Introduction

The Clean Energy Package presented by the European Commission in 2016 set goals for 2030 in terms of greenhouse gas emissions (Cucchiella et al. 2017b), renewable sources and energy efficiency. At the same time, the need to build a Union that ensures accessible energy from an economic, security and sustainability point of view is highlighted. These principles are recalled in the well-known Sustainable development goal 7, which aims to ensure access to affordable, reliable, sustainable and modern energy for all. According to (Agnew et al. 2020), despite the efforts that have been made for several decades in terms of increasing renewables, improving energy efficiency and accessibility to energy, the goal is still very far from being achieved. In particular, the Report argues, “Clean and sustainable energy should be at the heart of the COVID-19 response and fight against climate change”. This topic was debated by many researchers as, for example (Hoang et al. 2021), who highlight the importance of renewables and clean energy, especially in the electricity sector, in the difficult time of the pandemic. They also discuss the main sustainable energy strategies. While (Steffen et al. 2020) focus their research on the identification of policy frameworks to achieve carbon–neutral energy systems by defining three different policy scenarios, in the short, medium and long term. Finally (Andrijevic et al. 2020; Cucchiella and D'Adamo 2015) also analyse the issue in economic and investment terms.

Many European countries are aware of this condition and how important is to promote policies aimed at increasing the share of renewables. Therefore, it is possible to identify many studies that have analyzed possible pathways for this transition (Deane et al. 2015; Graabak et al. 2019; Jäger-Waldau 2020; Oh et al. 2014; Xunzhang et al. 2017). Among these, (Lowitzsch et al. 2019) highlights the importance of the European Union Renewable Energy Directive (RED II) that Member States had time to implement until mid-2021 to achieve the transition towards renewable energies. Among the Member States, Italy implemented it with legislative decree no. 199 at the end of November 2021. And it is precisely in Italy that the field of renewables for some time has received a lot of attention from the academic, professional and political world. In 2017, the Minister of Economic Development, together with the Minister of the Environment and Land and Sea Protection, approved the National Energy Strategy (SEN). The main purpose of the SEN is to make the national energy system more competitive, more sustainable and safer. It may represent the starting point for the preparation of the Integrated Energy and Climate Plan (PNIEC), drawn up by the Italian government to implement Regulation (EU) 2018/1999. Among the most important goals, the SEN establishes that by 2030, the 28% of total consumption have to be covered by renewable sources, while 55% of electricity have to be covered by renewables. Among the strategies to achieve these goals, the SEN defines rules and incentive solutions to support renewables, whose mechanisms will evolve towards market parity, from direct incentives on production to the activation of policies and regulatory simplification.

The goals just described above were later elaborated in the PNIEC, which became permanent in December 2019. It sets mandatory goals for 2030 on energy efficiency, renewables and reduction of CO2 emissions. In particular, Italy aims to increase by 30% the energy from renewable sources in gross final energy consumption, in order to reduce primary energy consumption by 43%, compared to the PRIMES 2007 scenario (Capros et al. 2008), and the greenhouse gases by 33%.

A second pillar of energy legislation in the Italian context is the National Recovery and Resilience Plan (PNRR). It includes one of the six main missions, the one of the green revolution and ecological transition; for its achievement eur 59.47 billion has been allocated (MISE 2022). This mission includes projects aimed at promoting the green transition by focusing on energy produced from renewable sources, increasing resilience to climate change, supporting investment in research and innovation and encouraging sustainable public transport. The PNRR will allow the achievement of the aforementioned goals of the SEN and the PNIEC, in compliance with which it was launched. The decree of 04.07.2019, known also as “FER 1”, aims to provide economic support for investments in renewables: this decree is particularly interesting for the purposes of this research. In fact, it is essential to investigate how important the role of incentives is in accelerating the energy transition process. Similar studies were identified by the authors that, however, do not refer to incentives. For instance, (Rocamora 2017) analyses the role of the carbon tax in France for a fair ecological transition. Others show how the energy/carbon taxes appear to be regressive and justify these results (2002). Therefore, it is clear that there is a research gap linked to the relationship between incentives and energy transition, also with regard to renewables. This paper is aimed at illustrating the research within this gap, using a multidisciplinary methodological approach aimed at analysing the cost-effectiveness of the investment on the basis of technical and economic criteria.

