Distribution automation is well recognized as an effective solution to enhance the reliability and efficiency of these grids in a timely manner. This paper introduces an effective probabilistic operation framework for the automated distribution networks (ADNs) incorporating the plug-in electric vehicles (PEVs) charging/discharging schemes in the presence of different renewable energy sources (RESs). To this end, this paper pursues four different strategic approaches. Firstly, an effective fuzzy based probabilistic method is proposed to model the forecast error in the wind and solar units well as the load demand through the cloud theory. Secondly, an appropriate framework is devised to model the PEVs random behaviour considering their essential parameters such as the charging/discharging rate and arrival/departure time to/from the parking lots (PLs), the discharging level at driving mode on the road and the effects of battery degradation. As the third goal, an appropriate objective function which can consider automation indices including the social welfare and reliability is considered. Since the operation problem is a nonlinear continuous non-numerical problem, it requires an applicable and effective optimization algorithm which is regarded as the fourth goal of this paper. In this regard, a new θ-modified bat algorithm is introduced to find the optimal solution of the problem. The proposed model is simulated and examined on the IEEE 69-bus standard test system wherein results reveal the effectiveness and applicability of the proposed operation management framework.

中文翻译：

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存在电动汽车和可再生能源的情况下自动配电网络的概率运行管理

配电自动化是公认的有效解决方案，可以及时提高这些电网的可靠性和效率。本文介绍了一种自动配电网络（ADN）的有效概率操作框架，该框架结合了在存在不同可再生能源（RES）的情况下插入式电动汽车（PEV）的充电/放电方案。为此，本文采用了四种不同的战略方法。首先，提出了一种有效的基于模糊的概率方法，通过云理论对风电和太阳能机组的预测误差以及负荷需求进行建模。其次，考虑到PEV的基本参数，例如充/放电率和到/离开停车场（PLs）的到达/离开时间，行驶模式下道路上的排放水平以及车辆的影响，设计一个合适的框架来模拟PEV的随机行为。电池老化。作为第三个目标，考虑了可以考虑自动化指标（包括社会福利和可靠性）的适当目标函数。由于运算问题是非线性连续非数值问题，因此需要一种适用且有效的优化算法，该算法被视为本文的第四目标。在这方面，引入了一种新的θ修改bat算法，以找到问题的最佳解决方案。