The high penetration of wind generation has prompted the development of stochastic hydrothermal unit commitment (SHTUC) models, which are more difficult to be solved than their thermal-based counterparts due to hydro generation constraints and inflow uncertainties. For handling the uncertainty, the problem is usually formulated as a two-stage stochastic model (2S-SHTUC), although multistage (MS-SHTUC) formulations have gained increasing attention due to their more realistic assumptions about on–off decisions over the planning horizon. Benders decomposition (BD) is one of the most common methodologies used for solving 2S-SHTUC and MS-SHTUC problems. To overcome the well-known slow convergence of the classical BD when applied to large problems, most authors use accelerating techniques. In this paper, we implement state-of-the-art stabilization methods tailored for speeding up the convergence of the classical BD: local branching and the level regularization. Our experiments are conducted in a real-life SHTUC problem with 11 thermal units, 16 hydro plants, 46 buses and 95 lines. The results show that 2S and MS-SHTUC can benefit from stabilization. The savings in computing times range from 69 to 95% for the 2S-SHTUC model and from 77 to 89% for the MS-SHTUC.

风力发电的高渗透率推动了随机热液机组承诺（SHTUC）模型的开发，由于水力发电的限制和流量的不确定性，与基于热力的热力机组相比，它们很难解决。为了处理不确定性，通常将问题表示为两阶段随机模型（2S-SHTUC），尽管多阶段（MS-SHTUC）公式由于对规划范围内的开关决策更为现实的假设而受到越来越多的关注。 。Benders分解（BD）是用于解决2S-SHTUC和MS-SHTUC问题的最常用方法之一。为了克服经典BD在应用于大问题时的缓慢收敛性，大多数作者使用了加速技术。在本文中，我们实施了专门为加速经典BD的收敛而量身定制的最新稳定方法：局部分支和级别正则化。我们的实验是在一个真实的SHTUC问题中进行的，该问题包含11个热力单元，16个水力发电厂，46条公交车和95条管线。结果表明2S和MS-SHTUC可以从稳定中受益。对于2S-SHTUC模型，计算时间节省了69％至95％，对于MS-SHTUC模型，节省了77％至89％。