Results 31 to 40 of about 8,860,352 (207)
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning [PDF]
Operations Research & Management Science in the Age of Analytics, 2019 Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training
D. Kuhn +3 more
semanticscholar +1 more source
Journal of Modern Power Systems and Clean Energy, 2019 This paper proposes an adjustable and distributionally robust chance-constrained (ADRCC) optimal power flow (OPF) model for economic dispatch considering wind power forecasting uncertainty.
Xin Fang +4 more
doaj +1 more source
A distributionally robust perspective on uncertainty quantification and chance constrained programming [PDF]
, 2015 The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability.
Hanasusanto, GA +3 more
core +1 more source
IEEE Transactions on Smart Grid, 2021 This paper proposes a data-driven distributionally robust co-optimization model for the peer-to-peer (P2P) energy trading and network operation of interconnected microgrids (MGs).
Jiayong Li +3 more
semanticscholar +1 more source
Distributionally Robust Frequency Constrained Scheduling for an Integrated Electricity-Gas System [PDF]
IEEE Transactions on Smart Grid, 2021 Power systems are shifted from conventional bulk generation toward renewable generation. This trend leads to the frequency security problem due to the decline of system inertia.
Lun Yang +3 more
semanticscholar +1 more source
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
doaj +1 more source
Distributionally Robust Domain Adaptation
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023 Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. Since DA methods rely exclusively on the given source and target domain samples, they generally yield models that are vulnerable to noise and unable to adapt to unseen samples ...
Awad, Akram S., Atia, George K.
openaire +2 more sources
Frameworks and Results in Distributionally Robust Optimization
Open Journal of Mathematical Optimization, 2022 The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these ...
Rahimian, Hamed, Mehrotra, Sanjay
doaj +1 more source
Energy and reserve dispatch with distributionally robust joint chance constraints
Operations Research Letters, 2021 We develop a two-stage stochastic program for energy and reserve dispatch of a joint power and gas system with a high penetration of renewables. Data-driven distributionally robust chance constraints ensure that there is no load shedding and renewable ...
Christos Ordoudis +3 more
semanticscholar +1 more source
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
doaj +1 more source