In the field of renewables, solar energy is certainly very accessible and easy to use (Chen 2011; Lynn 2011; Mackay 2015). It is also a field full of studies, especially in terms of design of the optimal tilt, since it has been shown how the orientation of the photovoltaic panel affects the production of electricity. Annibaldi et al. (2020) evaluate the economic and environmental performance of a photovoltaic system applied in Italy, demonstrating its validity even if the roof inclination angle is not optimal. (Conceição et al. 2019) develop a model that allows for the calculation of the optimal tilt angle for dirty photovoltaic systems, under conditions that are thus not optimal but realistic. Finally, (Jacobson and Jadhav 2018) provide estimates of the optimal tilt angles of photovoltaic panels for all countries in the world. Other authors have focused their research on the analysis of the factors that allow the increase of solar production thanks to the use of tracking panels (Nsengiyumva et al. 2018) or through the use of reflectors of different kind of material (Kim et al. 2021; Pavlović and Kostić 2015) or through a double-skin photovoltaic facade (Lee et al. 2019). Other authors have been interested in researching integration strategies (Jelle and Breivik 2012; Kuhn et al. 2021; Strong 2010). Lam and Yu (2016) study the framework of using third-party finance in the provision of photovoltaic facilities in buildings. (Mahzouni 2019) focuses on the need to find out how the introduction and enforcement of new energy policies and regulations have changed the norms and building practices. Cucchiella et al. (2017a) explore the profitability of photovoltaic systems under different usage conditions determined by the combination of the following variables “photovoltaic plant size, battery capacity, the increase of the share of self-consumption, and the useful lifetime of energy storage system”.

It is a constantly growing field of interest (Jacobson et al. 2017; Rotilio et al. 2015) also because it has been shown how “energy sector restructuring with regulations encourages sustainable energy” (Wen et al. 2022). Therefore, this paper illustrates the research carried out in the field of renewables, with particular attention to solar photovoltaics and technologies aimed at its exploitation. Within the Italian context (Fig. 1) and in compliance with the FER1 decree, the paper shows how the productivity of a photovoltaic system and the respective economic feasibility change, assuming different scenarios. These scenarios are defined assuming firstly that the location of the plant needs to change in different Italian regions; additionally, various photovoltaic technologies are employed. In this way, it is possible to analyse the actual validity of the incentive tools promoted in the national context according to the main parameters that affect the productivity of a photovoltaic system. To this end, the research has developed a methodology, tested on case-studies, which is based on technical and economic criteria.

Fig. 1
figure 1

Average GHI, global horizontal irradiation, for Italy (Solargis. https://solargis.com/maps-and-gis-data/download/italy)

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This paper is organized as follows. In Sect. 2, the FER1 decree is analysed; in Sect. 3 the developed methodology is illustrated; in Sect. 4 the case-study is described while in Sect. 5 there are the results achieved with the relative discussion. The paper closes with the main conclusions (Sect. 6).

Regulatory references

In July 2019, the Minister of Economic Development, in agreement with the Minister for the Environment and for the Protection of the Land and the Sea, issued a ministerial decree, known as FER1 [“Incentives for electricity produced by on shore wind, solar photovoltaic, hydroelectric and gas residues from purification processes plants”, Minister of Economic Development, in agreement with the Minister for the Environment and for the Protection of the Land and the Sea, 04.07.2019], with the aim of encouraging the production of electricity from plants powered by renewable sources. In fact, in the plan of the government, the implementation of FER 1 was aimed at the construction of plants with a total capacity of about 8000 MW (of which 5380 MW for wind and solar), with an increase in production from renewable sources of about 12 billion kWh and with activated investments estimated in the order of eur 10 billion.

This decree divides the plants that can access the incentives into four groups based on the typology, renewable energy source and category of intervention. Among these ones, group A includes, in addition to wind power plants, newly built photovoltaic systems. Moreover, group A-2 includes newly built photovoltaic systems installed to replace the asbestos roofs of buildings. In this way, after 7 years from the expiry of the Fifth Conto Energia, which entered into force in August 2012 and repealed on July 6, 2013 (Sgroi et al. 2014), photovoltaic systems can again benefit from incentives. These plants will have access to the incentives provided for by FER1 Decree only if they are newly built, realized with newly built components and are not installed on agricultural areas. According to the nominal capacity of the photovoltaic system, various incentives are provided: € 105/MWh for systems with power ranging from 20 to 100 kW, € 90/MWh for systems with power greater than 100 kW and less than 1000 kW and € 70/MWh for plants with power greater than or equal to 1000 kW. These incentives are paid on the basis of the net electricity produced and fed into the grid by the photovoltaic system for a period of 20 years. Furthermore, an additional bonus of € 10/MWh is recognized for power plants up to 100 kW, as long as the self-consumed energy rate is greater than 40% of the plant's net production. The photovoltaic systems belonging to the group A-2 are entitled, in addition to the incentives mentioned above, to a premium equal to € 12/MWh paid on all the energy produced.

