IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i18p4654-d1480340.html
   My bibliography  Save this article

Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems

Author

Listed:
  • Sile Hu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
    Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China)

  • Jiaqiang Yang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yu Guo

    (Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China)

  • Yue Bi

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jianan Nan

    (Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China)

Abstract

With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system’s adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system’s adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system’s net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels.

Suggested Citation

  • Sile Hu & Jiaqiang Yang & Yu Guo & Yue Bi & Jianan Nan, 2024. "Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems," Energies, MDPI, vol. 17(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4654-:d:1480340
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/18/4654/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/18/4654/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
    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. Debnath, Kumar Biswajit & Jenkins, David P. & Patidar, Sandhya & Peacock, Andrew D., 2024. "Remote work might unlock solar PV's potential of cracking the ‘Duck Curve’," Applied Energy, Elsevier, vol. 367(C).
    2. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    3. Yi Hao & Zhigang Huang & Shiqian Ma & Jiakai Huang & Jiahao Chen & Bing Sun, 2023. "Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation," Energies, MDPI, vol. 16(16), pages 1-17, August.
    4. Kanato Tamashiro & Talal Alharbi & Alexey Mikhaylov & Ashraf M. Hemeida & Narayanan Krishnan & Mohammed Elsayed Lotfy & Tomonobu Senjyu, 2021. "Investigation of Home Energy Management with Advanced Direct Load Control and Optimal Scheduling of Controllable Loads," Energies, MDPI, vol. 14(21), pages 1-14, November.
    5. Junchao Cheng & Yongyi Huang & Hongjing He & Abdul Matin Ibrahimi & Tomonobu Senjyu, 2023. "Optimal Operation of CCHP System Combined Electric Vehicles Considering Seasons," Energies, MDPI, vol. 16(10), pages 1-21, May.
    6. Chen, Jiahao & Sun, Bing & Li, Yunfei & Jing, Ruipeng & Zeng, Yuan & Li, Minghao, 2022. "Credible capacity calculation method of distributed generation based on equal power supply reliability criterion," Renewable Energy, Elsevier, vol. 201(P1), pages 534-547.
    7. Homeyra Akter & Harun Or Rashid Howlader & Ahmed Y. Saber & Paras Mandal & Hiroshi Takahashi & Tomonobu Senjyu, 2021. "Optimal Sizing of Hybrid Microgrid in a Remote Island Considering Advanced Direct Load Control for Demand Response and Low Carbon Emission," Energies, MDPI, vol. 14(22), pages 1-19, November.
    8. Khush Bakht & Syed Abdul Rahman Kashif & Muhammad Salman Fakhar & Irfan Ahmad Khan & Ghulam Abbas, 2023. "Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 16(7), pages 1-23, April.
    9. Harsh Wardhan Pandey & Ramesh Kumar & Rajib Kumar Mandal, 2023. "Ranking of mitigation strategies for duck curve in Indian active distribution network using MCDM," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1255-1275, August.
    10. Binghui Han & Younes Zahraoui & Marizan Mubin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Strategy for Comfort-Based Home Energy Management System Considering Impact of Battery Degradation Cost Model," Mathematics, MDPI, vol. 11(6), pages 1-26, March.
    11. 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).
    12. Pierre Cayet & Arash Farnoosh, 2022. "A robust structural electric system model with significant share of intermittent renewables under auto-correlated residual demand," EconomiX Working Papers 2022-6, University of Paris Nanterre, EconomiX.
    13. Sepúlveda-Mora, Sergio B. & Hegedus, Steven, 2021. "Making the case for time-of-use electric rates to boost the value of battery storage in commercial buildings with grid connected PV systems," Energy, Elsevier, vol. 218(C).
    14. Shukhobodskiy, Alexander Alexandrovich & Colantuono, Giuseppe, 2020. "RED WoLF: Combining a battery and thermal energy reservoirs as a hybrid storage system," Applied Energy, Elsevier, vol. 274(C).
    15. Gholami, M. & Sanjari, M.J., 2021. "Multiobjective energy management in battery-integrated home energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 967-975.
    16. Parrado-Hernando, Gonzalo & Pfeifer, Antun & Frechoso, Fernando & Miguel González, Luis Javier & Duić, Neven, 2022. "A novel approach to represent the energy system in integrated assessment models," Energy, Elsevier, vol. 258(C).
    17. Frate, Guido Francesco & Baccioli, Andrea & Bernardini, Leonardo & Ferrari, Lorenzo, 2022. "Assessment of the off-design performance of a solar thermally-integrated pumped-thermal energy storage," Renewable Energy, Elsevier, vol. 201(P1), pages 636-650.
    18. Efstathios E. Michaelides, 2021. "Thermodynamics, Energy Dissipation, and Figures of Merit of Energy Storage Systems—A Critical Review," Energies, MDPI, vol. 14(19), pages 1-41, September.
    19. Tahir, Muhammad Usman & Siraj, Kiran & Ali Shah, Syed Faizan & Arshad, Naveed, 2023. "Evaluation of single-phase net metering to meet renewable energy targets: A case study from Pakistan," Energy Policy, Elsevier, vol. 172(C).
    20. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Wen, Huiqing & Yan, Ke & Kirtley, James, 2020. "Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling," Applied Energy, Elsevier, vol. 268(C).

    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:17:y:2024:i:18:p:4654-:d:1480340. 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.