A Two-Stage Scenario Generation Method for Wind- Solar Joint Power Output Considering Temporal and Spatial Correlations

Yang, Mingwei; Liu, Wenying; Yin, Xin; Cui, Zhijiu; Zhang, Wencheng

doi:10.1109/acpee51499.2021.9437026

Cited by 7 publications

(6 citation statements)

References 1 publication

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“…The correlation coefficients of each Copula function are calculated and compared with the correlation coefficients of the sample data. The closer the absolute values, the better the Copula function. In the Euclidean distance discriminant method, the Euclidean distance between each Copula function and the empirical Copula function[26] of the example data is compared. The smaller the Euclidean distance, the better the Copula function. Clayton‐Copula, Frank‐Copula, Gumbel‐Copula and t‐Copula functions are used to fit the renewable energy power data in 2021 (8760 h) respectively, and the empirical Copula function for WT and PV power data is calculated.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Multi‐time scale adequacy evaluation of the power system with high penetration of renewable energy sources based on empirical mode decomposition

Zhang

Xing

Sun

et al. 2023

IET Generation Trans & Dist

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Adequacy, especially in the power system with high penetration of renewable energy sources, is a crucial consideration in power system planning and operational dispatching. A multi‐time scale adequacy evaluation method based on empirical mode decomposition (EMD) is proposed, taking into account the multi‐time scale characteristics of renewable energy sources and the response characteristics of flexible regulation resources. In this method, a net load curve is decomposed into several component curves by the EMD algorithm. Then, the adequacy demand on each time scale is obtained by waveform recognition. Furthermore, the available adequacy resources on different time scales are quantified according to the regulation models of flexible resources. By analysing the adequacy demand and available adequacy resources of the power system on the same time scale, the adequacy evaluation indices on each time scale can be calculated and weighted to form the comprehensive indices. Finally, the adequacy evaluation indices on each time scale with different capacities of renewable energy sources and energy storage systems are compared and analysed using a practical power system as a case. Simulation results show the validity of the multi‐time scale adequacy evaluation method.

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Section: Simulation Resultsmentioning

confidence: 99%

“…In the Euclidean distance discriminant method, the Euclidean distance between each Copula function and the empirical Copula function[26] of the example data is compared. The smaller the Euclidean distance, the better the Copula function.…”

Section: Simulation Resultsmentioning

confidence: 99%

Multi‐time scale adequacy evaluation of the power system with high penetration of renewable energy sources based on empirical mode decomposition

Zhang

Xing

Sun

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…In sustainability research promoting power energy transformation, typical scenarios of power systems hold great significance, as they directly influence relevant decisions of SO or DRO. GANs have been extensively applied in domains like transfer learning and data augmentation in the power field, and numerous improved GAN algorithms have been proposed to heighten data authenticity [17][18][19][20][21]. However, power system scenarios are commonly time-series scenarios.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

“…The other optimized the network structure for day-ahead scenario generation by using ReLU activation functions in the output layers of the generator and discriminator and removing the normalization layers and achieved good performance. References [19,20] introduced gradient penalties and the Wasserstein distance, effectively improving model generalization, slow convergence, and difficulty in convergence, but convergence issues may still exist under certain specific inputs. Reference [21] used a CGAN based on deep convolution (DCCGAN) to learn data from existing renewable energy power stations near a new plant, generating better scenario data for the new plant compared to CGAN, but the deep convolutional neural network structure required parameter initialization tuning based on the dataset size.…”

Section: Introductionmentioning

confidence: 99%

A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors

Wang,

Liu,

Zhong

et al. 2023

Sustainability

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Under the background of large-scale PV (photovoltaic) integration, generating typical operation scenarios of power systems is of great significance for studying system planning operation and electricity markets. Since the uncertainty of PV output and system load is driven by weather factors to some extent, using PV output, system load, and weather data can allow constructing scenarios more accurately. In this study, we used a TimeGAN (time-series generative adversarial network) based on LSTM (long short-term memory) to generate PV output, system load, and weather data. After classifying the generated data using the k-means algorithm, we associated PV output scenarios and load scenarios using the FP-growth algorithm (an association rule mining algorithm), which effectively generated typical scenarios with weather correlations. In this case study, it can be seen that TimeGAN, unlike other GANs, could capture the temporal features of time-series data and performed better than the other examined GANs. The finally generated typical scenario sets also showed interpretable weather correlations.

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“…Ref. [17] used the method of clustering to complete the clustering of wind power and PV historical data to form a typical scenario, and then used the Copula function to achieve the scenario federation of multiple wind-PV fields.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

Wang,

Zhong,

et al. 2023

Energies

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Regarding the generation and integration of typical scenes of PV and loads in urban photovoltaic distribution networks, as well as the insufficient consideration of the spatiotemporal correlation between PV and loads, this paper proposes a typical scene extraction method based on local linear embedding, kernel density estimation, and a joint PV–load typical scene extraction method based on the FP-growth algorithm. Firstly, the daily operation matrices of PV and load are constructed by using the historical operation data of PV and load. Then, the typical scenes are extracted by the dimensionality reduction of local linear embedding and the kernel density estimation method. Finally, the strong association rules of PV–meteorological conditions and load–meteorological conditions are mined based on the FP-growth algorithm, respectively. The association of PV–load typical daily operation scenarios is completed using meteorological conditions as a link. This experiment involved one year of operation data of a distribution network containing PV in Qingyuan, Guangdong Province. The typical scene extraction joint method, Latin hypercube sampling method, and k-means clustering-based scene generation method proposed in this paper are used for comparison, respectively. The results show that compared to the other two scenario generation methods, the error between the typical scenario obtained by this method and the actual operating scenario of the distribution network is smaller. The extracted typical PV and load scenarios can better fit the actual PV and load operation scenarios, which have more reference value for the operation planning of actual distribution networks containing PV.

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A Two-Stage Scenario Generation Method for Wind- Solar Joint Power Output Considering Temporal and Spatial Correlations

Cited by 7 publications

References 1 publication

Multi‐time scale adequacy evaluation of the power system with high penetration of renewable energy sources based on empirical mode decomposition

Multi‐time scale adequacy evaluation of the power system with high penetration of renewable energy sources based on empirical mode decomposition

A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

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