Advanced control, optimization and digitalization of sustainable power and energy systems

Moving towards climate neutral society, future energy systems will have features of more renewables, more power electronics and more distributed. There are many challenges to be addressed, including the critical demand-supply power balance under the intermittent renewable generations, the economic operation under renewable uncertainties and the stability issue emerged from the high penetration of power electronic converters. This talk will present advanced control, optimization and AI based strategies to address the above challenges. First, advanced control and power management strategies are proposed for energy storage systems, and hybrid energy storage systems to compensate for power fluctuations and guarantee stable operation. Next, distributed optimization strategies are proposed for economic operation of energy systems under uncertainties. Finally, AI based solutions are proposed, including physics informed neural network based modeling and stability evaluation for power converter dominated grids, neural network based reliability prediction for power converters, and deep reinforcement learning based decentralized control of distribution grids with smart inverters. The proposed works provide cost-effective, stable, reliable and sustainable energy systems.