Intelligent Optimization Algorithm to Synthesize Renewable Energy and Hydrogen Deployment Strategies
Bibliographic record
Abstract
Toward Zero-Net Emissions Hybrid renewable energy systems (HRES) have emerged as a pivotal technology for mitigating greenhouse gas emissions, minimizing electricity consumption costs, and addressing the ever-growing demand for electric power. Consequently, reducing grid dependence has become a critical challenge in recent years for achieving net-zero emissions. This paper proposes an intelligent optimization algorithm based on a mixed-integer linear programming (MILP) approach for synthesizing and optimizing renewable energy deployment strategies. This method aims to minimize electricity costs and greenhouse gas emissions for various applications, including buildings, houses, and hospitals. The system incorporates wind turbines, photovoltaic solar panels, hydrogen fuel cells, and hydrogen storage, operating in both grid-connected and off-grid modes, allowing for a comparison between both. The MILP optimization not only determines the optimal energy source combination and quantity but also manages energy flow, including power exchange with the grid during surplus production. Additionally, the method integrates these processes within an energy semantic network. The results demonstrate that the proposed MILP approach effectively reduces total power costs and greenhouse gas emissions. It also identifies the optimal contribution from each renewable source, favors longer-lasting hydrogen batteries over traditional batteries, and utilizes the combined capabilities of hydrogen batteries and fuel cells to address the inherent variability of wind and solar energy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".