  This paper addresses the challenge of efficiently solving the optimal power
flow problem in real-time electricity markets. The proposed solution, named
Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages
physical constraints and market properties to ensure physical and economic
feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the
active set learning technique and expands its capabilities to account for
curtailment in load or renewable power generation, which is a common challenge
in real-world power systems. The core of PIMA-AS-OPF is a fully-connected
neural network that takes the net load and the system topology as input. The
outputs of this neural network include active constraints such as saturated
generators and transmission lines, as well as non-zero load shedding and wind
curtailments. These outputs allow for reducing the original market-clearing
optimization to a system of linear equations, which can be solved efficiently
and yield both the dispatch decisions and the locational marginal prices
(LMPs). The dispatch decisions and LMPs are then tested for their feasibility
with respect to the requirements for efficient market-clearing results. The
accuracy and scalability of the proposed method is tested on a realistic
1814-bus NYISO system with current and future renewable energy penetration
levels.

Machine Learning;Systems and Control;Systems and Control;Optimization and Control