基于SA/WGAN的新能源场景生成方法

New Energy Scenario Generation Based on SA/WGAN

随着新能源渗透率逐年提高,其出力的随机性与波动特性难以准确预测,给电力系统的运行、规划和调度提出了严峻的挑战,因此新能源的不确定性建模受到越来越多的关注。为了更有效地获得新能源出力场景中的时序特征,提出了一种基于数据驱动的新能源场景生成方法,通过采用SA/WGAN模型,把自注意力机制和带有梯度惩罚的生成对抗网络判别器结合,构建基于两种模型结合的深度学习模型,有效突显新能源出力场景中时序特性,增强场景生成中非线性拟合能力。算例结果表明,所提模型的新能源生成场景相较于原始WGAN和WGAN-LSTM的场景生成结果,可以有效提高精准度,同时具备了WGAN-GP训练结果稳定和SA计算速度快的优势,更高效地生成与真实新能源场景分布接近的场景。

With the increased penetration rate of new energy year by year,it is difficult to accurately predict the randomness and fluctuation characteristics of its output,causing a severe challenge to the operation,planning and scheduling of electrical power system.Therefore,modeling for the uncertainty of new energy has attracted more and more attention.To obtain the time sequence characteristics of new energy output scenario more effectively,a new energy scenario generation method was proposed based on data drive,and combined selfattention mechanism with generative adversarial network discriminator with gradient penalty through applying the SA/WGAN model.Through building a deep learning model based on the combination of two models,effectively highlight the timing sequence characteristics of new energy output scenario and enhancing the nonlinear fitting capability in scenario generation.The example results show that,compared with the scenario generation results of original WGAN and WGAN-LSTM,the new energy generation scenario of proposed model can not only effectively improve the accuracy,but also possess the advantages of stable WGAN-GP training results and quick SA calculation speed,which can achieve a more efficient generation of scenarios that is close to the distribution of real new energy scenario.