Optimal Demand Energy Management for Smart Distribution Networks

Abstract

Utilities all over the world consider the wide spread integration of renewable energies in modern power systems which will, reduce cost and minimize the environmental impact. The main problem that faces these kinds of resources is theirnature which has changing profile curves of the power generated. Consequently,they have an effect on the ability to face the, needed demand. One of the solutions to solve this mismatching issue between both the needed demand curves and available resourcecurves is through reshaping the demand curves to match the available sourcecurves. Utilities useDemand Side Management (DSM) programs to help in reducing their peak power purchases on the wholesale market. Consequently DSM programs can decreasethe bill for customers. On the other hand DSM programs limit the cost that utilities needed to rebuild new stations for generation, transmission and distribution. This thesis proposes implementation for Genetic Algorithm (GA) technique in solving the problem for mismatching of supply and load curves using DSM program. The proposed algorithm is implemented on a day with realistic demand and supply profiles. The DSM strategy is implemented on the demand taking into consideration all different type of loads which are divided into different types of loads residential, commercial and industrial loads.