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Summary In recent years, stochastic programming has gained increasing attention as a tool to support the scheduling of generating units in the face of uncertain information. One approach that has a long‐standing history in stochastic programming is the Benders decomposition (BD). However, BD is known to suffer from a series of shortcomings, for example, oscillation and tailing‐off effect. To reduce these drawbacks, regularization techniques are appealing options. However, even if regularized, BD may still struggle to converge due to the growing computational burden of its master problem (MP) over the iterations — this is especially noticeable in mixed‐integer programming models. Thus, to tackle this growing MP, we propose decomposing it using dual decomposition. We test our methodology on a testbed comprised of 108 cases from a system with 46 buses. Our results show that our methodology is effective both in terms of running times as well as the optimality gap.

Independent System Operators (ISOs) worldwide face the ever-increasing challenge of coping with uncertainties, which requires sophisticated algorithms for solving unit-commitment (UC) problems of increasing complexity in less-and-less time. Hence, decomposition methods are appealing options to produce easier-to-handle problems that can hopefully return good solutions at reasonable times. When applied to two-stage stochastic models, decomposition often yields subproblems that are embarrassingly parallel. Synchronous parallel-computing techniques are applied to the decomposable subproblem and frequently result in considerable time savings. However, due to the inherent run-time differences amongst the subproblem’s optimization models, unequal equipment, and communication overheads, synchronous approaches may underuse the computing resources. Consequently, asynchronous computing constitutes a natural enhancement to existing methods. In this work, we propose a novel extension of the asynchronous level decomposition to solve stochastic hydrothermal UC problems with mixed-integer variables in the first stage. In addition, we combine this novel method with an efficient task allocation to yield an innovative algorithm that far outperforms the current state-of-the-art. We provide convergence analysis of our proposal and assess its computational performance on a testbed consisting of 54 problems from a 46-bus system. Results show that our asynchronous algorithm outperforms its synchronous counterpart in terms of wall-clock computing time in 40% of the problems, providing time savings averaging about 45%, while also reducing the standard deviation of running times over the testbed in the order of 25%.

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