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论文类型：期刊论文

论文编号：19244970

第一作者：Xiaoyuan Xu

通讯作者：Mohammad Shahidehpour

合写作者：Zheng Yan,Zhiyi Li,Mingyu Yan,Xiangrui Kong

发表刊物：IEEE Transactions on Sustainable Energy

收录刊物：SCI

刊物所在地：美国

学科门类：工学

一级学科：电气工程

文献类型：J

卷号：11

期号：1

页面范围：436-447

ISSN号：1949-3029

关键字：Wind power, correlation, uncertainty, data-driven optimization, integrated energy and reserve dispatch

DOI码：10.1109/TSTE.2019.2894693

发表时间：2020-01-01

影响因子：8.31

摘要：This paper proposes a data-driven optimization method to solve the integrated energy and reserve dispatch problem with variable and correlated renewable energy generation. The proposed method applies the kernel density estimation to establish an ambiguity set of continuous multivariate probability distributions and the optimization model for the integrated dispatch is formulated as a combination of stochastic and robust optimization problems. First, a risk-averse two-stage stochastic optimization model is formulated to hedge the distributional uncertainty. Next, the second-stage worst case expectation is evaluated, using the equivalent model reformulation, as a combination of conditional value-at-risk (CVaR) and the extreme cost in the worst case scenario. The CVaR is calculated using a scenario-based stochastic optimization problem. After describing the wind power correlation in ellipsoidal uncertainty sets, the robust optimization problem for finding the worst case cost is cast into a mixed-integer second-order cone programming problem. Finally, the column-and-constraint generation method is employed to solve the proposed risk-averse two-stage problem. The proposed method is tested on the 6-bus and IEEE 118-bus systems and validated by comparing the results with those of conventional stochastic and robust optimization methods.

发布期刊链接：https://ieeexplore.ieee.org/abstract/document/8625475