Browsing by Author "Cando Naula, Diego Jonnathan"

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Graph partitioning-based clustering for the planning of distribution network topology using spatial- temporal load forecasting
(Institute of Electrical and Electronics Engineers (IEEE), 2021) Cando Naula, Diego Jonnathan
Planning the expansion and the new topology of distribution networks requires knowing the location and characterization of the load as well as its future growth. Spatial load forecasting is a key tool in this task, providing high spatial resolution and adequate temporal granularity. Nowadays, with the penetration of distributed energy resources, multiple microgrid connection strategies, and implementation of self-healing and protection schemes, it is necessary to identify load blocks to plan the new active network architecture. Based on spatial load forecasting information, this paper proposes a graph partitioning technique to create load clusters in the distribution feeders. A weighted graph is constructed by means of a minimum spanning tree that allows to consider adjacency relations. The results of the simulation, carried out in a real distribution network, have demonstrated the effectiveness of the proposed method.
Modelo de optimización multietapa para la planificación de la expansión de subestaciones del sistema de subtransmisión de la Empresa Eléctrica Regional Centro Sur C.A.
(Universidad de Cuenca, 2021-03-26) Cando Naula, Diego Jonnathan; Chuqui Cajamarca, Freddy Hernán; Sanango Fernández, Juan Bautista
The next degree work, presents the development and application of coordinated This degree work, presents the development and application of coordinated multi-stage model to solving substation expansion planning (SEP) with the purpose of determination service areas and implementation times substations expansion and construction projects so that it allows serve the increasing electricity load demand in accordance with certain technical and economic constraints. The objective function implemented for solving SEP takes into consideration: the installed and/or reinforced projects costs as well the installed and/or reinforced projects maintenance costs. To analyze the best alternative solution and its associated cost, this work uses the present value method. Moreover, to reduce calculation time, the algorithm has implemented different techniques among which the cluster analysis stands out, which objective is to group the load nodes to avoid the SEP process with large dispersed data sets (nodes load) and instead deal with cohesive groups (clusters) that allow to each one them generate a representative impact on the system. The objective function includes different electrical constraints such as power flow, changes to voltage level, maximum permissible loading of substations and radiality constraints. In addition, the algorithm also contemplates spatial and penalty factors that help to assignation and reduction clusters changes to other neighboring substations allowing to determine an optimal service area associated to different analysis substations. In order to establish relations between economic, technical and spatial aspects, this work uses Python, DIgSILENT, and ArcGIS software which the used together, generate a powerful tool for SEP and generally for any study that is attempt to be achieved. Finally, the model and algorithms proposed were implemented at the “Empresa Eléctrica Regional Centro Sur C.A” sub-transmission system, and the results showed that the methodology used is an effective tool in real and large-scale grids.
Modelo optimización robusta para la planificación de la topología de un sistema de distribución a gran escala
(Universidad de Cuenca, 2025-03-10) Cando Naula, Diego Jonnathan; Torres Contreras, Santiago Patricio
The integration of technologies such as distributed energy resources, microgrids, and smart grids has significantly transformed the design and operation of distribution networks, requiring new planning models that ensure efficiency and continuity of supply. The lack of access to detailed data from real distribution networks motivates this project, which proposes synthetic networks that replicate real-world characteristics without exposing sensitive information. To achieve this goal, geographic information systems, graph theory, and network statistics are employed to design both secondary and primary distribution systems. In this context, the algorithms define service drops, locate and size distribution transformers, secondary network layouts, structure primary feeders, and position support elements. Within the secondary system, length and proximity constraints are enforced using a KD-Tree to optimize spatial organization, alongside a DFS-P algorithm that merges depth-first search with tree pruning. This methodology places transformers under spatial constraints and avoids unrealistic configurations. For the primary distribution system, minimum spanning trees and optimal routes are devised based on distances among substations, load nodes, and distribution transformers. The distribution system design incorporates street and load maps, processed to correct geometric and attribute inconsistencies. Finally, the algorithms were implemented in an area served by Empresa Eléctrica Regional Centro Sur C.A., demonstrating that this methodology yields realistic and robust networks with statistics comparable to realistic systems.