Method for wind power unit type optimizing selection in wind power plant construction

Abstract

Abstract: The optimized type selection of wind power unit influences on the income from investment of wind farm significantly. Using the neural network model to estimate its total investment, and carrying out the economical and technical comparison on various kinds of units by using the wind farm life cycle costbenefit model, the optimized unit type selection can be realized. Taking the unit type selection of a wind farm as an example, the optimized method was analyzed and demonstrated, the result shows that the method is feasible and has theoretical and practical values.

Key words: wind power, optimized type selection of unit, cost-benefit model, neural network model, investment estimation

ZHANG Wenzhong;FAN Wei;LIU Qingchao. Method for wind power unit type optimizing selection in wind power plant construction[J]. Huadian Technology, 2010, 32(12): 81-83.

[1]	YIN Linfei, MENG Yujie. Short-term wind power forecasting based on DenseNet convolutional neural networks [J]. Integrated Intelligent Energy, 2024, 46(7): 12-20.
[2]	ZHANG Xunxiang, WU Jiekang, SUN Yehua, PENG Qijian. Capacity allocation optimization of hybrid energy storage systems considering fluctuation control on offshore wind power [J]. Integrated Intelligent Energy, 2024, 46(6): 54-65.
[3]	JIANG Shanhe, LI Wei, XU Xiaoyan, WANG Dekai. Short-term wind power forecasting based on variational mode decomposition and generative adversarial networks [J]. Integrated Intelligent Energy, 2024, 46(2): 28-35.
[4]	MA Yanhong, LYU Qingquan, ZHANG Zhenzhen, ZHAO Long, ZHOU Qiang, GAO Pengfei. Wind power fluctuation mitigation strategy based on double Kalman filter considering the SOC of the energy storage system [J]. Integrated Intelligent Energy, 2023, 45(2): 61-68.
[5]	WANG Kaiting, LI Xiaobin, ZHANG Hongna, LIU Shen, QU Kaiyang, LI Fengchen. Comprehensive evaluation for energy saving and emission reduction performance of turbulent drag reducing agent in heating systems [J]. Integrated Intelligent Energy, 2022, 44(9): 40-50.
[6]	LI Hua, ZHENG Hongwei, ZHOU Bowen, LI Guangdi, YANG Bo. Two-part tariff for pumped storage power plants in an integrated intelligent energy system [J]. Integrated Intelligent Energy, 2022, 44(7): 10-18.
[7]	WANG Xin, CHEN Zucui, BIAN Zaiping, WANG Yeyao, WU Yumiao. Optimal allocation of a wind‒PV‒battery hybrid system in smart microgrid based on particle swarm optimization algorithm [J]. Integrated Intelligent Energy, 2022, 44(6): 52-58.
[8]	Leijiao GE, Qingxue CUI, Mingwei LI, Fang YAO, Xiaona YANG, Tianshuo DU. Review on water electrolysis for hydrogen production powered by fluctuating wind power and PV [J]. Integrated Intelligent Energy, 2022, 44(5): 1-14.
[9]	Chang YAN, Sheng HUANG, Yinpeng QU. Review on hydrogen production technology from offshore wind power to achieve carbon neutrality [J]. Integrated Intelligent Energy, 2022, 44(5): 30-40.
[10]	Feng WANG, Peng LU, Qingtao ZHANG, Hui ZHAO, Huaiming WANG, Yangyang RU. Development trend and prospects of hydrogen production from offshore wind power [J]. Integrated Intelligent Energy, 2022, 44(5): 41-48.
[11]	Xinye DU, Jianxi WANG, Yonghui SUN, Yi HE, Pengpeng WU, Wei ZHOU. Optimal planning of hybrid energy storage systems in microgrids considering seawater desalination and hydrogen production [J]. Integrated Intelligent Energy, 2022, 44(5): 49-55.
[12]	CHEN Tiantian, GAO Yajing, LU Zhanhui. Research on improved fuzzy mean curve clustering method based on two-scale measurement [J]. Integrated Intelligent Energy, 2022, 44(4): 1-11.
[13]	CHEN Yihui, LIN Lingqi, TIAN Xin, ZHANG Dongliang, WU Jun, LIU Zichen. Three-level wind power AVC coordinated control strategy [J]. Integrated Intelligent Energy, 2022, 44(4): 20-27.
[14]	GUAN Xin, CHEN Tao, GAO Ciwei. Study on optimal operation of the demand-side energy storage system for wind power participating in electricity market [J]. Integrated Intelligent Energy, 2022, 44(2): 35-41.
[15]	CUI Shuangshuang, SUN Shanxun. Study on the correlation of wind turbine variables under different conditions [J]. Integrated Intelligent Energy, 2022, 44(12): 49-55.

Method for wind power unit type optimizing selection in wind power plant construction

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments