A framework for the cost-optimal design of nearly zero energy buildings (NZEBs) in representative climates across Europe

Accepted Manuscript A framework for the cost-optimal design of nearly zero energy buildings (NZEBs) in representative climates across Europe Delia D'Agostino, Danny Parker PII:

S0360-5442(18)30248-2

DOI:

10.1016/j.energy.2018.02.020

Reference:

EGY 12320

To appear in:

Energy

Received Date: 21 August 2017 Revised Date:

20 November 2017

Accepted Date: 5 February 2018

Please cite this article as: D'Agostino D, Parker D, A framework for the cost-optimal design of nearly zero energy buildings (NZEBs) in representative climates across Europe, Energy (2018), doi: 10.1016/ j.energy.2018.02.020. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT A framework for the cost-optimal design of Nearly Zero Energy Buildings (NZEBs) in representative climates across Europe Delia D'Agostino a*, Danny Parker b a

European Commission, Joint Research Centre (JRC), Directorate C–Energy, Transport and Climate, Energy efficiency and Renewables Unit, Via E. Fermi 2749, I-21027 Ispra (VA), Italy b Florida Solar Energy Center, University of Central Florida.

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Corresponding author, Tel: +39.0332.783512, e-mail address: [email protected]

Abstract

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Combining cost-optimal solutions to reach nearly zero energy buildings (NZEBs) in compliance with European policies is an ongoing challenge. Energy consumption can be reduced evaluating different configurations at the design stage and implementing the most appropriate solutions according to the building and the location. This paper develops a simulation-based optimization framework of cost-optimal choices and energy efficiency measures for new buildings. It combines energy and cost simulations using a sequential search technique to find the most effective combination of energy efficiency and renewable energy measures starting from a base configuration. The method is applied to a residential building prototype, taking into consideration hourly climatic data, construction methods, cost data and energy consumption. A cost database and a library of potential measures, related to envelope, appliances and systems, have been established and used within the optimization process. The potential impact of climate change on the estimated cooling loads has been included in the calculations. The paper shows the feasibility of European requirements for new NZEBs located in different cities. It shows how to best achieve the NZEB design at the lowest cost in 14 locations across Europe. Results highlight how the cost-optimal measures vary with climate and how in each location final selected options differ. Insulation and building tightness appear essential in colder climates, while efficient appliances and lighting are key measures in warmer locations. A key finding of the research is that a source energy reduction of 90% and beyond is feasible for new constructions in all locations. Results also show how efficient lighting and appliances considerably impact the building energy performance. The importance of integrating renewables and energy efficiency measures is confirmed as crucial to reach the NZEBs target.

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Keywords: nearly zero energy buildings (NZEBs), building simulation, energy and economic optimization, energy efficiency, energy consumption, technology measures, renewables.

1. Introduction

Accounting for around 36% of final energy demand, buildings have a huge impact on greenhouse gas emissions and climate change at global level [1]. At the same time, the building sector offers the largest potential of energy savings [2]. The European Union is focused on limiting building environmental impact through specific policy actions [3]. The recast of the Energy Performance of Buildings (EPBD) Directive, the Energy Efficiency Directive (EED) and the Renewable Energy Directive (RED), set out the requirements for achieving ambitious savings in buildings [4] [5] [6]. One relevant regulatory obligation of the EPBD recast is that all new buildings have to be nearly zero-energy (NZEBs) by the end of 2020. Member States have to define what a NZEB is within their

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Lack of a standard systematic approach to derive cost-optimality Uncertainty of simulation model input Long computational time Missing information on costs, occupancy/lighting schedules Difficulty in problem definition Low models flexibility for exchange with design, construction, costs and optimization tools Difficulty in interpreting results that depend on many calculation factors Low integration of the process with industry Set consistent and comparable NZEBs targets.

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MANUSCRIPT national framework [7]. AsACCEPTED a common point, NZEBs combine a very high energy performance with renewable production to cover the energy needs in a building [8]. The Directive also establishes the assessment of cost-optimal levels related to minimum energy performance requirements leading to the lowest building costs. A methodology has to be followed to derive cost-effectiveness from a technical and economic perspective [9] [10]. After defining a reference building, a set of energy efficiency measures have to be selected and combined in packages to be compared. The relative energy consumptions are calculated through energy simulations together with the costs [11] [12]. The optimal configuration can be found in the lower part of the curve that reports global costs (€/m2) and energy consumption (kWh/m2y). In this way, the distance between the cost-optimal performance, leading to the minimum global cost over the calculation period, and the NZEB target can be assessed [13]. However, this approach cannot assure to find the absolute cost-optimal solution, because only a limited number of options and combinations are usually considered. Moreover, several parameters can alter the curve, among them geometrical features, technical systems, selected energy efficient measures, energy prices, discount rates, and costs [14][15][16][17]. A sensitivity analysis can limit the calculation variability, but the high number of independent parameters makes the cost-optimal research closer to an optimization problem [18]. An open debate concerns how to combine NZEBs requirements with the need to cover the involved investments and enhance the reduction in costs [6]. As a consequence, besides efforts to design new buildings with low energy demand and availability of renewables, combining NZEBs with cost-optimality remains challenging and in many cases still limited to a research level. The following common obstacles can be listed:

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Although different studies have highlighted that reaching the NZEBs target is achievable [19], it is not always be proven that the selected design choices are cost effective both from an environmental and economic prospective. Another open issue concerns the heterogeneous situation that characterizes European countries in relation to the cost-optimal assessment. As stated in recent cost-optimal evaluations, several interpretations of the procedure described within the EPBD framework have been adopted in Member States [20] [21]. Based on different assumptions and calculation methods, a lack of harmonization and used national references can be observed at European level. Moreover, the available cost-optimal studies deal with several building types, geometries and orientations [22]. This variability makes these studies not comparable as derived for different building types and level of detail in energy performance calculations, global costs and problem characterization. Other studies mainly focus on one climate and building, and results are limited at a case study level. Furthermore, the trade-off between the cost of incremental measures and renewable energy generation appears variable and still subject to debates [23]. As a result, information and data on energy savings, optimal solutions and costs, have not been harmonised across locations. Therefore, there is the need for comparable data and information to stimulate a large-scale diffusion of cost-optimal NZEBs in the market. This study aims at overcoming these issues

ACCEPTED MANUSCRIPT identifying primary energy levels and benchmarks for new buildings which can represent the cost-optimal and NZEB target across Europe. It provides consistent references obtained under common boundary conditions and calculation assumptions in representative European climates. 1.1 Building optimization

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A building design strategy depends on a complex interaction of factors including location, climate, costs, available resources and materials. Since the building design has a big impact on the environmental and economic life-cycle, it is important to assess the effectiveness of potential solutions in a comprehensive manner. Based on various algorithms and assumptions, a simulation-based optimization method can be used to derive the cost-optimal solution exploring several design options in more locations within a reduced computational time [24]. The optimization approach is based on a computer model running a building simulation programme coupled to an optimization engine [25]. An iterative method driven by optimization algorithms progressively solves the analysed problem. The solution is gradually approached until it is reached and it is established as the level that satisfies an optimality condition selected by the user [26]. Simulation-based optimization methods have increasingly revealed their effectiveness in decreasing energy consumption in buildings at the design stage [27] [28]. Thanks also to increased computer capacity over the last decade, there is a growing interest on these tools that are able to cover a wide range of applications [29] [30]. Different design variables can be analysed with the purpose of reducing energy consumption in buildings [31] [32]. Applications deal with exploiting the efficiency of HVAC systems, ventilation, and photovoltaic (PV) systems [33] [34], or on optimizing a single building component, such as windows or envelope [35] [36], internal comfort and relative humidity [37] [38]. Some applications also consider costs while optimizing high-performing buildings. Znouda et al. [39] optimized energy and costs in a Mediterranean climate building, while Bambrook et al. focused on an Australian low energy house [40]. Recent research developments include the integration of optimization tools within the NZEBs design [41]. Ferrara et al. [42] developed a simulation-based optimization model with the aim of increasing the analysed design options and dealing with a large number of simulations. They demonstrated the feasibility of the method in dealing with a large number of packages of measures while maintaining a manageable calculation scheme and minimizing the complexity of the global cost function. A simulation-based optimization approach has the advantage of evaluating more design options providing a better approximation of the cost-optimal configuration for the reference building [43] [44]. However, until now, there is a lack of studies that consider more climates obtaining comparable results from the simulations. Therefore, there is the need of increasing the studies able to identify the cost-optimal solution at a larger scale and across the variation of climate related challenges posed by European geography. 1.2 Technological measures for energy efficiency Important energy savings can be achieved through a proper combination of efficient technological measures. It is foreseen that technology costs are likely to be lower in reaction to more mature and larger markets in next years [45]. New technologies are becoming integral part of buildings, enabling more dynamic and interactive structures. An innovative market is also now conforming to the new building targets while promoting integrated solutions and packages [46]. The envelope is crucial to dynamically balance the interaction between indoors and outdoors [47]. Insulation can be implemented within the whole building or a part of it, such as external walls or roof [48]. Insulation materials are able to decrease the heat transfer through the

