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Peak shaving operation of hydro-thermal-nuclear plants serving multiple power grids by linear programming

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  • Feng, Zhong-kai
  • Niu, Wen-jing
  • Cheng, Chun-tian
  • Zhou, Jian-zhong

Abstract

With the rapid economic development in recent years, the peak load demands of China are experiencing a booming period. As a regional power grid with the maximum electrical load in the world, the East China Power Grid (ECPG) is in charge of coordinating simultaneously the power generation of its own power plants to several subordinate provincial power grids. However, due to unreasonable power structure, there is a lack of flexible energy to quickly respond the peak loads of multiple power grids, which has brought a new real challenge for the dispatching center of most regional power grids in China. Hence, to meet the practical requirement of peak shaving operation in China, a novel linear programming optimization model is proposed in this paper to find out the optimal quarter-hourly generation allocation plan while satisfying a group of complex constraints. In this model, the objective is to minimize the summation of peak-valley difference of the residual load series by subtracting the total allocated generation from the original load of each power grid. This model is used to solve the day-head peak operation of 14 hydro-thermal-nuclear plants serving multiple power grids in ECPG. The results from different cases show that compared with the current method used in practical engineering, the proposed model is capable of providing results with smoother remaining load series for each power grid. Thus, this method proves to be effective technique to provide scientific decision support for large-scale generation allocation of plants serving multiple interconnected power grids in China.

Suggested Citation

  • Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian & Zhou, Jian-zhong, 2017. "Peak shaving operation of hydro-thermal-nuclear plants serving multiple power grids by linear programming," Energy, Elsevier, vol. 135(C), pages 210-219.
    • Handle: RePEc:eee:energy:v:135:y:2017:i:c:p:210-219
    DOI: 10.1016/j.energy.2017.06.097
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    5. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2019. "China’s large-scale hydropower system: operation characteristics, modeling challenge and dimensionality reduction possibilities," Renewable Energy, Elsevier, vol. 136(C), pages 805-818.
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    14. Ak, Mümtaz & Kentel, Elcin & Savasaneril, Secil, 2019. "Quantifying the revenue gain of operating a cascade hydropower plant system as a pumped-storage hydropower system," Renewable Energy, Elsevier, vol. 139(C), pages 739-752.
    15. Tan, Luzhi & Dong, Xiaoming & Gong, Zhiqiang & Wang, Mingtao, 2018. "Analysis on energy efficiency and CO2 emission reduction of an SOFC-based energy system served public buildings with large interior zones," Energy, Elsevier, vol. 165(PB), pages 1106-1118.
    16. Wang, Peilin & Yuan, Wenlin & Su, Chengguo & Wu, Yang & Lu, Lu & Yan, Denghua & Wu, Zening, 2022. "Short-term optimal scheduling of cascade hydropower plants shaving peak load for multiple power grids," Renewable Energy, Elsevier, vol. 184(C), pages 68-79.
    17. Jianjian Shen & Xiufei Zhang & Jian Wang & Rui Cao & Sen Wang & Jun Zhang, 2019. "Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions," Energies, MDPI, vol. 12(1), pages 1-19, January.
    18. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
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    20. Qi Liu & Jie Zhao & Youguo Shao & Libin Wen & Jianxu Wu & Dichen Liu & Yuhui Ma, 2019. "Multi-Power Joint Peak-Shaving Optimization for Power System Considering Coordinated Dispatching of Nuclear Power and Wind Power," Sustainability, MDPI, vol. 11(17), pages 1-23, September.
    21. Zhongkai Feng & Wenjing Niu & Sen Wang & Chuntian Cheng & Zhenguo Song, 2019. "Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones," Energies, MDPI, vol. 12(11), pages 1-17, June.
    22. Chen, Xiaojiao & Huang, Liansheng & Liu, Junbo & Song, Dongran & Yang, Sheng, 2022. "Peak shaving benefit assessment considering the joint operation of nuclear and battery energy storage power stations: Hainan case study," Energy, Elsevier, vol. 239(PA).
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    24. Shuai Liu & Zhong-Kai Feng & Wen-Jing Niu & Hai-Rong Zhang & Zhen-Guo Song, 2019. "Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation," Energies, MDPI, vol. 12(11), pages 1-24, June.

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