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The Recent Change in the Italian Policies for Photovoltaics: Effects on the Energy Demand Coverage of Grid-Connected PV Systems Installed in Urban Contexts Aldo Orioli *, Vincenzo Franzitta, Alessandra Di Gangi and Ferdinando Foresta Dipartimento di Energia, Ingegneria dell’Informazione e Modelli Matematici (D.E.I.M.), Università degli Studi di Palermo, Viale delle Scienze Edificio 9, 90128 Palermo, Italy; [email protected] (V.F.); [email protected] (A.D.G.); [email protected] (F.F.) * Correspondence: [email protected]; Tel.: +39-091-2386-1914; Fax: +39-091-484-425 Academic Editor: Jayanta Deb Mondol Received: 28 September 2016; Accepted: 8 November 2016; Published: 12 November 2016

Abstract: In July 2013, the Italian photovoltaic (PV) support policies changed the feed-in tariff (FIT) mechanism and turned to a tax credits program, which is currently in force. The aim of this paper is to investigate how such a radical change has influenced the electricity demand coverage of the PV systems installed in urban contexts. A methodology, which connects the economic assessment to a detailed architectural and energy suitability analysis, was applied to some case studies to analyse the relationships between the physical parameters related to multi-storey buildings (roof shapes, number of floors and area of flats) and the most relevant economic and financial features affecting the viability of rooftop PV systems. The study, which considers only the electricity produced by the PV systems that are economically profitable, highlighted that the tax credits scheme is even more effective in covering the electrical consumption of densely urbanised Italian city districts. The results, which are significantly influenced by the latitude of the analysed districts, underline the opportunity for governments to adopt PV promoting policies that are more sensitive to the amount of solar energy available in the different regions of their national territory. Keywords: photovoltaic; feed-in tariff; tax credit

1. Introduction The actual energy coverage of the grid-connected photovoltaic (PV) systems installed in urban contexts largely depends on the impact of the cost-benefit analysis. The economic evaluation plays a key role in the investment decision of householders, which has to be very accurate not only because the savings on the electricity bills due to the PV generation offset the costs related to the installation of PV systems. The economic outcome related to PV systems placed on multi-storeys buildings roofs implies the painstaking study of the interaction between the miscellaneous parameters closely linked to the morphological configurations of urban areas (building roof shape, number of floors and overshadowing effects of the surrounding buildings) and financial, economic and running factors (discount rate, inflation rate, price of the purchased electricity, price of the sold electricity, devices replacement and maintenance, insurance costs). Obviously, also the support mechanisms Feed-in tariff (FIT, net metering, tax credits, cash rebates, capital subsidies and renewable portfolio standards), which governments have enacted to promote PV dissemination and enforce the reduction of CO2 emission due to the use of fossil fuels, can significantly impact on the expected return on the investment and determine the PV technology success. The effectiveness of the promoting policies implemented by numerous countries has been widely analysed in literature. Energies 2016, 9, 944; doi:10.3390/en9110944

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Net metering, which is the simplest incentive for renewables, allows residential and commercial electric customers to feed generated PV electricity that they not use back into the grid [1–7]. This allows customers to offset their electric consumption by the amount of energy that their PV systems generate. Using a specified meter that records the flow of electricity in both directions, consumers can benefit from the total amount of energy they produce, not just the electricity they consume locally. Unfortunately, due to the high costs of the PV electricity, net metering alone may be insufficient to put PV systems in competition with the other traditional energy production technologies [8]. By means of renewable portfolio standards a government places an obligation on electricity supply companies to produce a specified fraction of their electricity from renewable energy sources [3–6,9–12]. The companies are required to acquire renewable energy certificates, also known as tradable green certificates, which are typically equivalent to 1 MWh of electricity created by a renewable resource. To better stimulate the PV development, countries have established targets, known as set-asides or carve-outs, which are provisions that require utilities companies to use a specific renewable source to account for a certain amount of generating capacity or a percentage of their retail electricity sales. Tax credits are financial incentives based on a non-refundable personal income tax credit, which generally applies only to residential renewable energy systems [1,3–6,13]. Governments allow PV systems a credit equivalent to a fixed percentage of the installation price, which usually includes on-site preparation, equipment, labour cost, wiring and piping for connection to the grid. Some countries also adopted financial incentives in the form of cash rebates and capital subsidies, which are amounts of money paid to the PV system owners per watt-capacity of the installed PV field or to compensate a fixed percentage of the investment costs. The FIT scheme provides a constant tariff guaranteed for electricity generated from PV and fed into the grid over a given period of time [1–5,7,9–12,14–23]. FIT payments may depend on the market price of electricity or not. In a fixed-price FIT, the total per-kWh payment is independent from the market price; in a premium-price FIT payment, the total payment is determined by adding a premium tariff, which can be constant or sliding, to the spot market electricity price. Despite FITs seem to be an effective incentive to boost PV technology growth, an optimal FIT must cover the PV project costs, plus an estimated profit, to reduce the investment risk and ensure a return to investors. The implementation of severe FIT de-escalation rates, justified by the PV device prices decrement observed over the last years, or even a PV support policy moratorium, may not guarantee a reliable economic return over the time and compromise the fulfilment of the electricity demand coverage that may be realistically obtained from renewable energy sources. The FIT support, which was promoted in Italy since 2005 by means of some consecutive decrees named “Conto Energia”, was discontinued in July 2013. At present, PV systems are incentivised by tax credits that are granted for ten years. Such a new promotion policy has significantly modified the economic convenience of PV systems in Italy. Despite many pessimistic predictions, it seems that the new approach may yield greater advantages for domestic investors and, in turn, increase the amount of electrical consumption covered by the electricity generated by the PV systems installed in densely urbanised areas. With the aim of quantifying the energy effects of the change in the Italian PV support policy, the FIT and current tax credits schemes have been compared for the districts of three densely urbanised Italian cities that are fair representative of the Italian latitudes. The paper is organised as follows: the Italian FIT and tax credits schemes are discussed in Section 2. In Section 3, the methodology used for evaluating the viability of the investment is outlined and the case studies are described. The last sections show the results concerning the effectiveness of both the analysed PV policy schemes on the electricity coverage, also considering the effects of the solar energy shading and mismatch between electricity generation and consumption. 2. The Italian Support Policies for Photovoltaics Over the last years the electricity produced by the clean sources of renewable energies has significantly risen in Italy. The power generated by PV systems, which was only 39 GWh in 2007,

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reached 23,299 GWh at the end of 2014 [24,25]. An increase was observed for hydropower and wind power that were 32,815 GWh and 4030 GWh in 2007, respectively, and became 58,067 GWh and 14,966 2014. TheThe generation of geothermal electricity, which has varied 5569from to 5541 GWh 14,966 GWh GWhinin 2014. generation of geothermal electricity, which hasfrom varied 5569 to during the during same period of time, was constant. 1 depicts variation the electricity 5541 GWh the same period ofalmost time, was almostFigure constant. Figurethe 1 depicts theof variation of the generated by clean renewable sources since 2007.since 2007. electricity generated by clean renewable sources Power from clean renewable sources in Italy 120

Geothermal Hydro Wind Photovoltaic

Power [TWh]

100 80 60 40 20 0 2007

2008

2009

2010 2011 Years

2012

2013

2014

Figure in Italy. Italy. Figure 1. 1. Power Power generation generation from from clean clean sources sources of of renewable renewable energy energy in

At the end of 2014 the clean renewable electricity, which has reached 38.08% of the power At the end of 2014 the clean renewable electricity, which has reached 38.08% of the power produced in Italy, was 2.40 times the value measured in 2007. It is easy to observe that PV systems produced in Italy, was 2.40 times the value measured in 2007. It is easy to observe that PV systems have played a dominant role in increasing the clean renewable electricity generation. Such a result is have played a dominant role in increasing the clean renewable electricity generation. Such a result is the long-term effect of the effective promoting policies implemented some years ago, which the long-term effect of the effective promoting policies implemented some years ago, which enhanced enhanced the diffusion of PV systems in Italy. the diffusion of PV systems in Italy. The first PV promoting initiative dates back to 2000 when the Italian government issued the The first PV promoting initiative dates back to 2000 when the Italian government issued the “Photovoltaic roofs” program [26] that provided consistent financial supports—up to 75% of the “Photovoltaic roofs” program [26] that provided consistent financial supports—up to 75% of the investment costs—to install PV systems with a peak power in the range 1 to 20 kW. A maximum investment costs—to install PV systems with a peak power in the range 1 to 20 kW. A maximum investment cost of 8005.08 €/kW was fixed for the PV systems with a nominal power up to 5 kW, investment cost of 8005.08 €/kW was fixed for the PV systems with a nominal power up to 5 kW, whereas such a value was linearly decreased to 7230.40 €/kW for an installed power of 20 kW. The whereas such a value was linearly decreased to 7230.40 €/kW for an installed power of 20 kW. promoting initiative yielded the installation of PV systems for a global power of 27 MW in 2003. The promoting initiative yielded the installation of PV systems for a global power of 27 MW in 2003. Unfortunately, the program had an important drawback as it did not reward the PV system energy Unfortunately, the program had an important drawback as it did not reward the PV system energy efficiency and electricity production. Accordingly, many installed PV systems were out of order efficiency and electricity production. Accordingly, many installed PV systems were out of order after after few years because the producers thought it was not worth spending money to replace the few years because the producers thought it was not worth spending money to replace the damaged damaged PV modules and inverters, whose cost was very high at that time. PV modules and inverters, whose cost was very high at that time. In 2005, to put the European Union 2001/77/EC Directive [27] into effect, the Italian government In 2005, to put the European Union 2001/77/EC Directive [27] into effect, the Italian government issued a decree, named 1st “Conto Energia” [28,29], that adopted a FIT scheme to incentivise the PV issued a decree, named 1st “Conto Energia” [28,29], that adopted a FIT scheme to incentivise the systems diffusion. A premium of 0.445 €/kWh was paid for the electricity generated by the PV PV systems diffusion. A premium of 0.445 €/kWh was paid for the electricity generated by the PV systems with a peak power up to 20 kW and self-consumed by the producers; for the energy injected systems with a peak power up to 20 kW and self-consumed by the producers; for the energy injected into the grid by the PV systems with a nominal power in the range 20 to 1000 kW a premium, into the grid by the PV systems with a nominal power in the range 20 to 1000 kW a premium, varying varying from 0.460 to 0.490 €/kWh, was paid. In addition to the previous premiums, which had a from 0.460 to 0.490 €/kWh, was paid. In addition to the previous premiums, which had a duration of duration of 20 years, a simplified purchase and resale arrangement was offered by the “Gestore 20 years, a simplified purchase and resale arrangement was offered by the “Gestore Servizi Energetici” Servizi Energetici” (GSE), which is the state-owned company that promotes and supports renewable (GSE), which is the state-owned company that promotes and supports renewable energy sources energy sources in Italy, to pay the electricity fed into the grid. The electricity exported to the grid was in Italy, to pay the electricity fed into the grid. The electricity exported to the grid was paid at a paid at a guaranteed minimum price, which is updated annually by the Autorità per l’Energia guaranteed minimum price, which is updated annually by the Autorità per l’Energia Elettrica, il Gas e Elettrica, il Gas e il Sistema Idrico (AEEG), Italian Regulatory Authority for electricity and gas, or at il Sistema Idrico (AEEG), Italian Regulatory Authority for electricity and gas, or at an average monthly an average monthly price per hourly band, which is set on the Italian Power Exchange for the market price per hourly band, which is set on the Italian Power Exchange for the market area to which the area to which the PV system is connected. Such a purchase and resale arrangement is currently used. PV system is connected. Such a purchase and resale arrangement is currently used. The net metering The net metering service, with a balancing period of 3 years, was alternatively proposed for the energy injected into the grid by the only PV systems with a peak power up to 20 kW. The 1st “Conto Energia” expired in March 2006 when the 500 MW target of PV installed power was reached.

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service, with a balancing period of 3 years, was alternatively proposed for the energy injected into the grid by the only PV systems with a peak power up to 20 kW. The 1st “Conto Energia” expired in March 2006 when the 500 MW target of PV installed power was reached. In 2007 the 2nd “Conto Energia” [30], which set a 1200 MW target of PV capacity to be installed at national level, was implemented. Besides simplified purchase and resale arrangements, the net metering service was alternatively proposed for the energy fed into the grid by the PV systems with a peak power up to 20 kW. Different premiums varying from 0.360 to 0.490 €/kWh, depending on the PV system peak power and the used installation typology, were additionally paid for the electricity generated in the next 20 years. The greatest premiums were paid for the PV systems that used building integrated photovoltaic (BIPV) devices to replace architectural elements such as roofs, facades, balustrades, bus shelters, acoustic barriers, windows and sun blind shutters. A premium de-escalation rate of 2%/year was fixed for the PV systems installed after 2008. In 2009 the AEEG adopted a modified net metering service under which, besides the possibility of using the electricity injected into the grid to offset the electricity withdrawn from the grid for a period of 3 years, a compensatory contribution is paid to PV producers [31]. The contribution, which also covers part of the charges incurred by the customer for withdrawing electricity from the grid, is calculated considering the amount of electricity exported or imported by the PV system at the various different hours of each day in a given calendar year and the corresponding hourly market price of the electricity. In 2010 the Italian government implemented the 3rd “Conto Energia” [32] that planned the installation of a number of regular PV systems (i.e., using a PV field made by joining commercial PV modules), innovative BIPV systems and concentrating PV (CPV) systems sufficient to reach a capacity of 3500 MW. Besides simplified purchase and resale arrangements for the energy injected into the grid by all kind of PV systems, the possibility of using the modified net metering service was optionally offered for the PV systems with a peak power up to 200 kW. For each PV system typology different premiums, varying with the peak power of the installed PV field, were additionally paid settled for the electricity produced in the next 20 years. The premiums ranged from 0.251 to 0.402 €/kWh for regular PV systems, from 0.370 to 0.440 €/kWh for BIPV systems and from 0.280 to 0.370 €/kWh for CPV systems. At the beginning of the second semester 2011 the 4th “Conto Energia” [33] was implemented. Both the modified net metering service and simplified purchase and resale arrangements were alternatively offered for the energy injected into the grid by all types of PV systems. The additional premiums settled for the electricity generated by regular PV systems varied from 0.133 to 0.387 €/kWh, depending on the installed peak power and the month of the year. The premiums paid for the electricity generated ranged from 0.345 to 0.427 €/kWh for BIPV systems and from 0.261 and 0.359 €/kWh for CPV systems, depending on the installed peak power and the current semester of the year. The premiums had twenty years duration. The 4th “Conto Energia” expired at the end of August 2012 when the global amount of incentives paid by the Italian government reached the fixed target value of 6 M€/year. For the 5th “Conto Energia” [34], which started as soon as the previous “Conto Energia” ceased, the target value of 6.7 M€/year for the global amount of the paid incentives was fixed. For each type of PV systems a different FIT was fixed for the electricity injected into the grid, depending on the peak power of the photovoltaic arrays and the current month. In addition, for the PV electricity self-consumed by producers in the next 20 years a premium was paid by the GSE. Table 1 lists the values of the FIT and premiums for the regular PV systems installed on buildings, which had to be provided with the energy performance certification.

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Table 1. FITs and premiums for PV systems installed on buildings. PV System Rated Power (kWp) 1–3 3–20 20–200 200–1000 1000–5000 >5000

September 2012–February 2013 Feed-in Tariff Premium (€/kWh) (€/kWh) 0.208 0.196 0.175 0.142 0.126 0.119

0.126 0.114 0.093 0.060 0.044 0.037

March 2013–August 2013 Feed-in Tariff Premium (€/kWh) (€/kWh) 0.182 0.171 0.157 0.130 0.118 0.112

0.100 0.089 0.075 0.048 0.036 0.030

The values of the FITs ranged from 0.217 to 0.288 €/kWh for the BIPV systems and from 0.174 to 0.259 €/kWh for the CPV systems. The premiums varied from 0.135 to 0.186 €/kWh and from 0.092 to 0.157 €/kWh for the BIPV and CPV systems, respectively. In addition to the previous premiums, for the energy injected into the grid by all types of PV systems the simplified purchase and resale arrangements and modified net metering service were also offered as possible alternatives to the current FIT. The 5th “Conto Energia” had a short life, as it ceased on the first days of July 2013. To support the future installation of PV systems the Italian government opportunely implemented a tax credit program [35–37] that allows the owners or tenants of the building, on which the PV system is installed, to have ten annual tax refunds corresponding to a global benefit on the PV system costs equal to:

• • •

50% of the expenses incurred before the end of 2014; 40% of the expenses incurred during 2015; 36% of the expenses incurred after 2015.

