Optimal Scheduling of AC/DC Comprehensive Energy Network via Risk Embedded Two-stage Stochastic Optimization

In this paper, a risk embedded two-stage stochastic optimization model is proposed to optimize the scheduling of the AC/DC comprehensive energy network (CEN). In the day-ahead scheduling, based on the forecast photovoltaic (PV) output and various loads, the first stage optimization model is established to minimize the operation cost in a daily time interval. In the realtime scheduling, considering that the PV output and loads are stochastic, the scenarios reduction based Monte Carlo method is used to generate multiple scenarios, and the second stage stochastic optimization model is adopted to minimize the weight sum of the expected deviation costs in the scenarios and the corresponding variance. A case of the CEN system has been studied, and results have been compared to verify the effectiveness of proposed method.