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A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids

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  • Lingling Li

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China)

  • Jiarui Pei

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China)

  • Qiang Shen

    (State Grid Henan Xinxiang Electric Power Company Xinxiang Power Supply Company, Zhengzhou 450100, China)

Abstract

As fossil energy is increasingly depleted, promoting the integration of renewable energy into the grid and improving its utilization rate has become an irresistible development trend in China’s power industry. However, the volatility of wind power increases the difficulty of economic dispatch in power systems. With the rising participation of wind power in the system, the complexity of traditional microgrid dynamic scheduling problems has increased, transforming into a dynamic economic scheduling problem for wind power thermal power hybrid microgrids. Starting from the concept and research significance of economic dispatch, this article analyzes the current research status of microgrid economic dispatch as well as the impact and influencing factors of wind energy grid connection on it. It summarizes the research performed by scholars in two aspects: scheduling models and solving algorithms in static dispatch, as well as how to deal with wind power randomness in dynamic dispatch and how to balance environmental protection while ensuring economic maximization. Finally, the existing problems in current research were summarized and future development directions were prospected. This research has important application prospects in improving the economy of the system and protecting the ecological environment.

Suggested Citation

  • Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:3985-:d:1142727
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    References listed on IDEAS

    as
    1. Oracio I. Barbosa-Ayala & Jhon A. Montañez-Barrera & Cesar E. Damian-Ascencio & Adriana Saldaña-Robles & J. Arturo Alfaro-Ayala & Jose Alfredo Padilla-Medina & Sergio Cano-Andrade, 2020. "Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation," Energies, MDPI, vol. 13(17), pages 1-15, August.
    2. Xiuyun Wang & Shaoxin Chen & Yibing Zhou & Jian Wang & Yang Cui, 2018. "Optimal Dispatch of Microgrid with Combined Heat and Power System Considering Environmental Cost," Energies, MDPI, vol. 11(10), pages 1-23, September.
    3. Gang Zhang & Yaning Zhu & Tuo Xie & Kaoshe Zhang & Xin He, 2022. "Wind Power Consumption Model Based on the Connection between Mid- and Long-Term Monthly Bidding Power Decomposition and Short-Term Wind-Thermal Power Joint Dispatch," Energies, MDPI, vol. 15(19), pages 1-25, September.
    4. Kumar Biswajit Debnath & Monjur Mourshed, 2018. "Author Correction: Challenges and gaps for energy planning models in the developing-world context," Nature Energy, Nature, vol. 3(6), pages 528-528, June.
    5. Tingli Cheng & Minyou Chen & Yingxiang Wang & Bo Li & Muhammad Arshad Shehzad Hassan & Tao Chen & Ruilin Xu, 2018. "Adaptive Robust Method for Dynamic Economic Emission Dispatch Incorporating Renewable Energy and Energy Storage," Complexity, Hindawi, vol. 2018, pages 1-13, June.
    6. Kumar Biswajit Debnath & Monjur Mourshed, 2018. "Challenges and gaps for energy planning models in the developing-world context," Nature Energy, Nature, vol. 3(3), pages 172-184, March.
    7. 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.
    8. Elio Chiodo & Maurizio Fantauzzi & Giovanni Mazzanti, 2022. "The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and Its Bayes Estimation," Energies, MDPI, vol. 15(3), pages 1-26, January.
    9. Hanifa Teimourian & Mahmoud Abubakar & Melih Yildiz & Amir Teimourian, 2022. "A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution," Energies, MDPI, vol. 15(15), pages 1-15, August.
    10. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    11. Cazzaro, Davide & Fischetti, Martina & Fischetti, Matteo, 2020. "Heuristic algorithms for the Wind Farm Cable Routing problem," Applied Energy, Elsevier, vol. 278(C).
    12. F. Daniel Santillán-Lemus & Hertwin Minor-Popocatl & Omar Aguilar-Mejía & Ruben Tapia-Olvera, 2019. "Optimal Economic Dispatch in Microgrids with Renewable Energy Sources," Energies, MDPI, vol. 12(1), pages 1-14, January.
    13. Li Yan & Zhengyu Zhu & Xiaopeng Kang & Boyang Qu & Baihao Qiao & Jiajia Huan & Xuzhao Chai, 2022. "Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-22, July.
    14. Dai, Wei & Yang, Zhifang & Yu, Juan & Cui, Wei & Li, Wenyuan & Li, Jinghua & Liu, Hui, 2021. "Economic dispatch of interconnected networks considering hidden flexibility," Energy, Elsevier, vol. 223(C).
    15. Huifeng Zhang & Xiaohui Lei & Chao Wang & Dong Yue & Xiangpeng Xie, 2017. "Adaptive grid based multi-objective Cauchy differential evolution for stochastic dynamic economic emission dispatch with wind power uncertainty," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-25, September.
    16. Qiao, Baihao & Liu, Jing, 2020. "Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm," Renewable Energy, Elsevier, vol. 154(C), pages 316-336.
    17. Rongquan Zhang & Saddam Aziz & Muhammad Umar Farooq & Kazi Nazmul Hasan & Nabil Mohammed & Sadiq Ahmad & Nisrine Ibadah, 2021. "A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor," Energies, MDPI, vol. 14(11), pages 1-22, May.
    18. Zhang, Guo-Xing & Yang, Yang & Su, Bin & Nie, Yan & Duan, Hong-Bo, 2023. "Electricity production, power generation structure, and air pollution: A monthly data analysis for 279 cities in China (2015–2019)," Energy Economics, Elsevier, vol. 120(C).
    19. Ke Jiang & Feng Wu & Linjun Shi & Keman Lin, 2020. "Distributed Hierarchical Consensus-Based Economic Dispatch for Isolated AC/DC Hybrid Microgrid," Energies, MDPI, vol. 13(12), pages 1-23, June.
    20. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    21. Zhenhua Xiong & Yan Chen & Guihua Ban & Yixin Zhuo & Kui Huang, 2022. "A Hybrid Algorithm for Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(19), pages 1-11, October.
    22. Roy, Sanjoy, 2018. "The maximum likelihood optima for an economic load dispatch in presence of demand and generation variability," Energy, Elsevier, vol. 147(C), pages 915-923.
    23. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
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