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Optimizing Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation
With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning.
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems
Unit commitment (UC) is a fundamental problem in the day-ahead electricity market, and it is critical to solve UC problems efficiently.
TSO-DSO Operational Planning Coordination through "$l_1$-Proximal" Surrogate Lagrangian Relaxation
Difficulties behind creating such TSO-DSO coordination include the combinatorial nature of the operational planning problem involved at the transmission level as well as the nonlinearity of AC power flow within both systems.
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation
We further accelerate the convergence of the SLR by using quadratic penalties.
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