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Appl. Syst. Innov. 2018, 1(3), 21; https://doi.org/10.3390/asi1030021

Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation

1
Control Engineering College, Chengdu University of Information Technology, Chengdu 610225, Sichuan Province, China
2
Kills Training Center of State Grid Sichuan Electric Power Corporation, Chengdu 610031, Sichuan Province, China
3
Department of Electrical Engineering, Henan polytechnic, Zhengzhou 450046, Henan Province, China
*
Author to whom correspondence should be addressed.
Received: 1 May 2018 / Revised: 21 June 2018 / Accepted: 21 June 2018 / Published: 26 June 2018
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Abstract

Usually, the optimal spinning reserve is studied by considering the balance between the economy and reliability of a power system. However, the uncertainties from the errors of load and wind power output forecasting have seldom been considered. In this paper, the optimal spinning reserve capacity of a power grid considering the wind speed correlation is investigated by Nataf transformation. According to the cost–benefit analysis method, the objective function for describing the optimal spinning reserve capacity is established, which considers the power cost, reserve cost, and expected cost of power outages. The model was solved by the quantum-behaved particle swarm optimization (QPSO) algorithm, based on stochastic simulation. Furthermore, the impact of the related factors on the optimal spinning reserve capacity is analyzed by a test system. From the  simulation results, the model and algorithm are proved to be feasible. The method provided in this paper offers a useful tool for the dispatcher when increasing wind energy is integrated into power systems.
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Keywords: spinning reserve; wind speed correlation; Nataf transformation; cost-benefit analysis; QPSO spinning reserve; wind speed correlation; Nataf transformation; cost-benefit analysis; QPSO
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Zhang, J.; Zhuang, H.; Zhang, L.; Gao, J. Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation. Appl. Syst. Innov. 2018, 1, 21.

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