Efficient Embedding of Neural Network-Based Stability Constraints Into Power System Dispatch
Tian Xia, Ning Zhang, Weiran Li, Ershun Du, Yun Su, Chen Fang, Chongqing Kang
Abstract
Neural networks have shown great potential to learn complex stability constraints for power system operation with high renewable penetration. However, explicitly embedding neural network-based stability constraints into power system dispatch is computationally intensive for online applications. This letter presents an efficient method to embed neural network-based stability constraints into power system dispatch. The neural network-based stability constraints are embedded into the optimization problem in linear form iteratively. Case studies on NPCC 140-bus system and a realistic power system demonstrate the effectiveness and efficiency of the proposed method.
Topics & Concepts
Electric power systemArtificial neural networkComputer scienceStability (learning theory)EmbeddingEconomic dispatchPower system simulationMathematical optimizationPower (physics)Control theory (sociology)Control engineeringEngineeringArtificial intelligenceMathematicsMachine learningControl (management)Quantum mechanicsPhysicsPower System Optimization and StabilityPower Systems Fault DetectionOptimal Power Flow Distribution