Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration

Abstract

This paper proposes a three-layer model to find the optimal routing of an underground electrical distribution system, employing the PRIM algorithm as a graph search heuristic. In the algorithm, the first layer handles transformer allocation and medium voltage network routing, the second layer deploys the low voltage network routing and transformer sizing, while the third presents a method to allocate distributed energy resources in an electric distribution system. The proposed algorithm routes an electrical distribution network in a georeferenced area, taking into account the characteristics of the terrain, such as streets or intersections, and scenarios without squared streets. Moreover, the algorithm copes with scalability characteristics, allowing the addition of loads with time. The model analysis discovers that the algorithm reaches a node connectivity of 100%, satisfies the planned distance constraints, and accomplishes the optimal solution of underground routing in a distribution electrical network applied in a georeferenced area. Simulating the electrical distribution network tests that the voltage drop is less than 2% in the farthest node.

A Decision-Making Tool for Electric Distribution Network Planning Based on Heuristics and Georeferenced Data

“…Moreover, there is additional information where optimal planning tools are mentioned, but here the fundamental considerations for a design of distribution lines are carried out by the use of simulator programs [17][18][19]. Related with power distribution systems, some studies have focused on how to improve resilience in distribution grids by graph theory or integrating distributed generation to the main grid [10,11], using georeferenced information as the initial stage.…”

“…An efficient distribution system needs to be provided with the best possible topological configuration in order to provide and supply the increase in power and demand and in order to ensure minimum requirements demanded by the local distribution company, such as the voltage drop [1,3,9]. In the same way, considering the expansion of the system is as important as planning, because in this way the optimum levels of service can be maintained and can ensure that the coverage area of the distribution network will include all desired users and thus provide them with electrical service [10,11].…”

Modelo resiliente de redes de distribución considerando flujos de potencia basado en teoría de grafos

“…El árbol de expansión del gráfico G no es único y la suma de peso del árbol se llama árbol de expansión mínimo, por lo tanto, el algoritmo de árbol mínimo de expansión es importante en la teoría de grafos [27], [28] único e irrepetible.…”

Óptimo dimensionamiento y ubicación de generación fotovoltaica en una microred georeferenciada utilizando generación de columnas

“…El uso de GD parte desde la necesidad de obtener: sistemas eléctricos flexibles, reducción de pérdidas eléctricas, un bajo impacto ambiental y un ahorro económico en la inversión [1]. Junto con la inmersión de unidades de GD las consideraciones técnicas y económicas son un factor determinante al momento de elegir su ubicación y dimensionamiento, por lo que ambos factores resultan ser posibles variables para un modelo de optimización [2] [3].…”

“…Al problema de optimización, en [3], se plantea un modelo matemático de programación no lineal entera mixta que se basa en los costos de operación del SDE y en el flujo óptimo de potencia (FOP) para minimizar la relación del costo en cada nodo, para asegurar que la inclusión de los generadores distribuidos sea de forma óptima y el dimensionamiento sea correcto.…”