Finally, there are two different ways to access the incentives depending on the power of the plant and the group to which it belongs, i.e., through registration in registers or through participation in auction procedures. The first group includes plants with a power output of more than 1 kW (20 kW for photovoltaic plants) and less than 1 MW that belong to Groups A, A-2, B and C. On the other hand, plants with a power output greater than or equal to 1 MW that belong to Groups A, B and C has to participate in the Auctions, through which the available power quota is allocated. The procedure is followed by the Gestore Servizi Energetici (GSE).

Methodology

This paper analyses the technical and economic convenience of a newly built photovoltaic system that can benefit from the funding provided for in the FER1 decree. It will also be analysed how the results obtained change when input data change. For this purpose, a multidisciplinary methodology was defined aimed at analysing the investment convenience on the basis of technical and economic criteria: the same was then applied to the evaluation of a case-study. In order to follow this methodological approach, this chapter has also been organized into two main sections: the first (Sect. 3.1) describes the technical analysis and defines the variables; the second Sect. (3.2) describes the economic analysis. The methodological approach is represented in Fig. 2 and illustrated in Sects. 3.1 and 3.2.

Fig. 2
figure 2

The flowchart in the figure is intended to summarize the main steps of the research. Specifically, all technical parameters influencing photovoltaic production were first defined in order to identify different scenarios. Then, energy and economic analyses were developed in relation to each scenario. Finally, a summary matrix was produced to analyze and comment on the results obtained

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Technical analysis

In order to analyse the productivity of the photovoltaic system, all the main input factors that could affect the technical and economic performance of the system were investigated. In particular:

  • solar potential analysis;

  • individuation of shading elements;

  • installation place;

  • azimuth;

  • tilt of photovoltaic modules;

  • photovoltaic technology.

The study of the solar potential allows to identify areas in which there is a good value of incident solar radiation, which is closely linked to the installation site. The identified areas are potentially available for the installation of photovoltaic modules. They are also defined, excluding the portions of surfaces subject to shading by physical elements. The other two parameters of interest for this study are photovoltaic technology and module tilt. Regarding the first one, the three most common types were chosen, namely polycrystalline, monocrystalline and thin film. These typologies are mainly made up of silicon and have been the subject of many researches. Moreover, even if they have already been widespread for several years, they are not obsolete, as they are continuously improving and develo** (Jacobson et al. 2017; Rotilio et al. 2015). The main characteristics have been summarized in Table 1.

Table 1 Summary of the three technologies used
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With regard to the second variable, three hypothetical locations have been identified in the north, south and central part of Italy. Thanks to the definition of these variables, different scenarios have been identified: for each one the energy outputs have been calculated, i.e., the System power (kW) and the System output energy (kWh/year). Subsequently, the economic analysis was carried out in Sect. 3.2 and a summary matrix developed.

Economic analysis

For the economic valuations, the discounted cash flow (DCF) method was used. In particular, the two indicators of the net present value (NPV) and the discounted payback period (DPBT) were estimated. The DCF method is a valuation technique useful for understanding the convenience of an investment and is commonly used to compare costs and revenues that occur at different time points. “This technique involves the time value of money (TVM), which is based on the fact that the money available at the present time is worth more than the identical sum in the future” (Lee et al. 2020). NPV is the difference between the present value of cash inflows and the present value of cash flows out over a period of time. DPBT represents the time required for negative cash flows to be repaid by discounted positive cash flows. The following three sub-paragraphs identify the revenues and costs of the PV system and, finally, the description of the economic model is presented.

Revenues

Revenues from a PV plant can be summarized in the following items:

  • lower energy bill, which, therefore, identify savings in the bill;

  • incentives as defined in the FER1 decree;

  • revenue obtained from the recycling of the photovoltaic panel.

The first item concerns the unsupported cost of purchasing electricity since, in the event of a plant, the electricity is produced and self-consumed; this cost can be interpreted as revenue. The second item relates to the remuneration of the electricity produced by the PV plant on the basis of the FER1 decree.

