Rooftop PV: Potential and Impacts in a Complex Territory
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Cluster Approach
- A linear correlation between couples of attributes (each value representing a characteristic of the settlement) allows to exclude features that are strongly correlated and thus do not provide significant new information.
- A principal component analysis (PCA) determines whether linear relationships between all attributes exist and points out peculiar individual situations.
- The clustering of remaining data partitions the settlements into sets with very similar urban (and, thus, energy) features.
- The settlement closest to the center of gravity of each cluster is examined in depth to determine its key features (in the case at hand, the available roof surface and its potential productivity).
- The results obtained for this “central” settlement are extrapolated to its entire cluster, using the most significant attribute.
2.2. Application to Lombardy
- Population (number);
- Altitude of the urban center (m a.s.l.);
- Land area (km2);
- Urban surface area (km2);
- Density of the urban area (inhabitants per square km–inh km−2;
- Six classes of the number of dwellings per building (one—Class 1, two—Class 2, three or four—Class 3, five to eight—Class 5, nine to 15—Class 9, 16 or more—Class 16).
- (1)
- The most reasonable number of clusters was identified, comparing the values of 30 indices of distance in the data (R function NbClust). Then the hybrid clustering algorithm was applied (hkmeans function) using NbClust results as input;
- (2)
- The resulting clusters were validated using internal measures to evaluate the goodness of the partitioning obtained. These measures, formulated to reflect the compactness, connectedness, and separation of the obtained partitions, use internal information from the clustering process (e.g., minimum or maximum distance between objects or between clusters) to evaluate the goodness of fit of the clusters without referring to a priori or external information.Such measures were:
- The average distance within each cluster M of m elements, which must be as small as possible:
- The average distance between each couple of clusters M and P (of m and p elements, respectively), which must be as large as possible:
- The average silhouette width of the cluster measures how similar an object i is to other objects in its cluster compared to those in the neighboring cluster. Its value is between 1 and −1, with a value of 1 indicating a perfect partition. The average silhouette width of the observation i is defined by the formula:
- (3)
- Finally, the outermost cluster has been graphically identified and excluded from the sample data to be processed in the following iteration.
3. Results
- Cluster 7 contains the most populated cities with medium-to-large land area and diverse housing types with numerous vertical buildings.
- Cluster 6 comprises municipalities in high mountains, not necessarily of small size, but mainly characterized by low density and a very large land area. The prevailing building typology are single family or duplex;
- Cluster 5 contains the cities belonging to metropolitan areas, similar to those in Cluster 7 but with a much smaller land area;
- Cluster 4 groups the remaining sparsely populated mountain communities which differ from Cluster 6 in population and land area;
- Cluster 3 contains municipalities of the low hilly territory predominantly in southern-eastern Lombardy, which have a highly populated, concentrated and diverse urban area over a very large land extension;
- Cluster 2 is predominantly characterized by average size municipalities with mostly single or two-family housing type;
- Cluster 1 comprises the remaining municipalities of medium size, both in terms of inhabitants and extension, with quite inhomogeneous housing types.
4. Discussion
- The paper by Bodis et al. [24] combines statistical (Eurostat) and satellite data (European Settlement Map-ESM) to quantify the rooftop area available for PV systems and assesses the technical potential for rooftop photovoltaic electricity production by applying PVGIS, at a spatial resolution of 100m across the European Union.
