IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i10p810-d80226.html
   My bibliography  Save this article

Operation Modeling of Power Systems Integrated with Large-Scale New Energy Power Sources

Author

Listed:
  • Hui Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Gengyin Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Yaowu Wu

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

  • Zhidong Wang

    (State Power Economic Research Institute, Changping District, Beijing 102209, China)

  • Jiaming Wang

    (State Power Economic Research Institute, Changping District, Beijing 102209, China)

Abstract

In the most current methods of probabilistic power system production simulation, the output characteristics of new energy power generation (NEPG) has not been comprehensively considered. In this paper, the power output characteristics of wind power generation and photovoltaic power generation are firstly analyzed based on statistical methods according to their historical operating data. Then the characteristic indexes and the filtering principle of the NEPG historical output scenarios are introduced with the confidence level, and the calculation model of NEPG’s credible capacity is proposed. Based on this, taking the minimum production costs or the best energy-saving and emission-reduction effect as the optimization objective, the power system operation model with large-scale integration of new energy power generation (NEPG) is established considering the power balance, the electricity balance and the peak balance. Besides, the constraints of the operating characteristics of different power generation types, the maintenance schedule, the load reservation, the emergency reservation, the water abandonment and the transmitting capacity between different areas are also considered. With the proposed power system operation model, the operation simulations are carried out based on the actual Northwest power grid of China, which resolves the new energy power accommodations considering different system operating conditions. The simulation results well verify the validity of the proposed power system operation model in the accommodation analysis for the power system which is penetrated with large scale NEPG.

Suggested Citation

  • Hui Li & Gengyin Li & Yaowu Wu & Zhidong Wang & Jiaming Wang, 2016. "Operation Modeling of Power Systems Integrated with Large-Scale New Energy Power Sources," Energies, MDPI, vol. 9(10), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:810-:d:80226
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/10/810/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/10/810/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    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. Jie Wu & Ying Fan & Yan Xia, 2017. "How Can China Achieve Its Nationally Determined Contribution Targets Combining Emissions Trading Scheme and Renewable Energy Policies?," Energies, MDPI, vol. 10(8), pages 1-20, August.
    2. Michel Noussan & Roberta Roberto & Benedetto Nastasi, 2018. "Performance Indicators of Electricity Generation at Country Level—The Case of Italy," Energies, MDPI, vol. 11(3), pages 1-14, March.
    3. Jiang Zhu & Zhenyu Zhao, 2017. "Chinese Electric Power Development Coordination Analysis on Resource, Production and Consumption: A Provincial Case Study," Sustainability, MDPI, vol. 9(2), pages 1-19, February.

    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. Popović, Željko N. & KovaÄ ki, Neven V. & Popović, Dragan S., 2020. "Resilient distribution network planning under the severe windstorms using a risk-based approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Zhang, Sheng & Huang, Pei & Sun, Yongjun, 2016. "A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties," Energy, Elsevier, vol. 94(C), pages 654-665.
    3. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Mena, Rodrigo & Hennebel, Martin & Li, Yan-Fu & Zio, Enrico, 2014. "Self-adaptable hierarchical clustering analysis and differential evolution for optimal integration of renewable distributed generation," Applied Energy, Elsevier, vol. 133(C), pages 388-402.
    5. Sansavini, G. & Piccinelli, R. & Golea, L.R. & Zio, E., 2014. "A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation," Renewable Energy, Elsevier, vol. 64(C), pages 71-81.
    6. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    7. Sun, Can & Bie, Zhaohong & Xie, Min & Jiang, Jiangfeng, 2016. "Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation," Renewable Energy, Elsevier, vol. 93(C), pages 68-76.
    8. Rocchetta, R. & Li, Y.F. & Zio, E., 2015. "Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 47-61.
    9. Koppelaar, Rembrandt H.E.M. & Keirstead, James & Shah, Nilay & Woods, Jeremy, 2016. "A review of policy analysis purpose and capabilities of electricity system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1531-1544.
    10. Lennard Sund & Saber Talari & Wolfgang Ketter, 2022. "Stochastic Wind Power Generation Planning in Liberalised Electricity Markets within a Heterogeneous Landscape," Energies, MDPI, vol. 15(21), pages 1-21, October.
    11. Zahid Ullah & Arshad & Hany Hassanin, 2022. "Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources," Energies, MDPI, vol. 15(14), pages 1-16, July.
    12. Zahid Ullah & Arshad & Hany Hassanin & James Cugley & Mohammed Al Alawi, 2022. "Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty," Energies, MDPI, vol. 15(19), pages 1-16, October.
    13. Zeng, Bo & Wu, Geng & Wang, Jianhui & Zhang, Jianhua & Zeng, Ming, 2017. "Impact of behavior-driven demand response on supply adequacy in smart distribution systems," Applied Energy, Elsevier, vol. 202(C), pages 125-137.
    14. Amedeo Buonanno & Martina Caliano & Marialaura Di Somma & Giorgio Graditi & Maria Valenti, 2022. "A Comprehensive Tool for Scenario Generation of Solar Irradiance Profiles," Energies, MDPI, vol. 15(23), pages 1-18, November.
    15. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Aleatory uncertainty quantification of project resources and its application to project scheduling," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    16. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    17. Mo, Huadong & Sansavini, Giovanni, 2019. "Impact of aging and performance degradation on the operational costs of distributed generation systems," Renewable Energy, Elsevier, vol. 143(C), pages 426-439.
    18. Zahid Ullah & Arshad & Jawad Ahmad, 2022. "The Development of a Cross-Border Energy Trade Cooperation Model of Interconnected Virtual Power Plants Using Bilateral Contracts," Energies, MDPI, vol. 15(21), pages 1-16, November.
    19. Sui, Quan & Zhang, Rui & Wu, Chuantao & Wei, Fanrong & Lin, Xiangning & Li, Zhengtian, 2020. "Stochastic scheduling of an electric vessel-based energy management system in pelagic clustering islands," Applied Energy, Elsevier, vol. 259(C).
    20. Ossai, Chinedu I. & Boswell, Brian & Davies, Ian J., 2016. "A Markovian approach for modelling the effects of maintenance on downtime and failure risk of wind turbine components," Renewable Energy, Elsevier, vol. 96(PA), pages 775-783.

    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:jeners:v:9:y:2016:i:10:p:810-:d:80226. 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.