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envelope minimizing summer ACCEPTED heat gains andMANUSCRIPT winter heat losses. A frequent measure outside the envelope is the installation of external shading devices on windows [49]. Windows give a central contribution to the envelope performance. Low emissivity double or triple glazed windows can reduce energy consumption by more than 40% per m2 of glazed surface compared to single glazing. Films and coatings can be used on existing glazing to limit solar gains. Efficient mechanical and smart systems also contribute to a building energy performance [50]. Different systems can supply the need for heating, cooling, ventilation, and dehumidification. Measures for cooling include: ground source heat pumps, free cooling, district cooling, design orientation, selective glazing and centralized cooling plants with room air condition machines. Heat recovery plants can reduce HVAC energy consumption as they use heat exchangers to recover hot or cold air from ventilation exhausts and supply it to the incoming fresh air. Control automation and smart metering devices are among the most rapidly developing smart technologies. These devices allow the control of the energy demand/supply through ICT technologies considerably decreasing energy consumptions. Control systems relate to heating, cooling systems and ventilation, but are frequently applied to lighting (e.g. daylight control, occupancy control). Furthermore, they allow data collection for performance calculations and dynamic simulation modelling [51]. An efficient envelope, the use of high performing systems, passive heating and cooling techniques, a rational use of daylight to reduce lighting, and renewables are often part of the NZEBs design where new technologies are applied in a cost-optimal manner. The impact of such measures on the overall energy consumption demands for the development of specific tools and simulation techniques [52]. An appropriate building design that selects the most convenient and cost-effective measures according to the building needs and characteristics becomes crucial. 1.3 NZEBs and climate

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Climate is a key factor to choose specific technological choices. Member States present a wide range of building types and are characterized by variable technologies, climatic, and financial conditions. Depending on building and climate variability, different combinations of energy efficient measures can be found [53]. The use of a particular technology usually depends on available energy resources. For example, electricity might be more convenient for heating in one Member State (e.g. France) than in another where more biomass or gas is available (e.g. Finland). Buildings located in warm areas are subject to an elevated risk of internal overheating: for this reason, monitoring solar radiation and managing free gains are important to guarantee a high level of comfort [54]. Low-solar gain windows are more suitable in warm climates whereas high-solar gain windows are preferred in cold climates [55]. In this climate, multilayered walls with a low steady thermal transmittance are preferred with insulating materials having a low density and high thickness. This permits to reduce heating costs in cold winters, when internal heat preservation is the main goal [56]. Depending on climate, the envelope air-tightness can be improved to minimise air leakage. A minimum exchange of the air in the building, expressed as air changes per hour (ACH) for a given occupancy, is generally required in a building to maintain indoor air quality and comfort, although tight construction with enthalpy recovery can produce large savings [57]. Fundamental is the role of renewable energy sources in NZEBs. Evidence suggests that energy efficiency and renewable technologies can be sufficient to reach the NZEB target [58]. Both the EPBD and the RES Directives [4] [6] encourage Member States to introduce measures to increase the use of renewables depending on local conditions and climate. Renewable energy technologies mainly include PV, solar thermal, biomass, and geothermal. On-site renewables, such as solar thermal and PV systems, are more cost-effective in

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ACCEPTED MANUSCRIPT Mediterranean climates characterized by higher solar radiation [59]. However, PV systems are becoming ubiquitous and efficient throughout Europe (e.g. Germany), stimulating architects to integrate them as a building material. PV can produce electricity to cover direct consumption or delivery to the grid or local storage for a later consumption [60]. Solar thermal collectors contribute to space heating, hot water or thermal energy storage system and are common in Mediterranean countries (e.g. Spain, Italy, Greece). Biomass products (e.g. wood logs, pellets) used in heating systems and heat pumps (geo- and aero-thermal energy for ground-coupled and air-to-air heat exchange) are particularly diffused in Northern Europe cold climate countries (e.g. Finland, Sweden, Latvia). Apart from technologies, the energy performance of a building is influenced by several other factors, such as geometry and orientation, as well as building usage (occupancy behaviour and auxiliary gains) [61]. However, those parameters are not included in the building's evaluation to assess the energy performance. Given the variation of thermal conditions and solar insolation across Europe, a consistent analysis needs to consider how the wide climatic differences translate into differing opportunities and NZEBs solutions across regions. 2. Methodology

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The methodology developed in this study focuses on the optimization design of a residential building prototype located in 14 European locations. Given the potential complexity of housing across Europe, the research focused on a single family detached structure, although the methodology could be extended to other building types and orientations. The building has been modelled to evaluate, from an energy and economic prospective, several configurations obtained by a combination of different design options. EnergyPlus and TRNSYS have been used to carry out the dynamic simulations of the building together with the optimization software BEopt. The methodology identifies the lowest cost path to reach the NZEB target, which is defined as buildings saving 90% of source energy for all end uses in reference to the base case. Table 1 reports the main research assumptions while the flowchart in Fig.1 summarizes the methodological approach. Using a sequential search technique, the methodology is designed to find, starting from the base building, the most cost-effective set of energy efficiency measures related to envelope, appliances, and systems. After the base building set up, the optimization method evaluates the entire suite of energy efficient options and selects the option with the highest net present value savings, balancing investment and costs against energy and savings. Once the best performing option of one component is identified, this is incorporated in the following simulation to sequentially search for the cost-optimal building design depending on the location. Then the simulation is run again to identify the next best energy efficiency option with highest present value savings. Several measures are sequentially simulated and compared until the building design is optimized for all components. The research investigates how each technological measure impacts the building energy consumption. The process continues iteratively until the source energy savings are reached or until zero energy is achieved using renewables. The methodology is able to support the application of the costoptimal methodology with accuracy. 2.1 Building Modelling BEopt is an energy simulation software, developed by the U.S. National Renewable Energy Laboratory (NREL). It is able to include an economic evaluation within the optimization and identify optimal building design variants on the path towards the NZEBs target. This gives the advantage of selecting the design which best suits the financing available for a specific

