Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

Duan, Chao; Jiang, Lin; Fang, Wanliang; Liu, Juhua

doi:10.1109/tpwrs.2017.2741506

Cited by 110 publications

(56 citation statements)

References 36 publications

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“…Obviously, this approach is more intuitive than heuristically determining a proper weight term as suggested by Zhao and Guan (2013). Finally, the hybrid approaches by Duan et al (2018) and Zhao and Guan (2016), which are based upon the principles of distributionally robust optimization, tend to impose a high computational complexity. Given limited computational resources in practice, the fact that the structure of our proposed hybrid formulation lends itself to solution by decomposition schemes such as Benders decomposition is another beneficial feature of our novel HUC.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, in the face of continuous uncertainties such as the available wind power, reliability can no longer be ensured. Finally, the distributionally robust UC (DRUC) formulations by Zhao and Guan (2016) and Duan et al (2018) propose to minimize the dispatch costs under the worst-case distribution within a pre-defined ambiguity set D:…”

Section: Comparison With Previous Hybrid Approachesmentioning

confidence: 99%

“…As the probability distribution, which models the uncertainties, is often chosen as the one which fits historical data best, partial knowledge is available but it is uncertain itself to some extent. This observation has paved the way for various so-called distributionally robust UC formulations (see, Zhao and Guan, 2016;Duan et al, 2018 for example). Here, in order to account for the uncertainty due to the estimation, a family of possible distributions, commonly referred to as ambiguity set, is defined.…”

Section: Introductionmentioning

confidence: 99%

“…3. In comparison to known HUC formulation, the proposed approach is more user-friendly than the unified UC by Zhao and Guan (2013), more reliable than the robust stochastic UC by Blanco and Morales (2017) and computationally cheaper than the distributionally robust UC by Zhao and Guan (2016) and Duan et al (2018). 4.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

2018

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Section: Resultsmentioning

confidence: 99%

Section: Comparison With Previous Hybrid Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

2018

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“…In Ref. [18], a data-driven affinely adjustable distributionally robust optimization model for unit commitment with uncertain wind power was proposed, which provided a new insight into modeling under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Han

Yang

Sun

et al. 2019

IEEJ Transactions Elec Engng

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To facilitate energy system integration, it is imperative that a multienergy system produce and deliver in a coordinated way the energy in its different component forms. In particular, a small-scale integrated energy system must accommodate renewable energy resources, flexible loads, and energy coupling technologies, which creates new challenges to the interactions between the energy vectors. Hence, an energy management model for a microenergy system in grid-connected mode under uncertainties is proposed to perform the decision-making for shifting energy modes for all energy sources and end-use applications with the aim of optimally scheduling controllable energy resources in the system and minimizing the net management cost under uncertainty. To address the stochastic nature of the price of electricity, a data-driven robust optimization approach is introduced. It uses the available sample data to design the appropriate uncertainty set using statistical hypothesis tests, and a combination of conditional value-at-risk and a worst-case optimization problem is used to formulate the energy management problem under uncertainty. We explore a computationally tractable robust counterpart of the original optimization problem. The optimal energy scheduling solution obtained from the proposed approach is immune against any worst-case realizations. Numerical results demonstrate the effectiveness of the proposed approach and its performance of less conservation.

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Electric Power System with Renewable Generation

Wei¹,

Wang

2019

Modeling and Optimization of Interdependent Energy Infrastructures

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Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

Cited by 110 publications

References 36 publications

Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Electric Power System with Renewable Generation

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