IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1257-d1352278.html
   My bibliography  Save this article

Optimization and Scheduling Method for Power Systems Considering Wind Power Forward/Reverse Peaking Scenarios

Author

Listed:
  • Hao Yu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Yibo Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Chuang Liu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Shunjiang Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Chunyang Hao

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Jian Xiong

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

Abstract

With the promotion of the dual carbon target, the scale of the wind power grid connection will significantly increase. However, wind power has characteristics such as randomness and volatility, and its grid connection challenges the pressure of system peak shaving, making it increasingly difficult to regulate the power system. To solve the problem of wind power abandonment, the positive and negative peak shaving characteristics of wind power were first analyzed. Based on this, it is proposed that demand response resources and energy storage with adjustable characteristics are used as the new means of wind power consumption. Together with the thermal power units, they participate in the optimization and scheduling of the power grid, forming a coordinated and optimized operation mode of source load storage. With the goal of minimizing system operating costs, a two-stage economic scheduling model was formed for the day-ahead and intra-day periods. Finally, optimization software was used to solve the problem, and the simulation results showed the effectiveness of the proposed economic scheduling model, which can improve the system’s new energy consumption and reduce the system’s operating costs.

Suggested Citation

  • Hao Yu & Yibo Wang & Chuang Liu & Shunjiang Wang & Chunyang Hao & Jian Xiong, 2024. "Optimization and Scheduling Method for Power Systems Considering Wind Power Forward/Reverse Peaking Scenarios," Energies, MDPI, vol. 17(5), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1257-:d:1352278
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1257/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/5/1257/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Changming & Wu, Xueyan & Li, Yan & Zhu, Xiaojun & Li, Zesen & Ma, Jien & Qiu, Weiqiang & Liu, Chang & Lin, Zhenzhi & Yang, Li & Wang, Qin & Ding, Yi, 2021. "Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages," Applied Energy, Elsevier, vol. 302(C).
    2. Ceballos, Salvador & Rea, Judy & Robles, Eider & Lopez, Iraide & Pou, Josep & O'Sullivan, Dara, 2015. "Control strategies for combining local energy storage with wells turbine oscillating water column devices," Renewable Energy, Elsevier, vol. 83(C), pages 1097-1109.
    3. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    4. Yang, Hongming & Liang, Rui & Yuan, Yuan & Chen, Bowen & Xiang, Sheng & Liu, Junpeng & Zhao, Huan & Ackom, Emmanuel, 2022. "Distributionally robust optimal dispatch in the power system with high penetration of wind power based on net load fluctuation data," Applied Energy, Elsevier, vol. 313(C).
    5. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    6. Chen, Maozhi & Lu, Hao & Chang, Xiqiang & Liao, Haiyan, 2023. "An optimization on an integrated energy system of combined heat and power, carbon capture system and power to gas by considering flexible load," Energy, Elsevier, vol. 273(C).
    7. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
    8. Roy, Anindita & Kedare, Shireesh B. & Bandyopadhyay, Santanu, 2010. "Optimum sizing of wind-battery systems incorporating resource uncertainty," Applied Energy, Elsevier, vol. 87(8), pages 2712-2727, August.
    9. Zafirakis, Dimitrios & Chalvatzis, Konstantinos J. & Baiocchi, Giovanni & Daskalakis, George, 2013. "Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy," Applied Energy, Elsevier, vol. 105(C), pages 138-154.
    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. Gao, Qiang & Yuan, Rui & Ertugrul, Nesimi & Ding, Boyin & Hayward, Jennifer A. & Li, Ye, 2023. "Analysis of energy variability and costs for offshore wind and hybrid power unit with equivalent energy storage system," Applied Energy, Elsevier, vol. 342(C).
    2. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