The electricity injected into the grid is counted by the GSE considering the terms of the covenant with the PV producer, who can choose a simplified purchase and resale arrangement or the modified net metering service. Since the end of the 5th “Conto Energia”, the lack of PV incentives has diffused the idea that it was not worth installing new PV systems, because no money was paid by the government to the investors. Such an idea, which is shared by many PV investors who used to get the money regularly paid by the government, is clearly disproved by the results presented in this paper. 3. Methodology The assessment of the PV electricity potential at urban scale has been investigated by many authors. Some Israeli (Jerusalem, Tel Aviv, Haifa, Beersheba, Eilat) and Slovakian (Bardejov) cities were analysed by Hofierca et al. [38] and Vardimon [39] respectively. Bergamasco et al. [40] evaluated the PV energy collected by the available roof surface area in Turin (Italy). The Portuguese cities of Lisbon, Alcabideche and Oeiras were studied by Brito et al. [41], Redweik et al. [42], Santos et al. [43] and Amado et al. [44,45]. Kodysh et al. [46] presented a methodology for estimating solar potential on multiple building rooftops in Knoxville (TN, USA). A method to evaluate the adaptability of PV energy on urban façades was proposed and used by Esclapés et al. [47] for a representative area of Madrid (Spain). Klironomos et al. [48] and Portolan dos Santos et al. [49] evaluated the potential of building integrated photovoltaic (BIPV) systems in Oinofyta (Greece) and Florianopolis (Brazil) respectively. Some districts of State College (PA, USA) and Bombay (India) were analysed by Choi et al. [50] and Singh et al [51] respectively. The urban PV potential of Cottbus (Germany), Georgetown (Malaysia) and Tokyo (Japan) was assessed by Schwarz et al. [52], Latif et al. [53] and Stoll et al. [54] respectively. Strzalka et al. [55] used a modern district near Stuttgart (Germany) to evaluate the potential of PV energy and the local own consumption of electricity produced. An urban area of Valparaiso (Chile)

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constituting 366 houses was studied by Araya-Muñoz [56]. Talavera et al. [57] analysed the University of Jaén Campus (Spain). In order to evaluate the effects of the two different support schemes on the level of coverage of the electricity consumption of rooftop PV systems, three case studies regarding the districts of cities representative of the urban morphologies and latitudes of southern, central and northern Italy, were investigated. A specific methodology [58], which combines the analysis of the economic convenience of PV systems to a detailed rooftop architectural and energy suitability analysis, was adopted. Accordingly, the interaction between the number of floors and the roof shapes of the buildings was considered with the aim of inferring the specific sizes of the PV systems that can be installed by each building co-owner, the associated electricity generation and, in turn, the coverage of the electricity demand in the districts. Then, the economic analysis of the PV systems was performed by means of the net present value (NPV) assuming both the FITs and tax credits schemes and considering all costs (PV and electrical devices, maintenance and management, insurance, replacement of PV panels and Energies 2016,all 9, 944 6 ofthe 31 inverters), benefits (reduced electricity bills, sold PV electricity, FITs, premiums and tax credits) decrease in the PV panel efficiency and the variation of the electricity price and inflation rate. Only the decrease inproduced the PV panel efficiency and the variation of the price and rate. Only the electricity by the economically advantageous PV electricity systems was used toinflation assess the coverage of electricity produced by the economically advantageous PV systems was used to assess the coverage of the electricity consumption in the districts. Three different temporal scenarios were assumed: the electricity consumption in the districts. Three different temporal scenarios were assumed: 1. At the end of the FIT scheme (June 2013); 1. At the end of the FIT scheme (June 2013); 2. At the beginning of the tax credits scheme (July 2013); 2. At the beginning of the tax credits scheme (July 2013); 3. 18 months months after after the the beginning beginning of of the the tax tax credits credits scheme scheme (December (December 2014). 2014). 3. 18 A sensitivity outout considering different levels of load mismatch and solar sensitivityanalysis analysiswas wasalso alsocarried carried considering different levels of load mismatch and energy shading. solar energy shading. 3.1. Case Studies Studies 3.1. Case ◦ N, Longitude 13.4◦ E), Three Three districts districts located located in in the the Italian Italian cities cities of of Palermo Palermo (Latitude (Latitude 38.0 38.0° N, Longitude 13.4° E), ◦ N, Longitude 12.0◦ E) and Milan (Latitude 45.0◦ N, Longitude 9.0◦ E) were Rome Rome (Latitude (Latitude 41.0 41.0° N, Longitude 12.0° E) and Milan (Latitude 45.0° N, Longitude 9.0° E) were selected selected as as case case studies studies to to assess assess the the effects effects of of the the integration integration of of PV PV systems systems in in urban urban contexts; contexts; as as it it is is shown in Figure 2, the cities are fair representative of the Italian latitudes. shown in Figure 2, the cities are fair representative of the Italian latitudes.

Figure 2. The analysed cities (provided by Google Maps). Figure 2. The analysed cities (provided by Google Maps).

The analysed districts are shown in Figures 3–5. Each district, which was surveyed by means of The analysed districts are shown inby Figures district,and which was surveyed by Google Earth™ program is characterised specific3–5. roadEach alignments orientations (Palermo: means of Google Earth™ program is characterised by specific road alignments and orientations 117° East of South and 153° West of South; Rome: 107° East of South and 253° West of South; Milan: 147° East of South and 213° West of South).

Figure 2. The analysed cities (provided by Google Maps). Energies 2016, 9, 944

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The analysed districts are shown in Figures 3–5. Each district, which was surveyed by means of Google Earth™ program is characterised by specific road alignments and orientations (Palermo: ◦ Eastand 117° East of South 153° West of ◦South; Rome: 107° East107 of ◦South and 253°and West South; (Palermo: 117 of South and 153 West of South; Rome: East of South 253of◦ West ofMilan: South; ◦ ◦ 147° East South 213° West South). Milan: 147of East ofand South and 213ofWest of South).

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Figure 3. The study area of Palermo (provided by Google Earth™).

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Figure 4. The study area of Rome (provided by by Google Earth™). Earth™). Figure Figure 4. 4. The The study study area area of of Rome Rome (provided (provided by Google Google Earth™).

Figure 5. 5. The The study study area area of of Milan Milan (provided Figure (provided by by Google Google Earth™). Earth™). Figure 5. The study area of Milan (provided by Google Earth™).

The districts, which were measured using the images provided by Google Earth™, cover an The districts, which were measured using the images provided by Google Earth™, cover an area of 269,000 m2, 126,000 m2 and 154,000 m2 for Palermo, Rome and Milan, respectively; the area of 269,000 m2, 126,000 m2 and 154,000 m2 for Palermo, Rome2 and Milan, respectively; the corresponding surfaces occupied by the buildings measure 109,207 m , 78,826 m2 and 99,817 m2. corresponding surfaces occupied by the buildings measure 109,207 m2, 78,826 m2 and 99,817 m2. 3.2. Architectural Analysis

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The districts, which were measured using the images provided by Google Earth™, cover an area of 269,000 m2 , 126,000 m2 and 154,000 m2 for Palermo, Rome and Milan, respectively; the corresponding surfaces occupied by the buildings measure 109,207 m2 , 78,826 m2 and 99,817 m2 . 3.2. Architectural Analysis The size of the PV fields that can be installed on the rooftop of a building is affected by the surface available for each flat-owner. Accordingly, a reliable assessment of the PV systems that can be realistically installed involves the study of the relationships between the roof shapes the number of floors and the surface of the flats in the buildings. For this purpose, the slanted roofs were classified depending on their configuration (gable, hip and skillion) and the main orientations of the buildings in each district. Table 2 shows the selected roof shapes, direction of slope and the areas of the classified roofs.

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Roof Type Roof Type Roof Type Roof Roof Type Roof Type RoofType Type

T1 T1 T1 T1 T1 T1 T1

Table 2. Classified areas of the slanted roofs. Table 2. Classified areas of the slanted roofs. Table 2. Classified areas of the slanted roofs. Table 2. Classified areas of the slanted roofs. Table Table 2. Classified areas of the slanted roofs. Table 2. Classified areas of the slanted roofs. Table2. 2.Classified Classifiedareas areasof ofthe theslanted slantedroofs. roofs. T2 T3 T4 T5 T6 T2 T3 T4 T5 T6 T2 T3 T4 T5 T6

T2 T2 T2 T2 T2

T3 T3 T3 T3 T3

T4 T4 T4 T4 T4

T5 T5 T5 T5 T5

T7 T7 T7 T7 T7 T7 T7

T6 T6 T6 T6 T6

T8 T8 T8 T8 T8 T8 T8

City City City City City City City 2 2222 Palermo (m 8162 12901 2690 2031 2144 2219 1582 2880 Palermo (m 8162 12901 2690 2031 2144 2219 1582 2880 Palermo (m 8162 12901 2690 2031 2144 2219 1582 2880 8162 12901 2690 2031 2144 2219 1582 2880 Palermo Palermo (m 2690 2031 2144 2219 1582 2880 Palermo (m 8162 12901 2690 2031 2144 2219 1582 2880 Palermo (m 8162 12901 2690 2031 2144 2219 1582 2880 Palermo(m (m2)222))2))))) 8162 8162 12901 12901 2690 2031 2144 2219 1582 2880 22016, 2 Energies 2016, 9, 944 8 of 31 Energies 2016, 9, 944 8 of 31 Energies 2016, 9, 944 8 of 31 2 2 Energies 2016, 9, 944 8 of 31 Energies 9, 944 8 of 31 2 7495 5253 299 1032 536 628 1506 1134 Rome (m ) Rome (m ) 7495 5253 299 1032 536 628 1506 1134 Rome (m ) 7495 5253 299 1032 536 628 1506 1134 Rome (m ) 7495 5253 299 1032 536 628 1506 1134 2 2 2 2 2 Rome (m ))))Energies 5253 299 1032 536 628 1134 Rome (m 7495 5253 299 1032 536 628 1506 1134 Rome (m 7495 5253 299 1032 536 628 1506 Rome (m 7495 5253 299 1032 536 628 1506 1134 Energies 9, 944 81506 of 31 Energies 2016, 9, 944 Energies 2016, 81134 of 31 Energies 2016, 9,7495 944 8 of831of831 2016, 9, 9449, 944 of 31 22016, 16,194 31,850 4954 3910 4325 4150 1695 1256 Milan (m ) 2 2 2 2 Milan (m 16,194 31,850 4954 3910 4325 4150 1695 1256 Milan (m 16,194 31,850 4954 3910 4325 4150 1695 1256 Milan (m 16,194 31,850 4954 3910 4325 4150 1695 1256 2. Classified of areas the slanted roofs. Table 2.areas Classified ofslanted the slanted Table 2.areas Classified areas of the roofs.roofs. Table 2. Classified of the slanted roofs. Table 2.Table Classified ofareas the slanted roofs. Milan 4954 3910 4325 4150 1695 1256 Milan (m 16,194 31,850 4954 3910 4325 4150 1695 1256 Milan (m 16,194 31,850 4954 3910 4325 4150 1695 1256 Milan(m (m2222))2))))) 16,194 16,194 31,850 31,850 4954 3910 4325 4150 1695 1256 Table 2.Table Classified of areas theof slanted roofs. 2. Classified ofareas the slanted roofs. Table 2.areas Classified ofslanted the slanted Table 2. Classified the slanted roofs. Table 2.areas Classified areas of the roofs.roofs. Roof Type T9 T10 T11 T13 T14 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 Roof Type T1 T4 T5 T8 Roof T1T2 T3 T2T3T4 T3 T4T5 T5T6T7 T6T7T8 T7 Roof Type T1 T2 T2 T3 T4 T4 T5 T5 T6 T6T14 T7 T8 T8 T8 T16 Type T1T10 T3 T7 RoofRoof Type T1 Type T2 T6 Roof T9 T11 T12 T13 T15 Roof type T9 T10 T11 T12 T13 T14 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 Rooftype type T9 T10 T11 T12 T13 T14 T15 T16

RoofRoof Type T1 Type Roof Type Roof T1T2T3 T2 T3T4 T3T4T5 T4T5T6 T5T6T7 T6T7T8 T7T8 T8 T8 Type T1 T1T2 Roof Type T1 T2 T2 T3 T3 T4 T4 T5 T5 T6 T6 T7 T7 T8

CityCityCity CityCity City City City City City City City City City City Energies 2016, 9, 944 City 2016, 9,2016, 944 9, 944 Energies 2016, 9, 944 Energies Energies City 2016, City 9,Energies 944

