Power Efficiency Optimization in a Nanogrid Using a Nash Bargaining-Based Power Management Strategy
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Bibliographic record
Abstract
This paper presents a cooperative power management strategy for nanogrids using the Nash Bargaining Solution (NBS) to optimize energy flow between photovoltaic arrays, batteries, and loads. The approach frames the nanogrid control problem as a bargaining game that prioritizes system efficiency. Two key objectives are modeled as utility functions: maximizing the direct use of renewable energy and minimizing system losses. A simple yet effective NBS-based optimization algorithm computes hourly power setpoints, ensuring the solution lies on the Pareto-optimal front. The proposed strategy is validated through 24 -hour MATLAB/Simulink simulations, which incorporate varying solar irradiance and load demand profiles. The results demonstrate robust DC bus voltage regulation, high renewable penetration, and reduced power losses. The battery state of charge remains within healthy limits, improving its useful lifespan. Compared to traditional optimization approaches, the NBS framework enables fair and efficient resource allocation with minimal computational complexity.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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