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Record W4386852973 · doi:10.1149/ma2023-0154123mtgabs

Energy Transition Scenarios in Off-grid Communities using SOFC-CHP/Battery Hybrid Systems

2023· article· en· W4386852973 on OpenAlexaff
Laura Nousch, Marie-Lise Tremblay, Simon Besner, Guillaume Jeanmonod, Mathias Hartmann, Daniela Herold, Martin Simoneau, A. Michaelis

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsRenewable energySolid oxide fuel cellPrime moverFossil fuelElectricityPrimary energyProcess engineeringEnvironmental scienceGrid energy storageDistributed generationDiesel fuelCogenerationElectricity generationWaste managementAutomotive engineeringEngineeringElectrical engineeringPower (physics)Chemistry

Abstract

fetched live from OpenAlex

Today, most communities are connected to large energy infrastructures that constitute the electricity and gas grids. But these infrastructures may not be present in extremely low populated regions or regions that are difficult to access, thus considered as off-grid islands or remote regions. Currently, the electricity in these communities is still mostly supplied by centralized or decentralized diesel generators, even though the supplemental use of renewable energy from water, wind and solar is increasingly part of their energy mix. Additionally, centralized or decentralized furnaces, also based on fossil fuels or regenerative fuels (as wood), may be used to supply heat needed for room heating and domestic hot water. The first step for energy transitions towards low emissions in this specific application scenario has the goal to implement prime mover that have higher electrical efficiencies. Consequently, the amount of primary energy (fuel) and the associated CO2 emissions are reduced. Secondly, the efficiency of fuel utilization shall be increased by using the waste heat for room heating, thus representing a typical combined heat and power (CHP) system. While diesel generators are very load flexible, available in different power ranges at reasonable costs their electrical efficiency is limited to 40% or less due to the thermodynamic cycle with several energy conversion steps. Due to the low number of conversions steps in fuel cells, higher electrical efficiencies up to 65% are possible for Solid Oxide Fuel Cell systems specifically. Furthermore, Solid Oxide Fuel Cell systems have a high fuel variety due to operation at high temperatures and enables the CHP operational mode. The overall efficiency of CHP SOFC systems ranges to more than 90%. The main drawback for this technology is the limited flexibility to adapt to load variations, especially regarding start/stop cycles and associated power degradation. To overcome this drawback, batteries in conjunction with SOFC systems may be used for the off-grid scenario. In this regard, SOFC-/Battery Hybrid systems are investigated in an annual simulation analysis for diffent scenarios and 3 different sized example communities. In each scenario waste heat from the SOFC combined with decentralized furnaces is used to fulfill the heat demand. Figure 1 shows the shares of the electrical and the thermal power supplied with a 2.8 MWel SOFC system, the operational states of the system as well as the battery State of Charge (SOC) over one year. It is visible how the battery is charged and discharged and when back-up diesel gensets are necessary to supply sufficient electricity. Based on the annual simulations, the performance and the benefit of using such hybrid systems can be shown in terms of primary energy saving and reduction of CO2 emissions, as well as in terms of system configurations and operation patterns. The analysis also gives information about the necessary sizes of the SOFC systems and the battery capacity to be installed. Figure 1: Exemplary simulation of the SOFC-CHP / Battery Hybrid system with a battery capacity of 2.5 MWh Figure 1

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.030
GPT teacher head0.240
Teacher spread0.210 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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