An Adaptive Distributionally Robust Model for Three-Phase Distribution Network Reconfiguration

[1]  Bangyin Liu,et al.  Online 24-h solar power forecasting based on weather type classification using artificial neural network , 2011 .

[2]  José Fortuny-Amat,et al.  A Representation and Economic Interpretation of a Two-Level Programming Problem , 1981 .

[3]  Long Zhao,et al.  Solving two-stage robust optimization problems using a column-and-constraint generation method , 2013, Oper. Res. Lett..

[4]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[5]  Wenchuan Wu,et al.  Distributed optimal residential demand response considering operational constraints of unbalanced distribution networks , 2018 .

[6]  Luca Delle Monache,et al.  Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble , 2017 .

[7]  Mohammad Shahidehpour,et al.  Multi-stage planning of active distribution networks considering the co-optimization of operation strategies , 2018, 2017 IEEE Power & Energy Society General Meeting.

[8]  U. S. N. Renewable Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment , 2017 .

[9]  Xin Chen,et al.  Robust Restoration Method for Active Distribution Networks , 2016, IEEE Transactions on Power Systems.

[10]  M. J. Rider,et al.  Imposing Radiality Constraints in Distribution System Optimization Problems , 2012, IEEE Transactions on Power Systems.

[11]  S. Low,et al.  Feeder Reconfiguration in Distribution Networks Based on Convex Relaxation of OPF , 2015, IEEE Transactions on Power Systems.

[12]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[13]  Prabhat,et al.  Artificial Neural Network , 2018, Encyclopedia of GIS.

[14]  R. Jabr,et al.  Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming , 2012, IEEE Transactions on Power Systems.

[15]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[16]  Sanjay Mehrotra,et al.  Robust Distribution Network Reconfiguration , 2015, IEEE Transactions on Smart Grid.

[17]  O. A. Jaramillo,et al.  Bimodal versus Weibull Wind Speed Distributions: An Analysis of Wind Energy Potential in La Venta, Mexico , 2004 .

[18]  Yibing Liu,et al.  A Fully Distributed Reactive Power Optimization and Control Method for Active Distribution Networks , 2014, IEEE Transactions on Smart Grid.

[19]  Felix F. Wu,et al.  Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing , 1989, IEEE Power Engineering Review.

[20]  David J. Hill,et al.  Distributionally Robust Optimal Power Flow in Multi-Microgrids With Decomposition and Guaranteed Convergence , 2021, IEEE Transactions on Smart Grid.

[21]  Yarhands Dissou Arthur,et al.  Probability Distributional Analysis of Hourly Solar Irradiation in Kumasi-Ghana , 2013 .

[22]  Peng Wang,et al.  A Distributed Algorithm for Distribution Network Reconfiguration , 2018, 2018 China International Conference on Electricity Distribution (CICED).

[23]  Matti Lehtonen,et al.  Stochastic Operation Framework for Distribution Networks Hosting High Wind Penetrations , 2019, IEEE Transactions on Sustainable Energy.

[24]  Chen Chen,et al.  Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation , 2019, IEEE Transactions on Smart Grid.

[25]  Boming Zhang,et al.  Robust Reactive Power Optimization and Voltage Control Method for Active Distribution Networks via Dual Time-scale Coordination , 2016, ArXiv.

[26]  David J. Hill,et al.  A deep learning-based general robust method for network reconfiguration in three-phase unbalanced active distribution networks , 2020 .

[27]  Francisco de León,et al.  Determination of the Optimal Switching Frequency for Distribution System Reconfiguration , 2017, IEEE Transactions on Power Delivery.

[28]  Sue Ellen Haupt,et al.  Solar Forecasting: Methods, Challenges, and Performance , 2015, IEEE Power and Energy Magazine.

[29]  Mahmoud-Reza Haghifam,et al.  Risk-based reconfiguration of active electric distribution networks , 2016 .

[30]  A. Ruszczynski,et al.  Dual stochastic dominance and quantile risk measures , 2002 .

[31]  Xiu-Shen Wei,et al.  In Defense of Fully Connected Layers in Visual Representation Transfer , 2017, PCM.

[32]  Wei Wang,et al.  Dynamic Distribution Network Reconfiguration Using Reinforcement Learning , 2019, 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[33]  Farhad Samadi Gazijahani,et al.  Robust Design of Microgrids With Reconfigurable Topology Under Severe Uncertainty , 2018, IEEE Transactions on Sustainable Energy.

[34]  João P. S. Catalão,et al.  Energy Management Strategy in Dynamic Distribution Network Reconfiguration Considering Renewable Energy Resources and Storage , 2020, IEEE Transactions on Sustainable Energy.

[35]  Yongpei Guan,et al.  Data-driven risk-averse stochastic optimization with Wasserstein metric , 2018, Oper. Res. Lett..

[36]  Huaiyu Zhu On Information and Sufficiency , 1997 .

[37]  Soumyabrata Barik,et al.  Determining the sizes of renewable DGs considering seasonal variation of generation and load and their impact on system load growth , 2017, IET Renewable Power Generation.

[38]  Hongbin Sun,et al.  Distribution-Free Probability Density Forecast Through Deep Neural Networks , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  K. Sharma,et al.  Wind Power Scenario Generation and Reduction in Stochastic Programming Framework , 2013 .