Results 1 to 10 of about 6,180 (210)

Shortfall-Based Wasserstein Distributionally Robust Optimization

Mathematics, 2023
In this paper, we study a distributionally robust optimization (DRO) problem with affine decision rules. In particular, we construct an ambiguity set based on a new family of Wasserstein metrics, shortfall–Wasserstein metrics, which apply normalized ...
Ruoxuan Li, Wenhua Lv, Tiantian Mao
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Data-driven two-stage sparse distributionally robust risk optimization model for location allocation problems under uncertain environment

AIMS Mathematics, 2023
Robust optimization is a new modeling method to study uncertain optimization problems, which is to find a solution with good performance for all implementations of uncertain input.
Zhimin Liu
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Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Journal of Modern Power Systems and Clean Energy, 2022
Transmission network expansion can significantly improve the penetration level of renewable generation. However, existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable ...
Jingwei Hu, Xiaoyuan Xu, Hongyan Ma, Zheng Yan   +3 more
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Probabilistic Optimization Techniques in Smart Power System

Energies, 2022
Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy.
Muhammad Riaz, Sadiq Ahmad, Irshad Hussain, Muhammad Naeem, Lucian Mihet-Popa   +4 more
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Wasserstein distance-based distributionally robust optimal scheduling in rural microgrid considering the coordinated interaction among source-grid-load-storage

Energy Reports, 2021
The microgrid (MG) is an effective way to alleviate the impact of the large-scale penetration of distributed generations. Due to the seasonal characteristics of rural areas, the load curve of the rural MG is different from the urban MG.
Changming Chen, Jianxu Xing, Qinchao Li, Shengyuan Liu, Jien Ma, Jiaqian Chen, Lei Han, Weiqiang Qiu, Zhenzhi Lin, Li Yang   +9 more
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Decision‐dependent distributionally robust integrated generation, transmission, and storage expansion planning: An enhanced Benders decomposition approach

IET Renewable Power Generation, 2023
Integrated generation, transmission, and storage expansion planning (IGT&SP) is the cornerstone to realize low‐carbon transition considering security constraints in the long run.
Lu Qiu, Yangqing Dan, Xukun Li, Ye Cao
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Day-ahead Network-constrained Unit Commitment Considering Distributional Robustness and Intraday Discreteness: A Sparse Solution Approach

Journal of Modern Power Systems and Clean Energy, 2023
Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems. By considering the wind uncertainty and both binary and continuous decisions of quick-start generation units
Xiaodong Zheng, Baorong Zhou, Xiuli Wang, Bo Zeng, Jizhong Zhu, Haoyong Chen, Waisheng Zheng   +6 more
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Consensus Distributionally Robust Optimization With Phi-Divergence

IEEE Access, 2021
We study an efficient algorithm to solve the distributionally robust optimization (DRO) problem, which has recently attracted attention as a new paradigm for decision making in uncertain situations.
Shunichi Ohmori
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A New Data-Driven Distributionally Robust Portfolio Optimization Method Based on Wasserstein Ambiguity Set

IEEE Access, 2021
Since optimal portfolio strategy depends heavily on the distribution of uncertain returns, this article proposes a new method for the portfolio optimization problem with respect to distribution uncertainty.
Ningning Du, Yankui Liu, Ying Liu
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Multi-Energy Microgrid Data-Driven Distributionally Robust Optimization Dispatch Considering Uncertainty Correlation [PDF]

Dianli jianshe
[Objective] Multi-energy microgrids（MEMGs）can integrate multiple energy carriers to improve energy efficiency，thereby contributing to the achievement of "dual carbon" goals. [Methods] This study proposes a data-driven distributionally robust optimization
LI Jiawei, SUN Qinghe, WANG Qiong, YE Yujian, HU Heng, ZHANG Xi
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