期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 37, 期 1, 页码 456-466出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2021.3091879
关键词
Uncertainty; Optimization; Stochastic processes; Inference algorithms; Wind power generation; Power generation; Load modeling; Stochastic robust optimization; virtual power plant; data-driven; multiple uncertainties
资金
- National Key Research and Development Program of China [2018YFE0106600]
- Science and Technology Project of State Grid Corporation of China [SGJX0000KXJS1900321]
This paper proposes a data-driven two-stage stochastic robust optimization scheduling model for virtual power plant considering uncertainties. The objective of the model is to maximize profit in the energy market and minimize total system cost in the reserve market. By employing specific algorithms, the model demonstrates its effectiveness and superiority in a case study.
Virtual power plant (VPP) has gradually become a key technology to the increasing penetration of renewable energy. The uncertainty and variability of renewable energy and load demand pose significant challenges to the efficiency and stability of VPP's operation. In this paper, a data-driven two-stage stochastic robust optimization (SRO) scheduling model is proposed for a VPP considering wind power, solar power, load demand, and market price uncertainties. The objective is to maximize the profit of the VPP in the energy market and minimize the total system cost of the VPP in the reserve market under the worst-case realization of the uncertainties. The Dirichlet process mixture model (DPMM) and variational inference algorithm are employed for constructing the data-driven uncertainty ambiguity set considering the correlations among multiple uncertainties. The tailored column-and-constraint generation algorithm is developed to solve the SRO model iteratively by reformulating the second stage with the application of the Karush-Kuhn-Tucker conditions. Results from a case study illustrate the effectiveness and superiority of the proposed model.
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