Application of a genetic algorithm for planning loads of a power supply system with a network photo-power plant and a heat active consumer

Abstract

A novel approach is presented using a genetic algorithm to enhance the planning of household electrical loads in accordance with practical and user restrictions and homing signa, and sample results are shown. The goal is to minimize the end user's electricity bill in accordance with his / her preferences while taking into account the property of the energy services consumed. Circumscription are: the level of pledged power, end-user desire regarding the allowable and/or preferred times to operate each load, and the accessible power at each time period to account for fluctuations in the (unsteady) base load. The loading schedule is drawn up for one day. Internal load scheduling helps users to exploit of various energy service alternatives and reduce energy bills. Compared to the reference case in which there is no automatic scheduling. Thus, it is recognized that optimal adherence to thermal systems results in great savings for the utilities. Renewable liabilities are the problem of determining the generation schedule for units subject to design and operational constraints. The formulation of load planning was discussed, and the solution was obtained by the classical dynamic programming method. To tackle this issue, an algorithm was developed based on the swarm part optimization method, which is a population-based global search and optimization method. The performance of these algorithms has been tested on three-unit and four-unit systems and compared for total operating costs. In this article, a comparison of costs for different seasons was compared for normal heat load and the increase in heat load and power for normal heat load and the increase in heat load was a grid and solar power for a different season of simulations performed in MATLAB software.