The last item concerns the revenue from the recycling of crystalline silicon photovoltaic panels. According to (Markert et al. 2020), the total costs for recycling crystalline silicon photovoltaic panels are given by the following formula:

$${\text{Total cost of PV recycling}} = \sum {\text{Private Cost}} + \sum {\text{External Cost}} - \sum {\text{Benefits}}$$
(1)

This cost item amounts to USD 6.72 per square meter of c-Si PV module. On the other hand, external costs are the ones related to environmental damage resulting from pollutants released in the meantime: the recycling process, the transport and the incineration. The total of external cost is 5.71 USD per square meter of c-Si PV module. Finally, benefits are the positive cash flows deriving from materials and recovered energy and are equal to 13.62 USD per square meter of c-Si PV module. Therefore, by solving Eq. (1), the revenue from the recycling of panels is obtained and it is equal to 1.19 USD per square meter of c-Si PV module.

Costs

The costs associated with a PV plant can be classified into:

  • Investment cost;

  • Operating costs.

The investment cost represents the total cost of all the works that have to be carried out for the construction of the PV plant. This cost depends on the technology used: for the polycrystalline the investment cost is 960 €/kW, for the monocrystalline, it is 986 €/kW and for the thin film it is 1147 € / kW. These cost items were derived from (Anuta et al. 2019) and confirmed by market surveys.

On the other hand, operating cost is the costs to be faced to ensure the proper functioning of the system. They consist of the maintenance cost, the cost of replacing the inverter, the cost of insurance. Specifically, the annual maintenance cost of a PV system is set equal to 1% of its investment cost (Gholami and Røstvik 2020), the cost of replacing the inverter is equal 17% of the investment cost of the PV plant and occurs every 10 years of plant operation (Gholami and Røstvik 2020) and the annual insurance cost is 1% of the investment cost of the PV plant (Gholami and Røstvik 2020).

Description of the economic model

The model adopted for estimating the NPV and DPBT of a PV plant is described below:

$${\text{NPV}} = \mathop \sum \limits_{t = 0}^{N} \frac{{\left( {R_{ t} - C_{t} } \right)}}{{\left( {1 + i} \right)^{t} }}$$
(2)
$$\mathop \sum \limits_{t = 0}^{{{\text{DPBT}}}} \frac{{\left( {R_{ t} - C_{t} } \right)}}{{\left( {1 + i} \right)^{t} }} = 0$$
(3)

With:

\({\text{NPV}}\): Net present value (€).

DPBT: Discounted payback period (years).

\(R_{ t}\): Total revenue (€).

\(C_{t}\): Total cost (€).\(i\): Discount rate\(t\): Time of cash flow

\(R_{ t}\), as can be seen from Eq. 4, is equal to the sum of:

  • electricity costs not incurred, which, consequently, are a saving in the bill (\(R_{t}^{{\text{electric energy}}}\));

  • incentives regulated by the decree FER1 (\(R_{{{\text{FIT}},t}}^{{{\text{subsidies}}}}\) and \(R_{{{\text{bonus}},t}}^{{{\text{subsidies}}}}\));

  • revenue obtained from the recycling of the photovoltaic panel (\(R_{{{\text{PV recycling,}}t}}\)).

    $$R_{t} = \left\{ {\begin{array}{*{20}c} 0 & {t = 0} \\ {R_{{{\text{FIT}},t}}^{{{\text{subsidies}}}} + R_{t}^{{\text{electric energy}}} + R_{{{\text{bonus}},t}}^{{{\text{subsidies}}}} + R_{{{\text{PV recycling}},t}} } & {1 \le t \le N} \\ \end{array} } \right.$$
    (4)

Specifically, \(R_{{{\text{FIT}},t}}^{{{\text{subsidies}}}}\) subsidies are paid on the basis of the net electricity produced and fed into the grid by the photovoltaic system for a period of 20 years:

$$R_{{{\text{FIT}},t}}^{{{\text{subsidies}}}} = {\text{SOE}}_{t} \cdot \left( {1 - Q_{{{\text{selfcons}}}} } \right) \cdot S_{{u, {\text{FIT}}}}$$
(5)

where: \({\text{SOE}}_{t}\) System Output energy at time t (kWh/year).

\(Q_{{{\text{selfcons}}}}\): % of energy self-consumption.

\(S_{{u, {\text{FIT}}}}\): Unitary FIT subsidies (€/MWh).

