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The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

Author

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  • Yajing Gao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Huaxin Cheng

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Jing Zhu

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Haifeng Liang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Peng Li

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

With the growing influence of fog and haze (F-H) weather and the rapid development of distributed energy resources (DERs) and smart grids, the concept of the virtual power plant (VPP) employed in this study would help to solve the dispatch problem caused by multiple DERs connected to the power grid. The effects of F-H weather on photovoltaic output forecast, load forecast and power system dispatch are discussed according to real case data. The wavelet neural network (WNN) model was employed to predict photovoltaic output and load, considering F-H weather, based on the idea of “similar days of F-H”. The multi-objective optimal dispatch model of a power system adopted in this paper contains several VPPs and conventional power plants, under F-H weather, and the mixed integer linear programming (MILP) and the Yalmip toolbox of MATLAB were adopted to solve the dispatch model. The analysis of the results from a case study proves the validity and feasibility of the model and the algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:1:p:71-:d:62108
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    References listed on IDEAS

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    1. Yajing Gao & Jianpeng Liu & Jin Yang & Haifeng Liang & Jiancheng Zhang, 2014. "Multi-Objective Planning of Multi-Type Distributed Generation Considering Timing Characteristics and Environmental Benefits," Energies, MDPI, vol. 7(10), pages 1-16, September.
    2. Nobre, André M. & Karthik, Shravan & Liu, Haohui & Yang, Dazhi & Martins, Fernando R. & Pereira, Enio B. & Rüther, Ricardo & Reindl, Thomas & Peters, Ian Marius, 2016. "On the impact of haze on the yield of photovoltaic systems in Singapore," Renewable Energy, Elsevier, vol. 89(C), pages 389-400.
    3. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    4. Arslan, Okan & Karasan, Oya Ekin, 2013. "Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks," Energy, Elsevier, vol. 60(C), pages 116-124.
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    Citations

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    Cited by:

    1. 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.
    2. Jingmin Wang & Wenhai Yang & Huaxin Cheng & Lingyu Huang & Yajing Gao, 2017. "The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors," Energies, MDPI, vol. 10(7), pages 1-12, July.
    3. Jiafeng Ren & Haifeng Liang & Yajing Gao, 2019. "Research on Evaluation of Power Supply Capability of Active Distribution Network with Distributed Power Supply with High Permeability," Energies, MDPI, vol. 12(11), pages 1-17, June.
    4. Yajing Gao & Fushen Xue & Wenhai Yang & Yanping Sun & Yongjian Sun & Haifeng Liang & Peng Li, 2017. "A Three-Part Electricity Price Mechanism for Photovoltaic-Battery Energy Storage Power Plants Considering the Power Quality and Ancillary Service," Energies, MDPI, vol. 10(9), pages 1-21, August.
    5. Weibo Zhao & Dongxiao Niu, 2017. "Prediction of CO 2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    6. Carlos Roldán-Porta & Carlos Roldán-Blay & Guillermo Escrivá-Escrivá & Eduardo Quiles, 2019. "Improving the Sustainability of Self-Consumption with Cooperative DC Microgrids," Sustainability, MDPI, vol. 11(19), pages 1-22, October.
    7. Yajing Gao & Yanping Sun & Xiaodan Wang & Feifan Chen & Ali Ehsan & Hongmei Li & Hong Li, 2017. "Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique," Energies, MDPI, vol. 10(12), pages 1-20, December.
    8. 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.
    9. Wei Shang & Guifen Pei & Conor Walsh & Ming Meng & Xiangsong Meng, 2016. "Have Market-oriented Reforms Decoupled China’s CO 2 Emissions from Total Electricity Generation? An Empirical Analysis," Sustainability, MDPI, vol. 8(5), pages 1-12, May.
    10. Yajing Gao & Wenhai Yang & Jing Zhu & Jiafeng Ren & Peng Li, 2017. "Evaluating the Effect of Distributed Generation on Power Supply Capacity in Active Distribution System Based on Sensitivity Analysis," Energies, MDPI, vol. 10(10), pages 1-14, September.
    11. Xiaoyang Zhou & Canhui Zhao & Jian Chai & Benjamin Lev & Kin Keung Lai, 2016. "Low-Carbon Based Multi-Objective Bi-Level Power Dispatching under Uncertainty," Sustainability, MDPI, vol. 8(6), pages 1-23, June.

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