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

Document Type : Original Article


Department of Electric Power Engineering, Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran



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. In this paper, the short-term scheduling for DRP with asset-light retailers is proposed. The main idea is to determine an optimal for these retail electricity providers to cooperate cooperate with them and propose the optimal DRP base on the incentive in the market while having short-time benefits and load uncertainties in mind on locational marginal prices (LMP).It is assumed that the DISTCO’s do not participate in the DRP, and only REPs and consumers are involved. Short DRP schedules for light-asset retailers providing electricity for customers use a multi-objective optimization design with practical restrictions in performance. Short-term DRP scheduling of the light-asset retailers who provide the electricity consumers is formulated through a multi-objective optimization model with practical operational constraints.


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