\(R_{t}^{{\text{electric energy}}}\) represents the unpaid electricity purchase price:

$$R_{t}^{{\text{electric energy}}} = {\text{SOE}}_{t} \cdot Q_{{{\text{selfcons}}}} \cdot p_{t}^{e}$$
(6)

where: \(p_{t}^{e}\): Energy price at time t (€/kWh)

For power plants up to 100 kW, an additional bonus \(S_{{u, {\text{bonus}}}}\) equal to 10 €/MWh is recognized as long as the self-consumed energy rate is greater than 40% of the plant's net production. Therefore, revenues by bonus subsidies \(R_{{{\text{bonus}},t}}^{{{\text{subsidies}}}}\) is given:

$$R_{{{\text{bonus}},t}}^{{{\text{subsidies}}}} = {\text{SOE}}_{t} \cdot Q_{{{\text{selfcons}}}} \cdot S_{{u, {\text{bonus}}}}$$
(7)

\(R_{{{\text{PV recycling}},t}}\) is the revenue from the recycling of crystalline silicon photovoltaic panels:

$$R_{{{\text{PV recycling}},t}} = \left\{ {\begin{array}{*{20}c} 0 & {t \ne N} \\ & \\ {{\text{Recy}}_{u} \cdot {\text{sqm}}} & {t = N} \\ \end{array} } \right.$$
(8)

With:

\({\text{Recy}}_{u}\): Unitary revenue of PV recycling (€/m2).

\({\text{sqm}}\): Surface of PV module (m2).

The term \(SOE\) decreases over time due to the reduction in the efficiency of the plant through the term dse (% of system efficiency annual decrease) according to the expression (9):

$${\text{SOE}}_{t + 1} {\text{ = SOE}}_{t} \cdot \left( {1 - {\text{dse}}} \right)$$
(9)

While the term \(p^{e}\) increases over time due to inflation through the coefficient \({\text{inf}}^{e}\), according to the expression (10):

$$p_{t + 1}^{e} = p_{t}^{e} \cdot \left( {1 + {\text{inf}}^{e} } \right)$$
(10)
$$p_{t + 1}^{e} = p_{t}^{e} \cdot \left( {1 + {\text{inf}}^{e} } \right)$$
(10)

The costs of a plant PV (\(C_{ t}\)), as can be seen from Eq. (11), are given by the sum of:

  • Investment cost (\(I_{t}\));

  • Maintenance cost (\(C_{m,t}\));

  • Replacement cost (\(C_{r,t}\));

  • Insurance cost (\(C_{i,t}\)).

    $$C_{ t} = I_{t} + C_{m,t} + C_{r,t} + C_{i,t}$$
    (12)

Specifically, \(I_{t}\), \(C_{m,t}\), \(C_{r,t}\) and \(C_{i,t}\) are calculated respectively by Eqs. (12–15).

$$I_{t} = \left\{ {\begin{array}{*{20}c} {I_{ u} \cdot P_{sy} } & {t = 0} \\ & \\ 0 & {t \ne 0} \\ \end{array} } \right.$$
(13)

where: \(I_{ u}\): Unitary investment cost (€/kW).

\(P_{{{\text{sy}}}}\): System power (kW)

$$C_{ m,t} = \left\{ {\begin{array}{*{20}c} {{\text{Perc}}_{Cm} \cdot I_{0} \cdot \left( {1 + {\text{inf}}} \right)} & { t = 1} \\ & \\ {C_{m, t - 1} \cdot \left( {1 + inf} \right)} & {2 \le t \le N} \\ \end{array} } \right.$$
(14)

where:

\({\text{Perc}}_{Cm}\): % of maintenance cost

$$C_{ r,t} = \left\{ {\begin{array}{*{20}c} {{\text{Perc}}_{Cr} \cdot I_{0} } & { t = 10,20,30 {\text{V }}t{ < }N } \\ & \\ 0 & {t \ne 10,20,30} \\ \end{array} } \right.$$
(15)

where:

\({\text{Perc}}_{Cr}\): % of replacement cost

$$C_{ i,t} = \left\{ {\begin{array}{*{20}c} {Perc_{Ci} \cdot I_{0} \cdot \left( {1 + {\text{inf}}} \right)} & { t = 1} \\ & \\ {C_{i, t - 1} \cdot \left( {1 + {\text{inf}}} \right)} & {2 \le t \le N} \\ \end{array} } \right.$$
(16)

where: \({\text{Perc}}_{Ci}\): % of insurance cost.

The case study

The building chosen as a case-study and analysed on the basis of the model described in the previous paragraph, is located in the Central Italy, in Abruzzo Region and specifically in a village called San Demetrio ne' Vestini (Annibaldi et al. 2020). It is a school building belonging to the Municipal Administration. It has an "L-shaped” plan, it is isolated and distributed on three different floors (Fig. 3). It was chosen since it is particularly energy-intensive. Given the high availability of south-facing surface (azimuth = 0°) and the almost non-existence of shaded areas, the roof was chosen as the constructive element to install the photovoltaic modules. The subsequent analysis of the solar potential made it possible to identify the portions of the surface suitable for the installation of photovoltaics, as they are characterized by a good value of incident solar radiation.

