Optimal capacity planning with economic emission considerations in isolated solar-wind-diesel microgrid using combined arithmetic-golden jackal optimization
Bibliographic record
Abstract
• Optimization of isolated solar-wind-diesel microgrid to reduce reliance on diesel generators, lower operational costs, and mitigate environmental pollution in remote areas. • The objective is achieving optimal capacity planning by considering economic and emission dispatch factors • Optimization is carried out using combination of metaheuristic methods “arithmetic optimization algorithm” and “golden jackal optimization” to enhance the search process. • Performance analysis is conducted by simulating and comparing three scenarios of only diesel generators, solar-wind-diesel and solar-wind with low number of diesel generators. • Results demonstrate significant cost savings using the solar-wind-diesel microgrid under the proposed combined optimization method compared to the conventional methods. This study aims to optimize an isolated solar-wind-diesel microgrid to reduce reliance on diesel generators, lower operational costs, and mitigate environmental pollution in remote areas. In this optimization, arithmetic optimization algorithm and golden jackal optimization are combined for achieving optimal capacity planning, considering economic and emission dispatch factors. This combination enhances the optimization by considering the balance in exploration and exploitation offered by the arithmetic operators of the arithmetic optimization algorithm and the dynamic adjustment by the adaptive search of the golden jackal optimization. Performance analysis is conducted by simulating and comparing three scenarios of only diesel generators, solar-wind-diesel and solar-wind with low number of diesel generators. The results demonstrate significant cost savings using the solar-wind-diesel microgrid under the proposed combined optimization compared to the arithmetic optimization algorithm and golden jackal algorithm and conventional metaheuristic optimization based on genetic algorithms. Fig. 1. Methodology of the optimal capacity planning considering EED.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".