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A Chance-Constrained Economic Dispatch Model in Wind-Thermal-Energy Storage System

Author

Listed:
  • Yanzhe Hu

    (Institute of Water Resources and Hydro-electric Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Yang Li

    (Institute of Water Resources and Hydro-electric Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Mengjie Xu

    (State Grid Shaanxi Economic Research Institue, Xi’an 710065, China)

  • Li Zhou

    (State Grid Hubei Electric Economics and Technology Research Institute, Wuhan 430077, China)

  • Mingjian Cui

    (Department of Mechanical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA)

Abstract

As a type of renewable energy, wind energy is integrated into the power system with more and more penetration levels. It is challenging for the power system operators (PSOs) to cope with the uncertainty and variation of the wind power and its forecasts. A chance-constrained economic dispatch (ED) model for the wind-thermal-energy storage system (WTESS) is developed in this paper. An optimization model with the wind power and the energy storage system (ESS) is first established with the consideration of both the economic benefits of the system and less wind curtailments. The original wind power generation is processed by the ESS to obtain the final wind power output generation (FWPG). A Gaussian mixture model (GMM) distribution is adopted to characterize the probabilistic and cumulative distribution functions with an analytical expression. Then, a chance-constrained ED model integrated by the wind-energy storage system (W-ESS) is developed by considering both the overestimation costs and the underestimation costs of the system and solved by the sequential linear programming method. Numerical simulation results using the wind power data in four wind farms are performed on the developed ED model with the IEEE 30-bus system. It is verified that the developed ED model is effective to integrate the uncertain and variable wind power. The GMM distribution could accurately fit the actual distribution of the final wind power output, and the ESS could help effectively decrease the operation costs.

Suggested Citation

  • Yanzhe Hu & Yang Li & Mengjie Xu & Li Zhou & Mingjian Cui, 2017. "A Chance-Constrained Economic Dispatch Model in Wind-Thermal-Energy Storage System," Energies, MDPI, vol. 10(3), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:326-:d:92493
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    References listed on IDEAS

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    1. Díaz-González, Francisco & Hau, Melanie & Sumper, Andreas & Gomis-Bellmunt, Oriol, 2014. "Participation of wind power plants in system frequency control: Review of grid code requirements and control methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 551-564.
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    Cited by:

    1. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2020. "An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    2. Yangwu Shen & Mingjian Cui & Qin Wang & Feifan Shen & Bin Zhang & Liqing Liang, 2017. "Comprehensive Reactive Power Support of DFIG Adapted to Different Depth of Voltage Sags," Energies, MDPI, vol. 10(6), pages 1-20, June.
    3. Wei-Tzer Huang & Kai-Chao Yao & Ming-Ku Chen & Feng-Ying Wang & Cang-Hui Zhu & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2018. "Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch," Energies, MDPI, vol. 11(2), pages 1-19, February.
    4. Jianfeng Li & Dongxiao Niu & Ming Wu & Yongli Wang & Fang Li & Huanran Dong, 2018. "Research on Battery Energy Storage as Backup Power in the Operation Optimization of a Regional Integrated Energy System," Energies, MDPI, vol. 11(11), pages 1-20, November.
    5. Aliyu Hassan & Yskandar Hamam & Josiah L. Munda, 2019. "Minimizing the Impact of Intermittent Wind Power on Multiperiod Power System Operation with Pumped Hydro Generation," Energies, MDPI, vol. 12(18), pages 1-22, September.
    6. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    7. Pei Bie & Buhan Zhang & Hang Li & Yong Wang & Le Luan & Guoyan Chen & Guojun Lu, 2017. "Chance-Constrained Real-Time Dispatch with Renewable Uncertainty Based on Dynamic Load Flow," Energies, MDPI, vol. 10(12), pages 1-20, December.
    8. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
    9. Yongli Wang & Haiyang Yu & Mingyue Yong & Yujing Huang & Fuli Zhang & Xiaohai Wang, 2018. "Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss," Energies, MDPI, vol. 11(7), pages 1-21, June.

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