# Capacity Allocation Method Based on Historical Data-Driven Search Algorithm for Integrated PV and Energy Storage Charging Station

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## Abstract

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## 1. Introduction

_{2}emissions should be considered. By configuring a photovoltaic power generation system into the charging station system, both economic benefits can be gained and energy consumption can be improved. However, it is still a challenge to determine how to ensure that charging efficiency, economic benefits, and reducing the impact on the grid can be accomplished at the same time. If a certain scale of PV generation system and energy storage (ES) system can be configured according to local conditions and combined with charging stations, namely the integrated PV and energy-storage charging station (PES-CS), the above problems will be effectively solved.

## 2. System Model of PES-CS

#### 2.1. Benefit and Investment Models

#### 2.2. Evaluation Metrics

_{total}is the annual revenue of the charging station, OM is the annual fixed operating cost of the PES-CS, and r

_{0}is the annual discount rate.

## 3. Restrictions

## 4. The Proposed Capacity Allocation Method

#### 4.1. Main History Data of Charging Station

#### 4.2. PES-CS Scheduling Strategy

#### 4.3. Initial Configuration for PV and Energy Storage Capacity

^{2}, 60 kWh PV modules can be installed, and the daily average ${Q}_{\mathrm{peak}}+{Q}_{\mathrm{flat}}$ is (10718 kWh + 2979 kWh)/31 = 441.8 kWh. It can be seen from the above analysis that the maximum PV installation capacity ${Q}_{\mathrm{pv}}$ is 60 kWh, so the site area is the biggest limiting factor. The 60 kW photovoltaic data for 12 months was analyzed, as shown in Figure 4.

#### 4.4. Optimal Solution Calculation

^{3}of natural gas per kilowatt-hour of electricity. The calorific value and carbon emission coefficient of traditional fossil energy are shown in Table 5. Through the introduction of energy storage equipment, the monthly charging station can reduce the use of coal by about 506 kg, natural gas by about 427 m

^{3}, and reduce carbon emissions by about 344–384 kg per day. It can reduce coal consumption by 6072 kg or natural gas by 5124 m

^{3}per year and reduce carbon emissions by about 4000 tons per year.

## 5. Case Analysis

#### 5.1. PES-CS Operation Data Analysis

#### 5.2. Data Visualization

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 6.**(

**a**) Change in profit rate with energy storage; (

**b**) enlarged profit curve after increasing the energy storage.

**Figure 7.**(

**a**) Power demand of a power station. (

**b**) The power distribution curve of PES-CS within 24 h.

**Figure 9.**Distribution diagram of electricity charge before and after adding the energy storage device. (

**a**) Electricity price without energy storage device; (

**b**) electricity price with energy storage device.

Variable | Value |
---|---|

AC input voltage | AC three-phase five-wire system 380 V ± 15%, 50 Hz ± 10% |

Distribution capacity | 500 kVA |

Charging voltage/current | DC 50 V–750 V/0–100 A |

Charging mode | 120 kW dual terminal charging × 2 60 kW single terminal charging × 1 |

Time | Peak | Flat | Valley | Total |
---|---|---|---|---|

Electricity (kWh) | 10,719 | 2979 | 1592 | 15,280 |

Percentage | 70.2% | 19.5% | 10.3% | / |

ES Capacity | Income | Power Purchase Cost | Profit | ES Cost |
---|---|---|---|---|

0 kW | CNY 23,217 | CNY 10,533.96 | CNY 12,683.23 | CNY 0 |

200 kW | CNY 23,217 | CNY 6582.49 | CNY 16,634.70 | CNY 350,000 |

400 kW | CNY 23,217 | CNY 4440.65 | CNY 18,776.07 | CNY 700,000 |

600 kW | CNY 23,217 | CNY 3736.08 | CNY 19,481.12 | CNY 1,050,000 |

800 kW | CNY 23,217 | CNY 3796.43 | CNY 19,420.76 | CNY 1,400,000 |

1000 kW | CNY 23,217 | CNY 3859.21 | CNY 19,357.98 | CNY 1,750,000 |

**Table 4.**Comparison of daily average electricity purchase before and after increasing energy storage.

Daily Electricity Purchase | Energy Storage Margin | Peak | Flat | Valley | |
---|---|---|---|---|---|

Before | 11,459 kW | 0 kW | 8650 kW | 1217 kW | 1591 kW |

After | 10,202 kW | 335 kW | 79 kW | 298 kW | 9824 kW |

Calorific Value | Carbon Emission Coefficient | Price | |
---|---|---|---|

Coal | 7000 kcal/kg | 0.68 | 1.4 CNY/kg |

Natural gas | 9000 kcal/m^{3} | 0.90 | 2.89 CNY/m^{3} |

Peak | Flat | Valley | Daily Average | |
---|---|---|---|---|

Photovoltaic power generation | 72.1 kW | 65.7 kW | 2 kW | 145.8 kW |

Peak | Flat | Valley | |
---|---|---|---|

Power grid supply | 2048 kWh | 1710 kWh | 9683 kWh |

PV generation | 2075 kWh | 2194 kWh | 115 kWh |

EV demand | 9802 kWh | 3496 kWh | 971 kWh |

PES-CS system demand | 938 kWh | 615 kWh | 746 kWh |

Energy storage charge | 563 kWh | 1505 kWh | 7174 kWh |

Energy storage discharge | 7171 kWh | 1708 kWh | 51 kWh |

Time | No Energy Storage Device | Price | With Energy Storage Device | Price |
---|---|---|---|---|

Peak | 722 kWh | 543 CNY | 580 kWh | 1845 CNY |

Flat | 1016 kWh | 601 CNY | 758 kWh | 2375 CNY |

Valley | 1078 kWh | 701 CNY | 900 kWh | 2679 CNY |

Total | 2816 kWh | 1845 CNY | 2238 kWh | 6899 CNY |

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**MDPI and ACS Style**

Pan, X.; Liu, K.; Wang, J.; Hu, Y.; Zhao, J.
Capacity Allocation Method Based on Historical Data-Driven Search Algorithm for Integrated PV and Energy Storage Charging Station. *Sustainability* **2023**, *15*, 5480.
https://doi.org/10.3390/su15065480

**AMA Style**

Pan X, Liu K, Wang J, Hu Y, Zhao J.
Capacity Allocation Method Based on Historical Data-Driven Search Algorithm for Integrated PV and Energy Storage Charging Station. *Sustainability*. 2023; 15(6):5480.
https://doi.org/10.3390/su15065480

**Chicago/Turabian Style**

Pan, Xiaogang, Kangli Liu, Jianhua Wang, Yutao Hu, and Jianfeng Zhao.
2023. "Capacity Allocation Method Based on Historical Data-Driven Search Algorithm for Integrated PV and Energy Storage Charging Station" *Sustainability* 15, no. 6: 5480.
https://doi.org/10.3390/su15065480