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2(m 2)8162 2)(m 2(m 2)(m Palermo ) 8162 8162 12901 2690 2031 2144 2219 1582 2880 Palermo 8162 2690 2031 2144 2219 1582 Palermo 12901 26902031 20312144 21442219 22191582 15822880 28802880 Palermo )8162 12901 2690 Palermo (m 12901 269012901 2031 2144 2219 1582 2880 2) 2 2 8162 2(m 2)8162 2)(m Table 2. Classified areas of the slanted roofs. 2(m 2Table Table 2. Classified areas of the2144 slanted roofs. 27495 2)Rome Palermo (m 12901 2690 2031 2144 2219 1582 2880 222Palermo Table 2.1032 Classified areas of the slanted roofs. 2)))Rome Table 2.2690 Classified areas of the slanted roofs. 2. Classified areas of the slanted roofs. Palermo (m ) 8162 12901 2690 2031 2144 2219 1582 2880 Palermo 8162 12901 2690 2031 2144 2219 1582 2880 4183 (m ) 8162 12901 2690 2031 2219 1582 2880 Palermo 12901 2031 2144 2219 15821134 28801134 Rome (m ) 7495 5253 299 536 628 1506 1134 Rome (m ) 7495 5253 299 1032 536 628 1506 ) 7495 5253 299 1032 536 628 1506 1134 (m ) 5253 299 1032 536 628 1506 Rome (m 7495 5253 299 1032 536 628 1506 1134 Palermo (m 2296 1683 3298 2906 3787 3765 3618 Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 2 2 2 )22)2))) Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 Palermo(m (m 2296 1683 3298 2906 3787 3765 3618 4183 2296 1683 3298 2906 3787 3765 3618 4183 Palermo 22) 22)5253 227495 22(m Rome (m 7495 299 1032 536 628 1506 1134 Rome 7495 5253 299 1032 536 628 1506 1134 Rome 7495 5253 299 1032 536 628 1506 1134 Rome (m22(m )Milan 5253 299 1032 536 628 1506 1134 Rome )(m 7495 5253 299 1032 536 628 1506 11341256 Milan ) T1 16,194 31,850 4954 3910 4325 4150 1695 1256 16,194 4954 3910 4325 4150 1695 16,194 31,850 4954 3910 4325 4150 1695 1256 Milan 16,194 4954 3910 4325 4150 1695 Milan (m )Milan 16,194 31,850 4954 3910 4325 1695 1256 Roof Type T1 T2 T3 T4 T6 T7 T8 Roof Type T1 T231,850 T3 T54150 T6 T7 T8 Roof Type T1 T2 T3T5 T4 T5 T6 T7 T8 Roof Type T1 T2 T3 T4 T5 T6T8 T7 1256 T8 Roof Type T231,850 T3 T4 T5T4 T6 T7 2 2 2 2 2 Rome (m ) 5530 4794 3997 3822 2385 606 779 882 Rome (m ) 5530 4794 3997 3822 2385 606 779 882 Rome (m ) 5530 4794 3997 3822 2385 606 779 882 2 2 2 2 2 2 2 2 5530 4794 3997 3822 2385 606 779 882 2 2 Rome (m ) Rome )))) Milan 5530 4794 3997 3822 2385 606 779 (m )Milan 16,194 31,850 4954 3910 4325 4150 1695 1256 Rome (m 5530 4794 3997 3822 2385 606 779 882 Rome (m 5530 4794 3997 3822 2385 606 779 882 Rome(m (mMilan 5530 4794 3997 3822 2385 606 779 882 Milan (m ) 16,194 16,194 31,850 4954 3910 4325 4150 1695 1256 (m )16,194 16,194 31,850 4954 3910 4325 4150 1695 (mtype )Milan 4954 1695 (m )T9 31,850 4954 3910 4325 4150 1695 1256 Roof T9 T10 T11 T12 T13 T14 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 882 Roof type T931,850 T10 T113910 T124325 T134150 T14 T151256 T161256 Roof type T10 T11 T12 T13 T14 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 2Roof 2)222))) Roof City Citytype City City City 1803 1605 3278 2710 966 571 type T9 T10 T11 T12 T13 T14 T15 T16 Milan (m 1803 1605 3278 2710 493 966 571 887 Milan (m Milan (m 1803 1605 3278 2710 493 966 571 887 Roof type T9 T10 T11 T12 T13 T14 T15 T16 Roof T9 T10 T11 T12 T13 T14 T15 T16 Milan (m 1803 1605 3278 2710 493 966 571 887 type T9 T10 T11 T12 T13 T14 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 2 2 2 2 2 Milan 1803 1605 3278 2710 493 966 571 887 Milan (m 1803 1605 3278 2710 493 966 571 887 Milan (m 1803 1605 3278 2710 493 966 571 887 Milan(m (m)))) 1803 1605 3278 2710 493 966 571 887 City CityCity CityCity 2) (m8162 2) 2)12901 2) 12901 Palermo 2690 2031 2144 2219 1582 2880 2690 2031 2144 2219 1582 Palermo (m 8162 12901 2690 2031 2144 2219 1582 Palermo (m8162 8162 12901 2690 2031 2144 2219 15822880 2880 2880 Palermo (m2Palermo ) (m 8162 12901 2690 2031 2144 2219 1582 2880 CityCity City City City 2) (m2)74952 74955253 2) 299 2) Rome Rome (m7495 299 1032 536 628 1506 1134 5253 299 1032299 536 628536 1506 Rome 7495 5253 1032 628 1506 Rome (m ) (m7495 5253 299 1032 536 628 15061134 1134 1134 Rome (m 5253 1032 536 628 1506 1134 2(m 21683 2)(m 2(m 2)(m Palermo ) 2296 2296 1683 3298 2906 3787 3765 3618 4183 Palermo )2296 2296 1683 3298 2906 3787 3765 3618 Palermo 1683 32982906 2906 37873765 3765 36184183 41834183 Palermo )2296 1683 3298 3787 3618 Palermo (m 3298 2906 3787 3765 3618 4183 A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five 2) (m216,194 2)4954 2) 2 of Milan (m 31,850 4954 3910 4325 4150 1695 1256 Milan ) (m 16,194 31,850 4954 3910 4325 4150 1695 1256 A criterion classification was used for flat roofs. Flat roofs were subdivided into A different criterion classification was used for flat roofs. Flat roofs were subdivided into five Milan (m 16,194 31,850 4954 3910 4325 4150 1695 A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five Milan 16,194 31,850 4954 3910 4325 4150 1695 1256 1256 Milan (m 16,194 31,850 3910 4325 4150 1695 1256 Adifferent different criterion of classification was used for flat roofs. Flat roofs were subdivided intofive five 2) ) of 2 2 2 2 2 2 2 2 2 Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 Palermo (m ) 5530 2296 1683 3298 2906 3787 3765 3618 4183 Palermo )5530 2296 1683 3298 2906 3787 3765 3618 Palermo (m )5530 2296 1683 3298 2906 3787 3765 3618 4183 Palermo (m )(m 2296 16833997 32983822 29062385 3787 3765 3618 4183 Rome (m ) 5530 4794 3997 3822 2385 606 779 882 Rome (m ) 5530 4794 3997 3822 2385 606 779 882 Rome (m ) 4794 3997 3822 2385 606 779 882 4183 Rome (m ) 4794 606 779 882 Rome (m ) 4794 3997 3822 2385 606 779 882 Roof Roof type T9Roof T10 T11 T12 T13 T14 T15 T16 type T9type T10 T11 T12T11 T13T12 T14T13 T15T14 T16close T9 T10 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 Roof type T9 T10 T11 T12 T13 T14 T15 T16 groups according to their surface and a building, whose roof area was very to the average groups according to their surface and a building, whose roof area was very close to the average groups according to their surface and a building, whose roof area was very close to the average 2 2(m 22)4794 2surface groups according their surface and aaaa3997 building, whose roof was very close the 2)) groups according to their surface and building, whose roof area was very close to the average groups according to their and building, whose roof area was very close to the average Rome (m 5530 3822 2385 606 882 groupsaccording according to their surface and building, whose roof area was very close to theaverage average Rome 5530 4794 3997 3822 2385 606779 779 882887 Rome 5530 4794 3997 3822 2385 606 779 882 Rome (m22(m )Milan 5530 4794 3997 3822 2385 606 779 882 Rome )(m1803 5530 4794 3997 3822 2385 606 779887 882 toto their surface and a1605 building, whose roof area was very close to theto average value Milan ) 221803 3278 2710 493 966 571 887 1803 1605 3278 2710 493 966 571 887 Milan 1803 1605 3278 2710 493area 966 571 Milan 1605 3278 2710 493 966 571 Milan (m 1803 1605 3278 2710 493 966 571 887 2) City 2(m 2)1605 2was City Milan (m 1803 3278 2710 493 2710 966 City value of the group surfaces, was selected as representative of each group. Table 33887 lists the surface Milan ) was 1803 1605 3278 2710 1803 1605 3278 493966571 966571887 5718873 887 the value of the group surfaces, selected as representative of each group. Table lists the surface Milan (m2(m )Milan 1803 1605 3278 493493 966 571 887 City City Milan )(m 1803 1605 32782710 493 966 value of the group surfaces, was selected as representative of each group. Table 3333lists lists the surface value surfaces, selected as representative of each group. Table surface value of the group surfaces, was selected as representative of each group. Table lists the surface value of the group surfaces, was selected as representative of each group. Table lists the surface value ofthe thegroup group surfaces, was selected as representative of each group. Table lists theand surface of the of group surfaces, was selected as representative of2710 each group. Table 3 571 lists the surface the A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different A different criterion criterion of classification of classification was was used used for flat for roofs. flat roofs. Flat Flat roofs roofs were were subdivided subdivided into into five five 2representative 2) 2) 1683 2) 2Palermo and the shape of the selected representative buildings along with the areas of the classified flat roofs. and the shape of the selected representative buildings along with the areas of the classified flat roofs. and the shape of the selected buildings along with the areas of the classified flat roofs. Palermo (m ) 2296 1683 3298 2906 3787 3765 3618 4183 (m 2296 3298 2906 3787 3765 3618 4183 Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 Palermo (m 2296 1683 3298 2906 3787 3765 3618 4183 Palermo (m ) 2296 1683 3298 2906 3787 3765 3618 4183 and of the selected representative buildings along with the areas of the classified flat roofs. and the shape of the selected representative buildings along with the areas of the classified flat roofs. and the shape of the selected representative buildings along with the areas of the classified flat roofs. andthe the shape of the selected representative buildings along with the areas of the classified flat roofs. shape ofshape the selected representative buildings along with the areas of the classified flat roofs. A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five A different criterion of classification was used for flat roofs. Flat roofs were subdivided into five groups according to their surface and a building, whose roof area was very close to the average groups according to their surface and a building, whose roof area was very close to the average groups according to their surface and a building, whose roof area was very close to the average groups groups according according their to5530 surface surface and and building, a 5530 building, whose whose roof roof area area was was very very close close to 606 the to the average 2)their 2)5530 2a 2)5530 2to Rome (m 4794 3997 3822 2385 606 779 882 (m 4794 3997 3822 2385 779 882779 Rome (m )3997 4794 3997 3822 2385 882 Rome (m 5530 4794 3997 3822 2385 606 779average 882 Rome (m ) Rome 4794 3822 2385 606 779606 882 groups according their surface and building, whose roof area was very close to the average groups according to their surface and a1605 building, whose roof area was very the average groups according to their surface and a2710 building, roof area was very close to the groups according to surface and aselected building, whose roof area was very close the average groups according to their surface and a3278 building, whose roof area was very close to the average 2)their 2was value ofvalue the surfaces, was selected as representative of each group. Table 3to lists the surface of the group was selected as493 representative of each Table 3surface lists the surface value of the group was as representative of each group. Table 3to lists the surface 2a value of the group surfaces, was selected as representative ofwhose each group. Table 3close lists the value of the group surfaces, selected as representative of3278 each group. Table 3group. lists the surface 2surfaces, 2to Milan (m 1803 1605 3278 493 966 571 887 Milan (m )surfaces, 1605 2710 493 966 571 887 Milan (m )3278 1803 1605 2710 493 966 571 887average Milan (m )1803 1803 3278 2710 493 966 571 887 Milan (m )group 1803 1605 2710 966 571 887 value of the group surfaces, was selected as representative of each group. Table 3group. lists the Table 3. Representative flat roofs (FRs). value of the group surfaces, was selected as representative of each group. Table 3Table lists the surface value of the group surfaces, was selected asbuildings representative each 3surface lists the value of the group surfaces, was selected asbuildings representative of each group. Table 3the lists the Table 3. Representative flat roofs value of the group surfaces, was selected asbuildings representative of(FRs). each group. Table 3surface lists the surface Table 3. Representative flat roofs (FRs). and the shape of the selected representative buildings along with the areas of the classified flat roofs. and the shape of therepresentative selected representative along with the areas of the classified flatsurface roofs. and the shape of the selected representative along with the areas of classified flat roofs. and and the shape the shape of the of selected the selected representative buildings along along with with the areas the areas of the of classified the classified flat roofs. flat roofs. Table Table Representative Representative roofs (FRs). Table 3. Representative flat roofs (FRs). Table 3. Representative flat roofs (FRs). roofs (FRs). 3. flat 3. flat roofs (FRs). and and theand shape of the representative buildings along with the areas of the classified flat Ashape different criterion ofrepresentative classification was used for flat roofs. Flat roofs were subdivided into five Aselected different criterion of representative classification was used for flat roofs. Flat roofs were subdivided into five the of the selected representative buildings along with the areas of the classified flat roofs. and the shape of the selected buildings along with the areas of theroofs. classified flat the shape of the selected buildings along with the areas of the classified flat roofs. Aselected different criterion of classification was used for flat roofs. Flat roofs were subdivided intoroofs. five the shape of the buildings along with the areas of the classified flat roofs. A different criterion ofrepresentative classification was used for flat roofs. Flat roofs were subdivided into five Aand different criterion of classification was used for flat roofs. Flat roofs were subdivided into five

City City City City City City CityCity

according tosurface their surface and aand building, roof area was very close to the average groups toTable their surface awhose building, roof area was very close tovery thetoclose average groups according to their and aarea building, whose roof area was the average groups according to their surface and a whose building, whose roof area was very close the to average groupsgroups according toaccording their and aRepresentative building, roof was very close to the average Table 3. Representative flat roofs (FRs). Table 3. flat Representative flat roofs (FRs). Table 3.surface Representative flat roofs (FRs). Table 3. 3. Representative roofs flatwhose roofs (FRs). (FRs). Item FR1 FR2 FR3 FR4 FR5 Item FR1 FR2 FR3 FR4 FR5 Item FR1 FR2 FR3 FR4 FR5 Item FR1 FR2 FR3 FR5 Item FR1 FR2 FR3 FR4 FR5 Item FR1 FR2 FR3 FR4 FR5 Item FR1 FR2 FR3 FR4 FR5 value of the surfaces, was selected as representative offlat each group. Table 3 FR4 lists the surface ofgroup theof group was selected asflat representative of each group. Table 3FR4 lists the value ofsurfaces, the group surfaces, was selected as representative ofgroup. each group. 3 lists the surface value the group surfaces, was as representative of each Table 3Table listssurface the surface value of thevalue group surfaces, was selected as representative of each group. Table 3(FRs). lists the surface Table 3.Table Representative roofs (FRs). 3. Representative flat roofs (FRs). Table 3. Representative flat roofs Table 3. Representative flat roofs (FRs). Table 3.selected Representative roofs (FRs). Item FR1 FR2 FR3 FR5 2 2 2 2 2 2 2 2 2222 2 2 2 2 2 2 2 2 City Item FR1 FR2 FR3 FR4 FR5 City Item FR1 FR2 FR3 FR4 FR5 City Item FR1 FR2 FR3 FR4 FR5 City Item FR1 FR2 FR3 FR4 FR5 Citythe Item FR1 FR2 FR3 FR4 FR5 265 m 387 m 482 m 717 m 1394 m 265 m 387 m 482 m 717 m 1394 m 265 m 387 m 482 m 717 m 1394 m and the shape of the representative buildings along with the areas of the classified flat roofs. and the shape ofselected the selected representative buildings along with the areas of the classified flat roofs. 2 2 2 2 2 2 2 2 2 2 2 2 and the shape of the selected representative buildings along with the areas of the classified flat roofs. 2 2 2 2 and the shape of the selected representative buildings along with the areas of the classified flat roofs. and shape of the selected representative buildings along with the areas of classified flat roofs. 2 2 2 2 265 m 387 m 482 m 717 m 1394 265 m 387 m 482 m 717 m 1394 m 265 m 387 m 482 m 717 1394 m 265 m22 FR1 387 m 482 m 717 m 1394m m22222 2 m CityCity FR1 2 FR2 2 FR3 22 FR4 2m 2 FR5 2 FR3 2FR5 2387 2 FR3 2 2FR5 2 m22 2 FR1 22 FR2 2m 2 FR4 2 FR5 2 2m 2482 22 FR4 2 FR5 City Item FR2 CityItem Item FR1 FR2 FR3 FR4 FR2 FR4 CityItem Item FR3 265 mFR1 717 1394

m387 m 482 m 717 m m m 1394 265m m387 387m m482 482 m m 717 717 m m m 1394 m m 265265 m265 387 m387 482 m482 717 m717 1394 m 265 m 1394m m1394 Representative Representative Representative 2Representative 2 3. 2(FRs). Representative Representative Representative 2m 2(FRs). 2717 2 m2 m2 2Table 2m 2 21394 23.m 2m 2 (FRs). 2 m2 m 2 m1394 Representative Representative flat roofs 2flat 2 roofs 2 m 3.2m Representative roofs 3. Representative flat (FRs). Table Representative flat482 roofs (FRs). Table 3.Table flat roofs 265 m 387 482 m m 1394 m 265 387 482 m 265 m 387 m 482 m22 717 717 265 mTable 387 m 482 717 m717 1394 m 265 387 m m 1394 Representative Representative Representative Representative Representative Representative Palermo building Palermo building Palermo building Representative Representative Representative Representative Representative Palermo building Palermo building Palermo building Palermo building Palermo building City Item FR2 FR1 FR3 FR2 Palermo City ItemFR1 FR1 FR2 FR3 FR4FR5 FR5 FR5 FR5 Palermo building City Item FR4 Palermo building Palermo building City Item FR1 FR4 FR5 City Item FR2 FR1 FR3 FR2 FR4FR3FR4FR3 Palermo building building

