Scheduling of demand response program in the presence of retail electricity providers using multi-objective uncertainty-constrained optimization

This study discusses a new approach to demand response program scheduling (DRP) having retail electricity providers (REPs) with lighter tangible assets. The primary goal of this study is to present an optimal multi-objective function design for integrating REPs into a motivational DRP, while taking into account uncertainty of loads and LMP (location marginal prices). Designing with fuzzy functions are used for load uncertainty and Non-dominated sorting genetic algorithm (NSGA-II) is implemented to find solutions. The optimal compatibility between Pareto front-generated solutions was achieved by using a fuzzy, non-linear attribution method. In order to demonstrate the performance of the proposed model, simulations are performed on the IEEE 24 bus RTS test system. The results have shown that short-run benefits of REPs in electricity markets could be provided and ensured through the designed DRPs.