An intensive 45-minute masterclass on deep reinforcement learning for smart grid load balancing, microgrid dispatch, and renewable integration.
This advanced masterclass covers state-of-the-art AI methodologies for power grid engineering. Learn how Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) solve multi-period optimal power flow (OPF) problems and manage high-penetration renewable energy storage in real time.
Participants will analyze real grid telemetry data from Indian power pools (POSOCO/GRID-INDIA) and build a Deep RL agent in PyTorch capable of stabilizing voltage fluctuations during solar/wind ramp events.
Participants will get hands-on exposure to key industry standard toolkits during the session:
Intermediate Python, linear algebra, and basic neural network concepts.
PhD candidates in Electrical/Energy Engineering, Smart Grid Scientists, R&D Engineers.
Every registered attendee receives an official, tamper-proof digital certificate issued by the Deep Science & Technology Consortium (DSTC). Includes a unique QR code and SHA-256 hash for instant verification on employer or university registries.
IIT Bombay
IIT Bombay