Optimization Solutions for Demand Side Management

Abstract

In modern years, energy demand is drastically increasing due to change in life style of consumers. This increases the load demand on the system. The insufficient generation leads to mismatch between load demand and generation, and creates unscheduled shedding of loads. Earlier this issue was addressed by generation capacity expansion which has negative impacts on environment. This subsequently increases the operational cost. Introduction of smart grid technologies such as renewable (RES), demand side management (DSM) enables the environmental friendly solutions. RES like wind, solar are abundantly available in nature. DSM is defined as a utility program that aims to tune customer’s energy consumption pattern along with renewable power generation. The main objective is to analyze the load shifting as one of the DSM technique to diminish the peak loads, energy bills and peak to average ratio (PAR). The movable items are shifted by vigorous involvement of clients in response to tariff of residential customers. DSM with load shifting technique and RES like wind are considered for investigations. This chapter presents the application of hybridization of firefly with particle swarm optimization (hFF-PSO) in DSM studies for the first time. The DSM study system comprises of smart AC grid and integrated with wind, energy storage systems (ESS). The optimal solution for shifting of loads can be achieved by the evolutionary algorithms. Investigations reveal that the system with the integration of wind and ESS outperforms over the system with smart AC alone. Moreover, the superiority of hFF-PSO technique is compared and tested with firefly (FF), particle swarm optimization (PSO) and is found to be better in terms of lessening the peak demand and utility bills. Further hFF-PSO technique aims to reduce the PAR over FF and PSO algorithms. Furthermore, hFF-PSO reshapes the load profile more effectively and reduces the tariff of residential customers.