Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method
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-s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/11/12/3393 |
| _version_ | 1798025464281825280 |
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| author | Pingping Yun Yongfeng Ren Yu Xue |
| author_facet | Pingping Yun Yongfeng Ren Yu Xue |
| author_sort | Pingping Yun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:19:19Z |
| format | Article |
| id | doaj.art-f18a37d04a834da883dfe836750179ee |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:19:19Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f18a37d04a834da883dfe836750179ee2022-12-22T04:09:49ZengMDPI AGEnergies1996-10732018-12-011112339310.3390/en11123393en11123393Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO MethodPingping Yun0Yongfeng Ren1Yu Xue2College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, ChinaCollege of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, ChinaCollege of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, ChinaWind 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.https://www.mdpi.com/1996-1073/11/12/3393wind-power fluctuation smoothingenergy storage systemMarkov prediction modelparticle swarm optimization algorithmmulti-objective optimizationenergy-storage battery output level |
| spellingShingle | Pingping Yun Yongfeng Ren Yu Xue Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method Energies wind-power fluctuation smoothing energy storage system Markov prediction model particle swarm optimization algorithm multi-objective optimization energy-storage battery output level |
| title | Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method |
| title_full | Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method |
| title_fullStr | Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method |
| title_full_unstemmed | Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method |
| title_short | Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method |
| title_sort | energy storage optimization strategy for reducing wind power fluctuation via markov prediction and pso method |
| topic | wind-power fluctuation smoothing energy storage system Markov prediction model particle swarm optimization algorithm multi-objective optimization energy-storage battery output level |
| url | https://www.mdpi.com/1996-1073/11/12/3393 |
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