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At the heart of power system operations, alternating current optimal powerflow (ACOPF) studies the generation of electric power in the most economicalway under network-wide load requirement, and can be formulated as a highlystructured non-convex quadratically constrained quadratic program (QCQP).Optimization-based solutions to ACOPF (such as ADMM or interior-point method),as the classic approach, require large amount of computation and cannot meetthe need to repeatedly solve the problem as load requirement frequentlychanges. On the other hand, learning-based methods that directly predict theACOPF solution given the load input incur little computational cost but oftengenerates infeasible solutions (i.e. violate the constraints of ACOPF). In thiswork, we combine the best of both worlds -- we propose an innovated frameworkfor learning ACOPF, where the input load is mapped to the ACOPF solutionthrough a neural network in a computationally efficient and reliable manner.Key to our innovation is a specific-purpose "activation function" definedimplicitly by a QCQP and a novel loss, which enforce constraint satisfaction.We show through numerical simulations that our proposed method achievessuperior feasibility rate and generation cost in situations where the existinglearning-based approaches fail.

QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints