Certain investigation on drp dg optimization for performance Enhancement of the power System network

Abstract

The changes in demand driven by population growth necessitates the need for an efficient electric power system that is capable of producing sufficient energy to meet demands at all times in a variety of operational and weather situations. More than 50% of electricity production is to come from renewable energy sources to reach net zero by 2030. Distributed energy resources are expanding rapidly, and it is anticipated that wind and solar will contribute a growing portion of the renewable energy. The growth of renewable energy and the flexible demand are revamping the nature of resource management. Additionally, these variable energy sources are dispersed and they are mostly connected to the distribution network, which lead to additional challenges at the distribution level. These issues call for new flexibility measures for the whole power system, not only on the generation side but also on the demand side. Flexibility solutions are provided by the distribution-connected variable generation hybridized with Demand Response Programs (DRP). Modern power systems recommend the use of Distributed Generation (DG) sources to reduce the transmission losses, contingency and cost of energy and to increase the system reliability, efficiency and power system loadability. DRP can play a vital role in increasing the correlation between the loads and the available generation from the Renewable Energy Sources (RES). This research focuses on overcoming the setbacks analysed in the literature survey. Therefore integration of DRP with DG is given utmost priority to enhance the performance of the power system network. Better ihandling of DG with an effective DRP coordination is considered in the fitness function which aims in achieving the objective function. The congestion index is taken as an indexing parameter and the DRP-DG integration is accomplished thereby ensuring the test case optimization. The base line simulation is carried out using Genetic Algorithm (GA) and Micro Genetic Algorithm (MGA) techniques where all the DG pa