Design and simulation of a microgrid system for a university campus in Nigeria
Bibliographic record
Abstract
The thesis presents the design and simulation of a microgrid system for a university community in Nigeria. Firstly, the system sizing and design was done in Homer Pro software where the microgrid system obtained consist of the grid system, 3,726 solar panel of 0.5kW, diesel generator of 1.5MVA and inverter of 500kW installed in an area of 17,696m2 at a cost of ₦295 with a simple payback of 3 years and 5 months at a reduced cost of electricity bill by 88.0% and a reduce CO2 emissions. Due to the high PV size of 1,863kW required by this design, other software such as OpenSolar, PVWatt and REopt was used to design the same system to optimise the PV size. The resulting system design consist of a PV size of 675.2 kW comprising of 96 cell modules each of 500W, with 25 connected in series and 54 in parallel. Also, a utility grid system and a diesel generator set in case of emergency. The system was then simulated in MATLAB/Simulink environment to determine the dynamics of the university microgrid system. Simulated results indicate that the system has acceptable dynamics with changes in the electric load, but the dynamic simulation was extremely slow. To solve these challenges, the reduced order model of the microgrid system was design in MATLAB/Simulink environment to speed up the simulation time. Simulated results indicates that the reduced order model obtained is more than 4 times faster than the original microgrid system of the campus community. Lastly, the monitoring system of the campus microgrid system was designed. Analysis shows that to monitor the dc part of the network, 54 number dc current sensor and a dc voltage sensor would be required and for the ac portion, 9 number ac current sensor and 6 number ac voltage sensor would be required. These sensors are connected to a data logger that is directly connected to a computer system with internet for remote monitoring and control of the microgrid system. Complete details of system design, sizing, dynamic simulation, reduced order model and monitoring are presented and explained in this thesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".