Chapter 6 - Smart energy optimization using new genetic algorithms in Smart Grids with the integration of renewable energy sources

Abstract

A Smart Grid (SG), a network of electricity, connects users like producers, Prosumers and consumers are two different types of people to stake energy in smart way, also facilitate them to choose various scheduling techniques in order to manage the energy usage. In this chapter, we discuss about the domiciliary load management with inclusion of Renewable Energy Resource (RES) like solar energy. A genetic algorithm (GA) based technique is projected to manage electrical appliance power utilization with a goal of minimizing power cost, increasing user comfort, and reducing peak to average ratio (PAR) shares of energy deprived of disturbing priorities of the prosumers’ by considering real time energy price (RTP), user priorities and renewable sources of energy-related parameters as input parameters. In our work, we have combined the pricing models of RTP with the Inclining Block Rate (IBR), which integrates user preferences and RES to schedule load demand appropriately. Adopting a RTP+IBR pricing method should successfully minimize electricity bills and PAR and improve system stability. To assess the proposed algorithm performance, the provided mathematical models of used loads are then used to build a multiobjective optimization problem. Further, simulation was done, and the results shows a substantial drop in the cost of energy, as well as achieving grid stability in terms of reduced peak and high comfort.