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 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | čínština angličtina |
| Vydáno: |
Editorial Department of Power Generation Technology
31.12.2023
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| Témata: | |
| ISSN: | 2096-4528 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| ISSN: | 2096-4528 |
| DOI: | 10.12096/j.2096-4528.pgt.23094 |