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MANUSCRIPT housing project. It is possibleACCEPTED to evaluate both new and existing buildings and to consider how different measures influence the optimal choices in retrofits. The calculation in BEopt uses the hourly energy simulation EnergyPlus developed by the Lawrence Berkeley National Laboratory and the U.S. Department of Energy [62]. The model calculates energy savings with respect to a user-defined base case. It estimates hourly household heating, cooling, water heating and appliance loads. Fundamental building thermodynamics are estimated via finite difference conduction functions based on a multi-zone representation that allows a robust evaluation of transient thermal phenomena. The results of the simulations compared to real buildings measured data verified its potential to replicate measured energy use both in cold and in hot climates [63] [64]. The renewable energy production is evaluated using the transient simulation program TRNSYS [65]. Apart from PV, this tool predicts solar water heating performance relative to domestic hot water heating needs. It allows to directly assess the cost-effectiveness of energy efficiency measures as compared with the cost of renewable energy production. Even in cold climates, this method offers some advantages as it shows that it is possible to reach zero energy performance at relatively lower cost [66]. The simulation has been adapted to run in European climates by adding hourly International Weather for Energy Calculations (IWEC) weather data files [67], by converting to metric inputs, by adapting cost data to European values or by obtaining data for specific models and equipment [68]. Using similar inputs, favorable comparisons have also been produced relative to predicted energy use and measure savings against the Passivhaus Planning Package (PHPP) software [69]. The approach is consistent with the established methodology for NZEBs cost-optimality. 2.2 Energy and economic optimization

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In all locations, options are selected from available energy conservation measures (ECMs). A library of 200 energy efficient options has been defined with their characteristics as well as their specific costs, life expectancy, operation, maintenance, and replacement costs. The optimization searches for the most cost-effective options across a range of categories (e.g. walls, floor and ceiling insulation levels, window glass type, HVAC type) to identify the optimal building design able to reach the target performance at the lowest cost. These measures are evaluated against the cost of electricity and natural gas bought from the utility, and taking into account the cost of producing solar electricity using roof-top PV. Once evaluated the energy use of the base building, all the options are compared in a series of parametric evaluations with energy saving results calculated and stored for each implemented measure, as reported in the following formulae: (1) ESavingsi,n = (Base energyn – Measure energyn,i) Where: ESaving n,i = Energy savings within optimization iteration ‘n’ evaluated for option ‘i’ Base energyn = Calculated energy use of the base building at the beginning of iteration ‘n’ Measure energyn,i = Estimated energy use of the base building with measure ‘i’ installed within iteration ‘n’ The simulated energy demand from each energy option together with cost data are used to analyse the cost-effectiveness of individual measures. This has been derived by estimating the net present value (NPV) of the cost of the improvement or change over the life of the building consistent with established method of evaluating NZEB design optimizations [13] [52]. This is compared with the cost of the evolving base building through the optimization process. NPV n, i= I (Vn, an)

(2)


 =  (  ) + 

 

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   +   + ∑  ∑ 
  

(3)

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Where: PV = total present-value of life-cycle costs before taxes, associated with a given energy system I = total first costs associated with energy saving measure, including purchase, installation, building modification, and improvement Vn = residual or salvage value at year n, the last year in the evaluation (30 years) a = single-present-value formula computed for a designated year from j = 1 to n, and discount rate d; i.e. aj = (1 + d)-j Mj = maintenance costs in year j Rj = repair and replacement costs in year j Pk = the initial price of the kth type of conventional energy carrier for energy types k = 1 to H Qk = the quantity required of the kth type of energy bj = a formula for finding the present value of an amount in the jth year, escalated at a rate Θk, where k denotes the kth type of energy carrier, and discounted at a rate d; i.e. bj = [(1 + Θk)/(1 +d)] Each option, in each iteration, has a calculated NPV or the combination cost of the purchase and ownership of the measure as well as the costs for energy needs associated with the measure. The total costs over the life of the analysis are then annualized to a yearly cost of energy and additional mortgage expenses associated with the added incremental costs of that measure (Figure 2a) [68]. Within the optimization process, the base building is modified by adding the selected most cost-effective option at the end of an iteration and before proceeding to the next. All remaining options are then re-evaluated until the performance target is reached or the costeffective options are exhausted ( Figure 2b). The sequential search technique has a number of advantages. It allows to reach the established target and locates the least expensive path to achieve it. It further locates intermediate optimal points along the path, i.e. minimum-cost building design at different energy savings levels. Another advantage is that single building options are evaluated reflecting realistic features of available technologies. Finally, near-optimal alternative designs are identified within the optimization process. This is important since many competing solutions may be very close to the optimum while costs may be uncertain or variable for some options. 2.3 Economic Parameters

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Uniform costs of measures is not seen across European climates, and a detailed knowledge of all measure costs is subject to an extremely high level of variation at European level. Differences exist especially for energy and technologies, but variability can be also found within the same country in relation to economical or labour costs. In this research, average costs, conditions and building variants are assumed to obtain comparable results and an overview of the potential savings that can be reached. The approach allows the analysis to focus on realistic influences of climate on potential options with a consistent set of potential costs. According to the Guidelines supplementing Directive 2010/31/EU [10], it is up to Member States to determine the estimated economic lifetime of building elements as well as the entire building. Our cost calculations are based on the present value of life-cycle costs considering Standard EN 15459 [11] for energy systems and projections over an analysis period of 30 years. Sensitivity analysis performed on calculation periods revealed that increasing the calculation period decreases the cost-optimal target until the year 31. After that year, there is a slight increase as this is the year when the periodic replacement costs occur [11]. The

2.4 Geographic locations

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ACCEPTED MANUSCRIPT procedure for estimating life-cycle cost calculations are well documented [43] [49]. The assumed economic parameters are shown in Table 2. They are based on Guidelines supplementing Directive 2010/31/EU [10]. The considered electricity price in Milan is € 0.25/kWh while the current natural gas price is €10/ GJ or €0.058 kWh gas. As results can be influenced by regional differences in energy costs, a weighted European average has been assumed to evaluate endemic climate and solar related influences. The assumed costs, service lives, and maintenance fractions for the hundreds of efficiency measures considered are given in an Excel sheet linked to the simulation. The value of the energy price inflation rate implicitly approximates the European Emissions Trading Scheme with carbon pricing assumptions of 39€/tCO2 in 2020. Although the selected rates are based on the European guidance, a sensitivity analysis has been performed for the Milan case, given current prevailing conditions which suggest lower inflation and financing rates. The new parameters for the sensitivity analysis are: General Inflation Rate (GR) 1.0%, Energy Price Inflation Rate (ER) 0.5%, Financing Interest Rate (MR) 4.0%, Discount Rate (DR) 4.0% [70]. The development of energy price is an important input data as it has a strong national influence and forecasts have to consider long term political and economical developments. A higher Energy Price (5%) and Real Interest Rate (6%) have been also considered. The influence of wall construction typology in the optimization results has been also checked by sensitivity. Energy costs for electricity and natural gas are taken from [1]. No financial incentives have been assumed for either efficiency or renewable energy sources. As the lifetime of building elements can be variable, a differing lifetime is specified for each measure and it is appropriately implemented considering data from a number of sources, including Standard EN 15459 [11]. For instance, most insulation measures are assumed to last the life of the building. Other systems might require operation and maintenance during that time as well as replacement before the end of the analysis period. As example, a heat recovery unit, may last 15 years, so it is calculated twice in the global cost calculation having a calculation period of 30 years, i.e. at the beginning as initial investment cost and then as replacement cost after 15 year.

