An improved wind power uncertainty model for day-ahead robust scheduling considering spatio-temporal correlations of multiple wind farms

Tu, Qingyu; Miao, Shihong; Yao, Fuxing; Yang, Weichen; Lin, Yujun; Zheng, Zhong

doi:10.1016/j.ijepes.2022.108674

Cited by 19 publications

(7 citation statements)

References 28 publications

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“…Several classes of copula functions are available in the existing literature. These are broadly classified as Elliptical and Archimedean copula [25][26][27][28]. Gaussian copulas and t-copulas are standard multivariate Elliptical copulas used to model data has symmetrical tail dependence.…”

Section: R-vine Copula Modelmentioning

confidence: 99%

“…Copulas are commonly used to model dependencies among high-dimensional random variables [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. This approach has gained significant attention recently for WFs' dependency and uncertainty modelling.…”

Section: Introductionmentioning

confidence: 99%

“…R-Vine copula models, kernel density estimation, time series and hybrid models are used to capture spatiotemporal correlations of multiple WFs for the generation of wind power, wind speed, and market price scenarios. Kernel density estimation, ARIMA, generalized autoregressive conditional heteroskedasticity (GARCH) and ARIMA-GARCHt models can estimate the marginal distributions for scenario generation [24][25][26][27][28]. A two-stage spatiotemporal sampling approach employs R-Vine copula as a spatial sampling method to generate wind power scenarios for multiple WFs.…”

Section: Introductionmentioning

confidence: 99%

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Wind Power Scenario Generation Considering Spatiotemporal Correlations: A Distribution Free Hybrid VARMA-Copula Approach

Sharma,

Gupta,

Bhakar

2023

Smart Grids and Energy

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Wind power scenario forecast is a primary step for probabilistic modelling of power systems' operation and planning problems in stochastic programming framework considering uncertainties. Several models have been proposed in the literature to generate wind power scenarios using statistical and machine learning approaches. Most of these models are univariate and do not consider dependency between the Wind Farms (WFs), resulting in generated scenarios lacking spatiotemporal correlation. Furthermore, most scenario generation approaches assume a parametric distribution (i.e., Normal or Weibull distribution) for wind power uncertainty, which is unrealistic. This paper proposes a novel distribution-free hybrid approach that combines multivariate Vector Autoregressive Moving Average (VARMA) and Copula models to generate wind power scenarios. The VARMA model is used to forecast wind power considering spatiotemporal correlation, and then a regular vine (R-Vine) copula is applied on WFs' residuals to retain spatial correlation. The proposed approach is independent of the distribution type because the R-Vine copula can decompose residuals into a copula function and a marginal distribution. The proposed model is illustrated through a realistic case study based on nine Australian WFs. The results obtained are compared with benchmark models that show the efficacy of the proposed model for the generation of wind power scenarios. Minimum energy score, nearly accurate Kendall's correlation, and cross correlation plots show that the proposed method can produce high-quality wind power scenarios without sacrificing spatiotemporal correlation or making distribution assumptions. Generated scenarios using the proposed approach can help WFs and system operators improve decisions in the stochastic programming framework.

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Section: R-vine Copula Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wind Power Scenario Generation Considering Spatiotemporal Correlations: A Distribution Free Hybrid VARMA-Copula Approach

Sharma,

Gupta,

Bhakar

2023

Smart Grids and Energy

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“…With the rapid development of the global industrial economy and the continuous growth in the population, carbon dioxide emissions continue to rise, leading to increasingly serious environmental problems [1,2]. The goal of achieving a low-carbon transition by adjusting the energy layout has received global attention.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Microgrids Considering Offshore Wind Power and Carbon Trading

Fang,

Li,

Zou

et al. 2024

Processes

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Offshore wind energy entering the grid in coastal areas creates issues with the safe and stable operation of power systems. To control the carbon emission of power systems and increase the proportion of offshore wind consumption, a microgrid optimization model considering offshore wind power and carbon trading is proposed in this paper. To avoid the defect of Particle Swarm Optimization (PSO) falling into the local optimum prematurely, the PSO algorithm is improved by dynamically decreasing inertia weights and chaos factors. Combined with the powerful optimization capability of the genetic algorithm (GA), the improved PSO-GA algorithm is used to solve the model. The simulation results show that the improved algorithm iterates 11 times before the parameters reach the optimal value, with high convergence accuracy. The proposed approach can increase the proportion of offshore wind consumption and ensure the optimal economic performance of the system while reducing the carbon emission.

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“…Still, these methods will affect the energy allocation during the operation cycle, which will generate grid connection deviations and the corresponding penalties. Xu et al (2023) proposed a day-ahead scheduling method using a trapezoidal fuzzy number equivalence model describing the uncertainty of wind power forecasting and day-ahead scheduling to improve the robustness of wind farms on the grid; Tu et al (2023) balanced the economics and robustness of day-ahead scheduling for wind farms by fitting three typical features to the wind power output uncertainty and establishing a two-stage day-ahead scheduling model. The abovementioned literature reduces the impact of bias accumulation through the idea of improving the robustness of dayahead scheduling, but this may lead to a larger amount of wind abandonment as well as the possibility of not being able to meet the intraday scheduling demand when experiencing extreme forecast deviations.…”

mentioning

confidence: 99%

Hybrid energy storage configuration methodology, taking into account the accumulation of wind farm forecast deviations

Yuan,

He,

Meng

2024

Front. Energy Res.

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The accumulation of wind power prediction deviations will make it difficult to maintain the long-term stable operation of energy storage. To solve this problem, this paper proposes a hybrid energy storage system configuration method containing second-use batteries. This paper establishes a three-battery hybrid energy storage operation strategy that considers the accumulation of prediction deviation and prevents the accumulation of prediction deviation by changing the energy storage used at the end of the dispatch cycle. It also establishes an optimal allocation model for energy storage capacity, which takes into account the performance parameters and life loss of the second-use batteries and the new power battery. Finally, Gurobi is used to simulate the field data of a wind farm. The simulation results show that this method is effective in preventing the accumulation of prediction deviation while reducing wind power grid deviation and improving the level of energy storage utilization. It can play a certain reference role in the configuration of energy storage for wind farms.

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An improved wind power uncertainty model for day-ahead robust scheduling considering spatio-temporal correlations of multiple wind farms

Cited by 19 publications

References 28 publications

Wind Power Scenario Generation Considering Spatiotemporal Correlations: A Distribution Free Hybrid VARMA-Copula Approach

Wind Power Scenario Generation Considering Spatiotemporal Correlations: A Distribution Free Hybrid VARMA-Copula Approach

Optimal Scheduling of Microgrids Considering Offshore Wind Power and Carbon Trading

Hybrid energy storage configuration methodology, taking into account the accumulation of wind farm forecast deviations

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