期刊
APPLIED ENERGY
卷 308, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.118392
关键词
Community-integrated energy systems; Stochastic scheduling; Generative adversarial network; Integrated demand response; Stackelberg game; Renewable generation uncertainty
资金
- Natural Science Foundation of Jilin Province, China [YDZJ202101ZYTS149]
This study proposes a hierarchical stochastic optimal scheduling method for multi-community integrated energy systems, which handles multiple uncertainties by generating renewable scenarios and typical scenarios, and constructs a Stackelberg-based hierarchical stochastic schedule.
An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets. To solve the energy management and pricing problem of multi-community integrated energy systems (MCIESs) with multi-energy interaction, this study investigated a hierarchical stochastic optimal scheduling method for uncertain environments. To handle multiple uncertainties, a Wasserstein generative adversarial network with a gradient penalty was used to generate renewable scenarios, and the Kmeans++ clustering algorithm was employed to generate typical scenarios. A Stackelberg-based hierarchical stochastic schedule with an integrated demand response was constructed, where the MCIES operator acted as the leader pursuing the maximum net profit by setting energy prices, while the building users were followers who adjusted their energy consumption plans to minimize their total costs. Finally, a distributed iterative solution method based on a metaheuristic was designed. The effectiveness of the proposed method was verified using practical examples.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据