Bilateral Reserve Capacity Optimization of Power System Based on Renewable Energy Uncertainty

LYU Xiaofan, ZHOU Jie, JIANG Chuanwen

Electric Power Construction ›› 2016, Vol. 37 ›› Issue (4) : 16-21.

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Electric Power Construction ›› 2016, Vol. 37 ›› Issue (4) : 16-21. DOI: 10.3969/j.issn.1000-7229.2016.04.003

Bilateral Reserve Capacity Optimization of Power System Based on Renewable Energy Uncertainty

  • LYU Xiaofan1, ZHOU Jie2, JIANG Chuanwen1
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Abstract

 Under the background of global energy internet, renewable energy represented by wind power insertion into the power system brings new challenges to the security and stability of the system operation and puts forward higher requirements to the configuration of reserve capacity. For this reason, this paper proposes the optimization model of reserve capacity taking the minimum cost expectations on both sides as the objective function. Considering the interruptible load in the demand response and the uncertainty factors such as the deviations of wind speed prediction and load prediction, we set up the allocation principle of reserve capacity that can satisfy the requirements of economy and reliability. Finally, we solve the improved 10-units test system with using the genetic algorithm based on Monte Carlo stochastic simulation. The economy of reserve capacity optimization decision with considering demand response is better, which is obtained through the example. Compared with traditional reserve capacity configuration, the bilateral reserve capacity configuration can achieve the optimal allocation of resources within the system as a whole, and improve the absorption of wind power generation as well, which can achieve the optimum security and economy of power system.

Key words

wind field / demand response / bilateral reserve capacity / chance constrained

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LYU Xiaofan, ZHOU Jie, JIANG Chuanwen. Bilateral Reserve Capacity Optimization of Power System Based on Renewable Energy Uncertainty[J]. Electric Power Construction. 2016, 37(4): 16-21 https://doi.org/10.3969/j.issn.1000-7229.2016.04.003

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Funding

Project supported by National Natural Science Foundation of China(51577116 )

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