Ant colony optimization for feasible scheduling of step-controlled smart grid generation

Abstract The electrical energy grid is currently experiencing a paradigm shift in control. In the future, small and decentralized resources will have to responsibly perform control tasks like frequency or voltage For many use cases, scheduling of necessary. multi-dimensional discrete case–e.g., for step-controlled devices–this an NP-hard problem if some sort intermediate buffer involved. Systematically constructing feasible solutions during optimization, hence, becomes difficult task. We prove NP-hardness example co-generation plants demonstrate multi-modality systematically designing solutions. day-ahead scheduling, model-integrated solution based on ant colony optimization has already been proposed. By using simulation model deciding branches, artificial ants construct search graphs demand. Thus, exponential growth graph this combinatorial avoided. present extended work additional insight into complexity structure underlying feasibility landscape results.