Fig. 3
figure 3

Case study. The figure shows the plan of the L-shaped roof in its context (footpaths, trees, steps,..). At the top, in the central part of the figure, there is the excerpt of the south elevation of the building

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The suitable surface was equal to 324 square metres. The choice was made on the basis of many aspects including those of architectural integration, in order to technically and aesthetically integrate the system (Kuhn et al. 2021). Furthermore, all the analysis was replicated in different locations, assuming that the building was located in two other different cities, one located in northern Italy, i.e., Milan, the other located in the south part of Italy, i.e., Palermo (Fig. 4) and assuming the roof renovation, including the inclination.

Fig. 4
figure 4

Monthly in-plane irradiation for fixed angle. From top to bottom: Milan, San Demetrio ne Vestini, Palermo (PVGIS. https://re.jrc.ec.europa.eu/pvg_tools/en/#PVP)

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Regarding the tilt, the three most common angles of inclination have been chosen: 15°, 30° and 45°. On the other hand, in this research, the polycrystalline, monocrystalline and thin film technologies were analysed. All the technical parameters are summarized in Table 2.

Table 2 Technical parameters
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In summary, the PV system was defined as a function of:

  • geographical position of the plant (north, central and south of Italy);

  • plant technology used (Monocrystalline, Polycrystalline and Thin film);

  • roof inclination (15°, 30°, 45°).

Considering all the possible combinations of these parameters, it was possible to obtain 27 different scenarios, obtaining a wide range of solutions to evaluate the variation of the photovoltaic producibility for the most commonly used technologies.

The model described in paragraph 3.2.3 requires the definition of input variables related to economic analysis. These parameters, and how they are defined, are shown in Table 3.

Table 3 Economic parameters
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With reference to the service life, this value, in analogy to what is established in the FER1 Decree, was set at 20 years (N = 20).

In the case-study, \(Q_{{{\text{selfcons}}}}\) is equal to 35% because according to (Gautier et al. 2019), in the absence of specific actions and/or investments, on average 35% of the electricity is self-consumed. This implies that, in the specific case, \(S_{{u, {\text{bonus}}}}\) is equal to zero and, consequently, \(R_{{{\text{bonus}},t}}^{{{\text{subsidies}}}}\) is zero.

In thin film PV modules, since the power of the system is 19.4 kW and, therefore, less than 20 kW, \(S_{u, FIT}\) is equal to zero. As a result, also \(R_{{{\text{FIT}},t}}^{{{\text{subsidies}}}}\) is zero unlike the monocrystalline and polycrystalline modules, where \(S_{{u, {\text{FIT}}}}\) is equal to 105 €/MWh since the power of the plant is respectively 38.9 kW and 34 kW.

Results and discussion

Technical results and discussion

According to the geographical position of the plant, the plant technology and the roof, 27 scenarios characterized by different levels of System Output energy (kWh/year) and System power (kW) have been developed. Figure 5 shows an example of \({\text{SOE}}_{0}\) for some specific cases, while Fig. 6 shows in graphic form all the \({\text{SOE}}_{0}\). Finally, Fig. 7 summarizes all the energy results of all 27 scenarios. This matrix illustrates all the technical and economic outputs taken into account in the study. The details are discussed below.

Fig. 5
figure 5

System output energy. In order from top to bottom: 30° monocrystalline in Milano, San Demetrio ne Vestini (AQ) and Palermo

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Fig. 6
figure 6

System output energy (M: Monocrystalline; P: Polycristalline, T: Thin Film)

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Fig. 7
figure 7

Energy results of PV plants and profitability of PV plant in 27 scenarios

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The study allows to identify the optimal scenario, regardless of the economic assessments illustrated in the next paragraph. From a technical point of view, the solution that optimizes total production is the one of the scenarios that places the plant in Palermo, southern Italy, with an inclination angle of 30° and with monocrystalline technology. In fact, in this case, there is a producibility equal to 59,283 kWh/year. On the other hand, the absolute worst solution is the one that sees the plant located in Milan, northern Italy, with an angle of 15° and Thin Film technology, for a producibility of 21,313 kWh/year.

Looking at Fig. 6, it is evident that the monocrystalline technology is the one that allows the maximization of the producibility of the system in all three cities, followed by the polycrystalline and thin film, according to the different inclination angles. In all three different sites, the \({\text{SOE}}_{0}\) gap between the last two solutions is considerable, since it oscillates between 15,900 and 22,000 kWh/year.

