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.