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The optimization requires incorporating specific data on climate severity and solar irradiance in a location. The European climate can be defined as temperate-continental with the following main climatic zones: Mediterranean, Oceanic, Continental, and Nordic [71]. The climate is influenced by the ocean on the Western coasts and the Mediterranean see in the South. Keeping mild air over the North-Western regions during winter, the Gulf Stream strongly influences the climate. Whilst Western Europe has an oceanic climate, Eastern Europe has a drier and continental climate. Central European countries have a mixed oceanic and continental climate. Table 3 reports the 14 locations selected to represent the European climatic variability providing a good coverage of climatic conditions. Our analysis has been based on the IWEC datasets, consisting in hourly data of the main climatic variables arranged in typical weather years as result of the ASHRAE Technical Committee 4.2 Weather Information. IWEC data are then used by EnergyPlus to predict heating and cooling needs, and by TRNSYS to predict how solar power production varies over time. The IWEC data has been processed for each location to calculate Heating Degree Days (HDD) and Cooling Degree Days (CDD) derived from heating degree hours (HDH) and cooling degree hours (CDH) respectively using the following formulae [72]: CHH = T − 18.5 (4) HDH = 18.5 − T (5) if CDH <0 then CDH = 0, if HDH <0 then HDH = 0

ACCEPTED MANUSCRIPT where: T= hourly air temperature as obtained from EnergyPlus CDD =

HDD =

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Table 4 reports the main climatic data for the simulated locations. Climate-sensitive buildings such as NZEBs should be designed taking into account climate change [73]. One of the most remarkable agreed change is related to summer periods, where temperature will increase [74]. According to the General Circulation Model HadCM3 for future climate predictions [75], the most remarkable changes in future climate relate temperature and irradiation during summer [76]. In summer future decades, the weather will be significantly warmer (higher dry bulb temperature, +3.6°C in 2050), sunnier (higher solar radiation, +42 kWh/m2 in 2050, 7% higher) and dryer (lower relative humidity, -4.8% in 2050) [77]. An average temperature increase of around 2°C is foreseen between 2020 and 2050 in summer. We accounted for these predictions in our research, although aware that there will likely be other changes [78]. The assumptions are based on the overall agreement related to the increasing importance of cooling in next decades compared to heating [79]. Considering the 2020 horizon, its share has more than doubled, and it will increase significantly by 2050 [80]. As the simulated new building would be used for decades, we adjusted the simulation to account for this change. We increased the expected amount of cooling in the building by 2°C adopting a set-point of 23 °C to compensate for higher cooling loads in future. The goal of this choice is to obtain more realistic energy consumption estimations related to future NZEBs. However, while there is a general consensus on global warming as hotter summer periods, uncertainty relates to the Arctic polar vortex that could actually make winters in Europe not more extreme due to shifts [81] . Moreover, according to the model, future relative humidity and horizontal irradiation will almost be the same, with a slight increase of the average temperature. As a consequence, heating values were not altered in our research. This results in conservative estimates for insulation levels that may be important to weather extremes. To verify the suitability of these assumptions, a parametric analysis has been performed in two Italian locations (Milan and Rome), considering that the future climate in northern Italy may become similar to that of central Italy today. The comparison showed that the adjustment in the summer cooling thermostat in Milan well mirrored the cooling-related optimization assumptions. This was deemed a more satisfactory way of accounting for future climate related influences on building loads, since winter heating may experience more future extremes which are best approximated by the conservatism of not altering the winter heating parameters. Changing the interior thermostat preference provides sufficient weight to cooling related efficiency options. 2.5 Building prototype

A standard new house of 120 m2 above grade with a full cellar has been considered. This building is similar to a prototype used in a recent study by Ecofys GmbH and the Danish Building Research Institute [82] with some additional details on lighting and appliances. Improved appliance efficiency alters building internal heat generation rates and the resulting heating and cooling needs. Its main characteristics are summarized in Table 5. The same table reports system properties, insulation levels, and airtight equipment efficiencies. The used building is representative of the European national building stock [83]. This building represents a standard energy performance starting point in the optimization process. Particular care was made in specifying occupancy related parameters as these potentially exhibit an extreme influence on building energy consumption [53] [61]. A minimum air

3. Results and discussion

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ACCEPTED change at maximum occupation rate has beenMANUSCRIPT considered, coherently with occupation levels and ventilation design rates proposed by Standard EN 15251 [84] for very low-polluted buildings (0.5 h-1 for residential buildings). This value represents the average rather than specific conditions for an individual building. Both a heating and cooling system are potentially available in all climates. Some measures, such as window solar heat gain selection, will involve a trade-off between the balance of heating and cooling. Similarly, mechanical ventilation is seen as desirable across climates even in the base configuration for adequate indoor air quality. Results depend on the selected building typology, but the influence of building geometry and selection of intervening steps is not specifically called out in the paper. The chosen building is intentionally simplified so that results will focus on the influence of climate and the solar resource on the potential optimization problem. This highlights the specific challenges (e.g. very cold climates) as well as the role of the PV resource (sunny cold vs. cloudy cold). As previously explained, a set-point of 23 °C has been adopted in line with recent climate change predictions. This accounts for the likelihood that cooling loads could grow over Europe with warmer temperatures. A mini-split cooling system has been included as available in the optimization for all locations. This has the important advantage of balancing options that might reduce heating, but can adversely impact cooling loads. The exclusion of a cooling system would have favored options that may lead to overheating. This is particularly important for the choice of window types when simulating some climates such as those in the Mediterranean. For example, high-gain windows in moderate climates with a southern exposure, or even adding cellar insulation to walls, typically increase cooling loads. Thus, the energy consumption results from EnergyPlus as interpreted within BEopt will seek to balance various building elements to address heating, cooling and appliance uses to best reduce energy costs for the cost-optimal selection. A cooling system may be avoided in temperate climates based on the passive measures installed. The analysis tends to encourage efficiency options such as shading, insulation and low surface solar absorptance that can significantly reduce cooling needs.

3.1 Energy savings in the optimized NZEBs

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A comparison between the base and the optimized building related to simulated consumptions for electricity and natural gas is summarized in Table 6. Solar PV production, annual net electricity consumption, and savings are also reported for the simulated NZEBs in all locations. Source energy savings percentage is computed for both electricity and natural gas consumption. Normalized site energy use can be computed by dividing the annual amounts by 120 m2. Table 6 illustrates initial and optimized building electricity consumption together with net annual optimized electricity and solar PV output. From Table 6, natural gas use varies with climate-related heating severity by 6:1 from the lowest consumption location to the highest. Electricity consumption varies less (1.6 to 1.0), being elevated in warmer locations where greater space cooling is used. PV output varies approximately 2:1 from the sunniest location, to the cloudiest. Natural gas consumption is cut to a low level by the optimization, particularly for sites in colder climates with elevated heating consumption in the base building. Values range from 11.1 GJ to 85.6 GJ with an average of 48 GJ in the base building. The highest savings are obtained in Stockholm (66.7 GJ), Milan (53.3 GJ), Warsaw (53.2 GJ) and Berlin (46.5 GJ). Larnaca and Lisbon have the lowest natural gas consumption in the base (11.1 GJ and 16.7

