Vehicle-to-Grid Frequency Regulation Signal Optimization Based on Inhomogeneous Hidden Markov Model
Bibliographic record
Abstract
As one of the potential frequency regulation (FR) service providers for the independent system operator (ISO) or regional transmission organization (RTO), electric vehicles (EVs) can response the FR control signals generated by ISO or RTO by changing their real-time charging or discharging power. The target is to keep the area control error (ACE) at a low level while minimizing the cost. Recently, many of the ISOs and RTOs such as CAISO and PJM have offered performance-based pricing schemes for FR service providers. Yet, how to estimate the total FR capacity of EV owners managed by the EV aggregators (AGGs) and optimize the FR signals accordingly still need extensive research. In this paper, we proposed an optimal strategy for ISO (or RTO) to allocate differentiated FR signals to EV AGGs according to their actual FR capacity. Thereby, an inhomogeneous (or time-variant) hidden Markov model (HMM) is developed to estimate the FR capacity through observing the historical FR responses. In this paper, traditional BaumWelch algorithm is extended to be applied in the inhomogeneous scenario through decoupling the transition matrix. In this way, the computational complexity can be significantly reduced. The performance of the proposed algorithm is evaluated through extensive simulations based on the real FR signals from PJM.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".