Editorial: Evolutionary multi-objective optimization algorithms in microgrid power dispatching

With the increase of the scale of the micro-grid system, the optimization of microgrid power dispatching becomes a challenging issue. From the perspective of algorithm design, traditional heuristic intelligent algorithms are difficult to solve these complex problems, since they are easy to be fallen into the local optima of the problem. Evolutionary intelligent algorithms and machine learning methods have shown merits in dealing with complex optimization problems. Therefore, this issue aims to provide a platform for such forum to focus our minds on the intelligent optimization methods and their application in microgrid systems.

Bo et al., use the Stackelberg game to develop a collaborative optimal dispatch model for microgrid and electric vehicles. This model is based on a two-level framework, which takes into account the state of charge. In the upper layer, the charging and discharging of electric vehicles are optimized via minimizing the operating cost of the microgrid. In the lower layer, the electric vehicle users adjust the charging and discharging strategies to maximize their individual interests. In this way, the peak–valley differences of the microgrid load and the charging and discharging time cost of electric vehicles can be reduced, at the same time their state of charge can be maintained at a high level.