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ACCEPTED GJ) and NZEB configurations (9.7 GJ andMANUSCRIPT 13 GJ). The highest consumption after the optimization is obtained in Bucharest (21.7 GJ), starting from 56.9 GJ in the base building. The optimization assures a source energy reductions higher than 90% in most locations, and 100% or greater in locations with less than 2000 HDD. This has the advantage of pushing for greater efficiency levels in mild climates that might otherwise underinvest in energy efficiency improvements. It also makes possible to reach positive buildings in warm climates where this is easier accomplished. The location having the lowest saving is Warsaw (92%). The highest savings (between 119% and 128%) are obtained in Athens, Lisbon, Larnaca, and Rome. These locations present the highest electricity consumption in the base building (between 5334 and 4373 kWh), and the highest savings from solar PV production in the optimized configuration (between 5985 kWh and 4862 kWh). As expected, the highest annual net values are found in the same locations as well (between 2831 kWh and 2521 kWh). Figure 4 graphically illustrates how initial (red), optimized building (olive) and net annual electricity (green) compares to the solar PV output for each location. Also in the locations with the lowest PV production, Stockholm (3326 kWh) and Dublin (3309 kWh), the NZEB target is achievable with 94% and 97% source savings. The highest production is obtained in Larnaca (5985 kWh) and Lisbon (5413 kWh). The average annual electricity consumption is 3961 kWh. In line with climatic conditions, the location having the highest consumption in the base configuration are Larnaca (5334 kWh) and Athens (4938 kWh), while the lowest are Stockholm (3508 kWh) and Berlin (3537 kWh). Figure 5 reports on map the initial (dark red) and final (green) gas consumption in all simulated locations. As seen from Figure 5, after the optimization, the building natural gas consumption is much lower than in the initial state except for the sunniest locations where the absolute consumption was very low at the outset. Table 7 summarizes the results related to envelope insulation and other parameters obtained after the optimization. Table 7 shows how the selected options reflect climatic conditions. Greater insulation levels and air tightness are selected in colder and cloudier locations. A lower insulation level is obtained for warmer locations such as Athens, Larnaca and Lisbon. With the obtained level of air tightness, a 90% and beyond HRV mechanical ventilation system has been found justified. As seen from Table 7, solar water heating is indicated in most colder climates, such as Milan and Berlin, to offset water heating. However, the fact that solar water heating does not appear in locations such as Madrid should not be interpreted as a lower effectiveness, but rather as a minor necessity after a 4 kW PV array installed to reach the NZEB target in milder climates. The selected window type is highly dependent on the cost data used for the optimization. Triple-glazed windows have approximately a 50% cost premium over advanced doubleglazed design, which have a U-value of about 1.14 W/m2K. These windows are shown as cost-effective in colder climates, but not in milder locations. The window optimization process yield useful information showing where low gain versus high gain designs are desirable as well as where highly insulated assemblies are important. Interestingly, the analysis also shows where tight constructions with high efficiency heat recovery is selected. As seen from Table 7, tighter levels are desirable in colder climates (such as Stockholm and Warsaw) to reduce heating needs. Although a minor influence, building exterior finish for walls and roofs shows how colder locations call for darker surfaces, while hotter locations, such as Larnaca, indicate light colored roofs, walls and roofs to control cooling needs. As the optimization analysis always sought tighter construction to provide energy savings, the specific requirements for airtightness was met in all cases. Energy efficient lighting and appliances (refrigerator, washer, dryer, dishwasher) and home energy management systems are cost-effective in all locations and fundamental for reaching

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ACCEPTED MANUSCRIPT the NZEB target. A 98% efficient condensing boiler with 0.5 W/m2K hydronic pipe insulation is also sellected. Good pipe insulation is potentially important to prevent excessive internal heat gains which can drive cooling loads in milder locations. It should be noted that including a PV system in the analysis can exclude efficiency measures that are less cost-effective than obtaining the same savings from solar systems. Low gain windows, light colored tiles, efficient cooling and appliances and lighting are important to achieve a positive energy building at low incremental costs. Evaluation for colder locations show very tight construction, high insulation levels and triple glazed windows. CO2 emission reduction between the base and the optimized building are shown in Fig.6 for all energy uses in some of the simulated locations. Fig.6 shows how CO2 emissions are reduced with the optimization. Lowest saving are obtained in Larnaca and Lisbon (1.3 tons/y). Highest savings are found in cold locations such as Stockholm (from 7.6 to 2.7 tons/y), Warsaw (from 7 to 2.9 tons/y) and Berlin (from 6.5 to 2.7 tons/y) where heating needs (red color) have the highest reductions. From Fig.6 it is also possible to notice how the energy uses change among locations and how the amount of cooling (blue color) is particularly important in warm sites, such as Larnaca and Lisbon, while heating (red color) is predominant is cold climates such as Stockholm. The PV output (black line) appears variable in function of the climate and in many locations satisfies all the electricity needs (Larnaca, Rome, Lisbon, Madrid). The impact of appliance (green color) is also clear in all locations. 3.2 Selection of energy efficiency options

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Detailing the results for the Milan (Italy) case, the base building is estimated to use 3901 kWh of electricity per year and 54.3 GJ of natural gas for space and water heating (space heating is approximately 44 GJ). When normalized by the building floor area, this amounts to 32.5 kWh/m2 and 0.45 GJ/m2. The base building has approximately 72 m2 of South facing roof area. Table 8 reports the characteristics of the base building and the optimized choices for building appliances and renewables. The size of the potential PV system (4.0 kWp) has been chosen based on the available South facing roof area, selecting efficient modules and allowing the possibility for installing a 6m2 solar water heating system. A water heating load of 120 l/day has been considered. It has been assumed 80% of the lighting devices equipped with incandescent lamps, even though lighting is currently in a state of rapid change in Europe. It must be noted that the lighting segment is so cost-effective that even assuming a 30% saturation of incandescent lighting systems, the change to compact fluorescent lights (CFL) or light emitting diode (LED) devices would still be among the first measures chosen in the optimization process. Table 9 shows how the energy efficiency and the renewables measures have been selected in Milan to reach the final design configuration. The software ran a total of 2097 simulations in 43 iterations to reach the final target [85]. As shown in the table, the first selected options are the replacement of some appliances with more efficient ones, followed by insulating walls to R-7.2 (0.14 W/m2K), changing windows and other interventions on energy systems. The other selected measures are: improving ceiling insulation to R-10.6 (0.09 W/m2K), insulating the cellar walls on the interior (0.29 W/m2K), reducing building air leakage to 0.6 ACH at a 50 Pa blower door pressure (Passivhaus standard), a 98% efficiency fully condensing gas boiler with improved pipe insulation, 100% efficient lighting and a complete selection of A++ appliances (refrigerator, dishwasher, clothes washer, dryer). An electric feedback system with an automated system to shed plug loads is also selected. During the optimization process, a 4.0 kWp grid-connected PV system is added. Assuming net metering, it produces all the electricity needed at the site. When the PV system is selected in the analysis, its costeffectiveness becomes the key economic test for other competing measures, which should be installed before the PV system is considered. However, based on solar system performance

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ACCEPTED MANUSCRIPT (which depends on location weather data) and cost competitiveness with efficiency options, the PV system is often installed midway through the optimization process with further efficiency measures then added as needed to achieve the NZEB target. The example shows how it is possible to achieve more than 95% primary energy savings (from 103.4 GJ to 4.9 GJ) for a standard new building in Milan with cost-effective measures. This results in lower annualized costs for combined energy and investment expenses even when paying for the upgrades. Table 9 also shows the changes in primary energy consumption, site (final) electricity and natural gas use, incremental and cumulative costs as compared to the base building. This type of analysis, illustrated for Milan, has been run for all of the evaluated locations (Table 3). Figure 7 shows a summary of how electricity and natural gas consumption are reduced over the optimization process along with progress towards the NZEB goal. The sensitivity analysis performed on the economic parameters for the Milan case showed that, although the order of the measures selected in the optimization and the final NPV are changed, modified rates do not typically alter the final selection of measures within the optimization for the achieved energy savings. The analysis revealed that the order in which options were selected can vary but the final selection for NZEB appear much more robust and unchanging. However, the cost-optimal level appear to increase with an increasing real interest rate as future economic savings are more discounted at high interest rates. A higher energy prices increase the minimum global cost range giving a lower net primary energy cost-optimal level while the price of CO2 emissions increase. Lower energy cost and inflation rates actually result in a lower annualized cost of energy and financing costs. The final point on the curve goes from 2,470 € to 2,363 €. Sensitivity analysis related to wall construction topology also informed the process, for instance illustrating how optimal wall insulation levels were not particularly sensitive to wall construction type.The first group of selected measures, as seen in Table 9, are dominated by low or no-cost options (such as roof finish solar absorptance), by the choice of A++ appliances and efficient lighting devices. These measures are highly cost-effective and associated with a very steep drop in the annualized costs. Moreover, the building begins with equally distributed glazing, but the simulation later determines that moving the glazing area to the South face of a building, a no cost option for a new construction, is highly desirable. Architectural features, such as overhangs or awnings, are shown as useful to reduce overheating potential. Additional wall insulation shows very large energy reductions within the optimization. The optimization process spends much time parametrically analyzing more than a dozen window options with varying glass coatings, solar transmittance or G-factors, fill and framing types. The selection changes over the optimization when heating and cooling system sizes and efficiencies are altered. It is interesting to note that, as the building improved thermally, the incremental cost of more efficient heating and cooling systems becomes negligible as the required size is reduced. The cost of improving the heating system boiler and high efficiency cooling system changes during the optimization as the necessary size for both systems get smaller and less expensive. The cost of the fully-condensing boiler is that before sizing advantages are incorporated. The incremental costs of more efficient refrigerators and other appliances have been obtained by comparing standard versus A++ products costs for a single manufacturer. The efficiency improvements reduce household natural gas use by 71% (55 GJ to 16 GJ annually) and electricity consumption by 38% (3901 kWh/y to 2424 kWh/y). A 4.0 kW PV system is able to produce an amount of electricity (3532 kWh/y) that is 1,108 kWh more than the improved building annually requires. The annual source energy needed, considering both efficiency improvements and renewable power generation, is cut by 97% with a similar corresponding reduction in annual CO2 emissions from 6.0 to 2.6 tons/y (Fig.6). The final selected package of measures has a total incremental cost of 30,581 €. About half of this amount is for a 4.0-kWp PV system and 4,800 € are for a pumped solar water heating

