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

Application of Improved Ant Colony Algorithm in Optimizing the Charging Path of Electric Vehicles

“…Moreover, some of these studies overlook various constraints and limitations in their actual travel process. In addition, ant colony optimization (ACO) is often used in path optimization because it utilizes the positive feedback mechanism of remaining pheromones on the path between ants for information transmission [4,5]. However, there are still shortcomings in optimizing the charging path of EVs.…”

“…β represents the heuristic factor.α represents the expected factor. In addition, the traditional expression of pheromone concentration updates is shown in Equations (3) to(5).…”