Nowadays, due to the increasing use of electric vehicles, manufacturers are making more and more innovations in the batteries used in electromobility, in order to make these vehicles more efficient and provide them with greater autonomy. This has led to the need to evaluate and compare the efficiency of different batteries used in electric vehicles to determine which one is the best to be implemented. This paper characterises, models and compares three batteries used in electromobility: lithium-ion, lead-acid, and nickel metal hydride, and determines which of these three is the most efficient based on their state of charge. The main drawback to determine the state of charge is that there are a great variety of methods and models used for this purpose; in this article, the Thévenin model and the Coulomb Count method are used to determine the state of charge of the battery. When obtaining the electrical parameters, the simulation of the same is carried out, which indicates that the most efficient battery is the Lithium-ion battery presenting the best performance of state of charge, reaching 99.05% in the charging scenario, while, in the discharge scenario, it reaches a minimum value of 40.68%; in contrast, the least efficient battery is the lead acid battery, presenting in the charging scenario a maximum value of 98.42%, and in the discharge scenario a minimum value of 10.35%, presenting a deep discharge. This indicates that the lithium-ion battery is the most efficient in both the charge and discharge scenarios, and is the best option for use in electric vehicles. In this paper, it was decided to use the Coulomb ampere counting method together with the Thévenin equivalent circuit model because it was determined that the combination of these two methods to estimate the SOC can be applied to any battery, not only applicable to electric vehicle batteries, but to battery banks, BESS systems, or any system or equipment that has batteries for its operation, while the models based on Kalman, or models based on fuzzy mathematics and neural networks, as they are often used and are applicable only to a specific battery system.

The study carries out an economic technical analysis of the effect of the penetration of electric vehicles in a microgrid with various types of renewable energy sources and loads of different nature. The electric vehicle has been modeled through its main component, the battery, by using criteria of randomness in the entry and type of load used by each vehicle. Following this process, the energy demand curves and voltage profiles resulting from such penetration were obtained. In a second phase, three demand response programs based on incentives and prices were applied, and the impact of these decisions on the system through new demand curves and voltage profiles were quantified, the comparison with the base scenario is also carried out. In its final stage, the different scenarios proposed are evaluated financially and the NPV financial index is calculated, which is included in a binary-type optimization model, allowing through simulations to make decisions about the best demand response program alternative for the system.

In this research, an alternative methodology is proposed for the location of Static VAR Compensators (SVC) in power systems, considering the reconfiguration of reactive power flows through the optimal switching of the transmission stage, which resembles the contingency restriction N-1 usually considered in transmission expansion planning. Based on this methodology, the contingency index was determined, which made it possible to determine which is the contingency that generates the greatest voltage degradation in the system. For the quantification of reactive flows, optimal AC power flows were used, which minimize the operating costs of the power system subject to transmission line switching restrictions, line charge-ability, voltages and node angles. To determine the node in which the compensation should be placed, the contingency index criterion was used, verifying the voltage profile in the nodes. The proposed methodology was tested in the IEEE test systems of 9, 14 nodes and large-scale systems of 200, 500 and 2000 bus-bars; to verify that the proposed methodology is adequate, the stability of the EPS was verified. Finally, the model allows satisfactorily to determine the node in which the SVC is implemented and its compensation value.

This paper presents an optimization model based on minimum spanning tree required to deploy phasor measurement units PMU throughout the power system. Each candidate site for a PMU is placed at random from geo-referenced form and possible links between each release candidate site; therefore. The proposed algorithm allows to optimize the connections between each of the PMUs through the minimum spanning tree. The optimum placement of each PMU will facilitate failure analysis power system with the minimum cost for resources used. The work presented is based on the concept of smart grids seeking efficiency and reliability of the power system.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangements. By employing a linear additive model to solve a multi-criteria decision model, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare and installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in MATLAB R2023b, with results validated through simulations in DIALux evo 12.0. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

This document presents demand energy response optimization in residential sector which energy required for demand supply is provided by electric system, which may have distributed generation, demand optimization objective is to flatten the demand peak curve, thus this promotes energy maintenance by users without producing comfort affections, for which an storage energy system through batteries is used allowing a system energy decrease provided by the electric system to the user. Management energy system allow to respond relieving load to the electric system, especially when peak demand response presents and when energy cost is greater, providing energy to the battery set, besides this research routes usage of surplus energy of electric vehicles.