2 2 2 2 2 2 m2 2 2387 2m 2 27172 m 2 Palermo building 22 2482 2 717 Palermo building Palermo building 2 2 2 717 Palermo building Palermo building m 387 mm 482 m2482 717 m 1394 387 m m m 1394 m1394 m2 265 m 265 387 m2m m 482 m 265 m2265 m265 387m m 482m 717 m 1394 m1394 23065 24267 25501 2 m2 m22 2 2 15,834 2 mm 2m 2215,834 2m 22m 2 m9235 2 m215,834 22 m 2 2m 2 2m 22 m Representative Representative Representative Representative Representative 2m2229235 Flat roofs area 3065 4267 5501 15,834 m Flat roofs area m m9235 9235 mm 15,834 Flat roofs area 3065 m4267 4267 m5501 5501 9235 m Flat roofs area 3065 4267 m215,834 Flat roofs area 3065 m 4267 5501 m 2 2 2 Flat roofs area 2 2 2 2222 2 2 2 2 3065 m m 5501 m 9235 m m2 2 2 2 2 Flat roofs area 3065 m 4267 m 5501 m 9235 m 15,834 m Flat roofs area 3065 m 4267 m 5501 m 9235 m 15,834 m Flat roofs area 3065 m 4267 m 5501 m 9235 m 15,834 m 222building 222224267 2222m 22215,834 22m 22 2m 2 m25501 2m 2 m22 15,834 2m 25501 2 m 22 2 2m 2m 2mm 215,834 Palermo building building Palermo Palermo building Palermo building FlatPalermo roofs area 3065 m 4267 m 15,834 m 23065 29235 2m 22m 25501 2m 2 24267 2229235 2 15,834 223065 22m 224267 2m 225501 22m 22m 2m Flat roofs area 3065 m 4267 m 5501 9235 m m Flat roofs area m m29235 m 15,834 Flat roofs area m 5501 m2229235 9235 m 15,834 Flat roofs area 3065 m 4267 m m 9235 m 15,834 m Flat roofs area 3065 m 4267 m 5501 m m Flat roofs area 3065 m m Flat roofs area 3065 m 4267 m 9235 m 15,834 m22222 Flat roofs area 4267 5501 m 104 122 m25501 136 179 296 104 m 1222m m 136 m 179 296 mm 104 m 122 m 136 m 179 m 296 104 m3065 122 136 m 179 m9235 296 mm 104 m 122 m 136 179 m 296m m 2 2 2 2 2 222 2 2m2 22 2136 2222 179 2222 2m 2m 2 m136 2222 2 22 m104 22 2 m 22m296 2 m122 2m 2 m2 m 2 2122 2m 2 m 2 m 104 m 104m m 179 296 m 122 136 m2m 104 m 122 m 136 m22 179 179 296 m 104 122 m 136 m179 296 122 m 179 m 104 122 m 136 179 m 296 m 104 m 122 m 136 m 179 m 296 m 104 m 122 m 136 m 179 m 296 m Representative Representative Representative 22222104 2m 2222136 222222m 222296 Representative Representative 22 m 296 2 2 2 2136 2 2 2 m2 2122 2m m 122 136 m 179 m 296 m 104 m 122 m 136 179 m 296 m 104 m 122 m 136 m 179 m 296 m 225501 2m 2 2 15,834 2 104 m m m 179 m 296 m22222 2m 2m 2m 2 15,834 2 2 mm 2 m4267 2 m5501 2 9235 Flatarea roofs area 3065 m 4267 9235 m Flat 104 roofs area 3065 m 4267 m 5501 m 9235 Flat roofs area 3065 m m mm m 15,834 m Flat roofs area 3065 4267 5501 9235 m 15,834 m Flat roofs 3065 m 4267 m 5501 m 9235 15,834 m Representative Representative Representative Representative Representative Rome building Rome building Rome building Rome Rome building building 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Representative 122 136 m179m 296179 m296 Representative 122 mm 136 m136 m179 Representative Representative 104 122 m m m296 m 296 m 104 mm 122 mm m 136 104 m 104 m104 122m m 136m 179 m 179 296 mm Representative Representative Representative RomeRepresentative Rome building Rome building Rome building Rome building Rome building Representative Representative Representative building Representative Representative

Rome Rome Rome Rome Rome Rome Rome

building building building

22321 22660 22924 2 mm 2m 2m 2m 2 m3942 2 m23942 2 m26360 2 m 2 2m 2 2m 22 m 2 m Flat roofs area 2321 2660 2924 Flat roofs area m m3942 m Flat roofs area 2321 m2660 2660 m2924 2924 m2 3942 3942 m2 6360 6360 m2 m2 roofs area 2321 Flat Flat roofs area 2321 m 2660 2924 m 6360 m26360 building building building Rome building Rome building building Rome building Rome building Rome building 2 m22660 2 m22924 22m22 3942 26360 2 m22 m222 2 m 2mm 2m Flat roofs area 2321 m 2660 2924 m m 22321 2178 23942 23942 2 6360 22m 22m 22226360 222 m 222321 22m 222660 22m 222924 22m 2223942 22m 22m Flat roofs area m m m m m Flat roofs area 2660 m 2924 m m Flat roofs area 2321 m m m 6360 m Flat roofs area 2321 2660 2924 3942 m 6360 2 2 113 138 178 257 424 113 m 138 m 178 m 257 424 113 m 138 m 178 m 257 m 424 m 113 m 113 m 138 m 138 m 178 257 m 257 m 424 m 424 m Flat roofs area 2321 m 2660 m 2924 m 3942 m 6360m m 2 2 m222660 2 2 m22 3942 222 2m22 2 26360 2 area 22660 222 6360 222924 2m 2m 2m 2 2 2 2m 22 m 2 2m2924 2m 22660 2m 2m 2m 22321 22 mm 2m 22 m Flatarea roofs area 2321 m Flat roofs area 2660 m 2924 6360 Flat roofs area 2321 m mm 3942 m424 6360 m22 Flat 2321 m 2924 3942 6360 Flat roofs 2321 m 2660 mm 2924 m 3942 m mm 113roofs m 138 178 257 m 424 m 113 m 178 m 257 mm 113 m138 138 m 178 m3942 257 424 m 113 m 138 178 257 m 424 m 113 m 138 m 178 m 257 424 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 222222 m2 2660 2 2222222 2178 2m 2 m2m 2 Flat area 2321 m m 2924 6360 m Flatroofs roofs area 2321 m 2660 2924 m 3942 m22m2222424 6360 m222222222 Flat roofs area 2321 m 2660 m 2924 m 6360 m Representative Representative Representative Representative Representative 2m 2 2 2138 22m 2 3942 2 2 257 2 2 257 2 424 m 2113 2138 2m 2m 113 m m 257 m 257 m257 424 113 138 mm 178 m2178 m 424 113 m 138 m m m m 113 m2m 138 m 178 m222222178 m m424 113 m 138m m 178 257 m 424 m3942

Flat Flat roofs area Flat roofs area Flatroofs roofsarea area

Milan

Milan Milan Milan Milan Milan Milan Milan

2321 2321 m 2321 m 2321m m

2660 2660 m 2660 m 2660m m

2924 2924 m 2924 m 2924m m 2222 178 m 178 m 178 m 178 178 m 178 m 178m m22222

Representative Representative Representative Representative Representative Milan building Milan building2222 Milan building Milan building Milan building 2222 113 m 138 m 113 m 138 m 113 m 138 m 22222 Representative Representative Representative Representative Representative Representative 113 138 113 m 138 m 113 m 138 m 113m m 138m m22222 Milan building Milan building Milan building Milan building Milan building MilanMilan building building Milan building Milan building Milanbuilding building

3942 3942 m 3942 m 3942m m 2222 257 m 257 m 257 m 257 257 m 257 m 257m m22222

6360 6360 m 6360 m 6360m m 2222 424 m 424 m 424 m 424 424 m 424 m 424m m22222

21228 21632 22155 2 mm 2m 2m 2m 2 m2604 2 m22604 2 m24478 2 m 2 2m 2 2m 22 m 2 m Representative Representative Flat roofs area 1228 1632 2155 Flat roofs area m m2604 m Flat roofs area 1228 m1632 1632 m2155 2155 m2 2604 2604 m2 4478 4478 m2 m2 Representative roofs area 1228 Flat Flat roofs area 1228 m 1632 2155 m 4478 m24478 Representative Representative Representative Representative 2 2 2 2 2 22 2 m1632 2 m2155 2 m2 2604 24478 2 m2 m2 224478 2m 2m 2 m2604 2 m2604 2 m4478 2mm 2m 2 4478 FlatFlat roofs area 1228 m 1632 2155 m mm Flat roofs area 1228 1632 2155 2604 Flat roofs area 1228 1632 m 2155 m m Flat roofs area 1228 m m m m m Flat roofs area 1228 1632 2155 2604 m2604 4478 Flat roofs area 1228 m 1632 m 2155 m 2604 m 4478 m Flat roofs area 1228 m 1632 m 2155 m 2604 m 4478 m 2 2 2 2 Flat roofs area 1228 m 1632 m 2155 m 4478 Flat roofs area 1228 m 1632 m 2155 m 2604 m 4478 m roofs area 1228 m 1632 m 2155 m 2604 m 4478 m building building Flat roofs area building 1228 m 1632 m 2155 m 2604 m 4478 m m2 building building building building 4Table lists the roof ofareas the districts and the other as terraces or balconies, 4roof lists the roof ofand theand districts and thesurfaces, other surfaces, such as or balconies, Table 4roof lists the roof areas of the districts and the other surfaces, such asorterraces or balconies, Table Table 4Table lists 4 lists the the areas areas ofareas the of districts the districts the other the other surfaces, surfaces, such such assuch terraces as terraces balconies, orterraces balconies, 2

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2

2

Table 4lists lists the roof areas of the districts and the other surfaces, such assuch terraces or balconies, Table 4too lists the roof areas ofinstallation. the districts and the other surfaces, such as terraces balconies, Table 4Table lists the areas of the districts and the other surfaces, such as terraces or balconies, 4roof lists the roof of the districts and the other surfaces, such asterraces terraces or balconies, balconies, Table 4Table lists roof areas ofareas the districts and the other surfaces, such as terraces orbalconies, Table 4small the roof areas of the districts and the other surfaces, such as terraces or balconies, 4small the roof areas ofareas the districts and the other surfaces, as terraces or Table 4roof lists the of the districts and the other surfaces, such asor or Table 4lists lists the areas of the districts and the other surfaces, such as terraces or balconies, Table 4roof lists the roof areas of the districts and the other surfaces, such as terraces orbalconies, balconies, which are too small for the PV array which are small for the PV array installation. which are too small for the PV array installation. which are too for PV array installation. which are too for the PV array installation. which are too small for the PV array installation. which are too small for the PV array installation. which are too small for the PV array installation. which are too small for the PV array installation. which are small for the PV array installation. which are too small for the PV array installation. which are too small for the PV array installation. which are too small for the PV array installation. which aretoo too small for the PV array installation. which are too small for the PV array installation. 2222 Distribution 222of 2222 districts. 2222 2 the Table 4. Distribution of the roof surfaces of the districts. Table 4. the Distribution surfaces Flat roofs area 1228 m 1632 m 2155 2604 m Table of the surfaces ofm the Flat roofs area 1228 m 1632 m 2155 m 2604 m Table 4. Distribution of the roof of the districts. Table 4. Distribution roof surfaces of roof the districts. Flat roofs area 1228 m 1632 m 2155 m 2604 m 2224. 222surfaces 22of 222 the districts. 22 of 22 roof Flat m 1632 m 2604 Flat roofs area 1228 m 1632 m 2155 m 2604 m Flat roofs area 1228 m 1632 m 2155 m 2604 m Flatroofs roofsarea areaTable 1228 m 1632 m 2155 m 2604m m22222 Table 4. Distribution of the roof surfaces of2155 districts. Table 4. of Distribution the roof surfaces of them districts. 4.of Distribution of the roof surfaces of the districts. Table 4.Table Distribution of of the districts. Table1228 Distribution the roof surfaces of the the districts. 4.4.Table Distribution of surfaces ofroof theofsurfaces districts. 4.Table Distribution of the roof surfaces of the districts. 4. the Distribution of the roof surfaces of the districts. Table 4. Distribution ofroof the roof surfaces the districts.

2222 4478 m 4478 m 4478 m 4478 4478 m 4478 m 4478m m22222

Table 4. Distribution of the roof surfaces of the districts. Slanted Roofs Flat Roofs Other Surfaces Slanted Roofs Flat Roofs Other Surfaces Slanted Roofs Flat Roofs Other Surfaces Slanted Roofs Flat Roofs Other Surfaces Slanted Roofs Flat Roofs Other Surfaces City City City City City Slanted Roofs Flat Roofs Other Surfaces Slanted Roofs Flat Roofs Other Surfaces City City Slanted Roofs Flat Roofs Other Surfaces Slanted Roofs Flat Roofs Other Surfaces Slanted Roofs Flat Roofs Other Surfaces City City City Slanted Roofs Roofs City Slanted Flat Roofs Other Surfaces Slanted Roofs Roofs Other Surfaces Slanted Roofs Roofs Other Surfaces 2Roofs 22 Other 2m 22Flat 2Surfaces Slanted Roofs Surfaces City City 2Flat 2 11,160 2 m2 2 City 2Roofs 22m 22Flat 22 11,160 City 260,145 2 2Flat 2and 60,145 m 37,902 m 11,160 m 37,902 11,160 2Other m37,902 37,902 msurfaces, 2m 2as 2 2 60,145 2 m 2 11,160 60,145 m 37,902 m m 60,145 m 37,902 m Table 4 lists the roof areas of the districts and the other such terraces or balconies, Table 4 lists the roof areas of the districts the other surfaces, such as terraces or balconies, Table 4 lists the roof areas of the districts and the other surfaces, such as terraces or balconies, 60,145 m 37,902 m 11,160 m 60,145 m 37,902 m 11,160 m 60,145 m 37,902 m 11,160 m 60,145 m 37,902 m 11,160 m 60,145 m m 11,160 m Table areas of the districts and the other surfaces, such as terraces Table lists the roof areas of the districts and the other surfaces, such as terraces or balconies, Table lists the roof areas of the districts and the other surfaces, such as terraces or balconies, Table4444lists liststhe theroof roof areas of60,145 the districts and the other surfaces, such as terracesor orbalconies, balconies, Palermo Palermo Palermo Palermo Palermo Palermo Palermo Palermo Palermo Palermo 2 2 2 2 2 2 2 2 2 2 2 2 2 2 m 37,902 m 11,160 m 60,145 m 37,902 m 11,160 m 60,145 m(55.07%) 37,902 m 11,160 m2 60,145 m 37,902 m (34.71%) 11,160 m 60,145 m(34.71%) 37,902 m (34.71%) 11,160 m (55.07%) (34.71%) (10.22%) (55.07%) (34.71%) (10.22%) (55.07%) (34.71%) (10.22%) (55.07%) (34.71%) (10.22%) (55.07%) (34.71%) (10.22%) (55.07%) (34.71%) (10.22%) (55.07%) (34.71%) (10.22%) (10.22%) (55.07%) (10.22%) (55.07%) (10.22%) Palermo Palermo Palermo Palermo Palermo which are too small for the PV array installation. which are too small for the PV array installation. which are too small for the PV array installation. 2 (55.07%) 2 2 m 2 (10.22%) 22 (34.71%) 2 2 (10.22%) 2m 2m 2 218,206 219,943 22 2 2 (55.07%) 2 (34.71%) 2 (10.22%) which array installation. which are too small for the PV array installation. which are too small for the PV array installation. (55.07%) (10.22%) 2 2 2 2 2 whichare aretoo toosmall smallfor forthe thePV PV array installation. 2 2 2 2 2 (55.07%) (10.22%) (34.71%) (55.07%) (34.71%) (34.71%) 40,677 m 40,677 m 18,206 19,943 m 40,677 m 18,206 m 19,943 m 40,677 m 18,206 m 19,943 m 40,677 m 18,206 m 19,943 40,677 m 18,206 m 19,943 m 40,677 m 18,206 m 19,943 m m 18,206 m 19,943 m 40,677 m 18,206 m 19,943 m 40,677 m 40,677 m 18,206 m 19,943 Roma Roma Roma Roma Roma Roma Roma Roma Roma Roma

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Table 4 lists the roof areas of the districts and the other surfaces, such as terraces or balconies, which are too small for the PV array installation. Table 4. Distribution of the roof surfaces of the districts. City

Slanted Roofs

Flat Roofs

Other Surfaces

Palermo

m2

60,145 (55.07%)

m2

37,902 (34.71%)

11,160 m2 (10.22%)

Roma

40,677 m2 (51.60%)

18,206 m2 (23.10%)

19,943 m2 (25.30%)

Milano

80,647 m2 (80.79%)

12,097 m2 (12.12%)

7073 m2 (7.09%)

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distribution areas roof Slanted distribution areas roof Slanted

Each representative slanted and flat roof was connected to the respective number of building Each representativedistributions slanted and flat roof was connected to the the respective number ofare building storeys. The slanted andand flat roofs, versus of floors, shown The percentage percentage distributionsof of slanted flat roofs, versusnumber the number of floors, are storeys. The percentage distributions of slanted and flat roofs, versus the number of floors, are in Figures 6 and 7. shown in Figures 6 and 7. shown in Figures 6 and 7. 50% 50% 45% 45% 40% 40% 35% 35% 30% 30% 25% 25% 20% 20% 15% 15% 10% 10% 5% 5% 0% 0%