system that augmented a 98%ACCEPTED efficient fully MANUSCRIPT condensing gas boiler. The efficiency measures dominate the potential cost-effective savings (Table 9). Thermal building improvements greatly reduce gas consumption while appliance and lighting efficiency improvements are key factors to cut electrical energy use. There are also financial advantages in having a thermally efficient building with solar electric power production. The owner annually saves approximately 2,243 € the first year in utility costs (bringing the annual utility cost to less than zero) and, even after accounting for interest expenses, has a positive cash flow.

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4. Conclusions

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The EPBD recast requires that all new buildings have to be nearly zero energy (NZEBs) by the end of 2020. NZEBs combine efficiency measures and renewables production considering cost-optimal levels of minimum energy performance requirements. The integration between the implementation of NZEBs and the assessment of cost-optimality across climates represents one of the major challenges that Europe is facing. This paper describes the utilization and refinement of an energy and cost simulation model built around EnergyPlus simulation using a sequential optimization method to identify the cost-optimal design of a new residential building. This building has been located across Europe in 14 cities with different climates. Given consistent materials, equipment and energy cost data, the results highlight the differing challenges and opportunities presented by the large variation in thermal conditions and solar availability across European climates. Results demonstrate that it is possible to reach a very low energy design in new buildings with source energy savings beyond 90% compared to the base case. The way in which this achievement is accomplished at the lowest cost varies considerably by climate that strongly influence the NZEB design. Results vary between cold and cloudy locations, such as Berlin, and sunny ones, such as Lisbon. In warmer, sunny locations, appliance efficiency measures and light colored surfaces are selected earlier as heating loads are not significantly increased, while cooling loads may be reduced. In colder climates, insulation and building tightness appear much more important as thermal improvements are strongly dependent on heating and cooling loads in a city. As the NZEBs target can be missed if buildings are designed without considering climate change, the possibility that Europe may experience warmer periods in summer was considered in the calculations. The cooling set point was adjusted since climate model predictions foresee an average temperature increase between 2020 and 2050 in summer. Sensitivity analysis established that the developed procedure resulted in appropriate adjustments. According to the results, the energy excess in summer decreases over time due to the increased cooling loads. The same does not occur in winter, when the imbalance between generation and consumption is not reduced in the same proportion. This leads to a smaller yearly energy excess and even an energy shortfall at the 2050 horizon. Simulation results agree that the increase in cooling energy consumption is the most significant. A common point in all locations is the importance of integrating renewables and energy efficiency measures to reach cost-effective NZEBs. The most common optimized NZEB configuration foresees a combination of good insulation, building airtightness as well as Class A++ appliances, lighting, and home energy management systems along with PV. In each location, the optimized building has less than zero net electricity consumption on an annual basis. Natural gas consumption for space heating and water heating is reduced by 70% in most cases. However, electricity neutrality is achieved if home lighting and appliances are optimized at the same time that the building technical systems are addressed. Efficiency measures are able to cut household appliance electricity by 35% or more in most cases and appears vital to reach the NZEB target.

Acknowledgements

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MANUSCRIPT Solar irradiance is important ACCEPTED in each location against the encountered thermal conditions. Roof space availability for solar, building orientation and site shading play a role in the available renewable energy mix. Although solar energy may appear most attractive, our analysis indicates that efficiency improvements are most important and appear first in the optimization. Savings from efficiency improvements occur at the right time whereas electrical storage will likely become important for the implementation of solar electric systems as installed saturations increase. The paper shows that the developed tool is able to support the application of the cost-optimal methodology. It furnishes a comprehensive reference for cost-optimal NZEBs. NZEB benchmarks for new buildings are identified allowing comparison among countries and providing scientific and technical support for a harmonized overview of the NZEBs status in Europe. As cost data collection is a critical aspect of the research, references should be provided to avoid inaccurate data that could alter results. Although the EPBD framework gives cost-optimal guidelines, more guidance and detailed indications would boost a more harmonized implementation of cost-optimal calculations at European level. Different assumptions, boundary conditions and calculation methods applied by Member States make results difficult to be compared. One of the main research finding to be considered in future policies relates the importance of efficient appliances and lighting in the energy performance assessment. Results show how the inclusion of these non-mandatory energy uses can result in more optimal solutions when combined with high efficiency technologies and renewable production. The developed method demonstrates the feasibility of the requirements for NZEBs constructions. It can be useful to identify the cost-optimal solution in terms of high energy performance and global costs in different climates. An important future development relates the investigation of cost-optimality in existing buildings. The study supports a cost-effective NZEBs design and decision making, facilitating the management of many parameters and the selection of different configuration options in new constructions. Results can be useful in the view of the next EPBD recast that is currently under approval. The overall framework supports European policies, energy and costs savings in the light of the European Roadmap 2050 of reducing greenhouse gas emissions by at least 80% by 2050 compared to 1990 levels.

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The authors wish to thank Heinz Ossenbrink and Paolo Bertoldi (JRC) for their support. We thank Matteo Rambaldi who assisted with estimating cost and performance data for appliances. A thank is due to Katalin Bodis who helped maps. Our thanks also to Andreas Hermelink (Ecofys) for assistance in collecting European cost data. Nick Grant with the Energy Saving Trust in the UK helped with measured water heating loads and Passivhaus consultant, Bronwyn Barry, assisted with some questions. References

