# Synthetic Models of Distribution Networks Based on Open Data and Georeferenced Information

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^{2}

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## Abstract

**:**

## 1. Introduction

_{2}emissions and reaching climate goals. This trend, at the power distribution level, results in the increasing penetration of distributed generation (DG). Another trend at the distribution level is represented by new electric loads with high coincident peaks (e.g., electric vehicles, heat pumps, induction cookers, etc.) that impact the exploitation of existing distribution assets. In this context, the balance of generation and demand at every single point in time becomes more challenging and increases the final cost of electricity for the final users.

## 2. Distribution System Model

## 3. Modelling Energy Consumption and Production at the Distribution Level with Open Data

- Build databases containing the PS locations of the region/area, and subdivision of the NTG HV nodes into HV customers (user substation—US), pure producers (PP) and public distribution substations (PS themselves).
- Spatial classification: subdivide the region/zone into portions, gradually smaller, according, for instance, to the administrative boundaries, and combine with such subdivision the information regarding the territory and the population by resorting geographical information systems (GIS) applications and tools.
- Assign to each territorial portion the share of demand consumption and production of the region/area.
- Build an incidence matrix associating the territorial portions to one or more PSs.
- Assess the total energy consumption and generation for each PS.
- Define the active power time series for each PS.
- Associate the geographical information with each PS, for the grid modelling (see Section 4).

_{min}between the PS and the adjacent ones). In a second step, the circles have been adjusted according to the following heuristic rules, formulated by considering the typical lengths of the distribution networks lines:

- The radii of the circles around PSs predominantly urban have been increased, due to the too-small distance between them (e.g., if d
_{min}< 1 km, r = 3·d_{min}); - On the contrary, the radii of the circles around the PSs mostly rural have been reduced (e.g., if d
_{min}> 16 km, r = d_{min}/2). - The radii of the circles around the PS different from public (i.e., USs, railway substations, etc.) have been chosen equal to 250 m.

## 4. Modelling the Distribution System with Representative Network Portions

- Association of the geographical/socio-economical information to the territory supplied by a given PS, by exploiting GIS tools and applications (shapefiles and intersections).
- Assessment of the shares of rural, industrial, urban ambit according to the land usage, population, etc., related to the territorial portion.
- Combining elementary portions of representative networks for building the synthetic network that models the grid downstream of the HV/MV transformer.

- Rural feeder: long overhead lines with lateral branches, low demand, and spread LV customers (low power density), mostly with agricultural or residential profiles.
- Urban feeder: relatively short underground cable lines, high power density, mainly LV residential loads and low total demand per feeder (but in an urban area, normally, a PS supplies quite a high number of feeders).
- Industrial feeder: supplies various different load types, high rate of MV loads, relatively short line extension, and quite high total power demand.

- −
- nF(U), nF(R), and nF(I) are the number of urban, rural, and industrial feeders respectively, each associated with the lengths LF(U), LF(R), and LF(I).
- −
- U
_{%}(PS_{k}), R_{%}(PS_{k}), and I_{%}(PS_{k}) are respectively, the urban, rural, and industrial shares of land cover and usage in the area supplied by the k-th PS. - −
- EF(U), EF(R), and EF(I) are the annual energy consumptions for each of the three typical feeders.
- −
- E
_{tot}(PS_{k}) is the total annual consumption of the k-th PS as derived by the energy profile assessment defined in Section 3.

_{tot}(PS

_{k}) is the unknown total length of the k-th PS lines that will be eliminated from the list of the unknown variables by dividing member to member the Equations (2)–(4) by Equation (1). In this way, the system will be reduced to three equations with three unknown variables (nF(U), nF(R), and nF(I)). On the contrary, the lengths and the energy consumptions of the representative feeders are fixed and defined by Table 2 and Table 3.

_{%}= 0, the number of industrial typical feeder nF(I) is zero according to Equation (3)).

- At the beginning: close to the HV/MV transformer;
- In the middle: in the central part of the feeder;
- At the end: far from the HV/MV transformer.

## 5. Italian Case Study

^{2}, sparsely populated by 1.6 million inhabitants, supplied by 152 HV/MV primary substations, 79 of which are public PSs. The smallest portion of territory used for the territorial segmentation is the municipality. In Figure 14, such territorial segmentation of the region and the public PSs position are shown.

^{2}), supplied by 19 public PSs. The other steps of the procedure proceed by exploiting the land usage information. In the considered territory, the land usage may be classified as predominantly rural and only close to Cagliari and its hinterland does it change to urban and industrial classifications (Figure 15).

_{PS}(GWh/y)). It is worth noticing that the proposed modelling of the whole Sardinian distribution system goes wrong for only 2% of the total energy demand and 85% of the PS has been modeled with an accuracy less than 20%.

#### 5.1. Single Network Results

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Theoretical four quadrants capability curve of the virtual power plant representing the primary substation.

**Figure 2.**Setpoints at the transmission system operator (TSO)/distribution system operator (DSO) interface during typical days in the year.

