Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research.

The massive introduction of electric vehicles as a mobility alternative requires deploying an infrastructure of charging stations for electric cars (ICSEC). This new mobility concept will mitigate the environmental harm caused by the emission of CO2 generated by conventional internal combustion mobility methods. The sustainability of the ICSEC depends not only on the capacity to meet the demand for charging batteries for electric vehicles (EV) but also on an adequate number of public/private charging stations (CS) distributed in a geolocalized area. It is noted that the distribution of CS must respond to a set of real mobility constraints, such as vehicular flow capacity, road capacity, and trajectories. The planning, intelligent location of public charging stations (PCS), and contingency analysis will enable us to study the increase in demand for electrical distribution substations (EDS). Therefore, the present model explains the need to plan the massive introduction of EVs by observing the user’s conditions at the trajectory level through finite resource allocation processes, segmentation, and minimum spanning trees, by observing heterogeneous vehicular flow criteria through microscopic analysis, to understand the space–time relationship of vehicular flow in georeferenced scenarios. Consequently, the computational complexity of the model is of the combinatorial type, and it is defined as NP-Hard given the multiple variables and constraints within the transportation problem.

El aumento de la demanda en los distintos sistemas eléctricos de potencia (SEP) tiene un impacto negativo en la estabilidad de la tensión, la confiabilidad y la calidad del suministro eléctrico. El perfil de tensión disminuye cuando las unidades de generación no son capaces de suministrar potencia reactiva al sistema eléctrico en los momentos que se requiere. Con el desarrollo de la electrónica de potencia y los complejos sistemas de control, se han podido introducir dispositivos de sistemas flexibles de transmisión de corriente alterna (FACTS, del inglés Flexible Alternating Current Transmission System). En este artículo se analiza en detalle el impacto que genera la introducción de un tipo de FACTS que permite la compensación de potencia reactiva en el SEP. Además, se analizará una metodología para decidir la capacidad del compensador síncrono estático (STATCOM, del inglés Static Synchronous Compensator) y su ubicación óptima con la ejecución de flujos de potencia continuos (FPC). Finalmente, se estudiará el impacto positivo de la instalación de un control estabilizador de potencia (PSS, del inglés Power System Stabilizer) para asegurar la estabilidad de tensión en el SEP. Este artículo se desarrolla utilizando el sistema base IEEE de 14 barras bajo dos modelos matemáticos para el cálculo del flujo de potencia desarrollados en el software Matlab, que son: i) utilizando las ecuaciones de balance de potencia y ii) Newton Raphson con el toolbox de MATLAB (PSAT, del inglés Power System Analysis Toolbox).

The unpredictable increase in electrical demand affects the quality of the energy throughout the network. A solution to the problem is the increase of distributed generation units, which burn fossil fuels. While this is an immediate solution to the problem, the ecosystem is affected by the emission of CO₂. A promising solution is the integration of Distributed Renewable Energy Sources (DRES) with the conventional electrical system, thus introducing the concept of Smart Microgrids (SMG). These SMGs require a safe, reliable and technically planned two-way communication system. This paper presents a heuristic based on planning capable of providing a bidirectional communication that is near optimal. The model follows the structure of a hybrid Fiber-Wireless (FiWi) network with the purpose of obtaining information of electrical parameters that help us to manage the use of energy by integrating conventional electrical system with SMG. The optimization model is based on clustering techniques, through the construction of balanced conglomerates. The method is used for the development of the clusters along with the Nearest-Neighbor Spanning Tree algorithm (N-NST). Additionally, the Optimal Delay Balancing (ODB) model will be used to minimize the end to end delay of each grouping. In addition, the heuristic observes real design parameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes are built following the shortest path. Therefore, this paper presents a heuristic able to plan the deployment of Smart Meters (SMs) through a tree-like hierarchical topology for the integration of SMG at the lowest cost.

The unpredictable increase in electrical demand affects the quality of theenergy throughout the network. A solution to the problem is the increase of distributedgeneration units which burn fossil fuels.While this is an immediate solution to theproblem the ecosystem gets affected by the emission of CO2.A promising solutionis the integration of Distributed Renewable Energy Sources (DRES) to the conventionalelectrical system, thus, introducing the concept of smart microgrids (SMG) that requirea safe, reliable and technically planned two-way communication system. This documentpresents a heuristic based on planning capable of providing a bidirectional communicationnear optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with thepurpose of obtaining information of electrical parameters that help us to manage the useof energy by integrating conventional electrical system to SMG. A FiWi network is basedon the integration of wireless access and optical networks. This integration increases thecoverage and reliability at a lower cost. The optimization model is based on clusteringtechniques, through the construction of balanced conglomerates. The method is used forthe development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm(N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimizethe end to end delay of each grouping. In addition, the heuristic observes real designparameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes arebuilt following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for theintegration of SMG at the lowest cost.