Palermo Palermo Rome Rome Milan Milan

1 1

2 2

3 3

4 4

5 6 7 5 6of floors 7 Number Number of floors

8 8

9 9

10 10

11 11

distribution areas roof distribution areas roof FlatFlat

Figure 6. Distribution of the slanted roofs areas versus the number of floors. Figure 6. 6. Distribution Distribution of of the the slanted slanted roofs roofs areas areas versus versus the Figure the number number of of floors. floors. 50% 50% 45% 45% 40% 40% 35% 35% 30% 30% 25% 25% 20% 20% 15% 15% 10% 10% 5% 5% 0% 0%

Palermo Palermo Rome Rome Milan Milan

1 1

2 2

3 3

4 4

5 6 7 5 6of floors 7 Number Number of floors

8 8

9 9

10 10

11 11

Figure 7. Distribution of the flat roofs areas versus the number of floors. Figure versus the the number number of of floors. floors. Figure 7. 7. Distribution Distribution of of the the flat flat roofs roofs areas areas versus

In the city of Palermo the majority of slanted roofs belong to four-storey buildings, whereas In the city of Palermo the majority of slanted roofs belong to four-storey buildings, whereas mostIn ofthe thecity flat of roof surfaces eight-storey buildings. Most the slanted and flat roofs in Rome Palermo thecover majority of slanted roofs belong toof four-storey buildings, whereas most most of the flat roof surfaces cover eight-storey buildings. Most of the slanted and flat roofs in Rome belong five-storey buildings. A regular distribution ofslanted flat roofs, with in of the flattoroof surfaces cover eight-storey buildings. Most of the and flat roofsainmaximum Rome belong belong to five-storey buildings. A regular distribution of flat roofs, with a maximum in correspondence of seven-storey buildings, is observed Milan, whereas the majority of the slanted to five-storey buildings. A regular distribution of flatfor roofs, with a maximum in correspondence correspondence of seven-storey buildings, is observed for Milan, whereas the majority of the slanted roofs cover five-storey buildings. roofs cover five-storey buildings. 3.3. Electricity Production 3.3. Electricity Production The exact assessment of the PV electricity production of the districts is an arduous task. The exact assessment of the PV electricity production of the districts is an arduous task. Actually, the electricity production is different for each building because the number and size of the

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of seven-storey buildings, is observed for Milan, whereas the majority of the slanted roofs cover five-storey buildings. 3.3. Electricity Production The exact assessment of the PV electricity production of the districts is an arduous task. Actually, the electricity production is different for each building because the number and size of the PV arrays is affected by the shape of the building roof, whose area has to be parcelled out considering the number and surface of the flats in the building. For calculations it was assumed that each flat had a standard surface of 162 m2 and a fixed dimension (width or length) of 9 m. For each roof type, the roof surface was divided by the number of flats in order to establish the surface available for each co-owner and, in turn, the size of the PV array to be installed. Due to the presence of 21 roof-types and 11-storey buildings, 231 different types of PV systems had to be analysed. The PV fields were built with commercial PV panels Kyocera KD210GH-2PU that were located with the same pitch of the slanted roof surfaces (approximately 20◦ above the horizontal for the district of Rome, and 25◦ for Palermo and Milan) also considering a sufficient room for installation and maintenance. For the flat roofs it was assumed that the panels were oriented to the south with a pitch of 31◦ , which is the most efficient for the analysed cities. From calculations performed for the analysed districts, it was verified that, if the PV arrays were built selecting the two main building axes, a greater number of PV modules may be installed but a smaller amount of electricity would be produced. The distance between the rows of the PV array was set accordingly to the national electric standard [59], which requires that no part of the PV modules is shaded between 10:00 a.m. and 2:00 p.m. on the winter solstice. Moreover, in designing the shapes of the PV arrays, it was geometrically verified that the shadows thrown by balustrades, elevator housings and other obstructions never cover any part of each PV panel from 10:00 a.m. to 2:00 p.m. on the winter solstice. The electricity calculations were carried out by means of PVsyst 5.06 [60] that determines the electricity produced by the PV systems considering the electrical characteristics of the PV panels and solar inverters issued by the manufacturers and using data of solar irradiance and air temperature that are specific to the given location and different for each day of the year. SMA Sunny Boy solar inverters [61,62] adequately sized for the number of panels of each PV array were used. Thee SMA Sunny Boy solar inverters use a maximum power point tracking method based on the Optitrac algorithm [63]. The energy generated by each type of PV system was divided by the corresponding roof-type surface in order to get the per square meter value of the PV generation that is sensitive to the number of the building storeys. Such value was multiplied by the respective global roof type surface of the district that cover the same number of storeys and, finally, the results were added to calculate the global PV production of the district. Table 5 lists the results of the PV electricity calculation. With reference to the slanted and flat roof areas of the districts, the installed PV systems produce 91.03 kWh/m2 every year in Palermo, 81.89 kWh/m2 in Rome and 63.53 kWh/m2 in Milan. The differences in the values are obviously due to the different latitudes and urban morphologies of the analysed cities. Assuming a value of the yearly global horizontal solar irradiation of 1690 kWh/m2 for Palermo, 1549 kWh/m2 for Rome and 1255 kWh/m2 for Milan, the PV electricity is produced with values of the conversion efficiency varying from of 5.39%, 5.29% and 5.06% for the three cities, respectively. If the gross areas of the district are considered, the yearly PV production lowers to 33.18 kWh/m2 , 38.27 kWh/m2 and 38.26 kWh/m2 for Palermo, Rome and Milan, respectively. The corresponding conversion efficiencies are equal to 1.96%, 2.47% and 3.05% for Palermo, Rome and Milan, respectively. Such anomalous values, which apparently contrast with the yearly global horizontal solar irradiation of the cities, are mainly due to the different percentages of flat roofs in the analysed districts (34.71% for Palermo, 23.10% for Rome and 12.12% for Milan), which resulted to be less energy efficient than the slanted roofs.

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Table 5. PV electricity produced by slanted and flat roofs. PV Electricity Produced (kWh/Year) Number of Floors 1 2 3 4 5 6 7 8 9 10 11 Total

Palermo

Rome

Milan

Slanted Roofs

Flat Roofs

Slanted Roofs

Flat Roofs

Slanted Roofs

Flat Roofs

6751.50 107,472.26 579,218.44 2,540,303.18 1,085,471.38 749,020.15 246,406.33 475,982.47 242,950.62 0.00 0.00 6,033,576.32

0.00 0.00 0.00 166,946.48 14,596.56 265,273.49 610,023.03 1,281,022.04 480,309.51 73,344.52 0.00 2,891,515.63

5364.58 17,613.41 233,292.23 1,070,826.52 1,926,091.64 738,447.06 231,728.43 20,516.25 0.00 0.00 0.00 4,243,880.13

9674.55 10,976.14 29,500.64 189,063.26 250,710.37 75,476.66 12,851.51 0.00 0.00 0.00 0.00 578,253.13

0.00 95,021.24 222,645.90 606,795.84 2,447,805.09 1,720,776.51 387,016.84 122,127.48 24,828.47 28,602.08 0.00 5,655,619.45

0.00 8556.37 4605.91 34,858.78 39,258.28 32,608.42 35,804.41 27,064.24 27,368.88 16,410.02 10,138.82 236,674.13

3.4. Coverage of the Electricity Demand The level of integration of PV systems installed on buildings can be assessed by means of gross energy cover factor CPV [64,65], which compares the electricity produced to the energy demands of the analysed districts: E (1) CPV = PV · 100 DTotal in which EPV represents the electricity produced by the totality of the installed PV systems and DTotal is the electrical energy demand of the district. DTotal can be derived by the information issued for the districts by the major local electricity transmission grid operators. Table 6 lists the data officially issued by Terna [66], main Italian electricity transmission grid operator, and Istat, Italian National Institute of Statistics [67], which were used to calculate the yearly electricity consumption of a household living in a standard flat of 162 m2 . Table 6. Energy and statistic data of the analysed cities. Energy and Statistic Data

Palermo

Rome

Milan

1530.90

5703.80

4029.90

Area of inhabited flats in the city Number of inhabitants in the province Number of inhabitants in the city

24,920,851 1,243,585 657,561

103,499,074 3,997,465 2,617,175

50,824,652 3,038,420 1,242,123

Average number of inhabitants in the standard flat

4.27

4.10

3.96

Electricity consumption in the standard flat (kWh/year)

5262.1

5845.09

5251.12

Domestic electricity consumption in the province (GWh/year) (m2 )

The results of the calculations of factor CPV are depicted in Figure 8. The installed PV systems cover 42.8%, 39.5% and 34.4% of the domestic electrical consumptions of the analysed districts of Palermo, Rome and Milan, respectively. The energy production is mainly due to the roofs covering four-storey buildings in Palermo, and of five-storey buildings in Rome and Milan. Such figures represent the maximum values of the energy cover factor because the calculus of the PV electricity was performed without considering the lack of solar irradiance due to the shadowing of obstructions, which is quite likely in densely urbanised contexts. Moreover, a part of the PV generation may not be locally used and exported to the grid because it exceeds the electricity demand of the flat or because the instantaneous perfect correspondence between demand and generation is quite improbable. Obviously, the electricity consumed after sunset and before dawn will never be compensated by the energy produced by a PV system not equipped with batteries. To estimate

districts by the major local electricity transmission grid operators. Table 6 lists the data officially issued by Terna [66], main Italian electricity transmission grid operator, and Istat, Italian National Institute of Statistics [67], which were used to calculate the yearly electricity consumption of a household living in a standard flat of 162 m2. Energies 2016, 9, 944

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Table 6. Energy and statistic data of the analysed cities. Energy and Statistic Data

Palermo

Rome

Milan

Area of inhabited flats in the city (m2)

24,920,851

103,499,074 3,997,465 2,617,175

50,824,652 3,038,420 1,242,123

4.27

4.10

3.96

night-time demands assumed that the following appliances were working in the standard night it was Domestic D electricity consumption in the 1530.90 5703.80 4029.90 flat from dusk to dawn [68–71]: province (GWh/year)

• • •

Lamps: 85 W from Ti to 23:00 and from 07.00 to Tf ; Number of inhabitants in the province 1,243,585 Refrigerator: 90 Woffrom Ti to 24:00 to Tf ; Number inhabitants in theand cityfrom 00.00657,561 Television + P.C.: 75number W from Average of T inhabitants i to 23:00 and from 07.00 to Tf ; in the standard flat

in which Ti Electricity was set one hour before sunset and T one hour after dawn in order to consider that the consumption in the standard f electricity generated byflat the(kWh/year) PV systems is quite small at 5262.1 the beginning5845.09 and at the end5251.12 of the day; sunset and dawn on 15th of each month were considered. In Table 7 the calculated values of day-time demand Dday along with the values of Dnight of arefactor listed. The results of the calculations CPV are depicted in Figure 8.

20% 18%

Palermo : CPV Total = 42.8% Rome : CPV Total = 39.5% Milan : CPV Total = 34.4%

Gross energy cover factors

16% 14% 12% 10% 8%

6% 4% 2% 0% 1

2

3

4

5

6

7

8

9

10

11

Number of floors

Figure cover factors factors for for the the whole whole districts districts versus versus the the number number of of floors. floors. Figure 8. 8. Yearly Yearly gross gross energy energy cover

The installed PV systems 42.8%, 39.5% and 34.4%ofof domestic Table 7.cover Electricity yearly consumption thethe standard flat.electrical consumptions of the analysed districts of Palermo, Rome and Milan, respectively. The energy production is mainly due to the roofs covering four-storey buildings in Palermo,(kWh/Year) and of five-storey buildings in Rome and Consumption City Milan. Such figures represent the maximum values of the energy coverTotal factor because the calculus of Night-Time Day-Time the PV electricity was performed without considering the lack of solar irradiance due to the Palermo 609.6 4652.5 5262.1 shadowing of obstructions, which is quite648.1 likely in densely urbanised5845.1 contexts. Moreover, a part of Rome 5197.0 the PV generation may notMilan be locally used624.8 and exported 4626.3 to the grid because 5251.1 it exceeds the electricity The load mismatch, caused by the lack of simultaneity between generation and consumption, and its impact on the operation of the grid is a relevant topic in the assessment of PV systems [72–86]. Due to the load mismatch, the grid managers are expected to provide the grid with a greater degree of flexibility and adopt smart energy management strategies. The presence of the load mismatch due to the incorrect habits in using the electrical appliances can cause the occurrence of disadvantageously purchasing electricity from the grid and/or wasting the unused PV energy. The effects of the load mismatch are even more evident if the time-of-using and real-time electricity pricing are considered [87,88]. Because the grid parity has not been completely reached in many countries, when almost all PV electricity is used by the PV system owner, the economic investment can be very convenient because the selling price is usually greater than the purchase price. Adversely, if only a very small part of the PV electricity is generated, and simultaneously used by the household appliances, the surplus would be sold off and it would be necessary to pay for some electricity imported from the grid later on. Moreover, because some duties must be also paid for both the purchased and sold electricity, the economic convenience may result very little.

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Among the indicators proposed by some authors to consider the effect of the mismatch between generated and consumed electricity [81,83–85], the load match index, which represents the percentage of the electrical demand that is covered by the generation of all NPV PV systems over the considered evaluation period T, is used in this paper. Load match index γD can be calculated with the following equation:   NPV i + N   min E ( i ) , D ( i ) ∑ ∑ PV,j j γD =

j

i

NPV

∑ j



i+ N

∑ D j (i )

· 100



(2)

i

in which N is the number of samples in the considered evaluation period T; EPV,j (i) and Dj (i) are the PV generation and the demand of the j-th PV system during the generic i-th time interval, respectively. When only the yearly data of the energy are available, load match index γD of the district may be approximated with the expression: NPV

h i ∗ , D∗ ∑ min EPV,j j

γD =

j =1

NPV

· 100

(3)

∑ D ∗j

j =1

∗ in which EPV,j is the PV generation of the j-th PV system and D ∗j is the electrical demand of the flat powered by the j-th PV system, over the year. When the PV generation is sufficient, the day-time energy demand can be covered by the PV electricity only if there is a perfect simultaneity between PV generation and local consumption. Obviously, if no energy storage is installed, the electricity DNight consumed after sunset and before dawn can be compensated only by the grid. The portion of DDay that is covered by the PV electricity mainly depends on the habits of the households in using the electrical appliances. A household, which does not intend to waste the energy produced by its PV system, may only try to reschedule the use of the domestic appliances in order to increase the household consumption during the PV generation. Modern building automation may provide more efficient solutions to manage the appliances that have the highest consumption. Smart control systems, able to forecast the solar potential and the PV generation up to 2 days, with hourly resolution, have been recently presented [89]. It may be very difficult to settle realistic values of the energy demand that is satisfied by the electricity produced by each PV system of the district. This is the reason why, in this paper it was considered convenient to address the problem of the load mismatch by means of a sensitivity analysis based of the available yearly energy data. To reach that purpose it is useful to introduce the following utilization coefficient cu : D Match,j cu = (4) DDay,j

where DMatch,j and DDay,j represent the amount of the matched yearly demand and the day-time yearly demand of the j-th PV system, respectively. The electricity demand of a PV system can be split as: D ∗j = DDay,j + D Night,j = D Match,j + D Mism,j

(5)

where DMism,j and DNight,j are the amount of mismatched yearly demand and the night-time yearly demand of the j-th PV system, respectively. Coefficient cu varies from 0, when no PV electricity is used, to 1, when all PV electricity covers the day-time consumptions. By means of the above equations the load match index of the district can be related to utilization coefficient cu :

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NPV

NPV

h i ∗ , c D ∑ min EPV,j u Day,j

γD (cu ) =

j

NPV

∑ j

∗ (cu ) ∑ EPVload,j

· 100 = D ∗j

j

DTotal

· 100

(6)

