Study on Uncertainty Operation Optimization of Virtual Power Plant Based on Intelligent Prediction Model Under Climate Change

In order to effectively cope with climate change and promote the healthy development of virtual power plant, an uncertainty operation optimization model of virtual power plant (VPP) adapted to climate change was proposed based on the providing regional climate for impact studies (PRECIS), BP neural...

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Vydáno v:发电技术 Ročník 44; číslo 6; s. 790 - 799
Hlavní autoři: JIA Xiaoqiang, YANG Yongbiao, DU Jiao, GAN Haiqing, YANG Nan
Médium: Journal Article
Jazyk:čínština
angličtina
Vydáno: Editorial Department of Power Generation Technology 31.12.2023
Témata:
bp neural network
climate change
providing regional climate for impact studies (precis)
uncertainty optimization
virtual power plant (vpp)
ISSN:2096-4528
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Abstract In order to effectively cope with climate change and promote the healthy development of virtual power plant, an uncertainty operation optimization model of virtual power plant (VPP) adapted to climate change was proposed based on the providing regional climate for impact studies (PRECIS), BP neural network prediction model and interval optimization algorithm. PRECIS was used to simulate the changes in meteorological factors such as temperature,wind speed and radiation under different carbon emission scenarios in 2025. The BP neural network model was used to predict the power generation of photovoltaic power plants based on the simulation results of PRICES. The interval optimization algorithm was coupled with the power generation prediction results to reduce the impact caused by the influence of photovoltaic power generation uncertainty on the simulation results of the optimization model. The results show that the model can not only generate the optimal operation strategy of VPP under climate change, but also reduce operating costs and improve economic benefits.
AbstractList In order to effectively cope with climate change and promote the healthy development of virtual power plant, an uncertainty operation optimization model of virtual power plant (VPP) adapted to climate change was proposed based on the providing regional climate for impact studies (PRECIS), BP neural network prediction model and interval optimization algorithm. PRECIS was used to simulate the changes in meteorological factors such as temperature,wind speed and radiation under different carbon emission scenarios in 2025. The BP neural network model was used to predict the power generation of photovoltaic power plants based on the simulation results of PRICES. The interval optimization algorithm was coupled with the power generation prediction results to reduce the impact caused by the influence of photovoltaic power generation uncertainty on the simulation results of the optimization model. The results show that the model can not only generate the optimal operation strategy of VPP under climate change, but also reduce operating costs and improve economic benefits.
Author GAN Haiqing
YANG Nan
JIA Xiaoqiang
DU Jiao
YANG Yongbiao
Author_xml – sequence: 1
  fullname: JIA Xiaoqiang
  organization: State Key Laboratory of Power Grid Safety (China Electric Power Research Institute), Haidian District, Beijing 100192, China
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  fullname: YANG Yongbiao
  organization: School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu Province, China
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  fullname: DU Jiao
  organization: School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu Province, China
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  fullname: GAN Haiqing
  organization: State Grid Jiagnsu Electric Power Co., Ltd., Nanjing 210000, Jiangsu Province, China
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  fullname: YANG Nan
  organization: State Grid Jiangsu Electric Power Co., Ltd., Nanjing Power Supply Branch, Nanjing 210000, Jiangsu Province, China
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SubjectTerms bp neural network
climate change
providing regional climate for impact studies (precis)
uncertainty optimization
virtual power plant (vpp)
Title Study on Uncertainty Operation Optimization of Virtual Power Plant Based on Intelligent Prediction Model Under Climate Change
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