Varela Aldas, José
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Preferred name
Varela Aldas, José
Main Affiliation
Ambato
Email
josevarela@uti.edu.ec
ORCID
Scopus Author ID
57192546163

Research Output
Now showing 1 - 5 of 5
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Publication

Impact of Distributed Energy Resources with Photovoltaic Self-Consumption on an Electrical Distribution Network

2024 , Pedro Torres , Kevin López-Eugenio , Varela Aldas, José

The sustainability of electrical distribution networks is essential for ensuring reliable supply and minimizing environmental impact. This study focuses on analyzing the impact of Distributed Energy Resources with photovoltaic selfconsumption systems on a real electrical distribution network, aiming to identify the benefits and challenges these systems present. The methodology employed includes collecting energy consumption data, selecting candidates for the implementation of photovoltaic systems, and modeling and simulating load flows using CYMDIST software. Data collection was carried out in an electric utility company in Ecuador, identifying users with the highest probability of adopting these systems. Subsequently, the photovoltaic systems were modeled in the electrical network, and the technical impacts were evaluated. The results show that the integration of photovoltaic systems alleviates the load on the distribution network, decreases energy losses, and facilitates a more flexible and adaptive management of energy demand. Finally, it is concluded that photovoltaic systems are expected to be a strong base for the transition towards a more sustainable and resilient electricity system.

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Publication

Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models

2025 , Pedro Torres-Bermeo , Kevin López-Eugenio , Carolina Del-Valle-Soto , Guillermo Palacios-Navarro , Varela Aldas, José

The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.

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Publication

Optimization of Electric Transformer Operation Through Load Estimation Based on the K-Means Algorithm

2025 , Pedro Torres-Bermeo , Varela Aldas, José , Kevin López-Eugenio , Nancy Velasco , Guillermo Palacios-Navarro

This study presents an innovative methodology to optimize the operation of distribution transformers through the estimation of hourly load curves, aimed at minimizing technical losses due to oversizing, particularly in systems lacking advanced metering infrastructure. The proposed approach combines clustering techniques, K-Means with DTW, to identify representative daily consumption patterns and a supervised model based on LightGBM to estimate hourly load curves for unmetered transformers, using customer characteristics as input. These estimated curves are integrated into a process that calculates technical losses, both no-load and load losses, for different transformer sizes, selecting the optimal rating that minimizes losses without compromising demand. Empirical validation showed accuracy levels of 95.6%, 95.29%, and 98.14% at an individual transformer, feeder, and a complete electrical system with 16,864 transformers, respectively. The application of the methodology to a real distribution system revealed a potential annual energy savings of 3004 MWh, equivalent to an estimated economic reduction of 150,238 USD.

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Publication

Projection of Photovoltaic System Adoption and Its Impact on a Distributed Power Grid Using Fuzzy Logic

2025 , Kevin López-Eugenio , Pedro Torres-Bermeo , Carolina Del-Valle-Soto , Varela Aldas, José

The increasing adoption of photovoltaic systems presents new challenges for energy planning and grid stability. This study proposes a fuzzy logic-based methodology to identify potential PV adopters by integrating variables such as energy consumption, electricity tariff, solar radiation, and socioeconomic level. The approach was applied to a real distribution grid and compared against a previously presented method that selects users based solely on high energy consumption. The fuzzy logic model demonstrated superior performance by identifying 77.03 [%] of real adopters, outperforming the previous selection strategy. Additionally, the study evaluates the technical impact of PV integration on the distribution grid through power flow simulations, analysing energy losses, voltage stability, and asset loadability. Findings highlight that while PV systems reduce energy losses, they can also introduce voltage regulation challenges under high penetration. The proposed methodology serves as a decision-support tool for utilities and regulators, enhancing the accuracy of adoption projections and informing infrastructure planning. Its flexibility and rule-based nature make it adaptable to different regulatory and technical environments, allowing it to be replicated globally for sustainable energy transition initiatives.

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Publication

Impact of Distributed Energy Resources with Photovoltaic Self-Consumption on an Electrical Distribution Network

2024 , Pedro Torres , Kevin López-Eugenio , Varela Aldas, José

The sustainability of electrical distribution networks is essential for ensuring reliable supply and minimizing environmental impact. This study focuses on analyzing the impact of Distributed Energy Resources with photovoltaic selfconsumption systems on a real electrical distribution network, aiming to identify the benefits and challenges these systems present. The methodology employed includes collecting energy consumption data, selecting candidates for the implementation of photovoltaic systems, and modeling and simulating load flows using CYMDIST software. Data collection was carried out in an electric utility company in Ecuador, identifying users with the highest probability of adopting these systems. Subsequently, the photovoltaic systems were modeled in the electrical network, and the technical impacts were evaluated. The results show that the integration of photovoltaic systems alleviates the load on the distribution network, decreases energy losses, and facilitates a more flexible and adaptive management of energy demand. Finally, it is concluded that photovoltaic systems are expected to be a strong base for the transition towards a more sustainable and resilient electricity system

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