Abstract
Most optimization and decision problems in power systems include integer or binary variables, leading to combinatorial problems. In this paper, several approaches using metaheuristics and genetic algorithms are presented that deal with real problems of the power industry Most of these methodologies are now implemented in distribution management systems (DMS) used by several utilities.

Abstract
This paper presents a new strategy for recharging electric vehicles in residential buildings. The proposed approach minimizes the difference between desired and final state of charge (SOC) by the end of the charging period, by adjusting the charging power for each vehicle in real-time. A non-linear optimization problem is formulated, considering the initial and final SOC, as well as available charging time, and total available power. Results were compared to a baseline and show that the proposed solution outperforms the currently most used nonoptimized method, particularly in high demand scenarios, where we achieve values of 9.3% of curtailed range when compared with the non-optimized methodology.

Abstract
The DC/DC boost converter is described as a time variant system. State-Space is one of the methods used to approach a time variant system to an invariant time linear system. The present document focuses on a comparative approach of output voltage regulation and system stability and performance. For this document, there were made MatLab tests of PI and PD controllers, with and without fuzzy control.