Control Strategies for a Shared Diesel Generator in an Off-Grid Hybrid Power System for Urban Areas in Pakistan
Bibliographic record
Abstract
This paper presents control strategies for a hybrid off-grid power system serving an urban residential neighborhood in Pakistan, integrating solar photovoltaic (PV) generation, battery storage, and a shared diesel generator. The primary objective is to ensure power reliability and prevent generator overloading in a multi-household configuration. The system was initially optimized for component sizing and cost using HOMER Pro, followed by the development of a detailed dynamic model in MATLAB Simulink/Simscape. Building on this foundation, two complementary control schemes are designed: one for load distribution among the houses and another for battery bank charging. Simulation results demonstrate that the proposed controls stabilize the load supply—ensuring that the diesel generator consistently maintains single-phase voltage around 220 V while sharing a 2kW load per house without exceeding its capacity—and facilitate efficient battery charging on a common 48 V Direct Current (DC) bus. The diesel generator is time-shared across seven households, delivering approximately 9.65 A Root Mean Square (RMS) per active phase, which prevents overloading and ensures continuous power availability. The batteries are charged in a balanced manner whenever solar energy is insufficient, preserving system autonomy. By sharing the diesel generator, the system reduces battery size requirements, thereby lowering both capital and operational costs. This work offers a scalable, cost-effective control strategy for a PV–diesel–battery hybrid microgrid, providing a viable solution to Pakistan’s urban energy shortages.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".