Algorithm 1: The PSO procedure with its variants proposed in [26]

For each particle  Randomly Initialize the particle End

Do   For each particle   Compute its fitness value (the objective function optimized for residential consumer-centric DSM in this paper is described in Section 3.2)     If the fitness value is better than pbest in history, then    Set the current value as the new pbest   End  Choose the particle with the best fitness value (against all the other particles in the population) as the gbest   For each particle   Compute its particle velocity according to Equation (1)   Update its particle position according to Equation (5)   End  During the optimization process, operational constraints by the objective function in Section 3.2 need to be satisfied. While the pre-specified maximum iteration or the minimum error tolerance is not attained  The goal of the constrained PSO used in this paper is to minimize electricity costs and maximize user satisfaction; at the same time, all the constraints are respected.