针对实际电网运行中仍存在新能源消纳能力不足的问题，提出了一种基于生成对抗网络（generative adversarial networks，GAN）和强化学习（reinforcement learning，RL）最优新能源消纳的运行方式样本智能生成方法。该方法可以根据少量实际电网的运行方式样本集，通过深度神经网络建立生成器和判别器模型，并依托生成对抗网络的目标函数对生成器和判别器进行最大最小对抗训练，使生成的虚假运行方式样本分布特征趋近真实样本。在生成器训练过程中，定义了一个关于新能源消纳的奖惩反馈函数，以对“生成动作”给予量化的优劣响应，并在此基础上引入强化学习方法。在遵循使累积奖励最多的寻优原则下，通过基于策略梯度的强化学习方法不断实时优化生成器参数，智能生成具有最优新能源消纳特征的运行方式样本。所提方法在某地区实际电网进行了测试，结果表明该方法能够有效地生成满足最优新能源消纳的运行方式样本，为改善现阶段下新能源消纳不足的运行调度决策提供数据支撑。

Aiming at the insufficient new energy consumption capacity in actual power grid operation, an intelligent generation method is proposed for operating mode samples of optimal new energy consumption based on generative adversarial networks (GAN) and reinforcement learning (RL). The method is used to establish generator and discriminator models through deep neural networks with a small sample set of actual power grid operation modes. And based on the objective function of GAN, the generator and discriminator can be trained for maximum and minimum adversarial training to generate false operating mode samples, distribution characteristics of which approach the real samples. During the training process of the generator, a reward and punishment feedback function is defined for new energy consumption to provide a quantitative good or bad response to the “generation action”. On this basis, the reinforcement learning method is introduced. Following the optimization principle of accumulating the most rewards, the generator parameters are continuously optimized in real-time by the reinforcement learning method with policy gradients. Operating mode samples are intelligently generated with optimal new energy consumption characteristics. The proposed method is tested in the actual power grid of a certain region. The results show that it can effectively generate operation mode samples that meet the optimal new energy consumption, and provide data support for improving the current operation scheduling decision of insufficient new energy consumption.