The impact of tie line capacity on the energy planning of the multi-lateral grid by considering Bi-facial PV
Bibliographic record
Abstract
The global energy landscape faces the pressing challenges of resource scarcity and environmental degradation due to fossil fuel dependency. This study investigates the integration of Bi-facial Photovoltaic (BPV) panels within a multilateral grid framework, emphasizing their impact on cost, profit, and pollution. Employing a multi-objective optimization approach, the problem is modeled as Mixed Integer Linear Programming (MILP) and solved using GAMS with advanced solvers like CPLEX and Baron. Four distinct scenarios are analyzed to evaluate the proposed energy strategy. The findings highlight the substantial benefits of BPV integration, demonstrating optimized values: a total cost of ${\$}$ 19.751, a profit of ${\$}$ 270.628, and a pollution level of 1003.268 kg. Furthermore, increasing tie line capacity significantly enhances economic outcomes without compromising environmental standards, yielding a recalibrated profit of ${\$}$ 44. 269 for building $\mathbf{1}$, a reduced cost of ${\$}$ 265.671 for building 2, and a maintained pollution level. These results underscore the critical role of grid infrastructure optimization in balancing economic efficiency and sustainability. This research provides a novel perspective on renewable energy planning, showcasing BPV panels as a cost-effective, environmentally advantageous solution. It also highlights the strategic importance of enhancing transmission infrastructure to improve economic returns while maintaining ecological responsibility. The insights presented offer a robust framework for academics, policymakers, and industry stakeholders, advancing sustainable energy system optimization and long-term energy resilience.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".