Data-Driven Optimal Voltage Regulation Using Input Convex Neural Networks

Carvalho, 2008, Distributed reactive power generation control for voltage rise mitigation in distribution networks, IEEE transactions on Power Systems, 23, 766, 10.1109/TPWRS.2008.919203 Jahangiri, 2013, Distributed volt/var control by pv inverters, IEEE Transactions on power systems, 28, 3429, 10.1109/TPWRS.2013.2256375 Farivar, 2011, Inverter var control for distribution systems with renewables, 457 Farivar, 2012, Optimal inverter var control in distribution systems with high pv penetration, 1 Zhu, 2015, Fast local voltage control under limited reactive power: Optimality and stability analysis, IEEE Transactions on Power Systems, 31, 3794, 10.1109/TPWRS.2015.2504419 Zhang, 2014, An optimal and distributed method for voltage regulation in power distribution systems, IEEE Transactions on Power Systems, 30, 1714, 10.1109/TPWRS.2014.2347281 Qu, 2018, An optimal and distributed feedback voltage control under limited reactive power, 1 Bolognani, 2014, Distributed reactive power feedback control for voltage regulation and loss minimization, IEEE Transactions on Automatic Control, 60, 966, 10.1109/TAC.2014.2363931 Magnússon, 2019, Voltage control using limited communication, IEEE Transactions on Control of Network Systems, 10.1109/TCNS.2019.2905091 Li, 2018, Distribution system voltage control under uncertainties using tractable chance constraints, IEEE Transactions on Power Systems H. Li, Y. Weng, Y. Liao, B. Keel, K.E. Brown, Robust hidden topology identification in distribution systems, arXiv preprint arXiv:1902.01365(2019). Deka, 2016, Estimating distribution grid topologies: A graphical learning based approach, 1 Low, 2014, Convex relaxation of optimal power flow-part i: Formulations and equivalence, IEEE Transactions on Control of Network Systems, 1, 15, 10.1109/TCNS.2014.2309732 Xu, 2018, A data-driven voltage control framework for power distribution systems, 1 Q. Yang, G. Wang, A. Sadeghi, G.B. Giannakis, J. Sun, Real-time voltage control using deep reinforcement learning, arXiv preprint arXiv:1904.09374(2019). Sutton, 1998, 2 Miryoosefi, 2019, Reinforcement learning with convex constraints, 1 LeCun, 2015, Deep learning, nature, 521, 436, 10.1038/nature14539 Amos, 2017, Input convex neural networks, 146 Chen, 2019, Optimal control via neural networks: A convex approach, 1 Baran, 1989, Optimal capacitor placement on radial distribution systems, IEEE Transactions on power Delivery, 4, 725, 10.1109/61.19265 Baran, 1989, Network reconfiguration in distribution systems for loss reduction and load balancing, IEEE Transactions on Power delivery, 4, 1401, 10.1109/61.25627 Bottou, 2010, Large-scale machine learning with stochastic gradient descent, 177 Chen, 2017, Modeling and optimization of complex building energy systems with deep neural networks, 1368 Boyd, 2004 He, 2016, Deep residual learning for image recognition, 770 Wang, 2004, General constructive representations for continuous piecewise-linear functions, IEEE Transactions on Circuits and Systems I: Regular Papers, 51, 1889, 10.1109/TCSI.2004.834521 M. Grant, S. Boyd, Cvx: Matlab software for disciplined convex programming, version 2.1, 2014. Abadi, 2016, Tensorflow: A system for large-scale machine learning, 265 Y. Chen, Y. Shi, B. Zhang, Input convex neural networks for optimal voltage regulation, arXiv preprint arXiv:2002.08684(2020).