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The synchronization requirement is a bottleneck of many distributed optimization algorithms, particularly for solving problems with computationally heterogeneous subproblems and during the occurrence of communication failure/delay. This paper presents a double-loop learning-aided asynchronous alternating direction method of multipliers (LA-ADMM) that has information prediction capability and handles a considerable level of asynchrony between subproblems. A momentum-extrapolation prediction-correction technique is developed to enable subproblems to predict their neighbors missing shared variable information instead of using the latest received values. An online streaming-based anomaly classification is designed to observe the performance of predicted data and control Lagrange multipliers update over the course of iterations. The proposed LA-ADMM reduces under-utilization of computation resources, especially if subproblems are computationally heterogeneous. This algorithm also enhances distributed optimization robustness against communication failure/delay that may result in a considerable level of asynchrony between subproblems. LA-ADMM is applied to solve the optimal power flow problem for several test systems. Promising results are obtained as compared to the classical synchronous ADMM and asynchronous ADMM without the anomaly switch control. [ABSTRACT FROM AUTHOR]

This paper proposes a temporal decomposition strategy to reduce the computation time of security-constrained unit commitment (SCUC). The novelty of this paper is twofold. The scheduling horizon is decomposed into multiple subhorizons. The concept of coupling intervals is introduced, and a set of auxiliary counting variables and logical expressions along with their equivalent linear models are formulated to handle intertemporal ramp constraints and minimum on/off times between consecutive subhorizons. An accelerated analytical target cascading (A-ATC) algorithm is developed to coordinate SCUC subproblems and find the optimal solution for the whole operation horizon in a distributed manner. An initialization strategy is presented to enhance the convergence performance of A-ATC. The proposed algorithm is tested on four test systems. [ABSTRACT FROM AUTHOR]