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

Pingping Yun, Yongfeng Ren and Yu Xue
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Pingping Yun: College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, China
Yongfeng Ren: College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, China
Yu Xue: College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, China

Energies, 2018, vol. 11, issue 12, 1-23

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.

Keywords: wind-power fluctuation smoothing; energy storage system; Markov prediction model; particle swarm optimization algorithm; multi-objective optimization; energy-storage battery output level (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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