Optimal Sizing of a Hybrid Renewable Energy System for Auxiliary Services in Substations Through Genetic Algorithm and Variable Neighborhood Search
Bibliographic record
Abstract
Auxiliary services in substations are fundamental systems for the operation and coordination of power electrical systems. Because of their importance, backup systems are utilized to support critical loads during main power supply failures. Hybrid renewable backup systems, comprising renewable generation and batteries, offer a more sustainable alternative compared to the current use of fossil fuel generators in substations. However, sizing these systems can be a complex task due to the uncertainty associated with interruptions and renewable source production. This paper proposes an optimization model to size hybrid renewable energy systems for auxiliary services in substations. Uncertainties related to wind and photovoltaic generation, as well as power outages start time and durations, are addressed through Monte Carlo simulations. Furthermore, a hybrid algorithm, based on Genetic Algorithm (GA) and Variable Neighborhood Search (VNS), is introduced. The proposed approach considers the costs associated with diesel generator usage and instances of non-supply. A comparison of the metaheuristics GA, VNS, and the new hybrid algorithm is presented, emphasizing the advantages of the hybrid approach. The algorithms are validated by comparing them with results from the literature, demonstrating their ability to achieve solutions with high accuracy and low computational time. The results demonstrate that the proposed sizing approach successfully minimizes costs while ensuring reliability, highlighting its potential to optimize hybrid renewable backup systems for critical substation loads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".