Load match index γD (cu ) is sensitive to the load mismatch and, if cu = 0, then γD (cu ) = 0. The maximum value of the load match index can be less than 100% for two main reasons: (1) a PV system that has an undersized PV panel array will never cover the day-time electricity demand of the household; (2) even a PV system with an oversized PV panel array may not cover the household demand if some amount of the energy produced is not simultaneously consumed and no battery storage system is used. 3.5. Economic Analysis Different measures of the economic value of an investment can be used depending on the peculiarities of the analysed problem [90]. In the present paper the NPV, which is typically used when only one set of investments can be selected, is considered. The NPV considers the full cost of possible alternatives, including direct costs such as capital costs, operations and maintenance costs (O&M), as well as external costs, such as environmental costs. The NPV is calculated with the following formula: Nt CFt NPV = ∑ − C0 (7) t t =1 (1 + k ) where Nt is the lifetime of the investment, CFt is the cash flow related to the generic t-th year, C0 is the initial investment cost and k is the discount rate, which is the index that represents the average expected return on the investor’s assets. The cash flow, which is defined as the movement of money into or out of a business, project or financial product, corresponds to the difference between all yearly disbursements (device replacements, operations, maintenance, servicing, insurance, finance costs, taxes, etc.) and benefits (revenues from the sold electricity, paid incentives, savings from the electricity bills, etc.). The cash flows were calculated for 20 years, which is the duration for which FIT incentives provided in Italy. In Table 8 the costs of the PV components are listed; costs were assumed from the market prices of components, considering the cost for labour, fitter wages and value added tax (VAT). Table 8. Costs of PV system components. Component

June 2013 (€)

July 2013 (€)

December 2014 (€)

PV panel 210 Wp Roof-top mounting system (1 PV panel) On-roof mounting system (1 PV panel) Connectors for PV panel 4 mm2 DC cabling (1 metre) 6 mm2 AC cabling (1 metre) Inverter Maximum Power Point Tracker (MPPT) 1.1 kW Inverter MPPT 1.7 kW Inverter MPPT 2.5 kW Inverter MPPT 3.0 kW Inverter MPPT 3.6 kW Inverter MPPT 5.0 kW Inverter MPPT 7.0 kW Inverter MPPT 8.0 kW Main switchboard

269.50 76.95 48.90 10.50 2.00 2.40 826.20 1099.40 1442.90 1571.20 2046.00 2528.60 2687.80 2824.40 649.70

267.10 77.05 49.00 10.50 1.95 2.30 812.80 1081.60 1419.60 1545.70 2012.90 2487.60 2644.30 2778.70 650.5

223.10 76.90 48.90 10.50 1.85 2.20 735.00 978.00 1283.70 1397.80 1820.20 2249.50 2391.20 2512.70 649.20

The profits are related to the gain for the avoided electricity bill cost, sold electricity and incentives. The electricity bills were calculated considering the difference between the bills corresponding to the electricity demand and those which are referred to the energy consumed while the PV systems are

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producing electricity. To calculate the bills, the electricity tariffs issued by the AEEG for domestic consumers with an electricity capacity of 3 kW were used [91]. Table 9 lists the data issued for the third trimester of 2013 and the fourth trimester of 2014. Table 9. Electricity tariff in Italy. Electricity Cost

Energy (€/kWh)

4440 kWh/year

Power (€/kW/year) Fixed cost (€/year)

2nd Trimester 2013

3rd Trimester 2013

4th Trimester 2014

0.12981 0.18523 0.24893 0.29569

0.13209 0.18774 0.25176 0.29852

0.13099 0.18949 0.25729 0.30424

21.6050 5.7374

21.6050 5.7374

23.5386 5.9570

The economic analysis was performed also considering:

• • • • • • • • • •

• •

•

•

the yearly degradation rate in the efficiency of the PV panels, set equal to 0.7% of the nominal initial value [91–95]; the yearly maintenance and management costs, estimated to be 1% of the investment cost [1]; the replacement of 1% of the PV panels every year and of all inverters every ten years [96–98]; the insurance costs, varying from 184.00 to 334.00 €, for PV systems with peak-power of 3 kWp and 18 kWp respectively; the FIT and premium listed in Table 1, for June 2013; the tax credit rates indicated in paragraph 2; a selling price of the PV electricity in Palermo of 0.103 €/kWh and 0.079 €/kWh, for July 2013 and December 2014, respectively [99,100]; a selling price of the PV electricity in Rome of 0.061 €/kWh and 0.062 €/kWh, for July 2013 and December 2014, respectively [99,100]; a selling price of the PV electricity in Milan of 0.062 €/kWh and 0.061 €/kWh, for July 2013 and December 2014, respectively [99,100]; the net gain in selling the exported PV electricity, evaluated charging an income tax of 38%, 41% and 43%, which was estimated on the basis of the average income of the inhabitants of Palermo, Rome and Milan, respectively [101–103]; a VAT rate of 10%; the yearly increase in the price of electricity of 3.302%, 3.291% and 2.885% for June 2013, July 2013 and December 2014, respectively, which were derived by the trend line calculated with the data issued by the AEEG for the previous 10 years [90]; an average inflation rate of 1.983%, 1.972% and 1.706% for June 2013, July 2013 and December 2014, respectively, obtained from the data issued by the Italian National Institute of Statistics for the previous 10 years, which were used to time-discount the costs during that time [104]; a net discount rate current value of 4.230%, 4.296% and 2.795% for June 2013, July 2013 and December 2014, respectively, assumed equal to the interest paid by Italian long term public securities, which may be considered by the homeowners as an investment with a similar risk [105].

The NPV was calculated considering 16 slanted roof types and five flat roof types covering buildings with a number of storeys ranging from one to 11, which corresponds to 231 different calculations for each district. Considering that the calculus was repeated for each city and for various values of the utilization coefficient and percentage of shaded solar energy, the total calculated NPVs reach a number too large to be depicted in figures or tables. To illustrate the procedure, the data used to calculate the NPV of a PV system installed in Palermo in July 2013 on the roof type T2 of a building with two or five floors, assuming cu = 0.4 and a percentage of shaded solar energy of 10%, are listed in Tables 10 and 11.

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Table 10. NPV calculation for Palermo in 2016, roof type T2, two-storey building. Disbursements (€/Year) Year Installation 0 15,612.47 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 City: Palermo

O&M

Insurance

156.12 221.30 159.20 225.66 162.34 230.11 165.54 234.65 168.81 239.28 172.14 244.00 175.53 248.81 178.99 253.71 182.52 258.71 186.12 263.82 189.79 269.02 193.53 274.32 197.35 279.73 201.24 285.25 205.20 290.87 209.25 296.60 213.38 302.45 217.58 308.42 221.87 314.50 226.25 320.70 Roof type: T2

Inverter Substitution

Revenues (€/Year) Panel Substitution

95.87 97.77 99.69 101.66 103.66 105.71 107.79 109.92 112.08 114.29 3605.62 116.55 118.84 121.19 123.58 126.01 128.50 131.03 133.62 136.25 138.94 Number of floors: 2

Total

Electricity Bill Cut

15,612.47 473.30 482.63 492.15 501.85 511.75 521.84 532.13 542.62 553.32 564.23 4180.97 586.70 598.26 610.06 622.09 634.35 646.86 659.62 672.62 685.88

605.35 625.27 645.84 667.09 689.05 711.72 735.14 759.33 784.32 810.12 836.78 864.32 892.76 922.14 952.48 983.82 1016.20 1049.64 1084.17 1119.85

Tax Deduction

Exported Electricity

Total

780.62 346.02 1731.99 785.42 354.05 1764.74 795.23 361.87 1802.94 798.82 369.47 1835.39 802.48 376.86 1868.38 806.21 384.03 1901.96 810.02 390.99 1936.15 813.90 397.74 1970.97 817.86 404.27 2006.44 821.89 410.59 2042.60 45.38 416.69 1298.86 174.59 422.58 1461.49 169.05 428.26 1490.07 169.82 433.72 1525.68 170.61 438.97 1562.06 171.42 444.00 1599.24 172.24 448.82 1637.26 173.07 453.43 1676.14 173.93 457.82 1715.93 174.80 462.00 1756.65 NPV at the 20th year (€)

Net Cash Flow (€/Year)

−15,612.47 −14,353.78 −13,071.68 −11,760.88 −10,427.35 −9070.71 −7690.58 −6286.56 −4858.21 −3405.09 −1926.71 −4808.83 −3934.04 −3042.24 −2126.62 −1186.64 −221.75 768.64 1785.17 2828.47 3899.24 −2118.40

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Table 11. NPV calculation for Palermo in July 2013, roof type T2, five-storey building. Disbursements (€/Year) Year Installation 0 6936.55 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 City: Palermo

O&M

Insurance

69.37 182.25 70.73 185.84 72.13 189.51 73.55 193.24 75.00 197.05 76.48 200.94 77.99 204.90 79.52 208.94 81.09 213.06 82.69 217.26 84.32 221.54 85.98 225.91 87.68 230.37 89.41 234.91 91.17 239.54 92.97 244.26 94.80 249.08 96.67 253.99 98.58 259.00 100.52 264.10 Roof type: T2

Inverter Substitution

Revenues (€/Year) Panel Substitution

35.95 36.66 37.38 38.12 38.87 39.64 40.42 41.22 42.03 42.86 1935.64 43.71 44.57 45.45 46.34 47.26 48.19 49.14 50.11 51.09 52.10 Number of floors: 5

Total

Electricity Bill Cut

6936.55 287.57 293.24 299.02 304.91 310.93 317.06 323.31 329.68 336.18 342.81 2285.21 356.46 363.49 370.66 377.97 385.42 393.02 400.77 408.67 416.73

605.35 625.27 645.84 667.09 689.05 711.72 735.14 759.33 784.32 810.12 836.78 864.32 892.76 922.14 952.48 983.82 1016.20 1049.64 1084.17 1119.85

Tax Deduction

Exported Electricity

Total

346.83 55.84 1008.01 348.62 56.42 1030.31 352.33 56.92 1055.09 353.68 57.33 1078.11 355.05 57.67 1101.77 356.45 57.93 1126.10 357.88 58.11 1151.12 359.33 58.20 1176.87 360.81 58.22 1203.35 362.33 58.16 1230.61 17.04 58.02 911.84 86.50 57.79 1008.61 84.40 57.49 1034.65 84.69 57.10 1063.93 84.99 56.64 1094.11 85.29 56.10 1125.21 85.60 55.47 1157.26 85.91 54.77 1190.31 86.23 53.98 1224.39 86.56 53.12 1259.52 NPV at the 20th year (€)

Net Cash Flow (€/Year)

−6936.55 −6216.10 −5479.03 −4722.95 −3949.76 −3158.92 −2349.88 −1522.06 −674.88 192.29 1080.09 −293.28 358.87 1030.03 1723.30 2439.44 3179.23 3943.48 4733.02 5548.74 6391.53 1954.94

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Energies 2016, 9, 944 With reference

of 31 to the data listed in Table 10, the PV system installed on the two-storey18building presents a high installation cost, as the PV array can cover a large part of the available roof surface. Despite the obtained exported and the on significant reduction in the Withrevenues reference to the data from listed the in Table 10, the electricity PV system installed the two-storey building electricity bills, such PV system hasasa the negative NPV the end of the period and therefore presents a high installation cost, PV array canatcover a large partevaluation of the available roof surface. the revenues obtained from the The exported andsystem the significant in the it is Despite not considered economically viable. arrayelectricity of the PV installedreduction on the five-storey electricity bills, such PV system has a negative NPV at the end of the evaluation period and therefore it is building, whose data are listed in Table 11, uses only a part of the building roof as such surface is not considered economically viable. The array of the PV system installed on the five-storey building, shared with the other four co-owners. Despite the PV system does not cover the entire energy whose data are listed in Table 11, uses only a part of the building roof as such surface is shared with the demand of the household it results economically effective, as the NPV is positive at the end of the other four co-owners. Despite the PV system does not cover the entire energy demand of the household 20th year. It is realistic to suppose that only the PV systems that were considered economically it results economically effective, as the NPV is positive at the end of the 20th year. It is realistic to convenient by homeowners would be really installed on the buildings. For this reason, in order to suppose that only the PV systems that were considered economically convenient by homeowners evaluate the coverage of buildings. the electricity demand district, the results obtained from the would beeffective really installed on the For this reason,of inthe order to evaluate the effective coverage energy assessment to be with results of thefrom economic analysis. of the electricityhave demand of coupled the district, thethe results obtained the energy assessment have to be

coupled with the results of the economic analysis.

3.6. Results of the Energy and Economic Analysis 3.6. Results of the Energy and Economic Analysis

Assuming that only economically convenient PV systems were installed, the coverage of the only economically convenient PV systems were installed, the coverage of the electricityAssuming demand that of the districts was calculated by filtering out the electricity generated by the PV electricity demand of the districts was calculated by filtering out the electricity generated by the PV systems that have negative values of the NPV. Moreover, to take into account the possible mismatch systems that have negative values of the NPV. Moreover, to take into account the possible mismatch between PV generation and consumption, the coverage of the electricity demand was calculated between PV generation and consumption, the coverage of the electricity demand was calculated using usingthethe load match indexes the analysed districts. 9–11 the load load match indexes of theofanalysed districts. FiguresFigures 9–11 show theshow valuesthe of values the loadof match match indexes obtained after filtering out the contribution of the PV with negative indexes obtained after filtering out the contribution of the PV systems withsystems a negative NPV.a Such valuesNPV. Suchare values are the maximum ones they were calculated cu =the1 shading and omitting the maximum ones because theybecause were calculated assuming cu = 1assuming and omitting effect the shading of the obstructions. In thethe same figures values of cover the gross cover factor of theeffect obstructions. In the same figures values of thethe gross energy factorenergy calculated with Equation (1) Equation are depicted. calculated with (1) are depicted.

18%

: CPV Total = 42.8% Unfiltered : γDTotal = 37.5% June 2013 : γDTotal = 41.7% July 2013 December 2014 : γDTotal = 42.3%

16%

Gross energy cover factors Filtered load match indexes

14% 12% 10% 8% 6% 4%

2% 0%

1

2

3

4

5

6

7

8

9

10

11

Number of floors

Figure 9. Yearly gross energy cover loadmatch matchindexes indexes whole district Figure 9. Yearly gross energy coverfactors factors and and filtered filtered load forfor thethe whole district of of Palermo versus thethe number ofoffloors. Palermo versus number floors.

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19 of 31 18%

: CPV Total = 39.5% Unfiltered :: γCDTotal == 34.5% June 2013 Unfiltered PV Total 39.5% :: γγDTotal == 36.1% July 34.5% June2013 2013 DTotal = 37.2% December July 2013 2014 :: γγDTotal DTotal = 36.1% December 2014 : γDTotal = 37.2%

factors cover energy Gross factors cover energy Gross indexes match Filtered indexes match loadload Filtered

18% 16% 16% 14% 14% 12% 12% 10% 10% 8% 8% 6% 6% 4% 4% 2% 2% 0% 0%

1

2

3

4

1

2

3

4

5

6

7

Number 5 6of floors 7

8

9

10

11

8

9

10

11

Number of floors

Figure 10. Yearly gross energy cover factors and filtered load match indexes for the whole district of

Figure 10. Yearly gross energy cover factors and filtered load match indexes for the whole district of Figureversus 10. Yearly gross energy cover factors and filtered load match indexes for the whole district of Rome the number of floors. Rome versus the number of floors. Rome versus the number of floors. 18%

Unfiltered : CPV Total = 34.4% June 2013 :: γCDTotal == 28.6% Unfiltered PV Total 34.4% July :: γγDTotal == 30.8% June2013 2013 28.6% DTotal December July 2013 2014 :: γγDTotal == 32.7% 30.8%

factors cover energy Gross factors cover energy Gross indexes match Filtered indexes match loadload Filtered

18% 16%

16% 14%

DTotal

14% 12%

December 2014 : γDTotal = 32.7%

12% 10%

10% 8% 8% 6% 6% 4% 4% 2% 2% 0%

0%

1

2

3

4

1

2

3

4

5

6

7

Number 5 6of floors 7

8

9

10

11

8

9

10

11

Number of floors

Figure 11. Yearly gross energy cover factors and filtered load match indexes for the whole district of Figureversus 11. Yearly gross energy cover factors and filtered load match indexes for the whole district of Milan the number of floors. Figure 11. Yearly gross energy cover factors and filtered load match indexes for the whole district of Milan versus the number of floors.