    3. Cheekatamarla, Praveen K. & Kassaee, Saiid & Abu-Heiba, Ahmad & Momen, Ayyoub M., 2022. "Near isothermal compressed air energy storage system in residential and commercial buildings: Techno-economic analysis," Energy, Elsevier, vol. 251(C).
    4. Locatelli, Giorgio & Invernizzi, Diletta Colette & Mancini, Mauro, 2016. "Investment and risk appraisal in energy storage systems: A real options approach," Energy, Elsevier, vol. 104(C), pages 114-131.
    5. Moradi, Jalal & Shahinzadeh, Hossein & Khandan, Amirsalar & Moazzami, Majid, 2017. "A profitability investigation into the collaborative operation of wind and underwater compressed air energy storage units in the spot market," Energy, Elsevier, vol. 141(C), pages 1779-1794.
    6. Fan, Guozhu & Peng, Chunhua & Wang, Xuekui & Wu, Peng & Yang, Yifan & Sun, Huijuan, 2024. "Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC," Renewable Energy, Elsevier, vol. 226(C).
    7. Hou, Rui & Deng, Guangzhi & Wu, Minrong & Wang, Wei & Gao, Wei & Chen, Kang & Liu, Lijun & Dehan, Sim, 2023. "Optimum exploitation of an integrated energy system considering renewable sources and power-heat system and energy storage," Energy, Elsevier, vol. 282(C).
    8. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Gao, Chong & Ma, Zeyang, 2023. "A novel two-layer nested optimization method for a zero-carbon island integrated energy system, incorporating tidal current power generation," Renewable Energy, Elsevier, vol. 218(C).
    9. Ma, Lan & Xie, Lirong & Ye, Jiahao & Bian, Yifan, 2024. "Two-stage dispatching strategy for park-level integrated energy systems based on a master-slave-cooperative hybrid game model," Renewable Energy, Elsevier, vol. 232(C).
    10. Daghi, Majid & Sedghi, Mahdi & Ahmadian, Ali & Aliakbar-Golkar, Masoud, 2016. "Factor analysis based optimal storage planning in active distribution network considering different battery technologies," Applied Energy, Elsevier, vol. 183(C), pages 456-469.
    11. Kies, Alexander & Schyska, Bruno U. & Bilousova, Mariia & El Sayed, Omar & Jurasz, Jakub & Stoecker, Horst, 2021. "Critical review of renewable generation datasets and their implications for European power system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    12. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    13. Zhu, Xiaoxun & Hu, Ming & Xue, Jinfei & Li, Yuxuan & Han, Zhonghe & Gao, Xiaoxia & Wang, Yu & Bao, Linlin, 2024. "Research on multi-time scale integrated energy scheduling optimization considering carbon constraints," Energy, Elsevier, vol. 302(C).
    14. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units," Renewable Energy, Elsevier, vol. 95(C), pages 74-84.
    15. Wang, L.L. & Xian, R.C. & Jiao, P.H. & Liu, X.H. & Xing, Y.W. & Wang, W., 2024. "Cooperative operation of industrial/commercial/residential integrated energy system with hydrogen energy based on Nash bargaining theory," Energy, Elsevier, vol. 288(C).
    16. Vallati, A. & de Lieto Vollaro, R. & Oclon, P. & Taler, J., 2021. "Experimental and analytical evaluation of a gas-liquid energy storage (GLES) prototype," Energy, Elsevier, vol. 224(C).
    17. Wang, L.L. & Xian, R.C. & Jiao, P.H. & Chen, J.J. & Chen, Y. & Liu, H.G., 2024. "Multi-timescale optimization of integrated energy system with diversified utilization of hydrogen energy under the coupling of green certificate and carbon trading," Renewable Energy, Elsevier, vol. 228(C).
    18. Zhao, Xudong & Wang, Yibo & Liu, Chuang & Cai, Guowei & Ge, Weichun & Zhou, Jianing & Wang, Dongzhe, 2024. "Low carbon scheduling method of electric power system considering energy-intensive load regulation of electrofused magnesium and wind powerfluctuation stabilization," Applied Energy, Elsevier, vol. 357(C).
    19. Zhang, Ning & Lu, Xi & McElroy, Michael B. & Nielsen, Chris P. & Chen, Xinyu & Deng, Yu & Kang, Chongqing, 2016. "Reducing curtailment of wind electricity in China by employing electric boilers for heat and pumped hydro for energy storage," Applied Energy, Elsevier, vol. 184(C), pages 987-994.
    20. Chen, Long Xiang & Xie, Mei Na & Zhao, Pan Pan & Wang, Feng Xiang & Hu, Peng & Wang, Dong Xiang, 2018. "A novel isobaric adiabatic compressed air energy storage (IA-CAES) system on the base of volatile fluid," Applied Energy, Elsevier, vol. 210(C), pages 198-210.

    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:5:p:1257-:d:1352278. 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.