.259 EUR/MWh) is achieved when placing the gas boiler on a parallel line between the biomass boiler and the storage tank while sizing the two boilers using fractions of 0.33 and 0.73 of the maximum hourly demand, respectively, as well as a storage volume of 318.9 m 3 . Such relatively small storage reduces the lifecycle costs by a maximum of 1.34 % while reducing the specific carbon emissions by 17.33 %. Besides, it dampens the fluctuations in supply flow rates, minimizes unnecessary flow recirculation, and ensures a stable heat supply. Overall, thermal storage has a reasonable positive impact from an economic perspective, but a substantial impact on operational flexibility and mitigated carbon emissions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.191
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it