The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network. In this paper, a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network (IGAN). Firstly, both real and predicted data are used to train the IGAN consisting of a discriminator and a generator. The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction. Then, a new improved wolf pack algorithm (IWPA) is presented to solve the formulated robust voltage control model, since the accuracy of the solutions obtained by traditional methods is limited. The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads, which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods. Furthermore, IWPA has a better performance than traditional methods in terms of convergence speed, accuracy, and stability for robust voltage control.

中文翻译：

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通过改进的对抗网络，考虑可再生能源和负荷不确定性的稳健电压控制

可再生能源和负荷的输出功率的波动给配电网络的调度和运行带来了挑战。本文提出了一种鲁棒的电压控制模型，以解决基于改进的生成对抗网络（IGAN）的可再生能源和负荷的不确定性。首先，真实数据和预测数据都用于训练由鉴别器和生成器组成的IGAN。从高斯分布采样的噪声被馈送到生成器，以生成大量场景，这些场景在场景减少后用于鲁棒的电压控制。然后，提出了一种新的改进的Wolf Pack算法（IWPA），以解决公式化的鲁棒电压控制模型，因为通过传统方法获得的解决方案的准确性受到限制。仿真结果表明，IGAN能够准确捕获可再生能源和负荷的概率分布特征和动态非线性特征，这使得IGAN产生的场景比传统方法产生的场景更适合于鲁棒的电压控制。此外，在收敛速度，准确性和鲁棒性电压控制的稳定性方面，IWPA具有比传统方法更好的性能。