Box-Based Temporal Decomposition of Multi-Period Economic Dispatch for Two-Stage Robust Unit Commitment

Cho, Youngchae; Ishizaki, Takayuki; Ramdani, Nacim; Imura, Jun-ichi

doi:10.1109/tpwrs.2019.2896349

Cited by 39 publications

(26 citation statements)

References 30 publications

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“…π will be constrained to 0, and π will be entirely determined by 2 π . Mathematically, the reformulated problem in (22) by the CHR method is a MILP problem and can be solved by the commercial solvers. In terms of calculation scale, it adds two groups of mixed integer constraints and a group of linear constraint.…”

Section: B Chr Reformulation Technique For Max-min Dual Problemmentioning

confidence: 99%

“…Step 3: Fix * k X , solve the max-min SP based on the CHR reformulation in (22). Derive its optimal solution IV.…”

Section: Solving Proceduresmentioning

confidence: 99%

“…From the formulation perspective, the feasibility cannot always be guaranteed if significant variations are embedded. The existing work assumes that the TS-RUC always has a feasible solution [21], [22]. However, such an assumption does not hold when the system has limited flexibility resources to accommodate all the uncertainties.…”

mentioning

confidence: 99%

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Feasibility Identification and Computational Efficiency Improvement for Two-Stage RUC With Multiple Wind Farms

Zhang

Shuai

et al. 2020

IEEE Trans. Sustain. Energy

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Section: B Chr Reformulation Technique For Max-min Dual Problemmentioning

confidence: 99%

“…Step 3: Fix * k X , solve the max-min SP based on the CHR reformulation in (22). Derive its optimal solution IV.…”

Section: Solving Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Feasibility Identification and Computational Efficiency Improvement for Two-Stage RUC With Multiple Wind Farms

Zhang

Shuai

et al. 2020

IEEE Trans. Sustain. Energy

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“…However, as the true distribution is unknown and often approximated by an empirical distribution comprising a finite number of historical data, or samples, the solution may cause unexpectedly high operating costs. On the other hand, RO minimizes the worst-case total operating cost over a set of uncertain scenarios [3]. However, as probabilistic features of the uncertainty are ignored, which are at least partially observable from samples, the solution may be overly conservative.…”

Section: Introductionmentioning

confidence: 99%

“…First, the proposed model is formulated as a WDRO problem that nests an infinitedimensional worst-case expectation problem, relying on an affine policy different from the one used in [16]. The affine policy used in the proposed model builds on the reservemodeling method studied in [3] and thus meets the nonanticipativity constraint unlike the one in [16]. Subsequently, the proposed model is recast as a semi-infinite programming problem that can be efficiently solved using existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

On Affine Policies for Wasserstein Distributionally Robust Unit Commitment

Cho¹,

Yang²

2022

Preprint

Self Cite

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This paper proposes a unit commitment (UC) model based on data-driven Wasserstein distributionally robust optimization (WDRO) for power systems under uncertainty of renewable generation as well as its tractable exact reformulation. The proposed model is formulated as a WDRO problem relying on an affine policy, which nests an infinitedimensional worst-case expectation problem and satisfies the non-anticipativity constraint. To reduce conservativeness, we develop a novel technique that defines a subset of the uncertainty set with a probabilistic guarantee. Subsequently, the proposed model is recast as a semi-infinite programming problem that can be efficiently solved using existing algorithms. Notably, the scale of this reformulation is invariant with the sample size. As a result, a number of samples are easily incorporated without using sophisticated decomposition algorithms. Numerical simulations on 6-and 24-bus test systems demonstrate the economic and computational efficiency of the proposed model.

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Robust optimal scheduling of integrated energy systems considering multiple uncertainties

Wang

Yong-jian

et al. 2023

Energy Science & Engineering

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Multiple uncertainties such as renewable energy output, energy purchase prices and integrated demand responses have brought about severe challenges to the safe and economic operation of integrated energy system (IES). To meet this challenge, this study takes the combined cooling heating and power as an example of IES and proposes an optimization model considering multiple uncertainties. First, the structure of IES is given, and these mathematical models and constraints are listed according to the energy supply characteristics. Second, considering uncertain factors such as the wind and solar output, comprehensive demand response and energy purchase price, the different characterization methods are selected to model the uncertainty sources by identifying the characteristics of multiple uncertainty sources. Then, combining the stochastic scenario method and robust optimization method, a day‐ahead optimal scheduling model of IES with multiple uncertainties is established, and the whale optimization algorithm is improved to obtain the optimal solution of the model. Finally, the actual data of an IES is selected to verify the rationality and effectiveness of the model. Meanwhile, the influence of uncertain factors on scheduling is introduced to verify the perfect coordination of economy and robustness of the model at runtime.

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Box-Based Temporal Decomposition of Multi-Period Economic Dispatch for Two-Stage Robust Unit Commitment

Cited by 39 publications

References 30 publications

Feasibility Identification and Computational Efficiency Improvement for Two-Stage RUC With Multiple Wind Farms

Feasibility Identification and Computational Efficiency Improvement for Two-Stage RUC With Multiple Wind Farms

On Affine Policies for Wasserstein Distributionally Robust Unit Commitment

Robust optimal scheduling of integrated energy systems considering multiple uncertainties

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