Optimized energy system using a novel learning approach for low-carbon economic dispatch
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The study addresses rising global energy demand by optimizing an integrated cooling, heating, and power (CCHP) system. This study introduces a reverse-learning Whale Optimization Algorithm (RL-WOA) to accelerate convergence and improve dispatch optimal within the scheduling model. The CCHP is modeled to simulate multi-energy production, demand, and storage interactions, and evaluated on practical operational data. A carbon trading system (CTS) is embedded to quantify economic and environmental impact, incorporating tiered pricing and explicit treatment of storage-related emissions. RL-WOA achieves the advanced optima in ~200 iterations versus 350 for standard WOA, reducing computational time while enhancing solution quality. The CTS deployment lowers both cost and emissions, a tiered CTS produces the lowest emissions (2.15 t), and excluded storage emissions reduces costs by $14.58 t −1 . Results demonstrate that combining RL-WOA with CTS materially improves energy-carbon co-optimization in CCHP scheduling. The framework offers a practical pathway to balance efficiency, sustainability, and economic viability, and motivates future work on combined energy–carbon market dynamics.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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