A Wasserstein Distance-Based Distributionally Robust Chance-Constrained Clustered Generation Expansion Planning Considering Flexible Resource Investments

Chen, Baorui; Liu, Tianqi; Liu, Xuan; He, Chuan; Nan, Lu; Wu, Lei; Su, Xueneng; Zhang, Jian

doi:10.1109/tpwrs.2022.3224142

Cited by 4 publications

(2 citation statements)

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“…Comparatively, stochastic planning is more mature than robust planning. Chance constraint (Chen et al, 2018;Li et al, 2023b) is involved in transmission grid expansion planning containing wind farms besides the joint consideration of the Monto Carlo simulation and analytic methods to acquire wind output probabilistic distribution. Chen et al (2018) proposed a Wasserstein distance-based distributionally robust generation expansion planning that involves the uncertainty concerns and improves robust planning for conservative issues.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chance constraint (Chen et al, 2018;Li et al, 2023b) is involved in transmission grid expansion planning containing wind farms besides the joint consideration of the Monto Carlo simulation and analytic methods to acquire wind output probabilistic distribution. Chen et al (2018) proposed a Wasserstein distance-based distributionally robust generation expansion planning that involves the uncertainty concerns and improves robust planning for conservative issues. Rintamäki et al (2024) and Wang et al (2020b) proposed a short-time operational model of source-side planning that is adapted to large-scale renewable energy integration.…”

Section: Literature Reviewmentioning

confidence: 99%

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Power generation–network–load–energy storage co-planning under uncertainty

Ma,

Liu,

Cheng

2024

Front. Energy Res.

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With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning.

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Section: Literature Reviewmentioning

confidence: 99%