# A Novel Method for Fast Configuration of Energy Storage Capacity in Stand-Alone and Grid-Connected Wind Energy Systems

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Data Used

## 3. Methods Used

**P**is the wind power data vector,

**P**= [P[1], …, P[i], …, P[N]]

^{T}; F(

**P**) means the process of calculating the discrete Fourier transformation of the wind power data; S[i] is the amplitude at the ith f[i], S[i] = R[i] + jI[i], with R[i] and I[i] representing the real part and the imaginary part, respectively; f[i] = f(i-1)/N, with f denoting the sampling frequency.

^{−1}(

**S**

_{0}) means the inverse DFT; P

_{0}[i] is the wind power data at the ith sampling number without the DC component.

^{−1}(

**S**

_{1}) means calculating the inverse DFT of

**S**

_{1}; P

_{1}[i] is the filtering wind power data at the ith sampling number. Moreover, the period of the filtering wind power data, P

_{1}, ranges from 0 to T.

_{0}, P

_{1}, namely E

_{0}and E

_{1}, is calculated based on Equation (6). Also, the energy of the wind power data P

_{0}is chosen as the standard energy. The energy ratio can be computed by the following Equation (7).

- (a)
- Set the energy ratio η
_{0}. - (b)
- Calculate the different P
_{1}by changing the range of the pass band from low (f_{min}) to high (f_{max}) according to Equations (4) and (5) shown in the former step. Calculate the energy ratio between energy E_{1}and the standard energy using Equations (6) and (7). - (c)
- Determine the computing time T, which is the reception of the compensation frequency f. The program terminates and the final frequency f can be returned when the energy ratio η reaches the predefined value (η − η
_{0}< = 0.005).

## 4. Results and Discussion

^{−4}Hz. The magnitude of the signal amplitude in the spectrum characteristic curve, to a certain extent, reflects the strength of the signal at the corresponding frequency. It can be found that the energy of the wind power data concentrates at the frequency below 1 × 10

^{−4}Hz.

_{0}is set to be 70% (taking 70% as an example) to study the energy storage configuration time. Based on Equations (4)–(7) and the specific process of the third step, the special frequency is calculated as 1.2684 × 10

^{−5}Hz and the corresponding computing time is 21.9 h (Table 1). In the process of determining the computing time, the superposed remaining (70%) and filtering (30%) signal of the original wind power data are demonstrated in Figure 5 and Figure 6, respectively.

_{0}. As shown in Table 1, in order to extract 90% energy of the wind generation power, the configuration time should be set as 130.7 h. However, the computing time is too long to retain higher economic benefits. In contrast, if the energy ratios are 80%, 70%, and 60%, the corresponding computing time can decrease to 45.1 h, 21.9 h and 10.9 h. Correspondingly, the energy storage capacity is 266 kWh, 129 kWh, and 64 kWh, respectively. In these three situations, the economy of the entire system is better. Moreover, if there is a wind power failure, the energy storage system can still meet the power supply of the important load.

_{0}are shown in Table 2. When the energy ratios are 25%, 20%, and 15%, the corresponding energy storage capacity is 396 MWh, 257.4 MWh, and 128.7 MWh, respectively. The energy storage capacity is chosen based on the tradeoff between the investment demand of a large capacity storage reserve and the output stability of the wind farm.

## 5. Conclusions

_{0}(70%) as an example, the energy storage computing time is analyzed in detail in this work. It is obvious that the configuration time (130.7 h) is too long to retain higher economic benefits. The configuration results for other energy ratios (80%, 70%, and 60%) having better economy are shown in the tabulation. Moreover, the energy storage system can meet the power supply for some of the important load. The EES capacity results are compared with the configuration capacity using HOMER software. In addition, the configuration results for a grid-connected wind system are analyzed in this study. The configuration time (with better economic benefit) for the energy ratios (25%, 20% and 15%) is 12.0 h, 7.8 h and 3.9 h respectively. For different targets, the energy ratio and the corresponding configuration result can be chosen and carried out by the proposed method (based on DFT theory).

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 5.**The superposed remaining signal, the energy of which accounts for 70% of the energy of the wind power data.

**Figure 6.**The superposed filtering signal, the energy of which accounts for 30% of the energy of the wind power data.

**Table 1.**Configuration results of the energy storage system for the stand-alone wind energy system with different energy ratios.

Energy Ratio | Computing Time/h | Calculated Power/kW | Energy Storage Capacity/kWh |
---|---|---|---|

90% | 130.7 | 5.9 | 771 |

80% | 45.1 | 5.9 | 266 |

70% | 21.9 | 5.9 | 129 |

60% | 10.9 | 5.9 | 64 |

50% | 6.0 | 5.9 | 35 |

40% | 3.5 | 5.9 | 21 |

**Table 2.**Configuration results of the energy storage system for the grid-connected wind system with different energy ratios.

Energy Ratio | Computing Time/h | Calculated Power/MW | Energy Storage Capacity/MWh |
---|---|---|---|

40% | 30.0 | 33.0 | 990.0 |

35% | 23.8 | 33.0 | 785.4 |

30% | 19.2 | 33.0 | 633.6 |

25% | 12.0 | 33.0 | 396.0 |

20% | 7.8 | 33.0 | 257.4 |

15% | 3.9 | 33.0 | 128.7 |

10% | 1.5 | 33.0 | 49.5 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Zang, H.; Guo, M.; Qian, Z.; Wei, Z.; Sun, G.
A Novel Method for Fast Configuration of Energy Storage Capacity in Stand-Alone and Grid-Connected Wind Energy Systems. *Sustainability* **2016**, *8*, 1336.
https://doi.org/10.3390/su8121336

**AMA Style**

Zang H, Guo M, Qian Z, Wei Z, Sun G.
A Novel Method for Fast Configuration of Energy Storage Capacity in Stand-Alone and Grid-Connected Wind Energy Systems. *Sustainability*. 2016; 8(12):1336.
https://doi.org/10.3390/su8121336

**Chicago/Turabian Style**

Zang, Haixiang, Mian Guo, Zeyu Qian, Zhinong Wei, and Guoqiang Sun.
2016. "A Novel Method for Fast Configuration of Energy Storage Capacity in Stand-Alone and Grid-Connected Wind Energy Systems" *Sustainability* 8, no. 12: 1336.
https://doi.org/10.3390/su8121336