Therefore, the three most favourable scenarios are those of the plant located in Palermo, with monocrystalline technology, followed by the one located in San Demetrio né Vestini, which provides for the use of the same technology. Below there are the scenarios related to the use of polycrystalline technology in Palermo and the ones in San Demetrio né Vestini, that use the same technology.

Regarding the location, the site that allows to maximize the producibility is the one located in the city of Palermo, or in general the south of the nation, since it is characterized by higher values of incident solar radiation compared to the rest of Italy. Finally, with regard to the inclination, the best solution between the three cases is the one with the angle equal to 30°.

Economic results and discussion

The results achieved make it possible to analyse the financial feasibility of the energy redevelopment project based on 27 scenarios identified as previously described. This financial feasibility is assessed through the NPV and DPBT indicators:

  • If NPV > 0 the PV plant will give some economic utility;

  • If NPV = 0, the investment will only serve to repay the disbursements and interest at rate i in N years;

  • If NPV < 0 the PV system will give an economic loss.

On the other hand, DPBT, unlike the NPV which measures the profitability of the investment, have to be compared with the time that investors are willing to wait for the investment to be repaid.

The NPV and DPBT values for the 27 scenarios are shown in Fig. 7. The profitability of the PV plant is verified in 21 out of 27 cases. In particular, the value of the NPV varies from − 7717 € to 68,004 €, while the DPBT index varies from 6 years to > 20 years. The greatest profitability is obtained in the case-study located in Southern Italy with a monocrystalline technology and an inclination angle of 30°. Moreover, any case-study located in Southern Italy has a greater profitability than the respective case-studies located in Northern and Central Italy. This happens because the PV plant located in Southern Italy has a greater efficiency and, therefore, a greater \({\text{SOE}}_{0}\). However, even in the case-study located in Southern Italy with thin film technology, the profitability of the plant is not high and the negative cash flows are repaid by positive cash flows at the end of the plant life (20 years). In the other case-studies located in Northern and Central Italy with a thin film technology, the PV plant will give an economic loss. For the PV plant located in the northern part of Italy, the NPV value is equal to − 7717 € with an inclination angle of 15°, − 7053 € with 30° and − 7532 € with 45°. On the other hand, for plants located in the central part of Italy NPV is equal to − 2576 € (15°), − 1652 € (30°) and − 2219 € (45°). Clearly, in all these cases the time required for negative cash flows to be repaid by positive cash flows is > 20 years. There are several reasons for this result. First of all, using thin film technology, the PV system has a power (19.4 kW) that does not allow access to the incentive mechanisms provided for by the FER1 decree. Thus, in these cases \(R_{{{\text{FIT}},t}}^{{{\text{subsidies}}}}\) is zero. This means that the presence of incentives is a relevant condition for the plant profitability. In addition, the efficiency of the PV system decreases over the years at a rate equal to dse, producing a lower amount of energy than the previous year. For the remaining technologies (monocrystalline and polycrystalline), the value of the NPV is always greater than zero and ranges from 31,251 € (polycrystalline system with 15° inclination located in Northern Italy) to 68,004 € (monocrystalline system with 30° inclination located in Southern Italy). Therefore, these are quite profitable investments. This is also confirmed by the DPBT index. In fact, the time needed to recover the disbursements reaches a maximum of 9 years. Nevertheless, leaving only the technology to change and maintaining the other assumptions unchanged, it is clear that monocrystalline technology leads to greater economic profitability. This occurs despite the fact that monocrystalline technology has a higher investment cost. In fact, for the monocrystalline and polycrystalline technology \(I_{t = 0}\) is respectively 38,355 € and 32,640 €. Therefore, the higher investment cost is offset by greater plant efficiency demonstrated by the value of \({\text{SOE}}_{0}\).

Under equal conditions in terms of technology used and plant location, an inclination angle of 30° allows greater economic profitability. In this case, a greater economic profitability is due exclusively to technical choices that allows to maximize the output of the plant. In fact, for an inclination angle of 30°, \({\text{SOE}}_{0}\) assumes the highest value on equal hypotheses.

Furthermore, it becomes interesting to evaluate the investment of the PV plant in the absence of the national FER1 decree. Clearly, the features of the 27 case-studies remain unchanged since the goal is to see how the values of NPV and DPBT change, considering only \(R_{t}^{{\text{electric\;energy}}}\) and \(R_{{{\text{PV recycling}},t}}\). as revenue. The results are shown in Fig. 8. For northern Italy, the NPV values are always less than zero and, consequently, the recovery time is greater than the useful life of the plant. For central Italy, NPV takes negative values only using thin film technology, even if very low NPV values are reached for Monocrystalline and Polycrystalline technologies (the maximum value does not exceed 5561 €). In this case, the best value of DPBT is 17 years. Finally, in southern Italy, NPV ranges from € 237 to € 10,302 and DPBT varies between 16 and 20 years.

