Microgrid System Modelling for Hybrid Renewable Energy Market in Malaysia
Bibliographic record
Abstract
A growing concern over climate change and the depletion of conventional energy resources have led to the urgent need for sustainable and resilient energy solutions. Previous studies, microgrid sizing only focusing on islanded mode and have limited global exploration. In comparison, various studies try to consider the microgrid (MG) in the grid-connected mode. Due to this need, this paper presents an innovative approach to MG system modelling, focusing on grid-connected with hybrid renewable energy sources (HRES) configurations in Malaysia landscape combining photovoltaic (PV), small hydropower (SHP), Biomass (BM), diesel generator (DG) and battery energy storage system (BESS). Our research aims to address the growing need for sustainable and efficient energy management strategies in the context of evolving energy markets. We have conducted a comprehensive performance analysis comparing various system modelling in microgrids connected to the distribution network, accounting for the uncertainties inherent in HRES. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE-33 Bus Distributed System. The findings are anticipated to contribute significantly to the development of sustainable energy solutions in Malaysia, offering insights for policymakers and stakeholders in the renewable energy sector.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".