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ACCEPTED [56] Cristina Baglivo, Paolo Maria Congedo,MANUSCRIPT Matteo Di Cataldo, Luigi Damiano Coluccia, Delia D’Agostino, Envelope Design Optimization by Thermal Modelling of a Building in a Warm Climate, Energies 2017, 10, 1808; doi:10.3390/en10111808. [57] EN 15251:2007. Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics [58] Ecofys 2013, Towards nearly zero-energy buildings- Definition on common principles under the EPBD (http://ec.europa.eu/energy/sites/ener/files/ documents/nzeb_full_report.pdf), carried out by Ecofys for the European Commission, DG ENERGY. [59] European Commission, 2016, Commission Recommendation (EU) 2016/1318 of 29 July 2016 on guidelines for the promotion of nearly zero-energy buildings and best practices to ensure that, by 2020, all new buildings are nearly zero-energy buildings, Official Journal of the European Union, L 208/46. [60] Ergo Pikas, Jarek Kurnitski, Martin Thalfeldt, Lauri Koskela, Cost-benefit analysis of nZEB energy efficiency strategies with on-site photovoltaic generation, Energy, 128 (2017), 291-301. [61] Valentina Fabi, Rune Korsholm Andersen, Stefano Corgnati, Verification of stochastic behavioural models of occupants' interactions with windows in residential buildings, Building and Environment, 94 (2015), 371-383 [62] Christensen, C., Horowitz, S. Givler, T, Barker, G.,Courney, A., 2005. BEopt: Software for Identifying Optimal Building Designs on the Path to Zero Net Energy, NREL/CP-5503733, National Renewable Energy Laboratory, Golden, CO. [63] Horowitz, Polly, B., S, Booten, C., Kruis, N. and Christensen, C., 2012 Automated Comparison of Building Energy Simulation Engines, NREL/CP-5500-52651, National Renewable Energy Laboratory, Golden, CO. [64] Joe Huang, Development of over 2,500 Weather Files for International Locations, ASHRAE, White Box Technologies, Moraga, California, available at: http://ashrae.whiteboxtechnologies.com. [65] Straube, J. The Passive House Standard (Passivhaus): A Comparison to Other Cold Climate Low Energy Houses,” Building Science Corporation, BSI-025, Somerville, Massachusetts, September 2009. [66] Feist, W., Pfluger, R., Kaufmann, B., Schnieders, J., Kah, O., Passivhaus Projektierungs Paket 2004, Passivhaus Institut Darmstadt, 2004. [67] Henninger, Robert H, Michael J. Witte, Drury B. Crawley, Experience Testing EnergyPlus With the IEA Hvac Bestest E100-E200 Series, Proc. 8th Int.l IBPSA Conference, Eindhoven, Netherlands, August 11-14, 2003. [68] S. Horowitz, C. Christensen, M. Brandemuehl, M. Krarti, An enhanced sequential search methodology for identifying cost-optimal building pathways, IBPSA-USA Journal, 3 (1) (2008) 100-107. [69] Klein, S.A. et al, 2010, TRNSYS 17: A Transient System Simulation Program, Solar Energy Laboratory, University of Wisconsin, Madison, USA, http://sel.me.wisc.edu/trnsys [70] European Commission, Directorate-General for Economic and Financial Affairs European Economic Forecast, ISSN 1725-3217, http://ec.europa.eu/economy_finance/eu/forecasts/2015_spring_forecast_en.htm [71] Kurnitski, J., Buso, T., Corgnati, S., Derjanecz, A., Litiu, A., NZEB definitions in Europe, Rehva Journal, March 2014, http://www.rehva.eu/publications-and-resources/rehvajournal/2014/022014/nzeb-definitions-in-europe.html [72] B. Polly, M. Gestwick, M. Bianchi, R. Anderson, S. Horowitz, C. Christensen and R. Judkoff, "A Method for Determining Optimal Residential Energy Efficiency Retrofit Packages," National Renewable Energy Laboratory, NREL Technical Report DOE/GO102011-3261, Golden, CO, April 2011.

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ACCEPTED MANUSCRIPT [73] Kershaw T, Eames M, Coley D. Assessing the risk of climate change for buildings: a comparison between multi-year and probabilistic reference year simulations. Building and Environment (2011) 46(6): 1303-8. [74] IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Summary for policymakers, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 132. [75] Pope, V.D.; Gallani, M.L.; Rowntree, P.R.; Stratton, R.A. (2000). "The impact of new physical parameterizations in the Hadley Centre climate model – HadAM3". Climate Dynamics. 16 (2–3): 123–146. doi:10.1007/s003820050009. [76] Gordon, C.; Cooper, C.; Senior, C.A.; Banks, H.; Gregory, J.M.; Johns, T.C.; Mitchell, J.F.B.; Wood, R.A. (2000). The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics. 16 (2–3): 147–168. doi:10.1007/s003820050010 [77] IPCC,2000: Special report on emissions scenarios (SRES): summary for policymakers – a special report of IPCC Working Group III Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC; 2000. [78] Robert A., Kummert M., Designing net-zero energy buildings for the future climate, not for the past, Building and Environment (2012), 55: 150-158. [79] de Wilde P, Coley D. The implications of a changing climate for buildings. Building and Environment (2012), 55:1-7. [80] Li Danny HW, Yang Liu, Lam Joseph C. Impact of climate change on energy use in the built environment in different climate zones, a review. Energy June2012;42(1):103e12. http://dx.doi.org/10.1016/j.energy.2012.03.044. [81] Zhang, J., Tian, W., Chipperfield, M.P., Xie, F. and Huang, J. 2016. Persistent shift of the Arctic polar vortex towards the Eurasian continent in recent decades, Nature Climate Change 6, 1094-1099, October 2016. [82] Boermans, T., Hermelink, A., Schimschar, S., Groezinger, J., Offerman, M., Thomsen, K.E., Rose, J. and Aggerholm, S., Principles for Nearly Zero Energy Buildings, ECOFYS, and the Danish Buildings Research Institute, Brussels, November, 2011. [83] Ballarini I, Cognati SP, Corrado V. (2014). Use of reference buildings to assess the energy saving potentials of the residential building stock: the experience of TABULA project. Energy Policy: 273-84. [84] EN 15251:2007. Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics [85 ] Delia D'Agostino, Danny Parker, Data on cost-optimal Nearly Zero Energy Buildings (NZEBs) across Europe, Data in Brief, submitted.

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Table 2: Economic parameters for the optimization.

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Table 1: Research assumptions. Application area New buildings Building type Single house (120 m2) Climatic Amsterdam, Athens, Berlin, Bucharest, Dublin, Larnaca, Lisbon, conditions Madrid, Milan, Paris, Rome, Stockholm Categories of Envelope (walls, ceiling, cellar, roof) insulation, airflow, windows, measure options shading, mechanical ventilation, heating and cooling systems, solar water system, PV, lighting, appliances Building lifetime 30 years Calculation of Dynamic simulations with EnergyPlus and TRNSYS energy needs Solving method Sequential search optimization technique (BEopt) Energy uses Heating, cooling, ventilation, DHW, other technical systems, lighting, appliances Costs Energy, labour, materials, maintenance, replacement, disposal and taxes

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Category General Inflation Rate (GR) Energy Price Inflation Rate (ER) Financing Interest Rate (MR) Discount Rate (DR) Down Payment with Financing

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Table 3: Simulated locations across Europe. Location name Amsterdam Athens Berlin Bucharest Dublin Larnaca Lisbon Madrid Milan Paris Rome Stockholm Vienna Warsaw

Country Netherlands Greece Germany Romania Ireland Cyprus Portugal Spain Italy France Italy Sweden Austria Poland

Rate 2.0% 3.0% 5.0% 5.0% 10.0%

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Table 4: Minimum, maximum temperature, heating and cooling degree days, and daily average solar irradiance in the simulated locations. Daily average Minimum Maximum Cooling Heating global solar Location temperature temperature Degree Degree irradiance (°C) (°C) Days (d) Days (d) (kWh/m2/d) Amsterdam -8.4 32.7 92 3185 2.69 Athens 2.0 37.2 1035 1254 4.57 Berlin -9.1 32.8 204 3372 2.70 Bucharest -20.0 38.8 492 3305 3.81 Dublin -4.2 24.2 15 3185 2.59 Larnaca 1.0 36.5 1221 904 5.13 Lisbon 4.1 36.0 558 1353 4.52 Madrid -4.6 40.4 703 2222 4.30 Milan -11.2 33.0 366 3048 2.93 Paris -6.0 30.0 204 2889 2.93 Rome -4.0 31.8 640 1618 4.01 Stockholm -17.0 27.1 75 4460 2.53 Vienna -18.3 31.7 265 3382 3.08 Warsaw -16.0 32.0 160 3856 2.74

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Table 5: Characteristics of the base building. House Size 120 m2 over 2.5 m cellar containing heating equipment Neighbors Similar neighboring buildings on the two sides of the house Envelope Windows 23 m2 with double clear glass (2.2 W/m2K) Walls R 1.3 Insulated perlite filled masonry walls (~0.8 W/m2K) Attic R-5.3 insulation (~0.18 W/m2K) Doors Insulated wood entry door (~0.8 W/m2K) Air leakage Standard construction (4 ACH at 50Pa blower door pressure) System Heating Hydronic natural gas heating system, 82% efficiency Cooling COP 4.1 mini-split cooling system T Set point 20oC for heating, cooling 23oC Hot Water 155 l insulated boiler in cellar providing 120 l per day at 55oC Mechanical 20.3 l/s continuous with 72% efficient ERV ventilation

