Optimized energy system using a novel learning approach for low-carbon economic dispatch
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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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".