Novel intelligent model following controller and PQ droop controller operated nuclear-PV-biogas hybrid microgrid and EV charging station
Bibliographic record
Abstract
The increasing adoption of electric vehicles (EVs) has led to significant challenges in the management of renewable-powered grid-connected electric vehicle charging stations (EVCS), particularly in maintaining grid stability. This paper introduces a novel Intelligent Model-Following Controller (IMFC) for EVCS integrated with a hybrid microgrid consisting of nuclear, photovoltaic (PV), and biogas power sources. The proposed IMFC aims to improve voltage and frequency stability, as well as overall energy management, compared to traditional controllers such as the PQ Droop Controller (PQDC). A comprehensive simulation study is conducted to evaluate the performance of both controllers under various dynamic conditions. A comparative analysis is conducted between IMFC and a PQDC to assess their performance in real-world scenarios to control the power system responses (active power, reactive power, voltage and frequency) of the hybrid system. Two consecutive three-phase faults have been implemented within the system and the transient response have been analyzed for both the controllers. The results show that the IMFC achieves a renewable fraction of 89.1%, with a cost of energy of $0.0132/kWh, and an internal rate of return (IRR) of 73%, demonstrating its economic feasibility and environmental benefits. The IMFC outperforms the PQDC in terms of transient response and system resilience, reducing the transient recovery time to 1.5 s, compared to 2.2 s for PQDC. Additionally, the IMFC provides better frequency regulation with a peak deviation of ±0.04 p.u., as opposed to ±0.1 p.u. for PQDC. These findings highlight the superiority of the IMFC in ensuring stable, efficient, and sustainable operation of hybrid renewable-powered EVCS. • The study introduces an Intelligent Controller for hybrid microgrid with EV outperforming conventional controllers. • The IMFC reduces transient recovery time to 1.5 s compared to 2.2 s for PQ droop controllers. • It achieves superior frequency regulation with deviations of only ±0.04 p.u., while PQ droop controllers show ±0.1 p.u. • The IMFC delivers a renewable fraction of 89.1%, a low cost of energy ($0.0132/kWh), and an internal rate of return of 73%, proving its economic and environmental viability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".