IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v261y2022ipas0360544222018199.html
   My bibliography  Save this article

Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating

Author

Listed:
  • Akhlaghi, M.
  • Moravej, Z.
  • Bagheri, A.

Abstract

Wind power generations as renewable and sustainable energies are widely integrated into electric power systems due to their technical, environmental, and economic advantages. This integration affects the power system operation and planning studies. Network-constrained unit commitment (NCUC) is one of important operation studies of electric power systems in which the aim is to schedule the generating units for the next 24-h period with the minimum operating cost considering the constraints of generating units and transmission network. One of the essential constraints influencing the results of NCUC is thermal rating of lines and transformers. In most of the previous studies, static ratings have been used for this equipment. The static ratings are conservative values leading to non-optimal use of real capacity of lines and transformers and increasing the system's operational costs. This issue is highlighted when there exist wind energy generations in system. A new NCUC problem is addressed in this paper that considers dynamic line rating (DLR) and dynamic transformer rating (DTR) in the presence of wind power generations. The proposed approach has been implemented on the 24-bus IEEE-RTS system in the GAMS environment in different experiments. The simulation results demonstrate effectiveness of the conducted model in reducing the operational costs and maximizing the usage of wind energy.

Suggested Citation

  • Akhlaghi, M. & Moravej, Z. & Bagheri, A., 2022. "Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating," Energy, Elsevier, vol. 261(PA).
  • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222018199
    DOI: 10.1016/j.energy.2022.124918
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222018199
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.124918?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pan, Jeng-Shyang & Hu, Pei & Chu, Shu-Chuan, 2021. "Binary fish migration optimization for solving unit commitment," Energy, Elsevier, vol. 226(C).
    2. Meng, Anbo & Zeng, Cong & Wang, Peng & Chen, De & Zhou, Tianmin & Zheng, Xiaoying & Yin, Hao, 2021. "A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem," Energy, Elsevier, vol. 225(C).
    3. Nikoobakht, Ahmad & Aghaei, Jamshid & Mokarram, Mohammad Jafar & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Adaptive robust co-optimization of wind energy generation, electric vehicle batteries and flexible AC transmission system devices," Energy, Elsevier, vol. 230(C).
    4. Wang, Bo & Zhou, Min & Xin, Bo & Zhao, Xin & Watada, Junzo, 2019. "Analysis of operation cost and wind curtailment using multi-objective unit commitment with battery energy storage," Energy, Elsevier, vol. 178(C), pages 101-114.
    5. Liu, Yikui & Wu, Lei & Li, Jie, 2019. "Towards accurate modeling of dynamic startup/shutdown and ramping processes of thermal units in unit commitment problems," Energy, Elsevier, vol. 187(C).
    6. Michiorri, Andrea & Nguyen, Huu-Minh & Alessandrini, Stefano & Bremnes, John Bjørnar & Dierer, Silke & Ferrero, Enrico & Nygaard, Bjørn-Egil & Pinson, Pierre & Thomaidis, Nikolaos & Uski, Sanna, 2015. "Forecasting for dynamic line rating," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1713-1730.
    7. Jithendranath, J. & Das, Debapriya & Guerrero, Josep M., 2021. "Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation," Energy, Elsevier, vol. 222(C).
    8. Dong, Jizhe & Han, Shunjie & Shao, Xiangxin & Tang, Like & Chen, Renhui & Wu, Longfei & Zheng, Cunlong & Li, Zonghao & Li, Haolin, 2021. "Day-ahead wind-thermal unit commitment considering historical virtual wind power data," Energy, Elsevier, vol. 235(C).
    9. Mansourshoar, Paria & Yazdankhah, Ahmad Sadeghi & Vatanpour, Mohsen & Mohammadi-Ivatloo, Behnam, 2022. "Impact of implementing a price-based demand response program on the system reliability in security-constrained unit commitment problem coupled with wind farms in the presence of contingencies," Energy, Elsevier, vol. 255(C).
    10. Prajapati, Vijaykumar K. & Mahajan, Vasundhara, 2021. "Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources," Energy, Elsevier, vol. 215(PB).
