# Battery Storage-Based Frequency Containment Reserves in Large Wind Penetrated Scenarios: A Practical Approach to Sizing

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## Abstract

**:**

## 1. Introduction

## 2. Sizing Methodology

## 3. System Modelling

#### 3.1. Battery Energy Storage System

#### 3.1.1. Performance Model

_{nom}is the nominal capacity in Ampere hour, SOC(0) is the initial SOC value and i is the current flowing through the battery in Ampere.

_{t}and V are the variables in the SOC estimation part of the performance model which are not directly included in the Equation (1). C

_{t}represents the actual available BESS capacity at the given time instant t during the BESS operation. V represents the voltage level of the BESS unit. Knowing its value is required in order to determine i, the current flowing through the BESS. The voltage level is evaluated based on the SOC level at the time instant t. SOC and V do not have a linear relationship, but their correlation is shown by the means of a standard discharge characteristic of a Li-ion BESS.

_{BESS}representing the actual power being delivered or absorbed by BESS. This value is determined based on the grid frequency deviation df and current BESS availability represented through SOC. The control part of the model is designed in a way that it captures all imposed requirements for FCR provision. Those requirements need to be addressed when developing a suitable control strategy for any participating unit, including BESS. In Europe, there are certain countries, such as Denmark, which have developed technical regulations specific to BESS units [29]. However, those regulations comply with the imposed rules for European electricity grid. According to ENTSO-E system operation grid code [2], in case of a disturbance in the system causing frequency deviations equal to or larger than ±200 mHz:

- half of the reserves need to be provided in the first 15 s after the disturbance and full activation is expected within 30 s
- participating units need to have the ability to provide the reserves for 15 min at nominal power
- participating units are entitled to 15 min re-establishing period after 15 min of reserve provision

_{%}is droop value, Δf

_{%}is change in frequency, ΔP

_{%}is change in output power, ω

_{nl}is the frequency at no load conditions, ω

_{fl}is the frequency at full load conditions and ω

_{0}is nominal frequency.

_{req}is being determined for the input frequency deviation, df. This signal is then sent to the control block (CB) shown in Figure 3. There, it is examined if the BESS can meet the demanded power based on its current SOC. In general, a situation like that could occur if severe frequency deviations persist for more than 15 min. The output of the CB is the actual power dP

_{BESS}that is being delivered or absorbed by BESS.

#### 3.1.2. Lifetime Model

_{nom}which is degrading with time. Based on the developed lifetime model, a degradation of this parameter is estimated. In that way, time-domain analysis and BESS contribution to frequency regulation can be examined with the included accurate representation of the available capacity throughout the entire time in service. The lifetime model developed for the purposes of this study is presented in Figure 5.

_{fade_idling}is determined by using the expression (4). In order to obtain all input parameters for Equation (3) from the SOC_cycle, the Rainflow cycle counting algorithm is used. The resulting SOC_av, cd and nc are then used in Equation (3) to obtain the capacity fade during cycling, C

_{fade_cycling}. The overall capacity fade, C

_{fade}, is the sum of the aforementioned capacity fades due to cycling and idling conditions. The output of the lifetime model is the updated capacity of the BESS unit C

_{act}, which is decreasing as the BESS degradation evolves. Referring to the developed performance model outlined in Figure 3, C

_{nom}is replaced by C

_{act}after the first iteration of the capacity fade determination. C

_{act}is then being updated after each performance degradation calculation during BESS time in service. The BESS End-of-Life (EOL) is reached when its capacity degrades to 80% of the nominal capacity C

_{nom}[19].

#### 3.1.3. Economic Model

#### 3.2. Benchmark Power System Model

## 4. Assessment Study

#### 4.1. Stage I—Time-Domain Assesment

#### 4.1.1. Initialization

#### 4.1.2. Results

#### 4.2. Stage II—Lifetime Performance Assessment

#### 4.2.1. Initialization

#### 4.2.2. Results

#### 4.3. Stage III—Economic Assessment

#### 4.3.1. Initialization

#### 4.3.2. Results

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

_{1}= 0.021, a

_{1}= −0.0194, b

_{1}= 0.7162, c

_{1}= 0.5, k

_{2}= 0.1723, a

_{2}= 0.0074, b

_{2}= 0.8.

Component | Parameter | Value |
---|---|---|

Gen 01: generator—steam turbine set | Nominal active power output | 200.8 MW |

Droop value | 4% | |

Gen 02: generator—gas turbine set | Nominal active power output | 40 MW |

Droop value | 4% | |

WT | Installed capacity | 96 MW |

Load | Overall active power demand | 320 MW |

Load damping constant of each load | 1% |

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**Figure 10.**(

**a**) Capacity fade for reduced interval of BESS sizes, (

**b**) Relative difference of capacity fade with the referent capacity of BESS_20000.

Scenario | Wind Power Generation as Percentage of 96 MW Installed Capacity | Active Power Required by the Load | |
---|---|---|---|

First | Case I | Fluctuating Maximum: 95% Minimum: 25% | Low 160 MW or 50% of daily peak |

Case II | High 320 MW daily peak | ||

Second | High Maximum: 100% Minimum: 95% | Low 160 MW or 50% of daily peak | |

Third | Low Maximum: 10% Minimum: 0% | High 320 MW daily peak |

BESS Name for Given Size | Power/Energy Rating |
---|---|

BESS_100 | 0.1 MW/0.025 MWh |

BESS_1000 | 1 MW/0.25 MWh |

BESS_10000 | 10 MW/2.5 MWh |

BESS_20000 | 20 MW/5 MWh |

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**MDPI and ACS Style**

Sandelic, M.; Stroe, D.-I.; Iov, F.
Battery Storage-Based Frequency Containment Reserves in Large Wind Penetrated Scenarios: A Practical Approach to Sizing. *Energies* **2018**, *11*, 3065.
https://doi.org/10.3390/en11113065

**AMA Style**

Sandelic M, Stroe D-I, Iov F.
Battery Storage-Based Frequency Containment Reserves in Large Wind Penetrated Scenarios: A Practical Approach to Sizing. *Energies*. 2018; 11(11):3065.
https://doi.org/10.3390/en11113065

**Chicago/Turabian Style**

Sandelic, Monika, Daniel-Ioan Stroe, and Florin Iov.
2018. "Battery Storage-Based Frequency Containment Reserves in Large Wind Penetrated Scenarios: A Practical Approach to Sizing" *Energies* 11, no. 11: 3065.
https://doi.org/10.3390/en11113065