  This paper seeks to design a machine learning twin of the optimal power flow
(OPF) optimization, which is used in market-clearing procedures by wholesale
electricity markets. The motivation for the proposed approach stems from the
need to obtain the digital twin, which is much faster than the original, while
also being sufficiently accurate and producing consistent generation dispatches
and locational marginal prices (LMPs), which are primal and dual solutions of
the OPF optimization, respectively. Availability of market-clearing tools based
on this approach will enable computationally tractable evaluation of multiple
dispatch scenarios under a given unit commitment. Rather than direct solution
of OPF, the Karush-Kuhn-Tucker (KKT) conditions for the OPF problem in question
may be written, and in parallel the LMPs of generators and loads may be
expressed in terms of the OPF Lagrangian multipliers. Also, taking advantage of
the practical fact that many of the Lagrangian multipliers associated with
lines will be zero (thermal limits are not binding), we build and train an ML
scheme which maps flexible resources (loads and renewables) to the binding
lines, and supplement it with an efficient power-grid aware linear map to
optimal dispatch and LMPs. The scheme is validated and illustrated on IEEE
models. We also report a trade of analysis between quality of the
reconstruction and number of samples needed to train the model.
