A Methodology to Assess and Improve the Impact of Public Energy Policies for Retrofitting the Building Stock: Application to Italian Office Buildings

It is well known that the deep energy renovation of the building stock is fundamental to fight some of the most urgent problems of our generation, such as climate change and energy poverty [1]. However, it is a very arduous issue that involves two different perspectives, normally divergent: the public and the private ones [2]. The public perspective represents the interests of the collectivity (i.e., the state), which aims at minimizing energy consumption and polluting emissions. The private perspective represents the interests of building owners and/or tenants, who aim at minimizing the economic expenditure. At EU (European Union) level, in order to harmonize the two perspective, the Energy Performance of Buildings Directive (EPBD) recast (2010/31/EU) [3] and the Delegated Regulation (EU) No. 244/2012 [4] recommend the cost-optimal analysis to address building energy retrofit. This kind of analysis aims at minimizing global (i.e., lifecycle) costs (hereinafter denoted with GC) due to energy uses, considering both investment and operation costs. The cost-optimal energy retrofit is given by the package of energy retrofit measures (ERMs) that minimizes GC. This ensures a good trade-off between the public and private perspectives [5]. Nevertheless, the cost-optimal analysis cannot be applied to each building of a wide stock because it is quite computationally expensive. Indeed, several building retrofit scenarios should be investigated by means of reliable but time-consuming BPS (building performance simulation) tools [6]. Therefore, the European regulations [3, 4] recommend to apply the cost-optimal analysis only to representative buildings, the so-called reference buildings. These should represent all building categories in which the national building stocks can be subdivided. The cost-optimal retrofit solutions that are identified for the reference buildings are, then, considered valid for the members (buildings) of the represented categories [5, 7]. Definitely, this approach implies acceptable computational times and efforts because only a few buildings, i.e., the reference buildings, are investigated via the cost-optimal methodology. Nevertheless, the achieved outcomes are not robust for all categories’ members because a reference building is able to represent reliably only a very restricted part of a category, as shown in [2]. In this regard, the reference building approach can be replaced by the representative building sample (RBS) approach, proposed by the same authors in [2]. This latter employs a group of buildings (i.e., the RBS), instead of a single building, to represent a building category, thereby capturing the main heterogeneous energy characteristics of the category’s members. Thus, it ensures more reliable and representative results, which can be dependably extended to a wide segment of the category.

All told, the cost-optimal analysis is a powerful tool that yields the minimization of building lifecycle costs, thereby favouring the private perspective. Furthermore, it is advantageous also for the public perspective, since it normally implies significant energy savings and polluting emissions’ reductions [8, 9]. However, effective and well-thought-out energy policies [10] that provide financial incentives to ERMs are necessary in order to properly direct the cost-optimal solutions towards the reduction of buildings’ environmental impact. In this regard, several energy polices were enacted to support building energy retrofit at national and international levels. For instance, current Italian laws provide public financial incentives for ERMs addressed to both building thermal envelope and energy systems [11]. Definitely, assessing the impact of such policies on the energy performance of wide building stocks is highly worthwhile, because the implementation of the supported ERMs can ensure huge energy, economic and environmental benefits at large scale. The arduous issue is that when a whole building stock is explored, instead of single buildings, the assessment of energy performance and retrofit scenarios is much more complex.

 In the proposed study, this issue is tackled by using the aforementioned RBS approach, which is implemented within the methodology SLABE that was presented by the same authors in [2]. This performs a reliable analysis of energy retrofit scenarios for building categories. SLABE is here updated to address the energy retrofit of a wide building stock, including different categories, by identifying the cost-optimal solutions. The outcomes are exploited to evaluate the effectiveness of energy policies and to propose potentials improvements in order to harmonize the two mentioned perspectives. EnergyPlus [12] is used as BPS tool, and combined with MATLAB® [13] for data analysis and processing. For demonstration purposes, the methodology is applied to the Italian office building stock.

The Italian office building stock is investigated by applying the proposed methodology. In this regard, the ENEA (Italian national agency for new technologies, energy and sustainable economic development) examined this stock by conducting a comprehensive statistical analysis of geometry, building envelope and HVAC systems [14]. Such analysis is here exploited to set the energy characteristics of existing buildings. The explored stock includes about 64’900 units.

3.1 Current state of the building stock

The building stock covers six Italian climatic zones, namely A, B, C, D, E and F, which differ for the value of heating degree days, HDDs (see Table 1). The climatic zone A is not considered because it includes only two municipalities, and thus it does not provide representative outcomes. Therefore, the building stock is subdivided into five categories, which correspond to the climatic zones B, C, D, E and F, respectively. For each category, a typical weather data file [15] is chosen to be used in EnergyPlus simulations, as reported in Table 1.

