IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i7p3020-d1107491.html
   My bibliography  Save this article

Multi-Time-Scale Coordinated Optimum Scheduling Technique for a Multi-Source Complementary Power-Generating System with Uncertainty in the Source-Load

Author

Listed:
  • Zhengwei Huang

    (College of Economics & Management, China Three Gorges University, Yichang 443002, China)

  • Lu Liu

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Jiachang Liu

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

Abstract

An optimal dispatching strategy for a multi-source complementary power generation system taking source–load uncertainty into account is proposed, in order to address the effects of large-scale intermittent renewable energy consumption and power load instability on power grid dispatching. The uncertainty problem is first converted into common situations for study, such as load power forecasting and solar and wind power. The backward scenario reduction and Latin hypercube sampling techniques are used to create these common situations. Based on this, a multi-timescale coordinated optimum scheduling control method for a multi-source complementary power generation system taking the demand response into account is presented, and the optimal operation of a wind–PV–thermal-pumped storage hybrid system is examined. The time-of-use power price optimizes the electrical load in the day-ahead pricing mode, and the two types of demand response loads are selected in the day-ahead scheduling. Second, the lowest system operating cost and the minimal day-ahead and intra-day adjustment of each source are established as the optimization targets in the day-ahead and intra-day phases of the multi-timescale coordinated scheduling model of the multi-source complementary system. The example study demonstrates that the scheduling strategy may increase the amount of renewable energy consumed, minimize load fluctuations, increase system stability, and further reduce operating expenses, proving the viability and efficiency of the suggested strategy.

Suggested Citation

  • Zhengwei Huang & Lu Liu & Jiachang Liu, 2023. "Multi-Time-Scale Coordinated Optimum Scheduling Technique for a Multi-Source Complementary Power-Generating System with Uncertainty in the Source-Load," Energies, MDPI, vol. 16(7), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3020-:d:1107491
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/7/3020/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/7/3020/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cantão, Mauricio P. & Bessa, Marcelo R. & Bettega, Renê & Detzel, Daniel H.M. & Lima, João M., 2017. "Evaluation of hydro-wind complementarity in the Brazilian territory by means of correlation maps," Renewable Energy, Elsevier, vol. 101(C), pages 1215-1225.
    2. Reddy, S. Surender, 2017. "Optimal scheduling of thermal-wind-solar power system with storage," Renewable Energy, Elsevier, vol. 101(C), pages 1357-1368.
    3. Shabanzadeh, Morteza & Sheikh-El-Eslami, Mohammad-Kazem & Haghifam, Mahmoud-Reza, 2017. "An interactive cooperation model for neighboring virtual power plants," Applied Energy, Elsevier, vol. 200(C), pages 273-289.
    4. Sun, Kaiqi & Li, Ke-Jun & Pan, Jiuping & Liu, Yong & Liu, Yilu, 2019. "An optimal combined operation scheme for pumped storage and hybrid wind-photovoltaic complementary power generation system," Applied Energy, Elsevier, vol. 242(C), pages 1155-1163.
    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. Fu, Yiwei & Lu, Zongxiang & Hu, Wei & Wu, Shuang & Wang, Yiting & Dong, Ling & Zhang, Jietan, 2019. "Research on joint optimal dispatching method for hybrid power system considering system security," Applied Energy, Elsevier, vol. 238(C), pages 147-163.
    2. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Aruna Kanagaraj & Kumudini Devi Raguru Pandu, 2020. "Investigations of Various Market Models in a Deregulated Power Environment Using ACOPF," Energies, MDPI, vol. 13(9), pages 1-17, May.
    5. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    6. Rakshith Subramanya & Matti Yli-Ojanperä & Seppo Sierla & Taneli Hölttä & Jori Valtakari & Valeriy Vyatkin, 2021. "A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves," Energies, MDPI, vol. 14(5), pages 1-23, February.
    7. Alqahtani, Bader & Yang, Jin & Paul, Manosh C., 2024. "A techno-economic-environmental assessment of a hybrid-renewable pumped hydropower energy storage system: A case study of Saudi Arabia," Renewable Energy, Elsevier, vol. 232(C).
    8. Wang, Gang & Zhang, Zhen & Lin, Jianqing, 2024. "Multi-energy complementary power systems based on solar energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    9. Hou, Hui & Xu, Tao & Wu, Xixiu & Wang, Huan & Tang, Aihong & Chen, Yangyang, 2020. "Optimal capacity configuration of the wind-photovoltaic-storage hybrid power system based on gravity energy storage system," Applied Energy, Elsevier, vol. 271(C).
    10. Guo, Su & Zheng, Kun & He, Yi & Kurban, Aynur, 2023. "The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps," Renewable Energy, Elsevier, vol. 202(C), pages 1169-1189.
    11. Wesseh, Presley K. & Benjamin, Nelson I. & Lin, Boqiang, 2022. "The coordination of pumped hydro storage, electric vehicles, and climate policy in imperfect electricity markets: Insights from China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    12. Anubhav Kumar Pandey & Vinay Kumar Jadoun & Jayalakshmi N. Sabhahit, 2022. "Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources," Energies, MDPI, vol. 15(16), pages 1-30, August.
    13. Zhang, Y.Q. & Chen, J.J. & Wang, Y.X. & Feng, L., 2024. "Enhancing resilience of agricultural microgrid through electricity–heat–water based multi-energy hub considering irradiation intensity uncertainty," Renewable Energy, Elsevier, vol. 220(C).
    14. Yoo, Yoon-Sik & Newaz, S.H. Shah & Shannon, Peter David & Lee, Il-Woo & Choi, Jun Kyun, 2018. "Towards improving throughput and reducing latency: A simplified protocol conversion mechanism in distributed energy resources network," Applied Energy, Elsevier, vol. 213(C), pages 45-55.
    15. Moises Neil V Seriño, 2022. "Energy security through diversification of non-hydro renewable energy sources in developing countries," Energy & Environment, , vol. 33(3), pages 546-561, May.
    16. Zhang, Yusheng & Ma, Chao & Yang, Yang & Pang, Xiulan & Liu, Lu & Lian, Jijian, 2021. "Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems," Applied Energy, Elsevier, vol. 291(C).
    17. Xu, Shitian & Liu, Pan & Li, Xiao & Cheng, Qian & Liu, Zheyuan, 2023. "Deriving long-term operating rules of the hydro-wind-PV hybrid energy system considering electricity price," Renewable Energy, Elsevier, vol. 219(P1).
    18. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
    19. Canales, Fausto A. & Sapiega, Patryk & Kasiulis, Egidijus & Jonasson, Erik & Temiz, Irina & Jurasz, Jakub, 2024. "Temporal dynamics and extreme events in solar, wind, and wave energy complementarity: Insights from the Polish Exclusive Economic Zone," Energy, Elsevier, vol. 305(C).
    20. Vakalis, Stergios & Kaffas, Konstantinos & Moustakas, Konstantinos, 2020. "The water-energy-climate nexus concept of “Hydrobattery”: Storing excess Variable Renewable Energy (VRE) at the Canyon Ferry Dam," Renewable Energy, Elsevier, vol. 155(C), pages 547-554.

    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:16:y:2023:i:7:p:3020-:d:1107491. 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.