| Autor: |
Xiaohui Zhang, Yufei Liu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Engineering Reports, Vol 6, Iss 12, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2577-8196 |
| DOI: |
10.1002/eng2.13005 |
| Popis: |
Abstract Aiming at the optimal scheduling problem of virtual power plant (VPP) with multiple uncertainties on the source‐load side, this paper proposes a two‐stage stochastic robust optimal scheduling method considering the uncertainty of the source‐load side. This method combines the characteristics of robust optimization and stochastic optimization to model the source‐load uncertainty differentiation. The Wasserstein generative adversarial network with gradient penalty (WGAN‐GP) is used to generate electric and thermal load scenarios, and then K‐medoids clustering is used to obtain several typical scenarios. The min–max–min two‐stage stochastic robust optimization model is constructed, and the column constraint generation (C&CG) algorithm and dual theory are used to solve the problem, and the scheduling scheme with the lowest operating cost in the worst scenario is obtained. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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