Fig. 8
figure 8

Profitability of PV plant in 27 scenarios in the absence of a national decree (FER1)

Full size image

So, in the light of this further analysis, the above may be confirmed: the economic support measures regulated by the national FER1 decree are fundamental to guarantee the profitability of a PV plant located in Italy. In fact, many authors point out that among the main barriers to the adoption of systems exploiting renewables there are the initial investment costs, the lack of incentives, maintenance costs and financial rates imposed by banks (Donaduzzi Rigo et al. 2019; Qureshi et al. 2017). Overcoming one of these barriers through the introduction of incentives seems significant. This happens also in the light of some research showing that there is a positive correlation between the use of renewables and the deployment of government incentives (Washburn and Pablo-Romero 2019). In view of what has been argued, it is considered that government initiatives that go in the direction of supporting renewables are therefore crucial for Italy to comply with the European 'New Green Deal'. Furthermore, measures similar to the Fer 1 decree would reduce the Levelized Cost of Energy (LCOE), which, due to longer authorisation processes, is higher in Italy than in other European countries.

Conclusions

In the context of European policies aimed at promoting the energy transition and increasing the use of renewables, incentive solutions have demonstrated to be a strategic tool for many countries (Bai et al. 2021). Among these, Italy has issued the so-called “FER1” which provides economic support for investments in renewables and it is an interesting regulatory tool for the research illustrated in this paper. In fact, within the Italian context and in compliance with the FER1 decree, the paper shows how the productivity of a photovoltaic system, and, at the same time, the economic feasibility, change as the Italian regions and the photovoltaic technology used vary. In this way, there is the need to analyse the effective validity of the incentive tools promoted throughout the national context, according to the main parameters that affect the productivity of a photovoltaic system. In this regard, the research developed a methodology based on technical and economic standards, validated through some case-studies.

The results of the research have shown that the FER1 decree is suitable to promote solar photovoltaics in the southern regions but not for the northern ones, where it would probably be proper to encourage other types of renewables, such as hydroelectric and geothermal. Besides, the optimal tilt was equal to 30° but this type of slope is not very common in the south where low slopes and flat roofs prevail. Therefore, the optimal condition presented in the study will not often be reached due to smaller inclination angles; nonetheless, they still allow the profitability verification of the system. Concerning the technology, it has been shown that monocrystalline leads to greater economic profitability, despite having a higher investment cost. So, its dissemination should be promoted. On the other hand, thin film technology should not be chosen under this decree as it will hardly allow access to incentives.

The incentives diffusion plays a key role in order to facilitate the energy transition, but the study showed how it would be necessary to better calibrate political choices, according to the following actions:

  • to focus on territorial and morphological features and peculiarities, in the case of a nation with important differences between north and south, as Italy is;

  • to consider the combination of other incentives related to building heritage rehabilitation and energy efficiency in terms of building envelope and plant systems. In fact, according to some authors (Berry 2019) such solutions are able to achieve greater long-term effects.

Moreover, in order to better achieve national energy goals, including by improving the incentive system, a series of actions should be implemented. In particular:

  • to introduce a substantial simplification of the authorisation process. This is actually a problem that involves many administrative areas in Italy, but it is even more critical at this historic moment in time when the PNRR demands quality and speed of execution, especially in the construction industry. Also for this reason, national initiatives such as the Superbonus 110, despite the advantageous incentive conditions, have not led to the desired results;

  • to extend the incentive system should also to revam**/repowering existing plants, as well as to ground-mounted plants on abandoned, unproductive and uncultivated land, under the condition that the systems are able to have almost zero impact on the land itself.

Finally, combining authorisation aspects with industrial profiles, as argued at length in the article, it would be appropriate to think about mechanisms that take into account geographical differences in terms of producibility and hence economics. In fact, burden sharing has to leverage economic factors. This is also to allow a more harmonious development of the territory and to avoid congestions in zones and network infrastructures.

The research will be expanded in the future by the authors, through the combination of several types of incentives and the relative analysis, in the short and long term.

The importance of the research illustrated lies in the fact that it could be easily replicated in other buildings in different locations and in different conditions. It could easily support the decision-making process of designers and administrators to make energy transition choices, in order to promote renewables.