The problem of coordinating the charging of electric vehicles gains more importance as the number of such vehicles grows. In this paper, we develop a method for the training of controllers for the coordination of EV charging. In contrast to most existing works on this topic, we require the controllers to preserve the privacy of the users, therefore we do not allow any communication from the controller to any third party. In order to train the controllers, we use the idea of imitation learning -- we first find an optimum solution for a relaxed version of the problem using quadratic optimization and then train the controllers to imitate this solution. We also investigate the effects of regularization of the optimum solution on the performance of the controllers. The method is evaluated on realistic data and shows improved performance and training speed compared to similar controllers trained using evolutionary algorithms.
翻译:随着电动车辆数量的增加,协调电动车辆收费的问题变得更为重要。在本文件中,我们为协调EV收费制定了一种对控制员进行培训的方法。与大多数关于这一专题的现有工作不同,我们要求控制员保护用户的隐私,因此我们不允许控制员与任何第三方进行任何通信。为了培训控制员,我们采用了模仿学习的理念 -- -- 我们首先利用二次优化来找到对问题进行轻松处理的最佳解决方案,然后培训控制员模仿这一解决方案。我们还调查优化解决方案对控制员绩效的影响,根据现实数据对方法进行评估,并显示与使用进化算法培训的类似控制员相比,业绩和培训速度有所提高。