Milan versus the number of floors. The yearly filtered load match index for the district of Palermo is 37.5% in June 2013, which is Themonth yearlyoffiltered match district of Palermo 37.5%ininJuly June 2013, which is the last the FITload scheme set index by thefor 5ththe “Conto Energia”, and is 41.7%, 2013, and 42.3%, theDecember last month of the FIT the scheme set by the 5th “Conto and 2013, 41.7%, 42.3%, in 2014, when tax credits scheme was in Energia”, force. In June γDinisJuly 34.5% andand 28.6% for The yearly filtered load match index for the district of Palermo is 37.5% in 2013, June 2013, which is in December when the taxset credits scheme was in force. In June 2013, γD indexes is in 34.5% and 28.6% the districtsofof2014, Rome and Milan, respectively. yearly filtered load match the district the last month the FIT scheme by the 5thThe “Conto Energia”, and 41.7%, Julyfor 2013, andfor 42.3%, theRome districts Rome Milan, respectively. yearly filtered load2014 match forInthe of areofequal toand 36.1% 37.2% in JulyThe 2013 December respectively. thedistrict same in December 2014, when the taxand credits scheme was inand force. In June 2013, γindexes D is 34.5% and 28.6% for of Rome values are equal to 36.1%toand 37.2% in32.7% July 2013 and December 2014 respectively. In of theMilan. same months, of γ D equal 30.8% and are respectively calculated for the district the districts of Rome and Milan, respectively. The yearly filtered load match indexes for the district months, values of of γD the equal 30.8% and 32.7% on aretall respectively for theand district The contribution PVtosystems installed buildingscalculated is quite small tendsoftoMilan. fade of Rome are equal to 36.1% and 37.2% in July 2013 and December 2014 respectively. In the same The contribution of the of PVfloors. systems on tall quite small tends to fade according to the number As ainstalled consequence, it isbuildings likely thatissmaller valuesand of the coverage of months, valuestoof γnumber tofloors. 30.8%Asand 32.7% are respectively calculated for the district of of Milan. D equalof according the is likely that smaller values of the coverage the domestic electricity demand willa consequence, be reached init the districts more recently built, where tall The contribution of the PV systems installed on tall buildings is quite small and tends to fade according the domestic electricitymore demand will due be reached in theuse districts more recently buildings are generally frequent to the modern of reinforced concrete.built, where tall to thebuildings number ofgenerally floors. As a consequence, it likely thatofsmaller values of the coverage more frequent due toreduces theismodern use reinforced As it are was predictable, the data filtering the energy coverage ofconcrete. all cities. The effects areof the domestic electricity demand will be reached in the districts more recently built, tall buildings it was considering predictable, filtering reduces the2013 energy coverage of all cities. The effects are moreAs evident the data data calculated in June and, in particular, forwhere the PV systems more evident the to data calculated in of June 2013inand, in particular, forthe thedata PV filtering systems installed on theconsidering buildingsdue with a the greater number Palermo. Adversely, are generally more frequent modern use offloors reinforced concrete. installed the buildings with a greater of floors in Palermo. Adversely, the data filtering marginally modifies the values of the loadnumber match indexes in July 2013of and December 2014. As it wasonpredictable, the data filtering reduces theevaluated energy coverage all cities. The effects marginally modifies the values of the load match indexes evaluated in July 2013 and December are more evident considering the data calculated in June 2013 and, in particular, for the PV2014. systems

installed on the buildings with a greater number of floors in Palermo. Adversely, the data filtering marginally modifies the values of the load match indexes evaluated in July 2013 and December 2014.

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The difference between such months is negligible despite the fact that the PV systems installed in July 2013 whereas only a tax credit of 40% supported the the PV 2013 were were incentivised incentivisedwith withaatax taxcredit creditofof50% 50% whereas only a tax credit of 40% supported systems installed in December 2014.2014. Figure 12 shows the global values of theoffiltered load match index PV systems installed in December Figure 12 shows the global values the filtered load match beforebefore and after change in the promoting policies. index andthe after the change in the promoting policies.

50%

June 2013 July 2013 December 2014

45%

Filtered load match indexes

40% 35% 30% 25%

20% 15% 10% 5% 0% Palermo

Rome

Milan

Figure cities. Figure 12. 12. Filtered Filtered load load match match indexes indexes for for the the whole whole districts districts of of the the analysed analysed cities.

In December 2014, the electricity demand coverage of the PV systems in Milan, which benefit In December 2014, the electricity demand coverage of the PV systems in Milan, which benefit from from the tax credit scheme, is up to 14.3% higher than the coverage calculated in June 2013, when the the tax credit scheme, is up to 14.3% higher than the coverage calculated in June 2013, when the FIT FIT scheme was still in force. The analogous increase in the electricity demand coverage is equal to scheme was still in force. The analogous increase in the electricity demand coverage is equal to 7.8% in 7.8% in Rome and 12.8% in Palermo. The direct comparison between the load match indexes shows Rome and 12.8% in Palermo. The direct comparison between the load match indexes shows the great the great significance of the economic analysis in evaluating the effects of the two different support significance of the economic analysis in evaluating the effects of the two different support policies. policies. The gains for the reduced electricity bills and the sold electricity apart, the level of the The gains for the reduced electricity bills and the sold electricity apart, the level of the economic economic convenience of PV systems largely depends on the incentives. The established values of convenience of PV systems largely depends on the incentives. The established values of the FITs are the FITs are less effective than the tax credit rates to compensate the disbursements of PV less effective than the tax credit rates to compensate the disbursements of PV investments. investments. The FIT scheme, which has been in force in Italy over many years, provided a premium The FIT scheme, which has been in force in Italy over many years, provided a premium proportional to the generated energy. Despite the de-escalation rates, which were applied to ratchet proportional to the generated energy. Despite the de-escalation rates, which were applied to ratchet down the tariffs over the time, such direct monetary incentive was considered an attractive and efficient down the tariffs over the time, such direct monetary incentive was considered an attractive and instrument to achieve an acceptable energy and economic return. It was expected that the change in efficient instrument to achieve an acceptable energy and economic return. It was expected that the the Italian policies for photovoltaics could reduce the interest in installing PV systems and induce change in the Italian policies for photovoltaics could reduce the interest in installing PV systems and the investors to choose other opportunities. On the contrary, the present study shows that the new induce the investors to choose other opportunities. On the contrary, the present study shows that the tax credit scheme, which only allows taxpayers to reduce the amount of tax that they owe to the new tax credit scheme, which only allows taxpayers to reduce the amount of tax that they owe to the government, is very effective in assuring the energy demand coverage and enhancing the PV diffusion government, is very effective in assuring the energy demand coverage and enhancing the PV in Italy. Moreover, it can be observed that the policy context can hold the PV systems dissemination diffusion in Italy. Moreover, it can be observed that the policy context can hold the PV systems more than the natural potential of the considered location. Although the morphological conditions of dissemination more than the natural potential of the considered location. Although the the urban context, along with the energy and economic parameters, are relevant contributing factors morphological conditions of the urban context, along with the energy and economic parameters, are for a successful integration of PV systems on the building rooftops, the adopted supporting schemes relevant contributing factors for a successful integration of PV systems on the building rooftops, the are of crucial importance to household or professional investors, who obviously prefer to invest in adopted supporting schemes are of crucial importance to household or professional investors, who projects that provide a satisfactory return. obviously prefer to invest in projects that provide a satisfactory return. 4. Sensitivity Analysis 4. Sensitivity Analysis The results presented in the previous section refer to a condition in which the available solar The presented in theby previous section refer to a no condition in which the available solar energy isresults completely harnessed the producers, because obstruction shadows the PV fields, energy is completely harnessed by the producers, because no obstruction shadows the PV fields, and the electricity generated perfectly matches the energy demands of flats. Actually, such an ideal

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condition is quite far from the real operation of PV systems installed in densely urbanised districts in which, because both low and tall buildings are present, the PV arrays placed on the roofs of low and the electricity generated perfectly matches the energy demands of flats. Actually, such an ideal buildings, which are the most effective in covering the electricity demand, may be very likely condition is quite far from the real operation of PV systems installed in densely urbanised districts shadowed by the adjoining tall buildings. Moreover, the presence of an amount of mismatch in which, because both low and tall buildings are present, the PV arrays placed on the roofs of between generation andare consumption should be realistically considered because the most low buildings, which the most effective in covering the electricity demand, may be even very likely meticulous householder may tall notbuildings. be capable of usingthe allpresence the electricity generated by a PVbetween system not shadowed by the adjoining Moreover, of an amount of mismatch equipped with batteries. The accurate assessment of the effects of both the PV array shadowing generation and consumption should be realistically considered because even the most meticulous and the load mismatch requireof ausing dynamic analysis of the PVbysystems based numerous householder may would not be capable all the electricity generated a PV system noton equipped with batteries. accurateparameters. assessment of the effects of both the to PVconsider array shadowing and the load environmental andThe electrical It would be necessary the instantaneous values mismatch would require a dynamic analysis of the PV systems based on numerous environmental of the solar irradiance, air temperature and velocity, which affect the current-voltage characteristics and electrical parameters. It would necessary to consider theto instantaneous the solar the of the used PV panels, the value of thebe electrical load connected the inverter,values whichofinfluences irradiance, air temperature and velocity, which affect the current-voltage characteristics of the used PV operation of the maximum power point tracking, and the shape, position and height of the panels, the value of the electrical load connected to the inverter, which influences the operation of the surrounding buildings in order to check if the PV array may be shadowed by them. Moreover, maximum power point tracking, and the shape, position and height of the surrounding buildings in because it is unlikely that the PV system results totally shadowed, the analysis of the electrical order to check if the PV array may be shadowed by them. Moreover, because it is unlikely that the PV behaviour consider the shading of eachofsingle PV panel, or even PV cells, because it is known systemshould results totally shadowed, the analysis the electrical behaviour should consider the shading that of theeach global electrical performance of abecause PV array be reduced by theelectrical malfunction of a single single PV panel, or even PV cells, it iscan known that the global performance of a PV device. Evencan though the described analysis,of which would require a large extent of reliable data and PV array be reduced by the malfunction a single PV device. Even though the described analysis, computations, was carried out extent considering the data totality the PV systems of the districts, the results which would require a large of reliable andof computations, was carried out considering thenever totalityhave of thea PV systems of the districts, results wouldinformation, never have a general because would general validity becausethe some relevant such asvalidity the instantaneous some relevant information, such as the instantaneous values of the domestic electrical loads connected values of the domestic electrical loads connected to each inverter, may be just supposed on the basis to each inverter, may be In justaddition, supposedthe on the basiswould of hypothetical addition, results and of hypothetical scenarios. results be validscenarios. only for In the studiedthe districts would be valid only for the studied districts and such districts, even if selected to be representatives, such districts, even if selected to be representatives, are only a sample of the analysed cities. are only a sample of the analysed cities. For the above reasons, the assessment of the load mismatch and shading effects was carried out For the above reasons, the assessment of the load mismatch and shading effects was carried out by means of a sensitivity analysis based on the available yearly data and information. The described by means of a sensitivity analysis based on the available yearly data and information. The described results represent how thethe load averagelyaffect affect electricity coverage of the results represent how loadmismatch mismatchand and shading shading averagely thethe electricity coverage of the totality of the PVPV systems installed of the theanalysed analysed districts. The shading effects totality of the systems installedon on the the roofs roofs of districts. The shading effects are are evaluated using a shading coefficient zero(no (noshading) shading) 100% solar energy evaluated using a shading coefficientthat thatvaries varies from from zero to to 100% (no(no solar energy is is collected). In Figures 13–18 the curves of the load match indexes versus the utilization coefficient, collected). In Figures 13–18 the curves of the load match indexes versus the utilization coefficient, for some values of the shading coefficient, are depicted. somefor values of the shading coefficient, are depicted. 45% 40%

Load match index

35% 30% 25% 20%

γD = 17%

15% Shad.= 0% Unfiltered Shad.=10% Unfiltered Shad.=20% Unfiltered Shad.= 0% Filtered Shad.=10% Filtered Shad.=20% Filtered

10% 5% 0% 0.0

0.2

0.4 0.6 Utilization coefficient cu

0.8

1.0

Figure 13. 13. Yearly load match districtofofPalermo Palermo June versus Figure Yearly load matchindexes indexesfor for the the whole whole district in in June 20132013 versus the the utilization coefficient. utilization coefficient.

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45% 45% 45% 40% 40% 40% 35% 35% 35% 30% 30% 30% 25% 25% 25% 20% 20% 17% γ γ = =17% 20% D D γD = 17% 15% 15% 15% 10% 10% 10% 5%5% 5% 0% 0% 0.2 0% 0.0 0.0 0.2 0.0 0.2

index match Load index match Load

Load match index

22 of 31 22 of 31

0.4 0.6 0.4 0.6 Utilization coefficient0.6 c 0.4 Utilization coefficient cuu Utilization coefficient cu

Shad.= Shad.=0% 0%Unfiltered Unfiltered Shad.=10% Unfiltered Shad.=10% Unfiltered Shad.= 0% Unfiltered Shad.=20% Unfiltered Shad.=10% Shad.=20%Unfiltered Unfiltered Shad.= 0% Filtered Shad.=20% Unfiltered Shad.= 0% Filtered Shad.=10% Filtered Shad.= 0% Filtered Shad.=10% Filtered Shad.=20% Filtered Filtered Shad.=10% Shad.=20% Filtered Shad.=20% Filtered 0.8 1.0 0.8 1.0 0.8 1.0

Figure 14. Yearly load match indexes for the whole district of Palermo in July 2013 versus the Figure 14. Yearly load load match indexes indexes for the whole district of Palermo in July 2013 versus the 14.14.Yearly Figure Yearly loadmatch match indexes for for the whole district of Palermo in July 2013 versus the utilization coefficient. utilization coefficient. coefficient. utilization coefficient.

Load match index index match Load index match Load

40% 40% 40% 35% 35% 35% 30% 30% 30% 25% 25% 25% 20% γD = 17% 20% 20% γ = 17% 15% γD D= 17% 15% 15% 10% 10% 10% 5% 5% 5%0% 0.2 0% 0.0 0% 0.0 0.2 0.0 0.2

0.4 0.6 Utilization coefficient 0.4 0.6cu 0.4 Utilization coefficient0.6 cu

Shad.= 0% Unfiltered Shad.=10% Unfiltered Shad.= 0% Unfiltered Shad.= 0%Unfiltered Unfiltered Shad.=20% Unfiltered Shad.=10% Shad.=10% Unfiltered Shad.= 0% Filtered Shad.=20% Unfiltered Shad.=10% Filtered Shad.=20% Unfiltered Shad.= 0% Filtered Shad.=20% Filtered Shad.= 0%Filtered Filtered Shad.=10% Shad.=10% Filtered Shad.=20% Filtered 0.8 Shad.=20% Filtered 1.0 0.8 1.0

0.8

1.0

Utilization coefficient cu

Figure 15. Yearly load match indexes for Figure 15. coefficient. Yearly load match indexes for utilization Figure 15. Yearly load load match match indexes indexes for for Figure 15. Yearly utilization coefficient. utilization utilization coefficient. coefficient.

whole district of Rome in June 2013 versus the whole district of Rome in June 2013 versus the whole district district of of Rome Rome in in June whole June 2013 2013 versus versus the the

Load match index index match Load index match Load

40% 40% 35% 40% 35% 30% 35% 30% 25% 30% 25% 20% γD = 17% 25% 20% γD = 17% 15% 20% 15% γD = 17% 10% 15% 10% 5% 10%5% 0% 5%0% 0.0 0.0

the the the the

0%

0.2 0.2

0.4 0.6 Utilization coefficient 0.4 0.6cu Utilization coefficient cu

Shad.= 0% Unfiltered Shad.=10% Unfiltered Shad.= 0% Unfiltered Shad.=20% Unfiltered Unfiltered Shad.=10% Shad.= 0% Filtered Shad.=20% Shad.= 0%Unfiltered Unfiltered Shad.=10% Filtered Shad.= 0% Filtered Shad.=10% Unfiltered Shad.=20% Filtered Shad.=10% Shad.=20%Filtered Unfiltered Shad.=20% Filtered Shad.= 0.8 0% Filtered 1.0 Shad.=10% Filtered 0.8 Shad.=20% Filtered1.0

0.0 0.2 0.8 1.0 Figure 16. Yearly load match indexes for 0.4 the whole 0.6 district of Rome in July 2013 versus the Utilization coefficient cu Figure 16. Yearly load match indexes for the whole district of Rome in July 2013 versus the utilization coefficient. utilization coefficient. Figure 16. 16. Yearly July 2013 2013 versus versus the the Figure Yearly load load match match indexes indexes for for the the whole whole district district of of Rome Rome in in July utilization coefficient. utilization coefficient.

