| 引用本文: | 吴艳梅,陈红坤,陈 磊,等.基于改进MMD-GAN的可再生能源随机场景生成[J].电力系统保护与控制,2024,52(19):85-96. |
| WU Yanmei,CHEN Hongkun,CHEN Lei,et al.Stochastic scenario generation for renewable energy based on improved MMD-GAN[J].Power System Protection and Control,2024,52(19):85-96 |
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| 摘要: |
| 针对可再生能源出力不确定性的准确表征问题，提出了一种基于改进的最大均值差异生成对抗网络(maximum mean discrepancy generative adversarial networks, MMD-GAN)的可再生能源随机场景生成方法。首先，阐述了GAN及MMD-GAN的基本原理，提出了MMD-GAN的改进方案，即在MMD-GAN的基础上改进鉴别器损失函数，并采用谱归一化和有界高斯核提升生成器和鉴别器的训练稳定性。然后，设计了基于改进MMD-GAN的可再生能源随机场景生成流程。最后，分析了所提方法在可再生能源随机场景生成中的效果，比较了改进MMD-GAN方法与MMD-GAN方法及典型GAN方法的性能差异。结果表明，改进MMD-GAN方法在生成分布和真实分布的Wasserstein距离上较对比方法降低超过50%，生成的场景精度得到有效提升。 |
| 关键词: 场景生成 最大均值差异 生成对抗网络 可再生能源 数据驱动 |
| DOI：10.19783/j.cnki.pspc.231546 |
| 分类号: |
| 基金项目:国家自然科学基金联合基金项目资助(U23B20117) |
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| Stochastic scenario generation for renewable energy based on improved MMD-GAN |
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WU Yanmei1, 2, CHEN Hongkun1, 2, CHEN Lei1, 2, CHU Yulin1, 2, GAO Peng1, 2, WU Haitao3
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1. Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University,
Wuhan 430072, China; 2. School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China;
3. State Grid Wuhan Power Supply Company, Wuhan 430050, China
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| Abstract: |
| It is difficult to obtain an accurate characterization of the uncertainty of renewable energy output. Thus an approach for generating stochastic scenarios of renewable energy based on improved maximum mean discrepancy generative adversarial networks (MMD-GAN) is proposed. First, the fundamental principles of GAN and MMD-GAN are described, and an improved scheme of MMD-GAN is proposed, one which enhances the discriminator’s loss function on the basis of MMD-GAN and uses spectral normalization and the bounded Gaussian kernel to improve the training stability of the generator and discriminator. Then, the process of stochastic scenario generation for renewable energy based on the improved MMD-GAN is designed. Finally, the effects of the proposed methods are analyzed. The performance of improved MMD-GAN, MMD-GAN, and typical GAN are compared. The results indicate that the improved MMD-GAN method can reduce the Wasserstein distance between the generated distribution and the real distribution by more than 50% contrasted with the comparison method, and the generated scenario accuracy can be effectively improved. |
| Key words: scenario generation maximum mean discrepancy (MMD) generative adversarial networks (GAN) renewable energy data-driven |
