IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i1p71-d62108.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/1/71/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/1/71/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juan José Echavarría & Andrés González, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 14-66, December.
    2. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    3. Yukai Yang & Luc Bauwens, 2018. "State-Space Models on the Stiefel Manifold with a New Approach to Nonlinear Filtering," Econometrics, MDPI, vol. 6(4), pages 1-22, December.
    4. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    5. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    6. Simplice A. Asongu & Nicholas M. Odhiambo, 2019. "Governance, capital flight and industrialisation in Africa," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-22, December.
    7. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    8. Michał Brzoza-Brzezina & Jacek Kotłowski, 2009. "Bezwzględna stopa inflacji w gospodarce polskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 9, pages 1-21.
    9. Proietti, Tommaso, 2010. "Trend Estimation," MPRA Paper 21607, University Library of Munich, Germany.
    10. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
    11. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    12. Ivan Kitov & Oleg Kitov, 2013. "Does Banque de France control inflation and unemployment?," Papers 1311.1097, arXiv.org.
    13. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    14. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    15. Baker, Jessica & Carreras, Oriol & Kirby, Simon & Meaning, Jack & Piggott, Rebecca, 2016. "Modelling events: The short-term economic impact of leaving the EU," Economic Modelling, Elsevier, vol. 58(C), pages 339-350.
    16. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
    17. David Havrlant & Peter Tóth & Julia Wörz, 2016. "On the optimal number of indicators – nowcasting GDP growth in CESEE," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 54-72.
    18. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    19. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    20. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:8:y:2016:i:1:p:71-:d:62108. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.