Results 91 to 100 of about 8,860,352 (207)
The volatility of renewable energy generation accelerates the aging of water distributionally robust optimization within Power-to-Gas (P2G) stations. Existing optimization models fail to effectively account for the impact of wide power fluctuations on ...
SUN Yifan, WANG Lin, JIN Xiao
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Distributionally Robust Adaptive Beamforming
As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model or the data size of snapshots is limited, the performance of beamformers significantly degrades.
Shixiong Wang, Wei Dai, Geoffrey Ye Li
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A hierarchical deep learning and distributionally robust framework for urban multi-energy systems
This paper presents a hierarchical optimization framework for distributed energy management in urban multi-energy systems that integrates deep learning-based forecasting with distributionally robust optimization.
Mohammad Heydari +2 more
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Two-stage stochastic optimization is a framework for modeling uncertainty, where we have a probability distribution over possible realizations of the data, called scenarios, and decisions are taken in two stages: we make first-stage decisions knowing ...
Linhares, Andre, Swamy, Chaitanya
core
Optimization Scheduling of Integrated Energy Systems Considering Power Flow Constraints
To further investigate the complementary characteristics among subsystems of the combined electricity–gas–heat system (CEGHS) and to enhance the renewable energy accommodation capability, this study proposes a comprehensive optimization scheduling ...
Sheng Zou +4 more
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The integration of photovoltaic generators introduces significant uncertainties in distribution network (DN) operations. This study presents a novel distributionally robust energy management strategy for multiple DNs, addressing inherent variabilities in
Dongwon Lee +4 more
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This paper proposes a two-stage distributionally robust conditional value-at-risk constrained (TS-DR-CVaR) framework and its computable approximations for the economic self-scheduling of microgrid problems considering the uncertainty of renewable energy ...
Chen Zhang, Jinbao Jian, Linfeng Yang
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Sinkhorn Distributionally Robust Optimization
Entropy-Regularized Wasserstein Distributionally Robust Optimization Uncertainty in data poses a central challenge in operations research. Distributionally robust optimization (DRO) offers a principled framework for addressing this challenge by producing solutions resilient to distributional variations.
Jie Wang, Rui Gao, Yao Xie
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Distributionally Robust Graphical Models
In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---probabilistic graphical models and large margin methods---have their own distinct strengths but also possess significant drawbacks.
Fathony, Rizal +4 more
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Microgrid formation provides a viable solution for enhancing the resilience of distribution systems under extreme conditions. In general, the on-outage areas of the distribution system are partitioned into multiple islands to restore the critical loads ...
Weixu Tian +5 more
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