Scenario Generation for Wind Power Using Generative
In this paper, a data-driven artificial intelligence approach is presented to generate wind power output scenarios based on generative adversarial networks (GANs).
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Wind power scenario generation methods are implemented through clustering techniques. Depending on the specific research question and to ensure the temporal continuity of wind power scenario data, wind power output data for a continuous period are grouped into the same scenario.
It is important to account for these complicated char-acteristics in the design of wind power scenario generation methods. The GAN model is powerful in generating synthetic data with similar characteristics as the given real dataset, without the need of explicitly modeling the underlying data statistics.
Jamali et al. utilized a roulette-wheel mechanism to generate scenarios for wind power generation and market prices using the Kantorovich distance index to reduce the number of scenarios . This method in has also been applied to establish the uncertainty model of wind power and load demand. 4. Evaluation of SG methods
Wind power scenario generation flowchart. This study constructs typical scenarios of new energy based on wind power historical output data from a region. The data consists of wind and solar power output data in 15-min intervals for 365 days, totaling 8,760 h, with a total of 35,040 data points for clustering.
In this paper, a data-driven artificial intelligence approach is presented to generate wind power output scenarios based on generative adversarial networks (GANs).
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A comprehensive analysis of the advantages and disadvantages of wind power SG methods is provided to serve as a guideline for power systems with integrated wind power.
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In order to accurately characterize the uncertainty of wind power, this paper proposes a multi-wind turbine scenario generation prediction model based on the combination of Graph Convolutional
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In this paper, we suggest an enhancement of the GAN-based wind power generation approach by using graph convolutional network (GCN) to produce the correct spatial relations among multiple wind farms.
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In this paper, a model combining generative adversarial network (GAN) and long short-term memory (LSTM) is proposed to character the time series properties of wind power and generate
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First, based on GAN theory, the original wind farm output dataset is augmented to generate a large number of wind farm output scenarios, and the rationality of the wind power output scenario set is
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Based on an improved generative adversarial network algorithm, this paper generates single-site and multi-site scenarios for wind power generation data. The temporal and spatial...
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View a PDF of the paper titled Wind Power Scenario Generation based on the Generalized Dynamic Factor Model and Generative Adversarial Network, by Young-ho Cho and 3
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This study proposes a method for wind power scenario generation based on clustering algorithms, which can also be applied to photovoltaic power generation scenarios.
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This paper proposes the use of state space models to generate scenarios for the analysis of wind power plant (WPP) generation capabilities.
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