Representative meteorological data for long-term wind-driven rain obtained from Latin Hypercube Sampling – Application to impact analysis of climate change

Bourcet, John; Kubilay, Aytaç; Derome, Dominique; Carmeliet, JE Jan

doi:10.1016/j.buildenv.2022.109875

Cited by 12 publications

(3 citation statements)

References 45 publications

(73 reference statements)

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“…The more scenarios are included in the set of scenarios, the more uncertain situations are included in the scheduling problem characterized by uncertainty. In this paper, Latin hypercube sampling (LHS) [39] and imultaneous backward reduction (SBR) [40] are used to generate representative wind-photovoltaic scenarios. Secondly, generating high-quality stochastic scenarios can better track the wind-photovoltaic uncertainty, but this is based on accurate and high-quality forecasting, and the accuracy of forecasting directly affects the diversity of generated scenarios.…”

Section: B Wind-photovoltaic Uncertainty Handlingmentioning

confidence: 99%

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

Ye,

Xie,

et al. 2023

IEEE Access

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In the optimization scheduling of power systems containing renewable energy, to ensure that the scheduling results have higher security and better economy, this study proposes a multi-objective forecasting, scenario generation, and decision scheduling integrated stochastic optimal scheduling method. Firstly, to improve the accuracy and stability of wind-photovoltaic power forecasting, a novel multiobjective wind-photovoltaic forecasting model is proposed based on the Laguerre polynomial, pseudoinverse learning, and hybrid multi-objective Runge-Kutta algorithm (HMORUN). Secondly, to deal with wind-photovoltaic uncertainty, scenarios with representative wind-photovoltaic uncertainty characteristics are generated by scenario generation and reduction techniques using wind-photovoltaic power forecast results. Finally, considering wind-photovoltaic output fluctuations, the degree of source-load matching, wind-photovoltaic utilization, and system economic efficiency factors, with the objective of maximizing the tracking of the load curve and minimizing system economic costs, a stochastic optimized scheduling model for power systems is established. This study uses HMORUN as a solution tool for the multi-objective stochastic optimization scheduling problem (MOSSP). This study uses constraint repair techniques to deal with the complex constraints of the MOSSP model to avoid system load shedding and minimize wind and photovoltaic generation curtailment. To verify the effectiveness of the proposed model, a 10-generator power system, including a wind farm and a photovoltaic plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety and better economy.INDEX TERMS Laguerre polynomial, Pseudo-inverse learning, Forecasting, Hybrid multi-objective Runge-Kutta algorithm, Stochastic optimal scheduling.

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Section: B Wind-photovoltaic Uncertainty Handlingmentioning

confidence: 99%

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

Ye,

Xie,

et al. 2023

IEEE Access

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“…Its difference from Monte Carlo sampling is that the Latin hypercube sampling method uses a cumulative distribution function with equally spaced bins. Samples are generated at each bin, ensuring all bins are sampled uniformly [17]. The authors of [18] modeled renewable energy generation using the Latin hypercube sampling technique and evaluated the voltage stability.…”

Section: Literature Surveymentioning

confidence: 99%

A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors

Yoo,

Son,

Yoon

et al. 2023

Sustainability

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The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system.

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“…It is found that 200 samples generated by LHS show the same WDR load as compared to wetting over a period of 3 years. For more details on the method, we refer to [10]. Advantage of the method is that the WDR load on complex buildings for long periods can be determined with much less computational effort, which allows to study the influence of climate change on WDR.…”

Section: Determination Of Impact Of Climate Change On Wind Driven Rai...mentioning

confidence: 99%

How to predict wind driven rain in a changing climate?

Kubilay,

Bourcet,

Carmeliet

et al. 2023

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We are nowadays confronted with changes in moisture-related durability problems arising from climate change. As one example, building facades of historical buildings that before were hardly exposed to frost damage, may in future be exposed to an increase in frost-thaw cycling leading to a higher risk for moisture-related damage. An essential step in hygrothermal and durability analysis is the prediction of wind-driven rain (WDR). A computational fluid dynamics (CFD) Eulerian multiphase model provides WDR catch ratio charts. Building further on this work, methods are developed to predict WDR loads and moisture damage risks.

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Representative meteorological data for long-term wind-driven rain obtained from Latin Hypercube Sampling – Application to impact analysis of climate change

Cited by 12 publications

References 45 publications

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors

How to predict wind driven rain in a changing climate?

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