Modeling and optimal operation of sustainable thermoelectric microgrids with phase-change material thermal system
Bibliographic record
Abstract
This paper proposes an Energy Management System for a thermoelectric microgrid that incorporates the modeling of a unique Phase-Change Material-based thermal system, capable of operating in both active and passive modes to minimize operating costs while guaranteeing thermal comfort, while properly considering the microgrid thermal power requirements and indoor temperature control. The proposed model also includes a detailed thermal representation of buildings to consider relevant thermal sources and room heat exchange, as well as heat pumps, water tanks for thermal storage, and battery degradation. A Model Predictive Control approach is used to address uncertainties in demand and environmental conditions. The proposed Energy Management System model is applied to the Energy Smart Home Lab microgrid located at the Karlsruhe Institute of Technology, in Germany, taking into account the specific characteristics of the microgrid’s components, expected energy consumption, and indoor temperature control requirements. Simulation results demonstrate the feasible application of the developed Energy Management System for the optimal operation of the actual microgrid considered, illustrating the thermoelectric microgrid’s power balance and temperature fluctuations of the associated components, with particular emphasis on the operation of the Phase-Change Material system, to showcase its active and passive thermal contribution under extreme weather conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".