IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i6p3400-d520235.html
   My bibliography  Save this article

Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response

Author

Listed:
  • Jia Ning

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Sipeng Hao

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Aidong Zeng

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Bin Chen

    (State Grid Changzhou Power Supply Company, Changzhou 213003, China)

  • Yi Tang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.

Suggested Citation

  • Jia Ning & Sipeng Hao & Aidong Zeng & Bin Chen & Yi Tang, 2021. "Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3400-:d:520235
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/6/3400/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/6/3400/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jingshuang Shen & Chuanwen Jiang & Bosong Li, 2015. "Controllable Load Management Approaches in Smart Grids," Energies, MDPI, vol. 8(10), pages 1-16, October.
    2. Jia Ning & Yi Tang & Qian Chen & Jianming Wang & Jianhua Zhou & Bingtuan Gao, 2017. "A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential," Energies, MDPI, vol. 10(4), pages 1-16, April.
    3. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, March.
    4. Jun Dong & Shilin Nie & Hui Huang & Peiwen Yang & Anyuan Fu & Jin Lin, 2019. "Research on Economic Operation Strategy of CHP Microgrid Considering Renewable Energy Sources and Integrated Energy Demand Response," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    5. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
    6. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
    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. Jiajie Tang & Jie Zhao & Hongliang Zou & Gaoyuan Ma & Jun Wu & Xu Jiang & Huaixun Zhang, 2021. "Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM," Sustainability, MDPI, vol. 13(19), pages 1-20, September.

    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. Moradi-Dalvand, M. & Mohammadi-Ivatloo, B. & Amjady, N. & Zareipour, H. & Mazhab-Jafari, A., 2015. "Self-scheduling of a wind producer based on Information Gap Decision Theory," Energy, Elsevier, vol. 81(C), pages 588-600.
    2. Ghasemi, Ahmad & Jamshidi Monfared, Houman & Loni, Abdolah & Marzband, Mousa, 2021. "CVaR-based retail electricity pricing in day-ahead scheduling of microgrids," Energy, Elsevier, vol. 227(C).
    3. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    4. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    5. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    7. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    8. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    9. Grover-Silva, Etta & Heleno, Miguel & Mashayekh, Salman & Cardoso, Gonçalo & Girard, Robin & Kariniotakis, George, 2018. "A stochastic optimal power flow for scheduling flexible resources in microgrids operation," Applied Energy, Elsevier, vol. 229(C), pages 201-208.
    10. Hernán Gómez-Villarreal & Miguel Carrión & Ruth Domínguez, 2019. "Optimal Management of Combined-Cycle Gas Units with Gas Storage under Uncertainty," Energies, MDPI, vol. 13(1), pages 1-29, December.
    11. Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    12. Tan, Bifei & Lin, Zhenjia & Zheng, Xiaodong & Xiao, Fu & Wu, Qiuwei & Yan, Jinyue, 2023. "Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors," Applied Energy, Elsevier, vol. 350(C).
    13. Hanif, Sarmad & Alam, M.J.E. & Roshan, Kini & Bhatti, Bilal A. & Bedoya, Juan C., 2022. "Multi-service battery energy storage system optimization and control," Applied Energy, Elsevier, vol. 311(C).
    14. Heeyun Lee & Hyunjoong Kim & Hyewon Kim & Hyunsup Kim, 2025. "Optimal Vehicle-to-Grid Charge Scheduling for Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 18(5), pages 1-15, February.
    15. Thibaut Th'eate & S'ebastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Papers 2006.05784, arXiv.org, revised Dec 2020.
    16. Schulze, Tim & McKinnon, Ken, 2016. "The value of stochastic programming in day-ahead and intra-day generation unit commitment," Energy, Elsevier, vol. 101(C), pages 592-605.
    17. Savelli, Iacopo & Morstyn, Thomas, 2021. "Electricity prices and tariffs to keep everyone happy: A framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Omega, Elsevier, vol. 103(C).
    18. Athira M. Mohan & Nader Meskin & Hasan Mehrjerdi, 2020. "A Comprehensive Review of the Cyber-Attacks and Cyber-Security on Load Frequency Control of Power Systems," Energies, MDPI, vol. 13(15), pages 1-33, July.
    19. Moret, Fabio & Pinson, Pierre & Papakonstantinou, Athanasios, 2020. "Heterogeneous risk preferences in community-based electricity markets," European Journal of Operational Research, Elsevier, vol. 287(1), pages 36-48.
    20. Ali Reza Kheirkhah & Carlos Frederico Meschini Almeida & Nelson Kagan & Jonatas Boas Leite, 2023. "Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model," Energies, MDPI, vol. 16(21), pages 1-18, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:13:y:2021:i:6:p:3400-:d:520235. 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.