The Future of Sustaining Energy Using Virtual Power Plant: Challenges and Opportunities for More Efficiently Distributed Energy Resources in Indonesia
Bibliographic record
Abstract
Energy plays a vital role worldwide.In Indonesia, there are 40 million people living without electricity and this lack of electricity has hindered investment.Meanwhile, fossil energy sources are predicted to run out by 2015.This alarming condition encourages the researchers to find alternative renewable energy resources for sustainable energy.Virtual Power Plant (VPP) is a promising solution, especially in terms of storage and distribution of the energy.VPP collects energy from geothermal power, water, sun, wind, and so forth, operated by a single energy generator.The electricity produced is processed and distributed to several units.However, to date In donesia has not utilised VPP as a device for more effective energy distribution although VPP has successfully been implemented in US A, Australia, Canada, and Japan.This article is aimed to analyse possible chances and challenges to effectively distribute energy resources by utilising the technology of VPP to realise the concept of sustainable energy in Indonesia.The research result reveals that there are several challenges encountered over the implementation of VPP regarding, namely: (a) the policy of energy management that is not technology-based; (b) unavailability of infrastructure supporting VPP; (c) participation of societies in electricity procurement; and (d) cyber security threat.In other side, Indonesia has a big opportunity to implement VPP, which are: (a) the change in electrical energy consumption; (b) increasing renewable energy need; (c) better environmental awareness; (d) more complex market; and (c) increasing interest of developed countries in VPP.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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".