6 papers
Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
Shourya Bose, Helgi Hilmarsson, Dhruv Suri
Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on h…
WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
Dhruv Suri, Helgi Hilmarsson, Shourya Bose
Solving AC Optimal Power Flow (AC-OPF) is of central importance in electricity market operations, where interior-point methods (IPMs) such as IPOPT are the standard solvers. A grow…
Presolving Convexified Optimal Power Flow with Mixtures of Gradient Experts
Shourya Bose, Kejun Chen, Yu Zhang
Convex relaxations and approximations of the optimal power flow (OPF) problem have gained significant research and industrial interest for planning and operations in electric power…
Physics-Informed Gradient Estimation for Accelerating Deep Learning based AC-OPF
Kejun Chen, Shourya Bose, Yu Zhang
The optimal power flow (OPF) problem can be rapidly and reliably solved by employing responsive online solvers based on neural networks. The dynamic nature of renewable energy gene…
From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
Shourya Bose, Yijiang Li, Amy Van Sant +2
Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting usi…
Load Restoration in Islanded Microgrids: Formulation and Solution Strategies
Shourya Bose, Yu Zhang
Adverse circumstances such as extreme weather events can cause significant disruptions to normal operation of electric distribution systems (DS), which includes isolating parts of…