Frequency Reserve Service From Heat Pumps and Electrolyzers in Power Systems: A Sealed-Bid Auction Mechanism
Bibliographic record
Abstract
With the increasing integration of renewable energy sources (RESs), power system inertia is steadily declining, leading to more pronounced frequency variations during disturbances. Therefore, in future low-inertia grids, frequency reserve service (FRS) from the controllable load sector will become increasingly critical. While much research has focused on FRS from RESs and Active Controllable Loads (ACLs), such as Energy Storage Systems (ESSs) and Electric Vehicles (EVs), the potential of Passive Controllable Loads (PCLs) has been largely overlooked. Additionally, there is a lack of an appropriate framework in the literature to determine the optimal price and quantity of FRS from each supplier. Hence, this paper addresses these gaps by investigating FRS from heat pumps (HPs) and electrolyzers, two key PCLs expected to play crucial roles in future power grids. Furthermore, a sealed-bid auction (SBA) framework is proposed to determine the optimal price as well as quantity of FRS from various suppliers, using nadir point deviation (NPD) and the rate of change of frequency (RoCoF) criteria of the frequency response. According to the results, when the system inertia decreases from 5 seconds to 2 seconds, the required FRS reserve increases from 0.07 p.u. to 0.24 p.u., and the FRS price approximately doubles. Finally, the analyzed case studies show the effectiveness of the HPs and electrolyzers as well as the developed market model to improve the frequency response of the system.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".