Optimal allocation and economic evaluation for industrial PV microgrid
Bibliographic record
Abstract
An economic evaluation method and optimal allocation model of the industrial PV microgrids are proposed in this paper. The economic indexes include levelized energy cost, emission reduction benefits and payback period. The optimal model considering various relevant issues such as local insolation level and detailed investment costs is established and applied to improve the configuration of the PV microgrids for four different operation strategies. The optimization problem is solved by Particle Swarm Optimization to obtain the minimum annual energy cost of the system and bring more economic and emission reduction benefits for industrial users. The simulation results based on three months running data of a 500kW industrial PV microgrid in Dongguan City, China, verify that the optimal allocations by the proposed method can meet the technical indicators as well as the economic optimum, thus effectively optimizing industrial PV microgrid in both economic and environmental way. In addition, the added batteries can not only be used to maximize the utilization of renewable energy, but also enhance the system's ability of peak load shifting. Besides, the sensitivity of the impact of different parameters on the system is also performed. The economic evaluation and optimal allocation of the industrial PV microgrids in this paper provide a reference for the application of similar projects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".