# Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads

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## Abstract

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## 1. Introduction

- (i)
- A stochastic day-ahead economic power dispatch model with wind farms and SLBFLs at MW levels is developed. This model utilizes batteries retired from EVs as flexible loads for balancing power and also for minimizing the operating cost and environmental emissions.
- (ii)
- The charging and discharging characteristics of SLBs at different temperatures and currents are obtained and analyzed based on actual NASA battery data.
- (iii)
- The opportunity cost is calculated to compare between the reuse and the disposal of SLBs; an economic analysis is carried out to compare the utilization of SLBs and EV first-life batteries as flexible loads; the thermal power generating cost and the peak-valley difference of loads are also compared with the system involving SLBs in the power dispatch.
- (iv)
- This work has proved that SLBs are more economical to be utilized in large quantity for power dispatch. This will have significant economic implications and environmental benefits for both automotive industry and power industry.

## 2. Second Life Batteries Characteristics Analysis

#### 2.1. Battery Power Output under Different Operating Temperatures and Charging/Discharging Currents

#### 2.2. SLBFLs in Power Dispatch

## 3. Economic Power Dispatch Model with Wind Farms and SLBFLs

- $g(x)$ is the balance constraint;
- $h(x)$ is the unequal constraint;
- $\underset{\_}{h}(x)$ is the minimum value of the unequal constraint;
- $\overline{h}(x)$ is the maximum value of the unequal constraint.

#### 3.1. Objective Functions

_{2}emissions, which is shown in Equation (4):

#### 3.2. Constraint Functions

#### 3.3. Stochastic Variables

## 4. Case Study

- Case 1: the power dispatch with wind farms and without SLBFLs. The spinning reserve confidence degree is 0.9.
- Case 2: the power dispatch with wind farms and SLBFLs at wind power reserve confidence degree of 0.9, 0.95 and 0.98.
- Case 3: the power dispatch with SLBs on supply side and the spinning reserve confidence degree is 0.9.

#### 4.1. The Result Comparison of Case 1 and Case 2 with 0.9 Confidence Degree

#### 4.2. The Result Comparison of Case 2 and Case 3

#### 4.3. The Result Comparison of Case 2 with Different Confidence Degrees

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Battery actual characteristic curve fitting. (

**a**) is Battery B0039 actual power-SOC curve fitting; (

**b**) is Battery B0039 actual temperature-SOC curve fitting; (

**c**) is Battery B0006 actual power-SOC curve fitting and (

**d**) is Battery B0006 actual temperature-SOC curve fitting.

**Figure 3.**Battery cells are dismantled from electric vehicle (EV) and regrouped to a second life batteries (SLB) pack.

**Figure 6.**Flow charts. (

**a**) is the flow chart of economic dispatch optimization and (

**b**) is the flow chart of Interior point method.

**Figure 10.**Result comparisons of Case 1 and Case 2 with 0.9 confidence degree. (

**a**) is load peak-valley differences; (

**b**) is system operating cost, thermal power generating cost and thermal generator ramp cost and (

**c**) is the OC of SLBs to be disposed of and used as flexible loads.

**Figure 11.**Active power generating curves of Unit 1, Unit 4 and Units 7–10 in Case 1 and Case 2 with 0.9 confidence degree.

**Figure 14.**Results comparison of Case 3 and Case 2 with 0.9 confidence degree. (

**a**) is the thermal generating cost comparison; (

**b**) is the system operation cost comparison and (

**c**) is the SLB throughput and peak-to-valley comparison.

**Figure 15.**The results of Case 2 at different confidence degrees; (

**a**) is the spinning reserve cost; (

**b**) is the up-reserve and down-reserve of wind power and (

**c**) is the total output of SLBFLs.

Wind Farm | Installation Capacity (MW) | SLBFL | Installation Capacity (MW) |
---|---|---|---|

