Participation of Pumped Hydro Storage in Energy and Performance-Based Regulation Markets
Bibliographic record
Abstract
This paper examines the non-strategic and strategic participation of a pumped hydro energy storage (PHES) facility in day-ahead energy and performance-based regulation (PBR), which includes regulation capacity and mileage markets. The PHES is modeled with the capability of operating in hydraulic short-circuit (HSC) mode with detailed representation of its operational constraints, and integrated with an energy-cum-PBR market clearing model. For its strategic participation, a bi-level market framework is proposed to determine the optimal offers and bids of the PHES that maximize its profit. The operation of PHES is modeled at the upper level, while the market clearing is modeled in the lower level problem. The bi-level problem is formulated as a mathematical program with equilibrium constraints (MPEC) model, which is linearized and solved as a mixed integer linear programming problem. Several case studies are carried out to demonstrate the impact of PHES' non-strategic and strategic operations on market outcomes. Furthermore, stochastic case studies are conducted to determine the PHES strategies considering the uncertainty of the net demand and rivals' price and quantity offers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".