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A Study on the Conduction Mechanism and Evaluation of the Comprehensive Efficiency of Photovoltaic Power Generation in China

Author

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  • Jinpeng Liu

    (School of Economic and Management, North China Electric Power University, Changping District, Beijing 102206, China)

  • Yun Long

    (School of Economic and Management, North China Electric Power University, Changping District, Beijing 102206, China)

  • Xiaohua Song

    (School of Economic and Management, North China Electric Power University, Changping District, Beijing 102206, China)

Abstract

In the context of the global potential energy crisis and aggravating regional environmental pollution, Chinese photovoltaic power generation still faces the key problems of sustainable development, even given its favorable background in large-scale exploitation. Scientific evaluation of the comprehensive efficiency of photovoltaic power generation is of great significance because it will improve investment decision-making and enhance management level, evaluate the development conditions of photovoltaic power generation and then promote sustainable development capability. The concept of “comprehensive efficiency” is proposed in this paper on the basis of the resource development of solar energy and exploitation of photovoltaic power generation. A system dynamics model is used to study the conduction mechanism of the comprehensive efficiency of photovoltaic power generation. This paper collects data from 2005 to 2015 as research models, establishes the evaluation model of the comprehensive efficiency of photovoltaic power generation and conducts empirical analysis based on a super-efficient data envelopment analysis (SE-DEA) model. With the evaluation results, this paper puts forward political suggestions as to the optimization of the comprehensive efficiency of photovoltaic power generation. The research results may provide policy-oriented references on the sustainable development of photovoltaic power generation and give theoretical guidance on the scientific evaluation and diagnosis of photovoltaic power generation efficiency.

Suggested Citation

  • Jinpeng Liu & Yun Long & Xiaohua Song, 2017. "A Study on the Conduction Mechanism and Evaluation of the Comprehensive Efficiency of Photovoltaic Power Generation in China," Energies, MDPI, vol. 10(5), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:723-:d:99162
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    1. Huaimo You & Hong Fang & Xu Wang & Siran Fang, 2018. "Environmental Efficiency of Photovoltaic Power Plants in China—A Comparative Study of Different Economic Zones and Plant Types," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    2. Wang, Zihan & Li, Jiaxin & Liu, Jing & Shuai, Chuanmin, 2020. "Is the photovoltaic poverty alleviation project the best way for the poor to escape poverty? ——A DEA and GRA analysis of different projects in rural China," Energy Policy, Elsevier, vol. 137(C).
    3. Wu, Yunna & Ke, Yiming & Zhang, Ting & Liu, Fangtong & Wang, Jing, 2018. "Performance efficiency assessment of photovoltaic poverty alleviation projects in China: A three-phase data envelopment analysis model," Energy, Elsevier, vol. 159(C), pages 599-610.
    4. Fei Mei & Yi Pan & Kedong Zhu & Jianyong Zheng, 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    5. Zhang, Zumeng & Ding, Liping & Wang, Chaofan & Dai, Qiyao & Shi, Yin & Zhao, Yujia & Zhu, Yuxuan, 2022. "Do operation and maintenance contracts help photovoltaic poverty alleviation power stations perform better?," Energy, Elsevier, vol. 259(C).
    6. Jin-Peng Liu & Qian-Ru Yang & Lin He, 2017. "Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques," Energies, MDPI, vol. 10(7), pages 1-14, July.
    7. Gabriele C. Eder & Yuliya Voronko & Christina Hirschl & Rita Ebner & Gusztáv Újvári & Wolfgang Mühleisen, 2018. "Non-Destructive Failure Detection and Visualization of Artificially and Naturally Aged PV Modules," Energies, MDPI, vol. 11(5), pages 1-14, April.
    8. Nubia Ilia Ponce de León Puig & Leonardo Acho & José Rodellar, 2018. "Design and Experimental Implementation of a Hysteresis Algorithm to Optimize the Maximum Power Point Extracted from a Photovoltaic System," Energies, MDPI, vol. 11(7), pages 1-24, July.
    9. Abbas Mardani & Dalia Streimikiene & Tomas Balezentis & Muhamad Zameri Mat Saman & Khalil Md Nor & Seyed Meysam Khoshnava, 2018. "Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends," Energies, MDPI, vol. 11(8), pages 1-21, August.
    10. Xiangwu Yan & Jiajia Li & Ling Wang & Shuaishuai Zhao & Tie Li & Zhipeng Lv & Ming Wu, 2018. "Adaptive-MPPT-Based Control of Improved Photovoltaic Virtual Synchronous Generators," Energies, MDPI, vol. 11(7), pages 1-18, July.

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