Learning AC Optimal Power Flow by a Capped Regressor with Topological Attenuation

Efficiently solving the Alternating Current Optimal Power Flow (AC-OPF) problem is essential in power systems. Despite substantial progress in learning-based AC-OPF, an important question remains: which physics-motivated patterns can a learning-based load-to-generation predictor encode under the cost-minimization objective? To answer this question, we investigate the physical patterns of load-to-generation relationships: boundedness of total active-power generation and topological attenuation of generator response. Accordingly, we propose a capped regressor with topological attenuation (CReT). To encode the boundedness of total active-power generation, CReT controls the load-relative scale of total active generation by a capping operation, and then employs a label distribution learning model to proportionally allocate this total generation among individual generators. To encourage topological attenuation, CReT utilizes a novel physics-informed graph convolution encoding the nonlinear couplings in power flow, where the locality of message-passing mechanism is consistent with topological attenuation. Finally, CReT is evaluated against recent baselines on multiple bus systems.