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index match Load index match Load

30% 30% 25% 25% 20% 20%

γD = 17% γD = 17%

15% 15% 10% 10% 5% 5% 0% 0% 0.0 0.0

0.2 0.2

0.4 0.6 0.4 0.6c Utilization coefficient u Utilization coefficient cu

Shad.= 0% Unfiltered Shad.= Shad.= 0% 5% Unfiltered Unfiltered Shad.= 5% Unfiltered Shad.=10% Unfiltered Shad.=10% Shad.= 0% Unfiltered Filtered Shad.= Shad.= 0% 5% Filtered Filtered Shad.= 5% Filtered Shad.=10% Filtered Shad.=10% Filtered 0.8 1.0 0.8 1.0

Figure 17. 17. Yearly load match indexes for the whole district of Milan in June 2013 2013 versus versus the the Figure Yearly load load match match indexes indexes for for the the whole whole district district of of Milan Milan in in June June Figure 17. Yearly 2013 versus the utilization coefficient. utilization coefficient. coefficient. utilization 35% 35%

index match Load index match Load

30% 30% 25% 25% 20% 20%

γD = 17% γD = 17%

15% 15% 10% 10% 5% 5% 0% 0% 0.0 0.0

0.2 0.2

0.4 0.6 0.4 0.6c Utilization coefficient u Utilization coefficient cu

Shad.= 0% Unfiltered Shad.= Shad.= 0% 5% Unfiltered Unfiltered Shad.= 5% Unfiltered Shad.=10% Unfiltered Shad.=10% Shad.= 0% Unfiltered Filtered Shad.= Shad.= 0% 5% Filtered Filtered Shad.= 5% Filtered Shad.=10% Filtered Shad.=10% Filtered 0.8 1.0 0.8 1.0

Figure 18. Yearly load match indexes for the whole district of Milan in July 2013 versus the Figure 18. Yearly Figure 18. Yearly load load match match indexes indexes for for the the whole whole district district of of Milan Milan in in July July 2013 2013 versus versus the the utilization coefficient. utilization coefficient. utilization coefficient.

Both the unfiltered and filtered load match indexes are lower when the utilization coefficient is Both the unfiltered and filtered load match indexes are lower when the utilization coefficient is reduced. obviously evidentindexes for the are filtered load match because of the BothThe the reduction unfilteredisand filtered more load match lower when theindexes utilization coefficient reduced. The reduction is obviously more evident for the filtered load match indexes because of the omission of the PV systems that have a negative value of the NPV. Comparing the data that refer to is reduced. The reduction is obviously more evident for the filtered load match indexes because of omission of the PV systems that have a negative value of the NPV. Comparing the data that refer to same city,ofit the is possible to appreciate the curves of the filtered index the calculated in refer June the omission PV systems that havethat a negative value of the NPV.match Comparing data that the same city, it is possible to appreciate that the curves of the filtered match index calculated in June 2013 are lower than the ones evaluated in July 2013; such behaviour becomes more evident to the same city, it is possible to appreciate that the curves of the filtered match index calculated by in 2013 are lower than the ones evaluated in July 2013; such behaviour becomes more evident by increasing thelower shading coefficient. The district Milan particularly affectedmore by the shading June 2013 are than the ones evaluated in of July 2013;results such behaviour becomes evident by increasing the shading coefficient. The district of Milan results particularly affected by the shading coefficient;the actually, thecoefficient. values of the match with a shading coefficient of 10% increasing shading Theload district of index Milancalculated results particularly affected by the shading coefficient; actually, the values of the load match index calculated with a shading coefficient of 10% are comparable with the data obtained for Palermo and Rome for a value of the shading coefficient coefficient; actually, the values of the load match index calculated with a shading coefficient of 10% are are comparable with the data obtained for Palermo and Rome for a value of the shading coefficient of 20%. comparable with the data obtained for Palermo and Rome for a value of the shading coefficient of 20%. of 20%. for γγD of 17%, which is the target value established by the In the previous graphs also the lines for In the previous graphs also the lines for γD D of 17%, which is the target value established by the Directive 2009/28/EC 2009/28/EC for Italy [106], are depicted. the comparison with the calculated results, it for Italy [106], are depicted.From From the comparison with the calculated results, Directive 2009/28/EC for Italy [106], are depicted. From the comparison with the calculated results, it is is evident it evidentthat thatthe thetax taxcredits creditsscheme, scheme,ininforce forceafter afterJune June2013, 2013,results results more more effective effective in keeping the is evident that the tax credits scheme, in force after June 2013, results more effective in keeping the load match match index indexatatvalues valuesgreater greaterthan thanthe thetarget target value of 17%. Such a conclusion is confirmed by value of 17%. Such a conclusion is confirmed by the load match index at values greater than the target value of 17%. Such a conclusion is confirmed by the data shown in Figures 19–21, which to December data shown in Figures 19–21, which referrefer to December 2014.2014. the data shown in Figures 19–21, which refer to December 2014.

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index match match LoadLoad Load indexindex match

45% 45% 40% 45% 40% 35% 40% 35% 30% 35% 30% 25% 30% 25% 20% γD = 17% 25% 20% 15% γD = 17% 20% 15% γD = 17% 10% 15% 10% 5% 10% 5% 0% 5% 0.0 0% 0.0 0% 0.0

0.2 0.2 0.2

Figure 19. Yearly load match indexes for

0.4 0.6 Utilization coefficient cu 0.4 0.6 Utilization coefficient cu 0.4 0.6 the whole district Utilization coefficient cu

Shad.= 0% Unfiltered Shad.=15% Unfiltered Shad.= 0% Unfiltered Shad.=30% Unfiltered Shad.=15% Unfiltered Shad.= 0% Filtered Shad.= 0% Unfiltered Shad.=30% Unfiltered Shad.=15% Filtered Shad.=15% Unfiltered Shad.= 0% Filtered Shad.=30% Filtered Shad.=30% Unfiltered Shad.=15% Filtered Shad.= 0% Filtered Shad.=30% Filtered 0.8 Shad.=15% Filtered 1.0 Shad.=30% Filtered 0.8 1.0 0.8

1.0

of Palermo in December 2014 versus the

Figure 19. Yearly load match indexes for the whole district of Palermo in December 2014 versus the Figure 19. Yearly load match indexes for the whole district of Palermo in December 2014 versus the utilization coefficient. utilization coefficient.

index match match LoadLoad Load indexindex match

Figure 19. Yearly load match indexes for the whole district of Palermo in December 2014 versus the utilization coefficient. utilization coefficient. 40% 40% 35% 40% 35% 30% 35% 30% 25% 30% 25% 20% γD = 17% 25% 20% γD = 17% 15% 20% γD = 17% 15% 10% 15% 10% 5% 10% 5% 0% 5% 0.0 0% 0.0 0% 0.0

0.2 0.2 0.2

0.4 0.6 Utilization coefficient cu 0.4 0.6 Utilization coefficient0.6 cu 0.4

Shad.= 0% Unfiltered Shad.=15% Unfiltered Shad.= 0% Unfiltered Shad.=30% Unfiltered Shad.=15% Unfiltered Shad.= 0% Filtered Shad.= 0% Unfiltered Shad.=30% Unfiltered Shad.=15% Filtered Shad.=15% Unfiltered Shad.= 0% Filtered Shad.=30% Filtered Shad.=30% Shad.=15% Unfiltered Filtered Shad.= 0% Filtered Shad.=30% Filtered 0.8 Shad.=15% Filtered 1.0 Shad.=30% Filtered 1.0 0.8 0.8

1.0

index match match LoadLoad Load indexindex match

Figure 20. Yearly load match indexes for Utilization the whole district of Rome in December 2014 versus the coefficient cu Figure 20. coefficient. Yearly load match indexes for the whole district of Rome in December 2014 versus the utilization Figure 20. Yearly load match indexes for the whole district of Rome in December 2014 versus the Figure 20. coefficient. Yearly load match indexes for the whole district of Rome in December 2014 versus the utilization utilization coefficient. utilization coefficient. 35% 35% 30% 35% 30% 25% 30% 25% 20% 25% γD = 17% 20% γD = 17% 15% 20% γD = 17% 15% 10% 15% 10% 5% 10% 5% 0% 5% 0.0 0% 0.0 0% 0.0

0.2 0.2 0.2

0.4 0.6 Utilization coefficient cu 0.4 0.6 Utilization coefficient0.6 cu 0.4

Shad.= 0% Unfiltered Shad.=10% Unfiltered Shad.= 0% Unfiltered Shad.=20% Unfiltered Shad.=10% Unfiltered Shad.= 0% Filtered Shad.= 0% Unfiltered Shad.=20% Unfiltered Shad.=10% Filtered Shad.=10% Unfiltered Shad.= 0% Filtered Shad.=20% Filtered Shad.=20% Shad.=10% Unfiltered Filtered Shad.= 0% Filtered Shad.=20% Filtered 1.0 0.8 Filtered Shad.=10% Shad.=20% 0.8 Filtered 1.0 0.8

1.0

Figure 21. Yearly load match indexes for Utilization the whole district of Milan in December 2014 versus the coefficient cu Figure 21. coefficient. Yearly load match indexes for the whole district of Milan in December 2014 versus the utilization Figure 21. coefficient. Yearly load match indexes for the whole district of Milan in December 2014 versus the utilization Figure 21. Yearly load match indexes for the whole district of Milan in December 2014 versus the utilization coefficient. If no economic filtering of the data is considered, the values of cu in correspondence to which

utilization coefficient. If nomatch economic filtering of to the17% dataseem is considered, the values cu in correspondence to which the load index is equal almost unaffected byofthe percentage of shaded solar If nomatch economic filtering of to the17% dataseem is considered, the values cu in correspondence to which the load index is equal almost unaffected byofthe percentage of shaded solar thenoload match index is equal to data 17% seem almost unaffected by the of shaded solar If economic filtering of the is considered, the values of cupercentage in correspondence to which

the load match index is equal to 17% seem almost unaffected by the percentage of shaded solar

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energy; moreover, such values are much smaller than the values obtained considering the economic profitability of the PV systems. As a consequence, one may erroneously infer that the presence of shading buildings is not an issue and that, even if about 80% of the generated electricity does not match household consumption, the target value established by the Directive 2009/28/EC can be reached for each city. The filtered data gives more realistic results that highlight the importance of the latitude, the shading coefficient and the presence of the load mismatch. Table 12 lists the values of the utilization coefficient for which the filtered load match index is equal to 17%. Table 12. Utilization coefficients at filtered γD = 17%. City

Shading Coefficient

Month 0%

5%

10%

15%

20%

30%

Palermo

June 2013 July 2013 December 2014

0.334 0.282 0.244

0.340 0.287 0.247

0.359 0.301 0.251

0.392 0.310 0.256

0.428 0.328 0.266

0.400 0.318

Rome

June 2013 July 2013 December 2014

0.343 0.315 0.260

0.348 0.326 0.265

0.367 0.328 0.267

0.386 0.337 0.291

0.400 0.362 0.295

0.398 0.314

Milan

June 2013 July 2013 December 2014

0.373 0.339 0.275

0.393 0.343 0.278

0.454 0.352 0.285

0.390 0.290

0.455 0.318

-

For a fixed percentage of shaded solar energy, the coverage target set for Italy by the Directive 2009/28/EC can be met only if values of the utilization coefficient grater of the threshold data in Table 10 are achieved. When the listed data are small, the householders are allowed a larger flexibility in using their domestic appliances because a greater amount of mismatch between PV generation and household consumption is tolerated without affecting the energy performance of the districts. It can be observed that the tax credits scheme is always more effective than the FIT scheme and its validity tends to increase further in time. Actually, the target value, which cannot be reached in Milan in 2013 with a shading coefficient of 15%, is obtained in December 2014 if at least 71.0% of generated electricity is consumed by the household. Using the data of Table 12, similar observations can be made for the cities of Palermo and Rome. On the basis of the presented results, it is possible to claim that, if the future values of the tax credits will be wisely fixed considering the current costs of PV devices, the recent Italian promoting policy will be able to adequately support the diffusion of domestic PV systems in densely urbanised areas. The effectiveness of the promoting policy would increase further if the values of the tax credits were arranged to compensate the effects of the latitude on the solar energy available in the various geographical areas of Italy. 5. Conclusions A study on the electrical coverage of grid-connected rooftop PV systems was developed with the aim to evaluate the effectiveness of the energy strategies adopted over the 2013–2014 period: (1) the FIT mechanism that has guaranteed set prices for both generated electricity provided to the grid and self-consumed electricity; (2) the present tax credit scheme that, in contrast to the FIT program, does not include a remuneration, but directly allows the PV system owners to reduce their tax liability. The approach combines the annual data of PV generation and electricity consumptions with a detailed architectural analysis of three Italian districts. The roof morphology, orientation, tilt and available surface for each householder, shading effects and load mismatch were considered. The results of the energy analysis were filtered by an economic analysis that included all costs (investment, insurance, replacement, maintenance and management costs) and benefits (FIT and premium on the self-consumed electricity, percentage of tax credit, reduced electricity bills, profits for sold electricity) of each PV system over a period of 20 years. The results show a significant reduction in the energy cover factors when, as it can be realistically assumed, only the energy produced by the

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economically advantageous PV systems is considered. The comparison between the FIT policy at the end of the supporting program (June 2013) and the tax credits scheme in December 2014 highlighted an increase in the electrical coverage from 37.5%, 34.5% and 28.6% to 42.3%, 37.2% and 32.7% for the districts in Palermo, Rome and Milan, respectively. The results denote the major effectiveness of the energy policy based on the tax credits especially when solar energy shading and mismatch between PV generation and household consumption are considered. By means of a sensitivity analysis, the role of the shading and utilization coefficients, which respectively indicate the lack of solar energy, due to the surrounding buildings, and the mismatch between electricity generation and consumption, was considered. Aiming to assess the fulfilment of the target set for Italy by the Directive 2009/28/EC, the values of the utilization and shading coefficients, which allow 17% of the electrical coverage demand by renewable sources, were calculated. The presented study underlines how the results are significantly influenced by the latitude of the analysed districts. For this reason it would be desirable that the Italian government promoted supporting measures that were sensitive to the geographic position in order to compensate the penalizing effects of the latitude and reach the most homogeneous diffusion of the PV systems in each region of national territory. Author Contributions: Aldo Orioli and Alessandra Di Gangi conceived and designed the methodology; Aldo Orioli and Vincenzo Franzitta performed the analysis; Alessandra Di Gangi and Ferdinando Foresta carried out the architectural analysis; Vincenzo Franzitta analyzed the data; Aldo Orioli and Alessandra Di Gangi wrote the paper. Conflicts of Interest: The authors declare no conflict of interest.

Nomenclature CPV CFt cu C0 DDay DDay,j D ∗j Dj DMatch,j DMism,j DNight,j DNight DTotal EPV ∗ EPV,j EPV,j ∗ EPVload k N NPV NPV Nt t γD

gross energy cover factor of the district (%) cash flow at the generic t-th year (€) utilization coefficient cu initial investment cost (€) day-time yearly electricity demand of the standard flat (kWh) day-time yearly electricity demand of the generic j-th PV system (kWh) yearly electricity demand of the generic j-th PV system (kWh) electricity demand of the generic j-th PV system during the generic i-th time interval (kWh) matched yearly energy demand of the generic j-th PV system (kWh) mismatched yearly energy demand of the generic j-th PV system (kWh) night-time yearly electricity demand of the generic j-th PV system (kWh) night-time yearly electricity demand of the standard flat (kWh) yearly electricity demand of the district (kWh) electricity generated by the totality of installed PV system (kWh) yearly electricity produced by the generic j-th PV system (kWh) electricity generated by the generic j-th PV system during the generic i-th time interval (kWh) yearly electricity supplied to the load by the generic j-th PV system (kWh) discount rate (%) number of samples in the evaluation period number of PV systems net present value (€) life time of the investment (years) generic t-th year load match index of the district (%)

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