TY - JOUR
T1 - Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network
AU - Zhao, Qianyu
AU - Liao, Wenlong
AU - Wang, Shouxiang
AU - Pillai, Jayakrishnan Radhakrishna
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Robust voltage control
KW - generative adversarial network
KW - uncertainty
KW - wolf pack algorithm
UR - http://www.scopus.com/inward/record.url?scp=85097437102&partnerID=8YFLogxK
U2 - 10.35833/MPCE.2020.000210
DO - 10.35833/MPCE.2020.000210
M3 - Journal article
SN - 2196-5625
VL - 8
SP - 1104
EP - 1114
JO - Journal of Modern Power Systems and Clean Energy
JF - Journal of Modern Power Systems and Clean Energy
IS - 6
M1 - 9275598
ER -