1 | 60 | 1 | 200 |

2 | 120 | 2 | 150 |

3 | 180 | 3 | 150 |

4 | 240 | ||

Total Capacity | 600 | Total Capacity | 500 |

Unit | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

Pmax (MW) | 470 | 460 | 340 | 300 | 243 | 160 | 130 | 120 | 80 | 55 |

Pmin (MW) | 150 | 135 | 73 | 60 | 73 | 57 | 20 | 47 | 20 | 55 |

a ($/MW2h) | 0.00043 | 0.00063 | 0.00039 | 0.00070 | 0.00079 | 0.00056 | 0.00211 | 0.00480 | 0.10908 | 0.00951 |

b ($/MWh) | 21.60 | 21.05 | 20.81 | 23.90 | 21.62 | 17.87 | 16.51 | 23.23 | 19.58 | 22.54 |

c ($/h) | 958.20 | 1313.6 | 604.97 | 471.60 | 480.29 | 601.75 | 502.70 | 639.40 | 455.60 | 692.40 |

e ($/h) | 450 | 600 | 320 | 260 | 280 | 310 | 300 | 340 | 270 | 380 |

F (rad/MW) | 0.041 | 0.036 | 0.028 | 0.052 | 0.063 | 0.048 | 0.086 | 0.082 | 0.098 | 0.094 |

α (kg/MW2h) | 0.022 | 0.020 | 0.044 | 0.058 | 0.065 | 0.080 | 0.075 | 0.082 | 0.090 | 0.084 |

β (kg/MWh) | −2.86 | −2.72 | −2.94 | −2.35 | −2.36 | −2.28 | −2.36 | −1.29 | −1.14 | −2.14 |

γ (kg/h) | 130 | 132 | 137 | 130 | 125 | 110 | 135 | 157 | 160 | 137.7 |

UR | 120 | 120 | 120 | 100 | 100 | 100 | 50 | 50 | 50 | 50 |

DR | 120 | 120 | 120 | 100 | 100 | 100 | 50 | 50 | 50 | 50 |

Coe_{URi} ($/MWh) | 14.7 | 15.5 | 15.2 | 17.8 | 19.3 | 19.8 | 18.7 | 21.7 | 23.4 | 25.2 |

Coe_{DRi} ($/MWh) | 15.2 | 14.8 | 15.1 | 18.6 | 21.2 | 19.5 | 19 | 22 | 23.1 | 25.6 |

Coe_{ramp−upi} ($/MWh) | 3.13 | 3.08 | 3.75 | 4.17 | 5.88 | 9.71 | 9.09 | 13.7 | 16.67 | 28.57 |

Coe_{ramp−downi} ($/MWh) | 3.13 | 3.08 | 3.75 | 4.17 | 5.88 | 9.71 | 9.09 | 13.7 | 16.67 | 28.57 |

Component | Component Percentage | Recycling Rate | Recycle Price ($/kg) |
---|---|---|---|

Aluminum | 3.5% | 42% | 1.68 |

Cobalt | 15% | 89% | 33.59 |

Lithium | 1.8% | 80% | 62.5 |

Iron/steel | 50% | 52% | 0.05 |

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## Share and Cite

**MDPI and ACS Style**

Hu, S.; Sun, H.; Peng, F.; Zhou, W.; Cao, W.; Su, A.; Chen, X.; Sun, M. Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads. *Energies* **2018**, *11*, 1657.
https://doi.org/10.3390/en11071657

**AMA Style**

Hu S, Sun H, Peng F, Zhou W, Cao W, Su A, Chen X, Sun M. Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads. *Energies*. 2018; 11(7):1657.
https://doi.org/10.3390/en11071657

**Chicago/Turabian Style**

Hu, Shubo, Hui Sun, Feixiang Peng, Wei Zhou, Wenping Cao, Anlong Su, Xiaodong Chen, and Mingze Sun. 2018. "Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads" *Energies* 11, no. 7: 1657.
https://doi.org/10.3390/en11071657