The IEEE PES Singapore Chapter is pleased to announce an upcoming seminar featuring two esteemed speakers from the Chinese University of Hong Kong (CUHK). Date: 5th December 2024 (Thursday) Time: 2:30 PM – 4:00 PM Venue: LT 29, South Spine, NTU The event will include two talks focusing on advanced methodologies in power systems optimization: Speaker 1: Assistant Professor Yue Chen Time: 2:30 PM – 3:00 PM Title: A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow Prof. Chen will present her work on applying quantum neural networks to address probabilistic optimal power flow challenges while ensuring data privacy. The talk will also highlight related research on optimization and market design in power systems. For more information: https://site.ieee.org/singapore-pes/files/2024/11/Centre-for-Power-Engineering-Seminar.pdf Speaker 2: Assistant Professor Changhong Zhao Time: 3:00 PM – 3:30 PM Title: Accelerating Large-scale Power Flow Optimization via Distributed Learning and Computing Prof. Zhao will discuss distributed algorithms designed to enhance computational efficiency for large-scale nonlinear power flow problems. Broader insights into optimization and control for networked systems will also be shared. For more information: https://site.ieee.org/singapore-pes/files/2024/11/Centre-for-Power-Engineering-Seminar.pdf

We are pleased to invite you to an online Distinguished Lecture Program (DLP) on "Intelligent Power Management and Control of Electric Vehicles Using Reinforcement Learning" by Professor Rajesh Kumar, University of Johannesburg. Date: 16 December 2024 ⏰ Time: 3:00 PM SGT Venue: Online (Zoom) The seminar aims to discuss the energy management system (EMS) for hybrid electric vehicles (HEVs) based on a model-based and model-free reinforcement control mechanism. To improve the learning process and reliability of the EMS framework, various deep-Q-network (DQN), deep-deterministic-policy gradient (DDPG), reinforcement learning algorithms, namely Q learning, SARSA, Trust-Region-Policy-Optimization (TRPO), and Proximal-Policy-Optimization (PPO), Soft-Actor-Critic (SAC), Twin-Delayed-Deep-Deterministic-Policy-Gradients (TD3), Hindsight-Experience-Replay (HER), Distributional-Reinforcement-Learning-with-Quantile-Regression (QR-DQN), and Asynchronous-Advantage-Actor-Critic (A3C) are analysed. Don't miss this opportunity to gain insights into the future of EV power management! ➡️ Register here: https://singaporetech.zoom.us/meeting/register/tJAvf--qrTwuHNXlR168BFRNTex-ZcxhQ2tX #IEEE #PowerAndEnergy #ElectricVehicles #ReinforcementLearning #HybridElectricVehicles #Singapore