    11. Bai, Yang & Zhong, Haiwang & Xia, Qing & Kang, Chongqing & Xie, Le, 2015. "A decomposition method for network-constrained unit commitment with AC power flow constraints," Energy, Elsevier, vol. 88(C), pages 595-603.
    12. Zhu, Xiaodong & Zhao, Shihao & Yang, Zhile & Zhang, Ning & Xu, Xinzhi, 2022. "A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors," Energy, Elsevier, vol. 238(PC).
    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. Dong, Jizhe & Li, Yuanhan & Zuo, Shi & Wu, Xiaomei & Zhang, Zuyao & Du, Jiang, 2023. "An intraperiod arbitrary ramping-rate changing model in unit commitment," Energy, Elsevier, vol. 284(C).
    2. Mohamed A. M. Shaheen & Hany M. Hasanien & Said F. Mekhamer & Mohammed H. Qais & Saad Alghuwainem & Zia Ullah & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-23, August.
    3. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    4. Romain Dupin & Laura Cavalcante & Ricardo J. Bessa & Georges Kariniotakis & Andrea Michiorri, 2020. "Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation," Energies, MDPI, vol. 13(12), pages 1-21, June.
    5. Ewaoche John Okampo & Nnamdi Nwulu & Pitshou N. Bokoro, 2022. "Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook," Sustainability, MDPI, vol. 14(13), pages 1-26, June.
    6. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    7. Min Xie & Yuxin Du & Peijun Cheng & Wei Wei & Mingbo Liu, 2019. "A Cross-Entropy-Based Hybrid Membrane Computing Method for Power System Unit Commitment Problems," Energies, MDPI, vol. 12(3), pages 1-18, February.
    8. Jiping Xu & Ziyi Wang & Xin Zhang & Jiabin Yu & Xiaoyu Cui & Yan Zhou & Zhiyao Zhao, 2022. "A Rice Security Risk Assessment Method Based on the Fusion of Multiple Machine Learning Models," Agriculture, MDPI, vol. 12(6), pages 1-15, June.
    9. Saffari, Mohammadali & Crownshaw, Timothy & McPherson, Madeleine, 2023. "Assessing the potential of demand-side flexibility to improve the performance of electricity systems under high variable renewable energy penetration," Energy, Elsevier, vol. 272(C).
    10. Wang, Cheng & Liu, Chuang & Lin, Yuzhang & Bi, Tianshu, 2020. "Day-ahead dispatch of integrated electric-heat systems considering weather-parameter-driven residential thermal demands," Energy, Elsevier, vol. 203(C).
    11. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    12. Dong, Jizhe & Han, Shunjie & Shao, Xiangxin & Tang, Like & Chen, Renhui & Wu, Longfei & Zheng, Cunlong & Li, Zonghao & Li, Haolin, 2021. "Day-ahead wind-thermal unit commitment considering historical virtual wind power data," Energy, Elsevier, vol. 235(C).
    13. Zhu, Xiaodong & Zhao, Shihao & Yang, Zhile & Zhang, Ning & Xu, Xinzhi, 2022. "A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors," Energy, Elsevier, vol. 238(PC).
    14. O., Yugeswar Reddy & J., Jithendranath & Chakraborty, Ajoy Kumar & Guerrero, Josep M., 2022. "Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties," Applied Energy, Elsevier, vol. 307(C).
    15. Meng, Anbo & Wu, Zhenbo & Zhang, Zhan & Xu, Xuancong & Tang, Yanshu & Xie, Zhifeng & Xian, Zikang & Zhang, Haitao & Luo, Jianqiang & Wang, Yu & Yan, Baiping & Yin, Hao, 2024. "Optimal scheduling of integrated energy system using decoupled distributed CSO with opposition-based learning and neighborhood re-dispatch strategy," Renewable Energy, Elsevier, vol. 224(C).
    16. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Gino Astorga & Carlos Castro & José García, 2022. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review," Mathematics, MDPI, vol. 11(1), pages 1-32, December.
    17. Karimi, Soheila & Musilek, Petr & Knight, Andrew M., 2018. "Dynamic thermal rating of transmission lines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 600-612.
    18. Aml Sayed & Mohamed Ebeed & Ziad M. Ali & Adel Bedair Abdel-Rahman & Mahrous Ahmed & Shady H. E. Abdel Aleem & Adel El-Shahat & Mahmoud Rihan, 2021. "A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand," Energies, MDPI, vol. 14(23), pages 1-21, November.
    19. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2024. "Optimizing corrective maintenance for multistate systems with storage," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    20. Qun Niu & Lipeng Tang & Litao Yu & Han Wang & Zhile Yang, 2024. "Unit Commitment Considering Electric Vehicles and Renewable Energy Integration—A CMAES Approach," Sustainability, MDPI, vol. 16(3), pages 1-28, 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:eee:energy:v:261:y:2022:i:pa:s0360544222018199. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.