Dr. Dan li | Smart Energy System | Research Excellence Award
Dr. Dan li | China Three Gorges University | China
Dr. Dan Li is an accomplished researcher in intelligent energy systems, with a strong focus on optimal scheduling, risk measurement, and intelligent decision-making for multi-energy systems operating under a high proportion of renewable energy. His work addresses the increasing complexity and uncertainty of modern power grids by developing advanced computational approaches that integrate artificial intelligence with domain-specific physical principles. Through three significant completed and ongoing research projects, he has contributed to flexible resource regulation and control technology for new-energy pumped storage power stations, the development and application of key technologies for grid-connected performance testing and analysis of renewable energy stations, and the functional debugging and data-processing framework for intelligent network-source coordinated control and risk early-warning decision-making platforms. His key scientific contribution lies in accurately defining the physical constraints, operational limits, and security boundaries of unit commitment and economic dispatch problems, and translating these elements into optimization objectives and state spaces suitable for advanced algorithms. Dr. Li introduced a physics-informed exploration strategy for deep reinforcement learning by embedding prior knowledge, including power-flow equations and unit ramp-rate constraints, directly into the reward design, reducing invalid exploration and improving algorithmic convergence by over 50 percent. This approach demonstrates a powerful integration of domain expertise and advanced computational tools, contributing significantly to enhancing the reliability, efficiency, and intelligence of next-generation power systems. His published research, including his recent article on optimization solutions for power generation planning, reflects strong scientific rigor and technological relevance, reinforcing his position as a promising contributor to the advancement of intelligent and sustainable energy systems.
Featured Publications
Li, D., Zhang, L., Mi, N., & Zhong, H. (2025). Optimization solution for unit power generation plan based on the integration of constraint identification and deep reinforcement learning. Processes.