NEO-Grid trains ReLU networks as power-flow surrogates and applies deep equilibrium models for closed-loop volt-var optimization and control, reporting better voltage regulation than linear and heuristic baselines on the IEEE 33-bus test system.
Optimal capacitor placement on radial distribution systems
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A trained neural network surrogate for distribution grid voltage constraints is encoded exactly as mixed-integer linear constraints inside optimal power flow, delivering sub-1 V voltage error and faster solves than nonlinear models on networks with PV, EVs, and heat pumps.
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NEO-Grid: A Neural Approximation Framework for Optimization and Control in Distribution Grids
NEO-Grid trains ReLU networks as power-flow surrogates and applies deep equilibrium models for closed-loop volt-var optimization and control, reporting better voltage regulation than linear and heuristic baselines on the IEEE 33-bus test system.
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Enhanced Optimal Power Flow Using a Trained Neural Network Surrogate for Distribution Grid Constraints
A trained neural network surrogate for distribution grid voltage constraints is encoded exactly as mixed-integer linear constraints inside optimal power flow, delivering sub-1 V voltage error and faster solves than nonlinear models on networks with PV, EVs, and heat pumps.