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Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

Author

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  • Weiliang Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources of North China Electric Power University, Baoding 071003, China)

  • Changliang Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources of North China Electric Power University, Baoding 071003, China)

  • Yongjun Lin

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources of North China Electric Power University, Baoding 071003, China)

  • Liangyu Ma

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources of North China Electric Power University, Baoding 071003, China)

  • Feng Xiong

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources of North China Electric Power University, Baoding 071003, China)

  • Jintuo Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources of North China Electric Power University, Baoding 071003, China)

Abstract

Fog and haze (F-H) weather has been occurring frequently in China since 2012, which affects the output power of photovoltaic (PV) generation dramatically by directly weakening solar irradiance and aggravating dust deposition on PV panels. The ultra-short-term forecast method presented in this study would help to fully reflect the dual effects of F-H on PV output power. Aiming at the weakening effect on solar irradiance, estimation models of atmospheric aerosol optical depth (AOD) based on particle matter (PM) concentration were established with machine learning (ML) method, and the total irradiance received by PV panels was calculated based on simplified REST2 model. Aiming at the aggravating effect on dust deposition on PV panels, sample set of “cumulative PM concentration—efficiency reduction” was constructed through special measurement experiments, then the efficiency reduction under certain dust deposition state was estimated with similar-day choosing method. Based on photoelectric conversion model, PM concentration prediction and weather forecast information, ultra-short-term forecast of PV output power was realized. Experimental results proved the validity and feasibility of the presented forecast method.

Suggested Citation

  • Weiliang Liu & Changliang Liu & Yongjun Lin & Liangyu Ma & Feng Xiong & Jintuo Li, 2018. "Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather," Energies, MDPI, vol. 11(3), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:528-:d:134029
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    References listed on IDEAS

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    1. Kaldellis, J.K. & Kapsali, M., 2011. "Simulating the dust effect on the energy performance of photovoltaic generators based on experimental measurements," Energy, Elsevier, vol. 36(8), pages 5154-5161.
    2. Yajing Gao & Huaxin Cheng & Jing Zhu & Haifeng Liang & Peng Li, 2016. "The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather," Sustainability, MDPI, vol. 8(1), pages 1-22, January.
    3. Klugmann-Radziemska, Ewa, 2015. "Degradation of electrical performance of a crystalline photovoltaic module due to dust deposition in northern Poland," Renewable Energy, Elsevier, vol. 78(C), pages 418-426.
    4. Yajing Gao & Jing Zhu & Huaxin Cheng & Fushen Xue & Qing Xie & Peng Li, 2016. "Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories," Energies, MDPI, vol. 9(7), pages 1-15, July.
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    Cited by:

    1. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    2. Yao, Wanxiang & Zheng, Zhimiao & Zhao, Jun & Wang, Xiao & Wang, Yan & Li, Xianli & Fu, Jidong, 2020. "The factor analysis of fog and haze under the coupling of multiple factors -- taking four Chinese cities as an example," Energy Policy, Elsevier, vol. 137(C).
    3. Evgeny Solomin & Shanmuga Priya Selvanathan & Sudhakar Kumarasamy & Anton Kovalyov & Ramyashree Maddappa Srinivasa, 2021. "The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations," Energies, MDPI, vol. 14(9), pages 1-18, May.
    4. Yosui Miyazaki & Yusuke Kameda & Junji Kondoh, 2019. "A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets," Energies, MDPI, vol. 12(24), pages 1-14, December.
    5. Wei Li & Hui Ren & Ping Chen & Yanyang Wang & Hailong Qi, 2020. "Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review," Energies, MDPI, vol. 13(22), pages 1-25, November.

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