Table 1. Characterization of the Italian main climatic zones

It is noticed that considering different climatic zones is fundamental because weather conditions highly affect building energy performance, as shown by Genco et al. [16]. Buildings are assumed to have a rectangular geometry and a structural frame in reinforced concrete, with vertical walls in hollow bricks. The roofs and floors are in mix brick-reinforced concrete, whereas the internal partitions are composed of 15 cm thick concrete [2]. These hypotheses allow to cover a broad segment of the investigated stock. Each of the five categories is characterized by means of the n = 21 parameters of Table 2.

Each parameter is defined by assigning a range of variability within the category and a type of probabilistic distribution. Such values are set according to the mentioned ENEA statistical study [14] and to the authors’ experience, as reported in Table 3, for representing a significant amount of Italian office buildings.

Table 2. Characteristic building parameters (p_i)

Table 3. Range of variability and type of distribution of the characteristic parameters (p_i) for the five building categories

*the mean value is the average of the range and the standard deviation is given by (range length)/4 in such a way to provide a confidence interval of 95% [2]

3.2 Investigated ERMs

Some common ERMs are proposed to reduce PEC. These are investigated for the whole building stock and are slightly differentiated depending on the climatic zone, and thus on the building category. The following ERMs are investigated:

1. Thermal insulation of the external vertical walls by installing an external layer of polyurethane (thermal conductivity = 0.028W/m K, density= 25 kg/m³, specific heat = 1340 J/kg K [5]) of thickness t_iv. For each building, t_iv is such to yield the limit (maximum) value of walls’ thermal transmittance (U_v) prescribed by Italian law to obtain public financial incentives (see Table 4). The investment cost (IC) of this ERM is equal to 500 – 3000 t_iv€ per m³ of polyurethane [2].
2. Thermal insulation of the roof by installing an external layer of polyurethane of thickness t_ic. For each building, t_ic is such to yield the limit values of roof thermal transmittance (U_r) reported in Table 4. The value of IC is 500 – 3000   t_ic € per m³ of polyurethane [2].
3. Installation of energy efficient windows with thermal transmittance (U_w) equal or lower to the limit values of Table 4. Thus, the following windows’ types, all with PVC frames, are proposed in function of the climatic zone:

• Zone B: argon-filled double-glazed windows (U_w = 2.4 W/m² K, SHGC = 0.76). The value of IC is 210 €/m²;
• Zones C and D: argon-filled double-glazed windows with low-emissivity (low-e) coatings (U_w= 2.0 W/m² K, SHGC = 0.69). The value of IC is 250 €/m²;
• Zones E and F: argon-filled triple-glazed windows with low-emissivity (low-e) coatings (U_w = 1.6 W/m² K, SHGC = 0.59). The value of IC is 300 €/m²;

Definitely, low-e coatings [17] imply a significant reduction of U_w, and so they are particular effective for cold climates.

4. Replacement of the primary heating systems with highly energy efficient devices. The values of thermal capacity (P_t[kW]) of such devices vary in function of the considered building and are set equal to those of the reference boilers (RBs). The following options are investigated:

• natural gas hot water condensing boiler (CB) with η equal to 1.05 and IC equal to 80   P_t + 1’900 € [2];
• air-source electric heat pump (AHP) with COP (coefficient of performance) equal to 3.7 at rated conditions (water temperatures = 45/50 °C, external air temperature = 7 °C); IC is equal to 150   P_t+ 5’000 € [2];
• ground-source electric reversible heat pump (GHP) with geothermal vertical probes; the COP in heating operation is equal to 4.5 [5] at rated condition and IC is equal to 700 P + 20’000 €, where P is the maximum value between heating and cooling capacity.

5. Replacement of the primary cooling systems with highly energy efficient devices. The values of thermal capacity (P_c [kW]) of such devices vary in function of the considered building and are set equal to those of the reference chillers (RCs). The following options are investigated:

• efficient air-cooled electric chiller (ACH) with EER equal to 3.2 at rated conditions (water temperatures = 12/7 °C, external air temperature = 35 °C) and IC equal to 150   P_c+ 5’000 € [2];
• water-cooled electric chiller (WCH) with cooling tower; the EER at rated conditions is equal to 5.5 and IC is equal to 250   P_c + 8’000 € [2];
• the mentioned GHP with EER in cooling operation equal to 6.2 [5].

Concerning the ERMs (d) and (e), when the AHP and ACH are coupled, the installation of a single reversible air-source heat pump is considered (AHP +ACH). It is noted that most values of IC have been taken from [2]. When not available, they have been obtained from direct quotations of suppliers.

