An application of heuristic optimization algorithm for demand response in smart grids with renewable energy

This work presented power usage scheduling by engaging consumers in demand response program (DRP) with and without using renewable energy generation (REG). This power usage scheduling problem was modeled as an optimization problem, which was solved using an energy scheduler (ES) based on the crossover mutated enhanced wind-driven optimization (CMEWDO) algorithm. The CMEWDO was an enhanced wind-driven optimization (WDO) algorithm, where the optimal solution returned from WDO was fed to crossover and mutation operations to further achieve the global optimal solution. The developed CMEWDO algorithm was verified by comparing it with other algorithms like the whale optimization algorithm (WOA), enhanced differential evolution algorithm (EDE), and the WDO algorithm in aspects of the electricity bill and peak to average demand ratio (PADR) minimization without compromising consumers' comfort. Also, the developed CMEWDO algorithm has a lower computational time (measured in seconds) and a faster convergence rate (measured in number of iterations) than the standard WDO algorithm and other comparative algorithms.