Research on dynamic economic scheduling of plug in electric vehicles based on improved ant-lion algorithm

The dynamic economic dispatch (DED) problem involving plug-in electric vehicles (PEVs) has evolved into a complex constrained optimization challenge characterized by non-smooth, non-linear, and non-convex properties, particularly when considering valve point effects and transmission losses. The integration of grid-connected PEVs through vehicle-to-grid (V2G) technology can effectively mitigate grid fluctuations and facilitate peak shaving and valley filling. This study proposes a model of the dynamic economic dispatch problem that incorporates PEV charging to simulate the effects of day-ahead scheduling and PEV integration in the power system, thereby addressing grid fluctuations and energy consumption. Additionally, an enhanced ant-lion optimization algorithm (EALO) is developed to efficiently solve this model. An elitist transformation mechanism is incorporated to improve the algorithm's random wandering capability, resulting in faster convergence and enhanced global search performance. In addition, a simple and effective treatment of power balance constraints is proposed for infeasible solutions. The EALO's efficacy is evaluated through eight benchmark functions and applied to three different scales of DED scenarios, with comparisons made to existing methods in the literature. The experiment results indicate that the EALO algorithm offers significant advantages in addressing DED challenges.