Table 4. Limit (maximum) values of thermal transmittance (U^lim) for accessing to public financial incentives [11]

As for Italian energy policies, the ERMs (a), (b), (c) and (d) are supported by public financial incentives. In particular, a capital grant is accorded in ten years, covering the 65% of the investment cost [11]. However, the following limits subsist:

• the capital grant provided for the thermal insulation of the building envelope, and thus ERM (a) + ERM (b) +ERM (c), cannot exceed the threshold of 60’000 €;
• likewise, the capital grant provided for the replacement of the heating system, and thus ERM (d), cannot exceed the threshold of 30’000 €.

Therefore, the maximum value of public financial incentive is equal to 90’000 (accorded in ten years) per building. It is highlighted that, as concerns ERM (d), the incentive is provided also for reversible heat pumps, and thus AHP + ACH and GHP. Conversely, the other cooling systems are not supported by public grants, but they are nevertheless investigated because they can imply significant PEC savings, especially in the warmest climatic zones and in presence of envelope thermal insulation (risk of summer overheating [18]).

Finally, the ERMs introduce e = 5 discrete parameters, reported in Table 5. Thus the renovated building stock is defined by n + e = 26 characteristic parameters.

Table 5. Parameters associated to the ERMs

The paper proposes a methodology to investigate the energy retrofit of a wide building stock. The main goals are:

• detecting the cost-optimal energy retrofit solutions and the potential energy savings and polluting emissions’ reductions;
• assessing the effectiveness of current energy policies in supporting energy retrofit measures (ERMs);
• providing guidelines for increasing the effectiveness of such energy policies.

The methodology subdivides the investigated building stock into homogeneous categories, each of them characterized by the same use destination, construction type and climatic location. Each category is represented by generating a representative building sample (RBS) through Latin hypercube sampling. Then, the energy performance and the retrofit potentials of the RBSs are assessed by means of the automatic coupling between MATLAB® and EnergyPlus.

For demonstration purposes, the Italian office building stock has been investigated and subdivided into five categories, corresponding to the main Italian climatic zones, namely B, C, D, E, and F. The implementation of common ERMs, most of them financially supported by Italian energy policies, has been explored. The following main outcomes have been achieved concerning the energy retrofit of the mentioned categories:

• Category B: in presence of current incentives, the cost-optimal retrofit package (for most category’s members) includes the mere installation of an air-source reversible heat pump. Present energy policies are effective, but they can be improved by providing a financial support to the installation of efficient air-cooled chillers.
• Category C: in presence of current incentives, the cost-optimal retrofit package for most category’s members includes the installation of an air-source reversible heat pump and the thermal insulation of external walls and roof. Present energy policies are very effective.
• Category D: in presence of current incentives, the cost-optimal retrofit package includes the installation of a condensing boiler and the thermal insulation of external walls and roof. Present energy policies can be improved by reducing the incentives addressed to condensing boilers and roof thermal insulation and offering a financial support to efficient air-cooled chillers. This would imply further potential energy savings and polluting emissions’ reductions, per building, of around 12.3 MWh/a and 4.0 tCO2-eq/a, respectively.
• Category E: in presence of current incentives, the cost-optimal retrofit package includes the installation of a condensing boiler and the thermal insulation of external walls and roof. Present energy policies can be improved by reducing the incentives addressed to condensing boilers and thermal insulation of the opaque building envelope, and offering a higher financial support to energy-efficient windows and ground-source reversible heat pumps. This would imply further potential energy savings and polluting emissions’ reductions, per building, of around 59.4 MWh/a and 11.3 tCO2-eq/a, respectively.
• Category F: in presence of current incentives, the cost-optimal retrofit package includes the installation of a condensing boiler, the thermal insulation of external walls and roof, the installation of energy-efficient windows. Present energy policies can be improved by reducing the incentives addressed to condensing boilers and thermal insulation of the opaque building envelope and offering a higher financial support to ground-source reversible heat pumps. This would imply further potential energy savings and polluting emissions’ reductions, per building, of around 49.3 MWh/a and 7.0 tCO2-eq/a, respectively.

The application of the proposed guidelines would highly increase the effectiveness of Italian energy policies that support building retrofit, thereby producing huge energy, economic and environmental benefits at large scale. However, these guidelines are generic, whereas a throughout and comprehensive definition of energy policies requires to solve a complex multi-objective optimization problem, in order to find the optimal trade-off between the public and private perspectives. Further studies will carefully address this optimization problem by employing multi-objective optimization algorithms, such as genetic algorithms [5] or particle swarm optimization [21]. Finally, it is highlighted that a rigorous investigation of the energy performance of building stocks should also consider urban scenarios and inter-building effects [22]. Future studies will take into account this issue, too.