.png){kind=icon}
返回
A hybrid data-driven robust optimization approach for unit commitment considering volatile wind power
.png){kind=icon}
PRE
AI
DOI：10.1016/j.epsr.2021.107758.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a data-driven hybrid method combining kernel density estimation (KDE) and Wasserstein metric to solve unit commitment problems considering volatile wind power. The proposed approach adopts KDE to deduce the underlying probability distribution function (PDF) of wind power forecasting errors, and utilizes the confidence interval of the estimated PDF to construct the support of uncertainties. Wasserstein metric is applied to measure the distances among distributions belonging to the established support. We take the PDF estimated by KDE as the center, and take a certain Wasserstein distance, so as to form a Wasserstein ball of probability distributions, namely ambiguity set. The unit commitment plan is obtained by minimizing the expected cost with regard to the worst-case distribution in the ambiguity set. As a fusion of Wasserstein technique and KDE, the proposed method inherits good abilities of conventional Wasserstein-based method, such as statistics, tractability, extensibility and data mining capacity. More importantly, since the PDF estimated by KDE can converge to the true distribution with the increase of sample data, the proposed method can effectively further narrow the conservatism of the model without losing robustness. Computational results on IEEE 57-bus, 118-bus and 145-bus systems from MATPOWER 6.0 numerically demonstrate the favorable features of the proposed method.
Keyword:
Data-driven
kernel density estimation
robust optimization
uncertainty
unit commitment
期刊
IF:
4.2
论文数:
1.2W
被引数:
2.2W
机构
引用论文
Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China考虑大规模光伏发电集成的稳健水电机组承诺: 以中国为例.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertaintiesAPPLIED ENERGY
OAAI
APPLIED ENERGYIF11
OAAI分享收藏Distributionally Robust Chance-Constrained Approximate AC-OPF With Wasserstein Metric具有Wasserstein度量的分布鲁棒机会约束近似ac-opfOAAI
OAAI分享收藏A Distributionally Robust Optimization Model for Unit Commitment Considering Uncertain Wind Power Generation考虑不确定风力发电的机组组合分布式鲁棒优化模型
PREAI
分享收藏分享收藏
查看更多内容