Smart AC and Micro DC Grid Based DSM Using Battery Storage and Wind Energy

Abstract

Inadequate energy generation leads to unscheduled shedding of loads. Demand side management (DSM) finds a solution for it by shifting the controllable loads from peak hours to off-peak hours by active participation of customers in response to time of day tariff. This article evaluates the existing DSM for residential, commercial and industrial architecture by considering smart AC grid system and, smart AC grid and solar operated DC micro grid with battery storage system. The existing DSM structure is modified by further integrating generation from wind energy. The optimal load shifting for both existing and modified DSM architecture is done by using evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and hybrid particle swarm optimization (HPSO) with an objective to minimize the peak load demand which in turn reduces peak-to-average-ratio (PAR), energy bills and reshapes the load profile. The modified DSM architecture outperforms the existing one in terms of peak load demand reduction. Performance comparison among GA, PSO and HPSO witness the superiority of HPSO in terms of minimizing the peak load, PAR, energy bills and, reshaping the load profile.