PSGAI 2026 · BEIJING

Special Session · 4

Coordinated Optimization of Power Systems and AI Computing

📅 18–20 December 2026

Session Organizers Chairs

Jie Song Chair

Peking University Professor, Chang Jiang Distinguished Professor

IEEE RAS TC Chair (first Asian) · 100+ papers (Nature, Cell, UTD24) · Research: intelligent decision-making in industrial systems, energy & robotics.

Brief Introduction · Jie Song is Secretary of the CPC Committee and a Chang Jiang Distinguished Professor at the School of Advanced Manufacturing and Robotics, Peking University. Her research focuses on intelligent decision-making in emerging industrial systems. She has led and contributed to multiple projects funded by the National Natural Science Foundation of China, including Innovative Research Group, Major Program, and Key Program projects. She serves as Chair of the IEEE Robotics and Automation Society Technical Committee on Management of Robotics and Automation, becoming the first Asian scholar to hold this position. She is also Vice President of the Capital Association of Women Professors, Deputy Secretary-General of the Chinese Society for Industrial and Applied Mathematics, a Council Member of the Systems Engineering Society of China, and a Member of the National Steering Committee for Graduate Education in Engineering Management. She has published over 100 papers in leading journals, including Nature Portfolio, Cell Press, and UTD24 journals. Her research has received numerous honors, including the Science and Technology Progress Award of the China Electrotechnical Society, the Young Scientist Award in Systems Science and Systems Engineering, a Gold Medal at the International Exhibition of Inventions Geneva, Best Paper Awards from IISE and INFORMS, and the Best Supervisor Award from the National Steering Committee for Graduate Education in Engineering Management.

Lanqing Shan Chair

Peking University Associate Research Professor

PhD (Peking U.) · 10+ SCI/EI papers, 3 monographs · Research: intelligent decision-making in energy systems, distributed resource coordination, multi-level optimization.

Brief Introduction · Lanqing Shan is an Associate Research Professor at the School of Advanced Manufacturing and Robotics, Peking University. She serves as the liaison for the Beijing Key Laboratory of Green AI Computing Infrastructure and Power–Computing Coordination, currently under establishment, and is a member of the Capital Association of Women Professors. Her research focuses on intelligent decision-making in energy systems, intelligent coordination of distributed energy resources, and multi-level system optimization. She has published over ten SCI/EI-indexed papers, contributed to three Chinese-language monographs, and participated as a key team member in more than ten major national research and strategic advisory projects. Her government-commissioned research on energy system operation and key technologies has supported the development of China's first electricity spot market and informed national energy strategies and energy-sector policymaking.

Scope & Topics CFP

With the continued growth of large-scale AI training and inference, AI computing infrastructures, represented by AI data centers (AIDCs), are emerging as a new class of power system loads characterized by high power density, stringent reliability requirements, and substantial temporal and spatial flexibility. The growing interdependence among computing workloads, power supply systems, cooling loads, multi-level energy storage, and renewable energy introduces new challenges to power system planning, operation, and reliability, while also creating new flexibility resources for demand response, virtual power plants, and low-carbon operation.

Existing approaches remain insufficient for accurately characterizing the electrical behavior and flexibility boundaries of AI computing loads, coordinating heterogeneous energy resources across different levels, and effectively transmitting electricity price, carbon, and grid control signals to computing operations. This Special Session focuses on the coordinated optimization of power systems and AI computing loads. It welcomes the latest advances in modeling, planning, operation, control, market mechanisms, and AI-enabled methods, with the aim of transforming AI computing infrastructures from passive electricity consumers into grid-friendly, low-carbon, and flexible energy resources.

📌 Topics of Interest (include but are not limited to):

1. Modeling, forecasting, and flexibility assessment of the electrical characteristics of AI computing loads
2. Optimal sizing and power–computing coordinated operation of multi-level energy storage in AIDCs
3. Multi-resource coordinated scheduling and multi-timescale optimization of power, computing, cooling, and energy storage
4. Power grid planning, operational security, and supply reliability with the integration of AI computing loads
5. Computing workload scheduling and cross-region migration for renewable energy integration and low-carbon operation
6. Data center aggregation in virtual power plants, demand response, and electricity market participation
7. AI-enabled forecasting, optimization, decision-making, and intelligent control for power–computing coordination

🎤 Invited Speakers

to be confirmed + more invited

📝 Submission (Chinese Authors) 📄 Submission (International Authors)