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

doi:10.12096/j.2096-4528.pgt.23094

Abstract

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.

Key words: virtual power plant (VPP), providing regional climate for impact studies (PRECIS), BP neural network, uncertainty optimization, climate change

CLC Number:

TK 01

Xiaoqiang JIA, Yongbiao YANG, Jiao DU, Haiqing GAN, Nan YANG. Study on Uncertainty Operation Optimization of Virtual Power Plant Based on Intelligent Prediction Model Under Climate Change[J]. Power Generation Technology, 2023, 44(6): 790-799.

Figures/Tables 10

Fig. 1 Research roadmap

Tab. 1 System patameters

类别	参数	数值
经济	热电联产系统发电维护成本/(元/kW)	0.03
	热电联产系统供热维护成本/(元/kW)	0.03
	补燃锅炉供热维护成本/(元/kW)	0.02
	储能元件充放电成本/(元/kW)	0.02
	天然气价格/(元/m³)	2.74
工程	热电联产系统额定发电功率/MW	2.50
	热电联产系统额定供热功率/MW	2.00
	光伏额定功率/MW	1.00
	储能设备额定功率/MW	1.00
	余热锅炉效率	0.78
	补燃锅炉效率	0.85

Fig. 2 Hourly simulation results of various meteorological elements in 2025 under SSP2-4.5 scenario

Fig. 3 Hourly simulation results of various meteorological elements in 2025 under SSP2-8.5 scenario

Fig. 4 Comparison of simulation results and real data of predictive models under SSP2-4.5 scenario

Fig. 5 Comparison of simulation results and real data of predictive models under SSP2-8.5 scenario

Fig. 6 Photovoltaic output, electrical and heat load in typical day

Fig. 7 VPP power supply system operation strategy

Fig. 8 Dynamic process of charging and discharging of energy storage devices

Fig. 9 Hourly operation stratrgies of heating-supply

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