IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p10892-d1191773.html
   My bibliography  Save this article

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

Author

Listed:
  • Dagui Liu

    (Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Control, Xinjiang University, Urumqi 830047, China
    Power Dispatching Control Center, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830063, China)

  • Weiqing Wang

    (Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Control, Xinjiang University, Urumqi 830047, China)

  • Huie Zhang

    (College of Energy Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China)

  • Wei Shi

    (State Grid Urumqi Electric Power Supply Company, Urumqi 830001, China)

  • Caiqing Bai

    (Inner Mongolia Extra-High Voltage Power Supply Bureau, Hohhot 010080, China)

  • Huimin Zhang

    (Inner Mongolia Extra-High Voltage Power Supply Bureau, Hohhot 010080, China)

Abstract

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.

Suggested Citation

  • Dagui Liu & Weiqing Wang & Huie Zhang & Wei Shi & Caiqing Bai & Huimin Zhang, 2023. "Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10892-:d:1191773
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/10892/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/14/10892/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chao Fu & Guo-Quan Li & Kuo-Ping Lin & Hui-Juan Zhang, 2019. "Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    2. Musawenkosi Lethumcebo Thanduxolo Zulu & Rudiren Pillay Carpanen & Remy Tiako, 2023. "A Comprehensive Review: Study of Artificial Intelligence Optimization Technique Applications in a Hybrid Microgrid at Times of Fault Outbreaks," Energies, MDPI, vol. 16(4), pages 1-32, February.
    3. Matamala, Yolanda & Feijoo, Felipe, 2021. "A two-stage stochastic Stackelberg model for microgrid operation with chance constraints for renewable energy generation uncertainty," Applied Energy, Elsevier, vol. 303(C).
    4. Hussein Slim & Sylvie Nadeau, 2020. "A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    5. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    6. Ernesto Chavero-Navarrete & Mario Trejo-Perea & Juan-Carlos Jáuregui-Correa & Roberto-Valentín Carrillo-Serrano & José-Gabriel Rios-Moreno, 2019. "Pitch Angle Optimization by Intelligent Adjusting the Gains of a PI Controller for Small Wind Turbines in Areas with Drastic Wind Speed Changes," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    7. Miao Tang & Minghua Hu & Honghai Zhang & Long Zhou, 2022. "Research on Multi Unmanned Aerial Vehicles Emergency Task Planning Method Based on Discrete Multi-Objective TLBO Algorithm," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    8. Nandini K. Krishnamurthy & Jayalakshmi N. Sabhahit & Vinay Kumar Jadoun & Dattatraya Narayan Gaonkar & Ashish Shrivastava & Vidya S. Rao & Ganesh Kudva, 2023. "Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method," Energies, MDPI, vol. 16(4), pages 1-27, February.
    9. Zhengjie Li & Zhisheng Zhang, 2021. "Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting," Energies, MDPI, vol. 14(9), pages 1-14, April.
    10. Xiaofei Jia & Zhaobo Sun & Guanglun Lei & Chuanjin Yao, 2023. "Model for Predicting Horizontal Well Transient Productivity in the Bottom-Water Reservoir with Finite Water Bodies," Energies, MDPI, vol. 16(4), pages 1-13, February.
    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. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    2. Mohd Bilal & Pitshou N. Bokoro & Gulshan Sharma & Giovanni Pau, 2024. "A Cost-Effective Energy Management Approach for On-Grid Charging of Plug-in Electric Vehicles Integrated with Hybrid Renewable Energy Sources," Energies, MDPI, vol. 17(16), pages 1-35, August.
    3. Osório, G.J. & Lujano-Rojas, J.M. & Matias, J.C.O. & Catalão, J.P.S., 2015. "A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources," Energy, Elsevier, vol. 82(C), pages 949-959.
    4. Zhihan Shi & Weisong Han & Guangming Zhang & Zhiqing Bai & Mingxiang Zhu & Xiaodong Lv, 2022. "Research on Low-Carbon Energy Sharing through the Alliance of Integrated Energy Systems with Multiple Uncertainties," Energies, MDPI, vol. 15(24), pages 1-20, December.
    5. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    6. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    7. Spyridon Chapaloglou & Babak Abdolmaleki & Elisabetta Tedeschi, 2023. "Optimal Generation Capacity Allocation and Droop Control Design for Current Sharing in DC Microgrids," Energies, MDPI, vol. 16(12), pages 1-17, June.
    8. Flores, Francisco & Feijoo, Felipe & DeStephano, Paelina & Herc, Luka & Pfeifer, Antun & Duić, Neven, 2024. "Assessment of the impacts of renewable energy variability in long-term decarbonization strategies," Applied Energy, Elsevier, vol. 368(C).
    9. Zixiang Yan & Wen Zhou & Jinxiao Li & Xuedan Zhu & Yuxin Zang & Liuyi Zhang, 2024. "Skillful Seasonal Prediction of Global Onshore Wind Resources in SIDRI-ESS V1.0," Sustainability, MDPI, vol. 16(17), pages 1-16, September.
    10. Marc Deissenroth & Martin Klein & Kristina Nienhaus & Matthias Reeg, 2017. "Assessing the Plurality of Actors and Policy Interactions: Agent-Based Modelling of Renewable Energy Market Integration," Complexity, Hindawi, vol. 2017, pages 1-24, December.
    11. Min Xu & Wanwei Li & Zhihui Feng & Wangwang Bai & Lingling Jia & Zhanhong Wei, 2023. "Economic Dispatch Model of High Proportional New Energy Grid-Connected Consumption Considering Source Load Uncertainty," Energies, MDPI, vol. 16(4), pages 1-20, February.
    12. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
    13. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    14. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    15. Pan, Zehua & Shen, Jian & Wang, Jingyi & Xu, Xinhai & Chan, Wei Ping & Liu, Siyu & Zhou, Yexin & Yan, Zilin & Jiao, Zhenjun & Lim, Teik-Thye & Zhong, Zheng, 2022. "Thermodynamic analyses of a standalone diesel-fueled distributed power generation system based on solid oxide fuel cells," Applied Energy, Elsevier, vol. 308(C).
    16. Thangaraj Yuvaraj & Thirukoilur Dhandapani Suresh & Arokiasamy Ananthi Christy & Thanikanti Sudhakar Babu & Benedetto Nastasi, 2023. "Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems," Energies, MDPI, vol. 16(19), pages 1-31, September.
    17. 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).
    18. Kim, H.J. & Kim, M.K., 2023. "A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid," Applied Energy, Elsevier, vol. 332(C).
    19. Xu, Jiazhu & Yi, Yuqin, 2023. "Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach," Energy, Elsevier, vol. 263(PB).
    20. Zhao, Lulin & Yin, Linfei, 2024. "Knowledge-shareable adaptive deep dynamic programming for hierarchical generation control of distributed high-percentage renewable energy systems," Renewable Energy, Elsevier, vol. 228(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:jsusta:v:15:y:2023:i:14:p:10892-:d:1191773. 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.