Data generation scheme for photovoltaic power forecasting using Wasserstein GAN with gradient penalty combined with autoencoder and regression models

Park, Sungwoo; Moon, Jaeuk; Hwang, Eenjun

doi:10.1016/j.eswa.2024.125012

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.125012

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Data generation scheme for photovoltaic power forecasting using Wasserstein GAN with gradient penalty combined with autoencoder and regression models

Sungwoo Park,

Jaeuk Moon,

Eenjun Hwang

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2024

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Optimization Control Method for Low-Voltage DC Microgrid with Low Carbon, Economy, and Reliability

Zhu,

Wang,

Zeng

2024

ACS Omega

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From the perspectives of economy, low carbon, and safety in DC microgrids, a multiscenario optimization control method of low-voltage DC microgrids based on the nondominant sorting arctic puffin optimization algorithm (NSAPOA) is proposed in this paper. The Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is used to generate typical output scenarios of photovoltaic and loads that are reduced by the K-means clustering method to deal with the uncertainty of photovoltaic and load. Based on the time of use electricity price, the operating modes of the low-voltage DC microgrid system are divided to formulate relevant energy exchange strategies. The equivalent electricity weight method is used to evaluate the service life of energy storage systems, and a multiobjective optimization control model of low-voltage DC microgrid for multiple scenarios is established with the objective functions of maximizing daily net income, minimizing equivalent charging and discharging capacity of energy storage systems, and minimizing carbon dioxide emissions. The NSAPOA is used to iteratively obtain the Pareto solution set, and the final optimal solution is determined by employing the multiattributive border approximation area comparison (MABAC) algorithm. Analysis results show that this can achieve economic and low-carbon optimization operation of the system throughout the whole life cycle of energy storage systems.

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Optimization Control Method for Low-Voltage DC Microgrid with Low Carbon, Economy, and Reliability

Zhu,

Wang,

Zeng

2024

ACS Omega

View full text Add to dashboard Cite

show abstract

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Data generation scheme for photovoltaic power forecasting using Wasserstein GAN with gradient penalty combined with autoencoder and regression models

Cited by 1 publication

References 37 publications

Optimization Control Method for Low-Voltage DC Microgrid with Low Carbon, Economy, and Reliability

Optimization Control Method for Low-Voltage DC Microgrid with Low Carbon, Economy, and Reliability

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