**Figure 3.**Procedure for assigning load/generation profiles to each primary substation (PS) in a given region (flow diagram).

**Figure 4.**Illustration of the steps of the procedure for assigning load/generation profiles to each PS in a given region/area.

**Figure 5.**Procedure for assigning for creating an incidence matrix to associate each municipality area with one or more PSs: (

**a**) Territorial segmentation + PS location, (

**b**) circles of the first attempt, and (

**c**) adjusted circles.

**Figure 13.**Subdivision of the Italian territory in NTG zones [19].

**Figure 15.**Spatial downscaling: land usage and PS location of the province of Cagliari. On the right, the zoom of the area close to Cagliari (highlighted with the grey rectangle on the left). In the background, the circles used for building the impedance matrix are also visible.

**Figure 16.**Power exchange with the HV grid (balance of demand and production) of two PSs: one located in urban ambit and one predominantly rural with a high share of DG.

**Figure 17.**Example of the synthetic network (model of the PS N. 52 of Table 6).

Industrial Sector | Specific Energy Consumption |
---|---|

Water | 120 (kWh/t) |

Cement | 67.5 (kWh/t) |

Chemical | 67.5 (kWh/t) |

Refinery | 67.5 (kWh/t) |

Metallurgy | 500 (kWh/t) |

Paper | 900 (kWh/t) |

Mining | 300 (kWh/t) |

Mechanical | 10 (MWh/h) |

Electronic | 10 (MWh/h) |

Feeder | MV Nodes | Total Length (km) | Max Distance from PS (km) | Load (MVA) | LV Installed Power (%) |
---|---|---|---|---|---|

Rural | 22 | 40.11 | 20.85 | 3.51 | 99.6 |

Urban | 9 | 1.23 | 1.21 | 3.62 | 97.6 |

Industrial | 22 | 18.09 | 11.21 | 4.04 | 32.1 |

Dedicated | 1 | 0.01 | 0.01 | -- | -- |

Feeder | Agricultural cons. (%) | Industrial cons. (%) | Tertiary cons. (%) | Residential cons. (%) | Total Energy Consumption (GWh/y) |
---|---|---|---|---|---|

Rural | 37.16% | 0.38% | 0.00% | 62.46% | 16.98 |

Urban | 0.00% | 2.49% | 55.78% | 41.73% | 13.54 |

Industrial | 0.00% | 65.91% | 10.47% | 23.62% | 21.48 |

NTG Zone | PP | PS | US | Other | Total |
---|---|---|---|---|---|

SUD | 58 | 261 | 30 | 54 | 403 |

SARD | 42 | 79 | 22 | 9 | 152 |

SICI | 13 | 137 | 17 | 50 | 217 |

NTG Zone | Region | Agricultural cons. (%) | Industrial cons. (%) | Tertiary cons. (%) | Residential cons. (%) | Total Energy (GWh) |
---|---|---|---|---|---|---|

SUD | Basilicata | 2% | 54% | 24% | 20% | 2548.9 |

Calabria | 3% | 15% | 43% | 40% | 5140.8 | |

Molise | 3% | 46% | 29% | 22% | 1284.8 | |

Puglia | 3% | 44% | 28% | 25% | 16479.8 | |

SARD | Sardinia | 3% | 45% | 27% | 26% | 8403.46 |

SICI | Sicily | 2% | 33% | 32% | 33% | 17214.7 |

**Table 6.**Synthetic network composition of the distribution network supplied by the 79 public PS of Sardinia island (ED

_{PS}is the PS annual energy demand, ${\epsilon}_{model}$ is the accuracy of the representation, and R, I and U are used for rural, industrial and urban typical feeder, respectively).

PS No. | Number of Feeders | ED_{PS} (GWh/y) | ${\mathit{\epsilon}}_{\mathit{m}\mathit{o}\mathit{d}\mathit{e}\mathit{l}}\text{}(\%)$ | PS No. | Number of Feeders | ED_{PS} (GWh/y) | ${\mathit{\epsilon}}_{\mathit{m}\mathit{o}\mathit{d}\mathit{e}\mathit{l}}(\%)$ | PS No. | Number of Feeders | ED_{PS} (GWh/y) | ${\mathit{\epsilon}}_{\mathit{m}\mathit{o}\mathit{d}\mathit{e}\mathit{l}}\text{}(\%)$ | PS No. | Number of Feeders | ED_{PS} (GWh/y) | ${\mathit{\epsilon}}_{\mathit{m}\mathit{o}\mathit{d}\mathit{e}\mathit{l}}\text{}(\%)$ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 4 U | 66.6 | −19% | 21 | 1 R; 11 U | 176.6 | −6% | 41 * | 1 R; 3 U | 58.8 | −2% | 61 | 1 R; 6 U | 108.1 | −9% |

