Real-Time Energy Storage Management With Renewable Integration: Finite-Time Horizon Approach
Why this work is in the frame
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Bibliographic record
Abstract
We consider the design of cost-effective management of energy storage with renewable integration for load supply. We take a finite time horizon approach and formulate the control optimization problem aimed at minimizing the system cost over a fixed time period. Recognizing the unpredictable and nonstationary stochastic nature of system dynamics, we assume unknown arbitrary dynamics of renewable generation, load, and electricity pricing in formulating our problem. Furthermore, we incorporate detailed battery operation cost into the system cost. Different from the infinite time horizon problems in existing works, the coupling of control decisions over time, due to finite battery capacity, is more challenging to manage. We develop a special technique to tackle the technical challenges in solving the problem. Through problem modification and transformation, we are able to apply Lyapunov optimization to design a real-time control algorithm that relies only on the current system dynamics. The proposed control solution has a closed-form expression and thus is simple to implement. Through analysis, the proposed algorithm is shown to have a bounded performance gap to the optimal noncausal T-slot lookahead control policy. Simulation studies show the effectiveness of our proposed algorithm as compared with two alternative real-time and noncausal algorithms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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