Scenario Reduction Network Based on Wasserstein Distance With Regularization

Dong, Xiaochong; Sun, Yingyun; Malik, Sarmad Majeed; Pu, Tianjiao; Li, Ye; Wang, Xinying

doi:10.1109/tpwrs.2023.3234277

Cited by 6 publications

(6 citation statements)

References 35 publications

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“…The size of the typical scenario set S TYP is much smaller than the size of the generated scenario set S RES , with less computational burden. The objective of the scenario reduction problem is to find the reduced probability scenarios that approximately match the generated probability scenarios while minimizing the distance measure between the reduced and generated scenarios [32]. The scenario reduction problem can be stated as

$$\begin{equation} \def\eqcellsep{&}\begin{array}{l} \mathop {\min }\limits_\pi \sum_{_{s,s^{\prime}}} {{d}_{s,s^{\prime}}{\pi }_{s,s^{\prime}}} \\[6pt] s.t.

…”

Section: The Adaptive Decision‐making Frameworkmentioning

confidence: 99%

“…The generator and discriminator network structures of GAN are shown in Figure 2. In this paper, 10,000 sets of wind‐PV scenarios are generated based on GAN to form the full‐space scenario set S RES , and five sets of representative scenarios with corresponding probabilities are given based on the reduction network [32] to form the initial typical scenario set

{\bf{S}}_{{\mathrm{TYP}}}^1

at the first iteration.…”

Section: Case Studiesmentioning

confidence: 99%

“…The generator and discriminator network structures of GAN are shown in Figure 2. In this paper, 10,000 sets of wind-PV scenarios are generated based on GAN to form the full-space scenario set S RES , and five sets of representative scenarios with corresponding probabilities are given based on the reduction network [32] to form the initial typical scenario set S 1 TYP at the first iteration. The cumulative distribution function (CDF) of the generated scenarios is compared with the historical data to verify the effectiveness of GAN in scenario generalization, as shown in Figure 3.…”

Section: Scenario Generalization Based On Generative Networkmentioning

confidence: 99%

See 2 more Smart Citations

An adaptive decision‐making approach for transmission expansion planning considering risk assessment of renewable energy extreme scenarios

Zhao,

Xu,

Dong

et al. 2023

IET Generation Trans & Dist

Self Cite

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The extreme power output scenarios of renewable energy sources (RES) proposed new challenges to the safe and stable operation of the power system. Transmission expansion planning (TEP) with large‐scale RES grid integration needs considering the risk of extreme scenarios. In this paper, an adaptive decision‐making approach for the TEP problem based on planning‐risk assessment‐replanning iterative process is proposed. The method obtains massive temporal and spatial correlated wind‐photovoltaic (PV) power output scenarios by generalizing the historical data to describe the uncertainties. A data‐driven load loss risk assessment model (RAM) based on the power system's actual operating state is built, referring to the degree of extreme scenario risks on the balance of supply and demand, and the probability of extreme scenario occurrence. The planning decision is progressively revised according to the risk assessment result. The Garver's 6‐bus system and the IEEE RTS 24‐bus system are adopted as simulation cases. The results show that the optimal expansion plans achieve a balance between the economy and robustness, which verifies the effectiveness of the proposed method.

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$$\begin{equation} \def\eqcellsep{&}\begin{array}{l} \mathop {\min }\limits_\pi \sum_{_{s,s^{\prime}}} {{d}_{s,s^{\prime}}{\pi }_{s,s^{\prime}}} \\[6pt] s.t.

…”

Section: The Adaptive Decision‐making Frameworkmentioning

confidence: 99%

{\bf{S}}_{{\mathrm{TYP}}}^1

at the first iteration.…”

Section: Case Studiesmentioning

confidence: 99%

Section: Scenario Generalization Based On Generative Networkmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive decision‐making approach for transmission expansion planning considering risk assessment of renewable energy extreme scenarios

Zhao,

Xu,

Dong

et al. 2023

IET Generation Trans & Dist

Self Cite

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show abstract

“…Typically, scenario generation methods can be classified as statistical methods, deep learning methods, etc. [2] .…”

Section: Quotesmentioning

confidence: 99%

Medium and Long Term Scenario Generation of Multi-Temporal Renewable Energy Based on GAN

Hu,

Du,

Liu

et al. 2023

J. Phys.: Conf. Ser.

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With China’s “double carbon” target, the future power system will show A significant percentage of the energy generated comes from renewable sources, specifically wind power, which has obvious randomness, volatility and intermittency, and will have a greater impact on the safety of the power system. How to effectively characterize the randomness and volatility of its power output is a major challenge for power system dispatch. The method we suggest in this paper utilizes generative adversarial networks to produce medium- and long-term scenarios for wind power generation. This approach allows for the creation of multiple sets of wind power generation data that share the same characteristics as the historical data., Our approach takes into account the spatial correlation between wind power generation at wind farms situated in various geographic locations, fully describing the randomness and spatio-temporal correlation of wind power, and providing an important reference for power system planning and scheduling.

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“…Despite that the challenge of HCR computation can be sidestepped through direct feasibility assessment on generated scenarios, with the involvement of more energy units, it leads to an exponential growth in the scenario set scale. To deal with this alternative computation challenge, scenario reduction can be adopted for acceleration, relying on various probability measures and machine learning models [23]- [25]. Nevertheless, as the reduced scenario set might lose the generality of strictly guaranteeing the feasibility of all realized uncertainties, standing in a conservative role, the DSO is more interested in how to accelerate the whole assessment process that incorporates all scenarios.…”

Section: Introductionmentioning

confidence: 99%

Rapid Feasibility Assessment of Energy Unit Integration in Distribution Networks

Gong,

Kok,

Cobben

2024

Preprint

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Scenario Reduction Network Based on Wasserstein Distance With Regularization

Cited by 6 publications

References 35 publications

An adaptive decision‐making approach for transmission expansion planning considering risk assessment of renewable energy extreme scenarios

An adaptive decision‐making approach for transmission expansion planning considering risk assessment of renewable energy extreme scenarios

Medium and Long Term Scenario Generation of Multi-Temporal Renewable Energy Based on GAN

Rapid Feasibility Assessment of Energy Unit Integration in Distribution Networks

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