Design of Energy Storage Station Grouping Energy Management Strategies to Balance Short-Term Wind Power Fluctuations

2013 ◽  
Vol 448-453 ◽  
pp. 2866-2871 ◽  
Author(s):  
Jun Hui Li ◽  
Xing Xu Zhu ◽  
Gan Gui Yan ◽  
Gang Mu ◽  
Wei Hua Luo
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Energy Management ◽  
Management Strategy ◽  
Wind Farm ◽  
Management Strategies ◽  
Wind Farms ◽  
Energy Management Strategy ◽  
Storage Station ◽  
Power Fluctuations

This paper designs a grouping energy management strategy to reduce the influence of wind power fluctuations on the power system. To improve operational technicality and economy of energy storage stations, this paper designs a grouping energy management strategy with SOC correction. According to physical constrains of battery energy storage systems, technical and economic evaluation index of energy storage stations are established. Reasonable limit bands to an energy storage station installed 5MW×2h can balance the output power of a wind farm installed 49.3MW achieved through example analysis. Then the energy management strategy designed is proved to be able to control the change range of the battery SOC and distribute control tasks efficaciously, improving operational technicality and economy of the station effectively. This research provides a theoretical reference to design of energy management strategies for energy storage stations installed small at wind farms.

Analysis on the Fluctuation of Wind Power

2015 ◽  
Vol 733 ◽  
pp. 199-202
Author(s):  
Rui Hao Wang
Keyword(s):  
Neural Network ◽  
Energy Storage ◽  
Wind Power ◽  
Time Scales ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Power Fluctuation ◽  
Power Fluctuations ◽  
Different Time Scales

This paper is aimed at exploring the characteristic fluctuation of wind power based on samples from a certain wind farm. First, the paper is to analyze fluctuations of wind power at different time scales. According to a sliding difference algorithm to build wind power fluctuations evaluation. Wind power fluctuation index for different time scales are used to fit probability distributions, indicating that the best form of distribution of wind power fluctuations is t location scale distribution. Secondly, considering the wind power has the characteristics of non-linear, non-stationary signal of the data, it fully meets the wavelet neural network analysis of the characteristics of the data. Therefore, select wavelet neural network training and testing so as to make predictions about the future of the total power of wind farm. It points out the differences between different regions covered by the index from the fluctuation characteristics of wind power, thus further understanding the fluctuation characteristics of wind power: Influenced by the time and space distribution and other factors, there is a big difference between the output power fluctuation characteristics of single wind generator and wind farm, which is because of the different wind machine in the field by the wind energy differences, and the wake effect of organic groups, making frequent fluctuations in power distribution; the fluctuation of wind is gentle, i.e. with increasing spatial distribution scale, so gentle effect occurs to wind power fluctuations. Finally, through the analysis of the fluctuation characteristics of power, power factor and analyses the influence of the characteristics of fluctuation, the paper draws a conclusion of the following improvement programs to overcome the adverse effects of wind power fluctuation of power grid operation: the rational allocation of energy storage devices, expanding the coverage area of a wind farm, or improving the design of the windmill, which will make wind farms adapt to different wind directions, thus eliminating the impact of fluctuations on the power grid from the wind farm power output by the energy storage device, and covering the area of large wind farms can adapt to different wind directions, and with power complementary, it has achieved the amount of stable power transmission into the grid.

The Energy Management Strategy of Photovoltaic Energy Storage System

2014 ◽  
Vol 1006-1007 ◽  
pp. 727-731
Author(s):  
Feng Di ◽  
Wei Li ◽  
Fu Cheng Liu ◽  
Guang Ru Zhang ◽  
Guo Tao Hui
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Management Strategy ◽  
Storage System ◽  
Management Strategies ◽  
Energy Storage System ◽  
Energy Management Strategy ◽  
Photovoltaic Energy ◽  
Complete Set ◽  
Battery Energy

In order to prolong the service life of the battery, ensure the operation efficiency of photovoltaic energy storage system, the establishment of a complete set of battery energy management strategies for photovoltaic energy storage system is very necessary because of the intermittent of solar energy. A strategy based on the state of charge of the battery to realize energy management strategy for photovoltaic energy storage system is proposed in this paper. The simulation verifies the effectiveness of the proposed method and the results show that the method can realize the purpose better.

scholarly journals Optimal sizing and control of hybrid energy storage system for wind power using hybrid Parallel PSO-GA algorithm

2018 ◽  
Vol 37 (1) ◽  
pp. 558-578 ◽  
Author(s):  
Ming Pang ◽  
Yikai Shi ◽  
Wendong Wang ◽  
Shun Pang
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Energy Management ◽  
Management Strategy ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management Strategy ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System

This paper proposes a frequency-based method for sizing the hybrid energy storage system in order to smoothen wind power fluctuations. The main goal of the proposed method is to find the power and energy capacities of the hybrid energy storage system that minimizes the total cost per day of all the systems. The energy management strategy used in this paper is designed as a two-level energy distribution scheme: the first level is responsible for setting the output power of hybrid energy storage system, the second level manages the power flow between the battery and supercapacitor. The hybrid parallel particle swarm optimization-genetic algorithm (PSO-GA) optimization algorithm is proposed to solve the control parameters of energy management strategy. In addition, the proposed method uses the piecewise fitting function to describe the lifetime of battery. Obtained results show that the hybrid energy storage system with the proposed energy management strategy is able to offer the best performances for the wind power system in terms of cost and lifetime.

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