Distributed Optimization Frameworks for Large-Scale Nonlinear Programming in Power Systems
The ongoing energy transition is challenging centralized power system paradigms by rapidly integrating distributed energy resources (DERs), which introduce significant supply-demand variability. This variability complicates grid management and necessitates enhanced coordination among operators. Centralized data aggregation further exacerbates privacy risks and strains the communication infrastructure as the proliferation of controllable devices increases.
To address these challenges, this presentation introduces advances in distributed frameworks for nonconvex nonlinear programming (NLP). The first approach refines a distributed Sequential Quadratic Programming (SQP) framework that integrates the barrier method and Schur-complement-based communication reduction, enabling efficient parallelization through graph decomposition. Large-scale AC optimal power flow (OPF) benchmarks demonstrate its superiority over the centralized solver IPOPT. The framework solves problems with over 500,000 variables at speeds 2–8 times faster than IPOPT on standard workstations while maintaining numerical robustness. The second approach leverages the hierarchical structure of integrated transmission–distribution (ITD) systems and casts coordination as a non-iterative, two-layer optimization scheme. By communicating aggregated distribution-level flexibility to the transmission layer, the method eliminates the need for detailed distribution-network models in system-level coordination. Simulations under severe weather conditions in Germany demonstrate robustness to prediction errors and underscore the scalability and privacy-preserving properties of the proposed strategy.
Speaker: Dr. Xinliang DAI
Date: 4 Feb 2026 (Wednesday)
Time: 2:00pm – 3:00pm
Venue: LAU 6-209
Poster: Click here
Biography
Dr. Xinliang DAI received the B.Sc. degree from Jilin University, China, and the M.Sc. and Ph.D. degrees from the Karlsruhe Institute of Technology (KIT), Germany. He is currently a Postdoctoral Research Associate with the Zero-carbon Energy Systems Research and Optimization Laboratory (ZERO Lab) at Princeton University, USA. His research interests include graph-based distributed optimization, flexibility aggregation, and GPU acceleration for large-scale optimization.