IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i2p518-d307098.html
   My bibliography  Save this article

Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

Author

Listed:
  • Yue Chen

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Zhizhong Guo

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Hongbo Li

    (Electric Power Research Institute of HITZ, Harbin Institute of Technology at Zhangjiakou, Zhangjiakou 075000, China)

  • Yi Yang

    (Shenyang Power Supply Company, State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang 110000, China)

  • Abebe Tilahun Tadie

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Guizhong Wang

    (Electric Power Research Institute of HITZ, Harbin Institute of Technology at Zhangjiakou, Zhangjiakou 075000, China)

  • Yingwei Hou

    (Electric Power Research Institute of HITZ, Harbin Institute of Technology at Zhangjiakou, Zhangjiakou 075000, China)

Abstract

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

Suggested Citation

  • Yue Chen & Zhizhong Guo & Hongbo Li & Yi Yang & Abebe Tilahun Tadie & Guizhong Wang & Yingwei Hou, 2020. "Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:518-:d:307098
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/2/518/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/2/518/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Van Ky Huynh & Van Duong Ngo & Dinh Duong Le & Nhi Thi Ai Nguyen, 2018. "Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources," Energies, MDPI, vol. 11(10), pages 1-12, October.
    2. Xuexia Zhang & Zhiqi Guo & Weirong Chen, 2017. "Probabilistic Power Flow Method Considering Continuous and Discrete Variables," Energies, MDPI, vol. 10(5), pages 1-17, April.
    3. Sui Peng & Huixiang Chen & Yong Lin & Tong Shu & Xingyu Lin & Junjie Tang & Wenyuan Li & Weijie Wu, 2019. "Probabilistic Power Flow for Hybrid AC/DC Grids with Ninth-Order Polynomial Normal Transformation and Inherited Latin Hypercube Sampling," Energies, MDPI, vol. 12(16), pages 1-21, August.
    4. Jae-Kun Lyu & Jae-Haeng Heo & Jong-Keun Park & Yong-Cheol Kang, 2013. "Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects," Energies, MDPI, vol. 6(11), pages 1-21, October.
    5. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    6. Xiaoyang Deng & Jinghan He & Pei Zhang, 2017. "A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables," Energies, MDPI, vol. 10(10), pages 1-21, October.
    Full references (including those not matched with items on IDEAS)

    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. Pinto, Edwin S. & Gronier, Timothé & Franquet, Erwin & Serra, Luis M., 2023. "Opportunities and economic assessment for a third-party delivering electricity, heat and cold to residential buildings," Energy, Elsevier, vol. 272(C).
    2. Qais Alsafasfeh & Omar A. Saraereh & Imran Khan & Sunghwan Kim, 2019. "Solar PV Grid Power Flow Analysis," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    3. Domenech, B. & Ferrer-Martí, L. & Pastor, R., 2015. "Including management and security of supply constraints for designing stand-alone electrification systems in developing countries," Renewable Energy, Elsevier, vol. 80(C), pages 359-369.
    4. Shahmohammadi, Ali & Sioshansi, Ramteen & Conejo, Antonio J. & Afsharnia, Saeed, 2018. "Market equilibria and interactions between strategic generation, wind, and storage," Applied Energy, Elsevier, vol. 220(C), pages 876-892.
    5. Ismail, M.S. & Moghavvemi, M. & Mahlia, T.M.I., 2013. "Energy trends in Palestinian territories of West Bank and Gaza Strip: Possibilities for reducing the reliance on external energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 117-129.
    6. Cruz-Peragon, F. & Palomar, J.M. & Casanova, P.J. & Dorado, M.P. & Manzano-Agugliaro, F., 2012. "Characterization of solar flat plate collectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1709-1720.
    7. Ba, Birome Holo & Prins, Christian & Prodhon, Caroline, 2016. "Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective," Renewable Energy, Elsevier, vol. 87(P2), pages 977-989.
    8. Hang, Yin & Du, Lili & Qu, Ming & Peeta, Srinivas, 2013. "Multi-objective optimization of integrated solar absorption cooling and heating systems for medium-sized office buildings," Renewable Energy, Elsevier, vol. 52(C), pages 67-78.
    9. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    10. Carmen de la Cruz-Lovera & Francisco Manzano-Agugliaro & Esther Salmerón-Manzano & José-Luis de la Cruz-Fernández & Alberto-Jesus Perea-Moreno, 2019. "Date Seeds ( Phoenix dactylifera L. ) Valorization for Boilers in the Mediterranean Climate," Sustainability, MDPI, vol. 11(3), pages 1-14, January.
    11. Domenech, B. & Ferrer-Martí, L. & Pastor, R., 2015. "Hierarchical methodology to optimize the design of stand-alone electrification systems for rural communities considering technical and social criteria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 182-196.
    12. Sharafi, Masoud & ELMekkawy, Tarek Y., 2014. "Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach," Renewable Energy, Elsevier, vol. 68(C), pages 67-79.
    13. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    14. Yuxiao Qin & Guodong Zhao & Qingsong Hua & Li Sun & Soumyadeep Nag, 2019. "Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization," Sustainability, MDPI, vol. 11(12), pages 1-20, June.
    15. Attahiru, Yusuf Babangida & Aziz, Md. Maniruzzaman A. & Kassim, Khairul Anuar & Shahid, Shamsuddin & Wan Abu Bakar, Wan Azelee & NSashruddin, Thanwa Filza & Rahman, Farahiyah Abdul & Ahamed, Mohd Imra, 2019. "A review on green economy and development of green roads and highways using carbon neutral materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 600-613.
    16. Maleki, Akbar & Ameri, Mehran & Keynia, Farshid, 2015. "Scrutiny of multifarious particle swarm optimization for finding the optimal size of a PV/wind/battery hybrid system," Renewable Energy, Elsevier, vol. 80(C), pages 552-563.
    17. Maroufmashat, Azadeh & Elkamel, Ali & Fowler, Michael & Sattari, Sourena & Roshandel, Ramin & Hajimiragha, Amir & Walker, Sean & Entchev, Evgueniy, 2015. "Modeling and optimization of a network of energy hubs to improve economic and emission considerations," Energy, Elsevier, vol. 93(P2), pages 2546-2558.
    18. Abu Bakar, Nur Najihah & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah & Bandi, Masilah, 2015. "Energy efficiency index as an indicator for measuring building energy performance: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 1-11.
    19. Hernández-Escobedo, Q. & Rodríguez-García, E. & Saldaña-Flores, R. & Fernández-García, A. & Manzano-Agugliaro, F., 2015. "Solar energy resource assessment in Mexican states along the Gulf of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 216-238.
    20. Sameh Monna & Ramez Abdallah & Adel Juaidi & Aiman Albatayneh & Antonio Jesús Zapata-Sierra & Francisco Manzano-Agugliaro, 2022. "Potential Electricity Production by Installing Photovoltaic Systems on the Rooftops of Residential Buildings in Jordan: An Approach to Climate Change Mitigation," Energies, MDPI, vol. 15(2), pages 1-15, January.

    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:12:y:2020:i:2:p:518-:d:307098. 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.