# Planning Underground Power Distribution Networks to Minimize Negative Visual Impact in Resilient Smart Cities

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Problem Formulation

- D is the distance in km from a point i to a point j;
- $lat1$, $lon2$ are the coordinates of point 2;
- $\u25b5lat$ is the difference between the latitude coordinates of point 1 and point 2;
- $\u25b5lon$ is the difference between the longitude coordinates of point 1 and point 2;
- R is the earth’s radius, with a value of 6372.8 km.

## 4. Analysis of Results

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Author, Year | Objectives | Parameters Considered | Thematic | |||||
---|---|---|---|---|---|---|---|---|

Voltage | Capacity | Cost | Georeferenced | Planning | Resilience | Graph Theory | ||

Ciechanowicz, 2016 [43] | Electrical network evaluation and modeling | ✓ | ✓ | ✓ | - | ✓ | ✓ | - |

Li, 2016 [10] | Reduces active power losses | ✓ | - | - | - | ✓ | - | - |

Sowmya, 2016 [44] | Use of geographic data in power grids | - | - | - | ✓ | ✓ | - | - |

Roshanagh, 2016 [45] | Planning new electrical network at minimum cost | ✓ | ✓ | ✓ | - | ✓ | - | ✓ |

Xie, 2018 [46] | Distribution network planning | ✓ | ✓ | ✓ | - | ✓ | - | - |

Pinzón, 2020 [47] | Location of substations | - | ✓ | - | ✓ | ✓ | - | - |

Cresta, 2021 [48] | Management of electrical distribution networks | ✓ | ✓ | - | ✓ | - | ✓ | ✓ |

Ayalew, 2022 [49] | Expansion of the power grid with distributed generation | ✓ | ✓ | ✓ | - | ✓ | - | - |

Kostelac, 2022 [50] | Planning and operational issues of microgrids | ✓ | - | ✓ | - | ✓ | - | - |

Present work | Planning and sizing of electrical distribution network | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |

Strata | Rate of Consumption by Customer (kWh/Month/Customer) |
---|---|

E | 0–100 |

D | 101–150 |

C | 151–250 |

B | 251–350 |

A | 351–500 |

A1 | 501–900 |

Residential User | MDD (kW) | MDD (kVA) | RL (kW) | RL (kVA) |
---|---|---|---|---|

B | 3.4 | 3.58 | 9.8 | 10.31 |

A | 4.7 | 4.95 | 15.82 | 10.65 |

Symbol | Description | Unit |
---|---|---|

${C}_{s}$ | Restricts the capacity of the substation. | ${\mathbb{R}}^{+}$ |

$C{p}_{i}$ | Partial capacity of residential user. | kVA |

n | Number of nodes | ${\mathbb{R}}^{+}$ |

${C}_{f}$ | Multiplication factor projection future growth. | % |

$Ca{p}_{c}$ | Limit of the capacity of each group. | ${\mathbb{R}}^{+}$ |

m | Vector length in the case study. | ${\mathbb{R}}^{+}$ |

z | Verification variable. | ${\mathbb{R}}^{+}$ |

${R}_{c}$ | Restricts the maximum allowable distance. | Meters |

r | Maximum allowed distance. | Meters |

$C{t}_{i}$ | Restricts the maximum load of the transformers. | kVA |

$\alpha $ | Number of clusters. | ${\mathbb{R}}^{+}$ |

$Vd$ | Voltage drop. | V |

${\gamma}_{2}0$ | Conductivity according to the type of conductor. | Siemens |

$Un$ | Applied voltage of the conductor. | V |

$Sc$ | Underground conductor section. | mm${}^{2}$ |

$Lg$ | Length of the conductor from the transformer to the user. | Meters |

P | Power losses. | kW |

$i,j$ | Source and target, respectively. | Index |

Item | Parameters | Description |
---|---|---|

Final user | 715 | |

Residential users | f.p. 0.95 | |

Scenario | Commercial users | f.p. 0.85 |

Total demand | variable (4.2 MW) | |

Connections associated with each transformer | 15, 20, 25, 30 | |

Primary feeder | 1 | |

Voltage level | 11 kV | |

Medium-voltage network | Type of installation | Underground |

Network settings | Radial | |

Type of conductor | Insulated power cord TCLPE 15 kV | |

Distribution transformer | 11/0.22 kV immersed in oil | |

Voltage level | 220 V | |

Low voltage network | Network settings | Radial |

Type of conductor | Insulated power cord TCLPE 2 kV |

Scenarios | Transformer Demand (kW) | Transformer Power without Tap (kVA) |
---|---|---|

Scenario A | 124 | 150 |

Scenario B | 106 | 112.5 |

Scenario C | 88 | 112.5 |

Scenario D | 71 | 75 |

Description | Scenario A | Scenario B | Scenario C | Scenario D |
---|---|---|---|---|

Length from source (m) | 490 | 495 | 500 | 610 |

No. of transformers | 35 | 40 | 49 | 60 |

Network length MV (km) | 30 | 43 | 55 | 58 |

Voltage drop | 2.1% | 2.4% | 2.7% | 3% |

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## Share and Cite

**MDPI and ACS Style**

Pabón, F.; Inga, E.; Campaña, M.
Planning Underground Power Distribution Networks to Minimize Negative Visual Impact in Resilient Smart Cities. *Electricity* **2022**, *3*, 463-479.
https://doi.org/10.3390/electricity3030024

**AMA Style**

Pabón F, Inga E, Campaña M.
Planning Underground Power Distribution Networks to Minimize Negative Visual Impact in Resilient Smart Cities. *Electricity*. 2022; 3(3):463-479.
https://doi.org/10.3390/electricity3030024

**Chicago/Turabian Style**

Pabón, Francisco, Esteban Inga, and Miguel Campaña.
2022. "Planning Underground Power Distribution Networks to Minimize Negative Visual Impact in Resilient Smart Cities" *Electricity* 3, no. 3: 463-479.
https://doi.org/10.3390/electricity3030024