Given the uncertainty in load demand and renewable energy sources, the distribution network reconfiguration (DNR) problem is a stochastic mixed-integer nonlinear optimization program with a running time that scales exponentially with the number of sectional and tie line switches. Stochastic optimization techniques require knowledge of the stochastic processes of the uncertain parameters, which may not be available in practice. In this seminar, we introduce a deep reinforcement learning algorithm to solve the DNR problem by determining the optimal network configuration using a deep neural network architecture.
Vincent Wong is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Vancouver, Canada. His research areas include protocol design, optimization, and resource management of communication networks, with applications to the Internet, wireless networks, smart grid, mobile edge computing, and Internet of Things. Dr. Wong is the Editor-in-Chief of the IEEE Transactions on Wireless Communications.