2 | 2 U | 32.7 | −17% | 22 | 1 R; 5 U | 79.9 | 6% | 42 * | 5 R; 15 U | 294.3 | −2% | 62 | 1 R; 2 U | 52 | −5% |

3 | 3 U | 43.3 | −6% | 23 | 1 R; 2 U | 32.3 | 36% | 43 | 1 R; 3 U | 59.6 | −4% | 63 | 3 U | 49 | −17% |

4 | 2 U | 23.8 | 14% | 24 | 2 R; 3 U | 70.4 | 6% | 44 | 2 U | 29.7 | −9% | 64 | 2 U | 25.7 | 5% |

5 | 1 R; 3 U | 58 | −1% | 25 | 1 R; 4 U | 78.4 | −9% | 45 | 1 R; 3 U | 69 | −17% | 65 | 4 U | 57.9 | −6% |

6 * | 2 R; 1 U | 53.7 | −12% | 26 | 2 R; 3 U | 71.9 | 3% | 46 | 1 R; 2 U | 35 | 26% | 66 | 1 R; 2 U | 29.4 | 50% |

7 | 3 R; 6 U | 127.3 | 4% | 27 | 1.U | 8.3 | 63% | 47 | 1 R; 4 U | 73 | −3% | 67 | 2 U | 24.4 | 11% |

8 | 3 U | 50.7 | −20% | 28 * | 2 U | 38.6 | −30% | 48 * | 1 R; 3 U | 49.6 | 16% | 68 | 1 R; 4 U | 70.3 | 1% |

9 | 1 U | 24.1 | −44% | 29 * | 3 U | 41.6 | −3% | 49 | 3 R; 11 U | 212.7 | −6% | 69 * | 1 R; 4 U | 82.7 | −14% |

10 | 1 R; 3 U | 62.6 | −8% | 30 * | 3 U | 44.5 | −9% | 50 * | 2 R; 9 U | 171.3 | 1% | 70 * | 2 R; 4 U | 92.3 | −5% |

11 | 3 U | 39 | 4% | 31 | 2 U | 31.2 | −13% | 51 | 1 R; 2 U | 44.8 | −2% | 71 | 1 R; 2 U | 36.3 | 21% |

12 | 1 U | 19.4 | −30% | 32 * | 4 U | 61 | −11% | 52 * | 2 R; 3 U | 81.4 | −9% | 72 | 1 R; 1 I; 10 U | 171.7 | 1% |

13 | 1 I; 3 U | 59.1 | 5% | 33 | 1 R; 8 U | 137.5 | −9% | 53 * | 1 R; 4 U | 63.4 | 12% | 73 | 2 U | 38.3 | −29% |

14 * | 1 R; 1 I; 3 U | 77.6 | 2% | 34 * | 3 R; 1 I; 8 U | 181.4 | −1% | 54 * | 1 R; 5 U | 84.1 | 0% | 74 | 2 R; 5 U | 104.8 | −3% |

15 * | 1 R; 5 U | 83.8 | 1% | 35 | 1 R; 13 U | 203.6 | 3% | 55 * | 1 R; 3 U | 51.4 | 12% | 75 | 1 R; 10 U | 160.9 | −6% |

16 | 2 R; 1 I; 7 U | 143 | 5% | 36 | 2 R; 4 U | 86.9 | 2% | 56 | 3 R; 6 U | 128.9 | 2% | 76 | 1 U | 15 | −9% |

17 | 1 R; 8 U | 129.1 | −3% | 37 | 2 U | 36.3 | −26% | 57 | 1 I; 4 U | 72.4 | 4% | 77 | 1 R; 5 U | 87.1 | −3% |

18 * | 2 U | 25.4 | 7% | 38 * | 4 R; 1 I; 11 U | 227.3 | 5% | 58 | 12 U | 173.1 | −6% | 78 * | 3 R; 7 U | 141.6 | 3% |

19 * | 1 U | 25.9 | −48% | 39 * | 2 R; 2 U | 60.5 | 1% | 59 * | 14 U | 201.6 | −6% | 79 | 10 U | 139.9 | −3% |

20 * | 4 R; 5 U | 132.3 | 2% | 40 | 2 U | 29.9 | −10% | 60 * | 2 R; 9 U | 153.4 | 1% |

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**MDPI and ACS Style**

Pisano, G.; Chowdhury, N.; Coppo, M.; Natale, N.; Petretto, G.; Soma, G.G.; Turri, R.; Pilo, F.
Synthetic Models of Distribution Networks Based on Open Data and Georeferenced Information. *Energies* **2019**, *12*, 4500.
https://doi.org/10.3390/en12234500

**AMA Style**

Pisano G, Chowdhury N, Coppo M, Natale N, Petretto G, Soma GG, Turri R, Pilo F.
Synthetic Models of Distribution Networks Based on Open Data and Georeferenced Information. *Energies*. 2019; 12(23):4500.
https://doi.org/10.3390/en12234500

**Chicago/Turabian Style**

Pisano, Giuditta, Nayeem Chowdhury, Massimiliano Coppo, Nicola Natale, Giacomo Petretto, Gian Giuseppe Soma, Roberto Turri, and Fabrizio Pilo.
2019. "Synthetic Models of Distribution Networks Based on Open Data and Georeferenced Information" *Energies* 12, no. 23: 4500.
https://doi.org/10.3390/en12234500