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

• URL: http://arxiv.org/abs/2401.06820v1
• Date: Thu, 11 Jan 2024 20:17:44 GMT
• Title: QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
• Authors: Sihan Zeng, Youngdae Kim, Yuxuan Ren, Kibaek Kim
• Abstract summary: We propose an innovated computational learning ACOPF, where the input is mapped to the ACOPF network in a computationally efficient manner. We show through simulations that our proposed method achieves superior feasibility rate and cost in situations where the existing-based approaches fail.
• Score: 4.1920378271058425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At the heart of power system operations, alternating current optimal power flow (ACOPF) studies the generation of electric power in the most economical way under network-wide load requirement, and can be formulated as a highly structured 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 meet the need to repeatedly solve the problem as load requirement frequently changes. On the other hand, learning-based methods that directly predict the ACOPF solution given the load input incur little computational cost but often generates infeasible solutions (i.e. violate the constraints of ACOPF). In this work, we combine the best of both worlds -- we propose an innovated framework for learning ACOPF, where the input load is mapped to the ACOPF solution through a neural network in a computationally efficient and reliable manner. Key to our innovation is a specific-purpose "activation function" defined implicitly by a QCQP and a novel loss, which enforce constraint satisfaction. We show through numerical simulations that our proposed method achieves superior feasibility rate and generation cost in situations where the existing learning-based approaches fail.

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