Smart demand management based on economic and technical objective functions in the autonomous energy system

This article introduces a novel approach to scheduling the autonomous energy system (AES) by taking into account both technical and economic considerations. To enhance the performance of the system during operation, demand management is proposed. This involves offering bid prices to consumers in order to reduce demand during emergencies. The study aims to achieve multiple objectives, including minimizing operational costs and improving voltage indices, through the use of multi-objective modeling. The Particle Swarm Optimization (PSO) algorithm is employed to optimize this multi-objective problem. The fuzzy max-min method is then applied to obtain the desired optimal solution from the set of non-dominated solutions. To demonstrate the effectiveness and validity of the proposed strategy, two case studies are conducted on the 33 bus IEEE test system. The participation of demand management by consumers leads to improving operational costs and voltage indices by 6.12%% and 20% in comparison with non-participation of the consumers.