DOI:10.35833/MPCE.2020.000210 Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network Page view: 0        Net amount: 151 Author: Qianyu Zhao1,Wenlong Liao1,Shouxiang Wang1,Jayakrishnan Radhakrishna Pillai2 Author Affiliation: 1.Key Laboratory of Smart Grid of Ministry of Education and Tianjin Key Laboratory of Power System Simulation and Control, Tianjin University, Tianjin, China;2.Department of Energy Technology, Aalborg University, Aalborg, Denmark Foundation: This work was supported by the Science and Technology Project of State Grid Corporation of China. Abstract: 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. Keywords: Robust voltage control ; uncertainty ; generative adversarial network ; wolf pack algorithm Received:April 14, 2020               Online Time:2020/12/03 View Full Text       Download reader