- ENergy Systems Potential Renewable Energy Sources (ENSPRESO) [28] provides a report of potential energy values implemented for three different solar plant capacities (low 85 kW/m2, medium 170 kW/m2, high 300 kW/m2) processed through JRC-EU-TIMES model at national (NUTS1) and regional (NUTS2) levels for the period 2010–2050.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Population | Altitude | Land Area | Urban Area | Density | Class 1 | Class 2 | Class 3 | Class 5 | Class 9 | Class 16 | |
---|---|---|---|---|---|---|---|---|---|---|---|
POPULATION | 1 | ||||||||||
ALTITUDE | −0.06 | 1 | |||||||||
LAND AREA | 0.27 | 0.22 | 1 | ||||||||
URBAN AREA | 0.91 | −0.16 | 0.39 | 1 | |||||||
DENSITY | 0.31 | −0.06 | −0.12 | 0.30 | 1 | ||||||
CLASS 1 | 0.47 | −0.13 | 0.43 | 0.68 | 0.24 | 1 | |||||
CLASS 2 | 0.48 | −0.13 | 0.38 | 0.73 | 0.39 | 0.86 | 1 | ||||
CLASS 3 | 0.66 | −0.04 | 0.40 | 0.83 | 0.44 | 0.73 | 0.90 | 1 | |||
CLASS 5 | 0.88 | −0.06 | 0.35 | 0.94 | 0.39 | 0.63 | 0.73 | 0.90 | 1 | ||
CLASS 9 | 0.98 | −0.05 | 0.29 | 0.93 | 0.32 | 0.51 | 0.54 | 0.73 | 0.93 | 1 | |
CLASS 16 | 0.99 | −0.03 | 0.25 | 0.86 | 0.25 | 0.38 | 0.37 | 0.57 | 0.82 | 0.96 | 1 |
Population | Altitude | Land Surface | Density over Urban Area | Buildings Class 1 | Buildings Class 3 | Residential Rooftop | PV Area over Land Area | |
---|---|---|---|---|---|---|---|---|
[inh] | [m a.s.l.] | [km2] | [inh/km2] | # | # | [km2] | % | |
CLUSTER 1 | 1930 | 115 | 13.62 | 1276 | 279 | 57 | 37.67 | 0.4% |
CLUSTER 2 | 4506 | 261 | 7.88 | 2571 | 291 | 131 | 80.56 | 1.3% |
CLUSTER 3 | 10,501 | 162 | 38.35 | 1994 | 1013 | 314 | 34.26 | 0.2% |
CLUSTER 4 | 961 | 757 | 18.14 | 1704 | 251 | 92 | 10.96 | 0.1% |
CLUSTER 5 | 17,212 | 199 | 9.32 | 4540 | 539 | 312 | 45.72 | 1.1% |
CLUSTER 6 | 2894 | 1058 | 122.69 | 1550 | 455 | 312 | 3.18 | 0.02% |
CLUSTER 7 | 66,155 | 175 | 39.19 | 3980 | 2836 | 1162 | 37.55 | 1.0% |
PV Production [TWH] | Consumption [TWH] | Coverage % | |
---|---|---|---|
CLUSTER 1 | 1.86 | 0.95 | 196% |
CLUSTER 2 | 3.88 | 3.42 | 113% |
CLUSTER 3 | 1.64 | 2.02 | 81% |
CLUSTER 4 | 0.52 | 0.25 | 208% |
CLUSTER 5 | 2.21 | 2.59 | 85% |
CLUSTER 6 | 0.15 | 0.06 | 237% |
CLUSTER 7 | 1.83 | 2.01 | 91% |
TOTAL | 12.11 | 11.46 |
Emission Factors [MG/KWH] | Emissions Reduction [TON] | ||
---|---|---|---|
NITROGEN OXIDES | NOx | 227.4 | 2752.8 |
SULFUR OXIDES | SOx | 63.6 | 769.9 |
NON-METHANE VOLATILE ORGANIC COMPOUNDS | NMVOC | 83.8 | 1014.4 |
AMMONIA | NH3 | 0.5 | 6.05 |
PARTICULATE MATTER | PM10 | 5.4 | 65.4 |
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Bernasconi, D.; Guariso, G. Rooftop PV: Potential and Impacts in a Complex Territory. Energies 2021, 14, 3687. https://doi.org/10.3390/en14123687
Bernasconi D, Guariso G. Rooftop PV: Potential and Impacts in a Complex Territory. Energies. 2021; 14(12):3687. https://doi.org/10.3390/en14123687
Chicago/Turabian StyleBernasconi, Diana, and Giorgio Guariso. 2021. "Rooftop PV: Potential and Impacts in a Complex Territory" Energies 14, no. 12: 3687. https://doi.org/10.3390/en14123687