We present a physics-informed, market-aware machine learning-driven algorithm to solve the DC optimal power flow problem. Namely, we employ active set learning to construct a system of linear equations whose solution is equivalent to the primal and dual solution of DC-OPF. The scheme, which we refer to as physics-informed, market-aware, active-set OPF (PIMA-AS-OPF), is validated on the New York ISO 1814-bus system, with results shown for different unit commitment, levels of wind penetration, and levels of noise. In addition, we demonstrate that the LMPs and dispatches adhere to established principles of market design, including revenue adequacy and cost recovery. Finally, we discuss how similar ML-driven schemes may be developed for unit commitment, which is mixed-integer and thus inherently more challenging in nature than DC-OPF.  

I will be rehearsing a conference talk, so feedback on the clarity of the presentation and ways to make the content more concise/accessible would be appreciated. I am not necessarily expecting detailed questions about the topic, though I welcome them as well.