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Energies 2018, 11(12), 3393; https://doi.org/10.3390/en11123393

Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

1
College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, China
2
Inner Mongolia Engineering Research Center for Wind Power Technology and Testing, Hohhot 010051, Inner Mongolia Autonomous Region, China
*
Author to whom correspondence should be addressed.
Received: 30 October 2018 / Revised: 20 November 2018 / Accepted: 27 November 2018 / Published: 4 December 2018
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Abstract

Wind power penetration ratios of power grids have increased in recent years; thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation. View Full-Text
Keywords: wind-power fluctuation smoothing; energy storage system; Markov prediction model; particle swarm optimization algorithm; multi-objective optimization; energy-storage battery output level wind-power fluctuation smoothing; energy storage system; Markov prediction model; particle swarm optimization algorithm; multi-objective optimization; energy-storage battery output level
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yun, P.; Ren, Y.; Xue, Y. Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method. Energies 2018, 11, 3393.

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