Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms

Nowadays, modern power networks have to face a number of challenges such as growing electricity demand, aging utility infrastructure and not to forget the environmental impact of the greenhouse gases produced by conventional electric generation. In order to increase renewable energy penetration but without disregarding security and reliability matters during the process, distribution power networks need to evolve to a flexible power network, better known as smart grid, in which distributed intelligence, communication technologies and automated control systems work as the driving factors. Taking into consideration this new frame, intelligent optimization techniques emerge as the only suitable way to optimally design this smart grid. In this paper, a generalized optimization formulation is introduced to determine the optimal location of distributed generators to offer reactive power capability. In order to find a suitable solution to such Reactive Power Management problem, genetic algorithms are applied in those cases where different multiobjective functions are to be considered. A more detailed description of the genetic algorithm evolution process is shown in a microgrid example.