This paper presents the optimal scheduling model of the active distribution network (ADN) containing renewable energy sources (RESs) and flexible sources (FSs) such as non-RESs (NRESs) and electric vehicles (EVs) parking lot based on the adaptive robust optimization (ARO). In the deterministic programming, a two-objective optimization model is expressed. It minimizes the difference between the network and NRES operation cost and the revenue of the RES, NRES and FS due to the sale of active and reactive power in the first objective function, and the second objective function considers the minimizing of the voltage deviation. Also, this problem considers AC optimal power flow constraints for the ADN in the presence of the RES, NRES and EVs parking lot. This method is as the non-linear programming (NLP). It is converted to the linear programming (LP) model based on the conventional linearization approaches to achieve the optimal solution at a short computational time. Then, the proposed approach is modeled as a single-objective optimization formulation using $\epsilon$-constraint-based Pareto optimization. This problem contains uncertainty of load, market price, EVs and RES characteristics; therefore, the ARO is used in this paper to model/obtain these uncertain parameters/robust capabilities of renewable and flexible sources to improve the network operation indices. The proposed scheme is implemented on the IEEE 33-bus ADN to improve the network indices such as voltage profile with determining optimal and robust scheduling of RES and FS according to numerical results.

本文提出了基于自适应鲁棒优化的包含可再生能源（RES）和灵活资源（FS）（例如非RES（NRES）和电动汽车（EV）停车场）的主动配电网（ADN）的最优调度模型（ARO）。在确定性编程中，表达了一个两目标优化模型。由于第一目标函数中有功功率和无功功率的出售，它使网络和NRES运营成本之间的差异以及RES，NRES和FS的收入之间的差异最小，而第二目标函数则考虑了电压偏差的最小化。同样，此问题考虑了在有RES，NRES和EV停车场的情况下ADN的AC最佳潮流约束。此方法称为非线性编程（NLP）。基于常规的线性化方法，将其转换为线性规划（LP）模型，以在较短的计算时间内实现最佳解决方案。然后，使用以下方法将提出的方法建模为单目标优化公式$\epsilon$基于约束的帕累托优化。这个问题包括负荷，市场价格，EV和RES特性的不确定性；因此，本文使用ARO来建模/获取这些不确定参数/可再生资源和灵活资源的鲁棒能力，以改善网络运行指标。所提出的方案在IEEE 33总线ADN上实施，以根据数值结果确定RES和FS的最佳和鲁棒调度，从而改善网络指标（如电压曲线）。