Solar and battery-oriented grid connected microgrid for peak and off peak hour operation
Bibliographic record
Abstract
The rising demand for sustainable and stable energy solutions has intensified interest in hybrid microgrids that integrate renewable sources with storage systems. However, frequent connection and disconnection to the main grid during peak and off-peak hours often introduce voltage and frequency instabilities, threatening system reliability. This study proposes a grid-connected solar and hydrogen-battery microgrid, optimized using advanced dispatch strategies and power plant controllers to mitigate such instabilities. Three control methods—Constant Q, Voltage Iq-Droop, and Voltage Q-Droop—are implemented and comparatively analyzed for their effectiveness in stabilizing voltage, frequency, power factor, and active/reactive power. Additionally, two dispatch strategies, Load Following (LF) and Cycle Charging (CC), are evaluated under derivative-free optimization to ensure cost-effective and low-emission operation. Results show the Constant Q controller delivers superior system stability and power quality. The optimized system achieves a renewable energy fraction of 89.1%, sells 192,161 kWh/year to the grid, and operates at an exceptionally low cost of $0.0132/kWh. Furthermore, the system demonstrates a 73% internal rate of return, a simple payback period of 1.4 years, and minimal CO 2 (18,647 kg/year) and SO 2 (80.8 kg/year) emissions. These outcomes affirm the feasibility and scalability of the proposed hybrid system for clean and stable power generation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".