The unit commitment (UC) is highly complex to solve the increasing integrations of wind farm due to intermittent wind power fluctuation in nature. This paper presents a hybrid methodology to solve the stochastic unit commitment (SUC) problem depending on binary mixed integer generator combination with renewable energy sources (RESs). In this combination, ON/OFF tasks of the generators are likewise included to satisfy the load requirement as for the system constraints. The proposed hybrid methodology is the consolidation of grey wolf optimization algorithm (GWOA) and artificial neural network (ANN), hence it is called the hybrid GWOANN (HGWOANN) technique. Here, the GWOA algorithm is used to optimizing the best combination of thermal generators depending on uncertain wind power, minimum operating cost and system constraints – that is, thermal generators limits, start-up cost, ramp-up time, ramp-down time, etc. ANN is utilized to capture the uncertain wind power events, therefore the system ensures maximal application of wind power. The combination of HGWOANN technique guarantees the prominent use of sustainable power sources to diminish the thermal generators unit operating cost. The proposed technique is implemented in MATLAB/Simulink site and the efficiency is assessed with different existing methods. The comparative analysis demonstrates that the proposed HGWOANN approach is proficient to solve unit commitment problems and wind integration. Here, the HGWOANN method is compared with existing techniques such as PSO, BPSO, IGSA to assess the overall performance using various metrics viz. RMSE, MAPE, MBE under 50 and 100 count of trials. In the proposed approach, the range of RMSE achieves 9.26%, MAPE achieves 0.95%, MBE achieves 1% in 50 count of trials. Moreover, in 100 count of trials, the range of RMSE achieves 7.38%, MAPE achieves 1.91%, MBE achieves 2.87%.

单位承诺（UC）非常复杂，无法解决自然界中间歇性的风电波动引起的风电场集成度提高的问题。本文提出了一种混合方法来解决随机单位承诺（SUC）问题，该问题取决于二进制混合整数生成器与可再生能源（RESs）的组合。在这种组合中，发电机的开/关任务同样包括在内，以满足系统要求的负载要求。提出的混合方法是灰狼优化算法（GWOA）和人工神经网络（ANN）的合并，因此被称为混合GWOANN（HGWOANN）技术。在这里，根据不确定的风力，最小的运行成本和系统约束，GWOA算法用于优化火力发电机的最佳组合-即，发电机限制，启动成本，加速时间，减速时间等。ANN用于捕获不确定的风电事件，因此系统可确保最大程度地利用风电。HGWOANN技术的结合保证了可持续能源的显着使用，从而降低了热力发电机组的运行成本。所提出的技术在MATLAB / Simulink站点中实现，并且使用不同的现有方法评估了效率。比较分析表明，所提出的HGWOANN方法足以解决机组承诺问题和风能集成。在这里，将HGWOANN方法与现有技术（例如PSO，BPSO，IGSA）进行比较，以使用各种指标来评估整体性能。RMSE，MAPE，MBE在50和100下的测试次数。在建议的方法中，在50个试验中，RMSE范围达到9.26％，MAPE达到0.95％，MBE达到1％。此外，在100个试验中，RMSE的范围达到7.38％，MAPE的范围达到1.91％，MBE的范围达到2.87％。