Load Scheduling Using Demand Response for Demand-Side Management in Smart Grid

The advent of smart grids (SG) has provided residents the opportunity to incorporate renewable energy and demand response (DR) into the electricity market for optimal demand-side management (DSM). This study introduces a generic DSM model featuring an Energy Consumption Scheduler (ECS) to optimally schedule the operation of smart appliances. The ECS leverages various optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Optimization Algorithm (GOA), Wind-Driven Optimization (WDO), and our developed Hybrid Genetic Wind-Driven Optimization (HGWDO) algorithm, to schedule the operation of smart appliances for DSM under real-time DR. One of the key challenges in efficiently integrating RESs into SG is their time varying and intermittent nature. This study employs batteries to smooth out fluctuations in RE generation. Simulation results demonstrate that our proposed approach optimally solves DSM and achieves the desired objectives of utility bill payment, and peak-to-average demand ratio (PADR) minimization compared to existing models.