TY - JOUR
T1 - An adaptive distributionally robust model for three-phase distribution network reconfiguration
AU - Zheng, Weiye
AU - Huang, Wanjun
AU - Hill, David J.
AU - Hou, Yunhe
N1 - Funding Information:
This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region through General Research Fund under Project 17209419; in part by the State Key Laboratory of Power System and Generation Equipment under Project SKLD20M06; in part by the National Natural Science Foundation of China under Grant 51677160; and in part by the Research Grant Council, Hong Kong, under Grant GRF17207818. Paper no. TSG-00428-2020.
Publisher Copyright:
© 2020 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - Distributed generator (DG) volatility has a great impact on system operation, which should be considered beforehand due to the slow time scale of distribution network reconfiguration (DNR). However, it is difficult to derive accurate probability distributions (PDs) for DG outputs and loads analytically. To remove the assumptions on accurate PD knowledge, a deep neural network is first devised to learn the reference joint PD from historical data in an adaptive way. The reference PD along with the forecast errors are enveloped by a distributional ambiguity set using Kullback-Leibler divergence. Then a distributionally robust model for three-phase unbalanced DNR is proposed to obtain the optimal configuration under the worst-case PD of DG outputs and loads within the ambiguity set. The result inherits the advantages of stochastic optimization and robust optimization. Finally, a modified column-and-constraint generation method with efficient scenario decomposition is investigated to solve this model. Numerical tests are carried out using an IEEE unbalanced benchmark and a practical-scale system in Shandong, China. Comparison with the deterministic, stochastic and robust DNR methods validates the effectiveness of the proposed method.
AB - Distributed generator (DG) volatility has a great impact on system operation, which should be considered beforehand due to the slow time scale of distribution network reconfiguration (DNR). However, it is difficult to derive accurate probability distributions (PDs) for DG outputs and loads analytically. To remove the assumptions on accurate PD knowledge, a deep neural network is first devised to learn the reference joint PD from historical data in an adaptive way. The reference PD along with the forecast errors are enveloped by a distributional ambiguity set using Kullback-Leibler divergence. Then a distributionally robust model for three-phase unbalanced DNR is proposed to obtain the optimal configuration under the worst-case PD of DG outputs and loads within the ambiguity set. The result inherits the advantages of stochastic optimization and robust optimization. Finally, a modified column-and-constraint generation method with efficient scenario decomposition is investigated to solve this model. Numerical tests are carried out using an IEEE unbalanced benchmark and a practical-scale system in Shandong, China. Comparison with the deterministic, stochastic and robust DNR methods validates the effectiveness of the proposed method.
KW - deep neural network
KW - distribution network reconfiguration
KW - Distributionally robust optimization
KW - three-phase unbalanced distribution system
UR - http://www.scopus.com/inward/record.url?scp=85101956695&partnerID=8YFLogxK
U2 - 10.1109/TSG.2020.3030299
DO - 10.1109/TSG.2020.3030299
M3 - Article
AN - SCOPUS:85101956695
SN - 1949-3053
VL - 12
SP - 1224
EP - 1237
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 2
ER -