Real-Time Power Balancing in Electric Grids With Distributed Storage
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Bibliographic record
Abstract
Power balancing is crucial for the reliability of an electric power grid. In this paper, we consider an aggregator coordinating a group of distributed storage (DS) units to provide power balancing service to a power grid through charging or discharging. We present a real-time, distributed algorithm that enables the DS units to determine their own charging or discharging amounts. The algorithm accommodates a wide spectrum of vital system characteristics, including time-varying power imbalance amount and electricity price, finite battery size constraints, cost of using external energy sources, and battery degradation. We develop a modified Lyapunov optimization framework for real-time power balancing and provide a fast iterative method for distributed implementation. The two components interact through a novel cost cushion parameter that tunes the trade-off between system performance and convergence speed. We show analytically that the algorithm converges quickly and provides asymptotically optimal performance as the capacity of DS units increases. We further study through simulation the algorithm performance over a wide range of parameter values and demonstrate that it is highly competitive over a greedy alternative.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it