Data-Driven Two-Stage Distributionally Robust Dispatch of Multi-Energy Microgrid
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910910703992832 |
|---|---|
| author | Sun, Xunhang Cao, Xiaoyu Zeng, Bo Li, Miaomiao Guan, Xiaohong Başar, Tamer |
| author_facet | Sun, Xunhang Cao, Xiaoyu Zeng, Bo Li, Miaomiao Guan, Xiaohong Başar, Tamer |
| contents | This paper studies adaptive distributionally robust dispatch (DRD) of the multi-energy microgrid under supply and demand uncertainties. A Wasserstein ambiguity set is constructed to support data-driven decision-making. By fully leveraging the special structure of worst-case expectation from the primal perspective, a novel and high-efficient decomposition algorithm under the framework of column-and-constraint generation is customized and developed to address the computational burden. Numerical studies demonstrate the effectiveness of our DRD approach, and shed light on the interrelationship of it with the traditional dispatch approaches through stochastic programming and robust optimization schemes. Also, comparisons with popular algorithms in the literature for two-stage distributionally robust optimization verify the powerful capacity of our algorithm in computing the DRD problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Two-Stage Distributionally Robust Dispatch of Multi-Energy Microgrid Sun, Xunhang Cao, Xiaoyu Zeng, Bo Li, Miaomiao Guan, Xiaohong Başar, Tamer Optimization and Control Systems and Control This paper studies adaptive distributionally robust dispatch (DRD) of the multi-energy microgrid under supply and demand uncertainties. A Wasserstein ambiguity set is constructed to support data-driven decision-making. By fully leveraging the special structure of worst-case expectation from the primal perspective, a novel and high-efficient decomposition algorithm under the framework of column-and-constraint generation is customized and developed to address the computational burden. Numerical studies demonstrate the effectiveness of our DRD approach, and shed light on the interrelationship of it with the traditional dispatch approaches through stochastic programming and robust optimization schemes. Also, comparisons with popular algorithms in the literature for two-stage distributionally robust optimization verify the powerful capacity of our algorithm in computing the DRD problem. |
| title | Data-Driven Two-Stage Distributionally Robust Dispatch of Multi-Energy Microgrid |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2504.09638 |