Primary Frequency Control in Isolated Microgrids Using Thermostatically Controllable Loads
Bibliographic record
Abstract
Hybrid renewable energy sources (RES)-diesel systems are now being considered as an economic, attractive and clean option for remote isolated microgrids, to offset diesel consumption by displacing generation from conventional units; however, system security and stability is a challenge as the penetration of RES increases. In this context, Demand Response (DR) can be used to increase grid flexibility, improve efficiency, and facilitate the penetration of RES. Thermostatically controllable loads (TCLs), i.e., electric water heaters (EWHs), air conditioners (ACs), and ground source heat pumps (GSHPs), are ideal candidates to participate in such a DR strategy, since their power consumption can be controlled without affecting consumer comfort. Therefore, this paper presents novel, computationally efficient, and adequate thermo-electrical dynamic models of ACs and GSHPs, along with an existing model of EWHs, to adapt and improve a decentralized DR strategy for Direct Load Control (DLC) for primary frequency regulation in hybrid isolated microgrids. Different cases studies are presented for a real microgrid to analyze and compare the system frequency response and determine the adequacy of the proposed approach and models, demonstrating their effectiveness for primary frequency control provision to facilitate higher RES penetrations.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".