An Examination of Hybrid PV-Biogas Power Plants for Electric Vehicle Charging Station Development in Indonesia
Bibliographic record
Abstract
Renewable energy is being created to replace traditional energy sources due to the depletion of fossil fuel reserves.Constructing appropriate infrastructure, such as charging stations, is essential to enable the expansion of electric vehicles.Renewable energy sources power the EV charging station.This study assesses the feasibility of constructing PV-biogas hybrid power plants to power EV charging stations in the Indonesian cities of Denpasar, Surakarta, Bekasi, and Semarang.The HOMER program was utilized for simulating and optimizing the Hybrid Optimization Model for Electric Renewables.The research design incorporated an anticipated daily power consumption of 232 kWh and a project lifespan of 25 years.The optimal city is found by considering various factors, including total power output, total power consumption, breakeven point (BEP), net present cost (NPC), and cost of energy (COE) figures.Semarang City has demonstrated the highest potential for building a hybrid production system among all cities, mostly due to its better economic advantages.Semarang has the lowest NPC, COE, and slowest return on investment.The initial investment cost for establishing a hybrid generating system is IDR 2,454,489,904.74.In Semarang City, the system design generates 625.88 kWh/year of electricity and consumes 551.03 kWh/year.It has an NPC value of IDR 20,964,400,000.00,a COE value of IDR 1,673.18,and a BEP in year 7.05.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".