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ACCEPTED Table 6: Simulated initial electricity, naturalMANUSCRIPT gas and PV electric output for the base and optimized buildings. Base Building NZEBs Annual Annual Solar Annual Natural Source Location Electricity Natural PVH Net Gas Savings (kWh) Gas (kWh) (kWh) (GJ) (%) Amsterdam 3482 60.2 3437 -1210 15.2 97% Athens 4938 18.4 5358 -2521 12.5 120% Berlin 3537 65.1 3371 -1152 18.6 93% Bucharest 4112 56.9 4660 -2090 21.7 100% Dublin 3441 59.0 3309 -1143 14.6 97% Larnaca 5334 11.1 5985 -2708 9.7 123% Lisbon 4103 16.7 5413 -2831 13.0 128% Madrid 3889 33.9 5200 -2732 18.1 114% Milan 3901 54.3 3532 -1108 16.0 95% Paris 3590 52.6 3605 -1471 18.3 97% Rome 4373 24.2 4862 -2468 12.5 119% Stockholm 3508 85.6 3326 -1090 18.9 94% Vienna 3687 63.5 3801 -1518 17.6 98% Warsaw 3567 74.6 3447 -1146 21.4 92%

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Table 7: Building properties by location as evaluated in optimization. Windows: D= double glazed with low-e coating, H= high solar transmittance (G-factor); L= low-gain, Ins=insulated window frame; Ar= argon fill, TH_H= Triple-glazed window with highly insulated frame and Hi gain characteristics (U= 0.74 W/m2-K, G=0.5), ACH= house air tightness at 50 Pa blower door pressurization, Hot Water Boiler: fully condensing = 98%, Solar Hot Water = with 6 m2 solar hot water heating system as auxiliary to boiler, Exterior Finish= indicated optimal color (solar absorptance) of roof and wall elements. Ceiling Window ACH at Solar Exterior Wall Location Insulation Insulation type 50 Pa hot Finish name 2 2 (W/m K) water (W/m K) Amsterdam 0.12 0.07 DH Ins, Ar 0.3 yes Dark Athens 0.25 0.15 DH Ins 0.6 no Light Berlin 0.12 0.07 TH H 0.3 yes Dark Bucharest 0.15 0.09 DH Ins, Ar 0.6 yes Dark Dublin 0.12 0.07 DH Ins, Ar 0.6 yes Dark Larnaca 0.25 0.19 DL 0.6 no Light Lisbon 0.25 0.15 DH Ins, Ar 0.6 no Med Madrid 0.25 0.12 DH Ins 0.6 no Med Milan 0.14 0.09 DH Ins, Ar 0.6 yes Dark Paris 0.12 0.07 DH Ins, Ar 0.6 yes Dark Rome 0.25 0.12 DH Ins 0.6 no Med. Stockholm 0.12 0.07 TH H 0.3 yes Dark Vienna 0.12 0.07 DH Ins, Ar 0.6 yes Dark Warsaw 0.12 0.07 TH H 0.3 yes Dark

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Table 8: Base and optimized building characteristics for appliances and renewables. Base building Optimized Appliances A+ Option A+++ Refrigerator 340 kWh/y 201 kWh/y Cooking 334 kWh/y 302 kWh/y (Induction) Dishwasher 319 kWh/y 258 kWh/y Clothes dryer 0.98 kWh/kg 0.59 kWh/kg Clothes washer 183 KWh/y 150 kWh Lighting 80% incandescent:600 kWh/y 100% CFL/LED: 175 kWh/y Renewables PV System None 4.0 kWp with 95% efficient inverter Solar Hot Water None 6m2 closed-loop system

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Table 9: Selected order of energy efficiency measures in Milan (Italy). Category Measure Total Electric Gas Incremen Cumulative (GJ) (kWh) (GJ) tal costs costs (€) (€) Base Case None 103.4 3901 54.3 0 0 Appliance A++Air Dryer 99.4 3365 56.1 250 250 Appliance Efficient lighting 97.1 3115 56.7 320 570 Appliance A++Refrigerator 95.8 2963 57.0 160 730 Appliance A++Clothes Washer 93.2 2808 58.3 150 880 Wall Walls +R.3.3 (RSI 3.3 73.0 2685 39.0 1177 2057 insulation walls: U: 0.303), ACH 50 (Air changes at 50 Pascal tested building pressure) Windows 40% glazing to South 72.4 2655 38.8 75 2132 Distributio Hydronic piping to R72.0 2647 38.5 39 2171 n 2 (RSI 2; U:0.5) Air 2 ACH50 70.5 2647 37.1 107 2278 tightness Air 1 ACH50 + High 66.8 2582 34.3 325 2603 tightness efficiency Mini-split Mechanic 90%+ ERV (enthalpy 64.9 2550 32.9 349 2952 al recovery ventilator) Ventilatio n Heating 98% efficient fully 61.5 2550 29.9 392 3344 condensing boiler Air 0.6 ACH 61.1 2553 29.4 134 3478 tightness Roof Dark Tile 61.0 2562 29.2 0 3478 finish Ceiling Insulation to R6.7 60.5 2559 28.8 202 3680

ACCEPTED Total MANUSCRIPT Electric Gas (GJ)

Ins Appliance Windows

Solar PV Windows

Water Heat Cellar Walls Ceiling Ins Wall Ins Wall Ins

(U=0.15) A++ Dishwater Double glass, low-e film with hi-gain G factor, air fill 4.0 kW PV system Double glass, low-e film with hi-gain G factor, air fill, Insulated frame Fully Condensing Gas Water heater Cellar Wall: +R1.8 (RSI 1.8: U= 0.56) Ceiling to R8.6

(kWh)

Incremen Cumulative tal costs costs (€) (€)

(GJ)

59.8 59.3

2515 2518

28.7 28.2

160 148

3840 3988

19.2 17.8

-1014 -1005

28.2 26.8

14484 546

18472 19018

16.6

-1005

25.6

429

19447

15.8

-994

24.9

447

19894

15.8

-996

12.8 12.0

-1017 -1020

9.7

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Measure

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Category

286

20180

22.3 21.6

2246 782

22426 23208

-1275

22.2

620

23828

9.3

-1269

21.7

1732

25560

9.1

-1272

21.5

-986

24574

-1266

20.8

751

25325

Cellar 7.9 -1260 Walls Solar Hot 4.9 -1108 Water *R= insulation thermal resistance. U-factor = 1/R.

20.5

456

25781

16.0

4800

30581

Cellar Walls Windows

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Windows

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Appliance

Wall to R6.3 (U=0.19) Wall to R7.2 (U=0.14) Feedback & home EMS (energy management system) Cellar W to R1.8 (U=0.56) Double glass, low-e film with hi-gain G factor; argon fill Double glass, low-e film with hi-gain G factor, argon fill, Insulated frame Walls to R3.5 (U=0.29) Solar water heater 6m2

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Fig. 1: The methodological approach of the research.

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Figure 2: a) Evaluation method to reach NZEBs; b) the sequential search process to reach the NZEB target (modified from [1)].

Figure 3: Building prototype for the optimization analysis.

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Figure 4: Base building electricity (red) vs optimized building (olive), annual PV power production (orange) and net electricity (green).

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Figure 5: Simulated natural gas consumption in the base building (red) vs the optimized building (green) before and after the optimization.

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Figure 6: CO2 emission reduction in the base and the optimized building (G=gas, E=electricity).

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Figure 7: The optimization process in Milan reduces electricity (red) and natural gas consumption (blue) while progress is made towards zero energy (green) at a modest cumulative initial cost for the investment (yellow).

ACCEPTED MANUSCRIPT Highlights The cost-optimal design of a new residential building prototype is optimized

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An energy and cost simulation model is developed in several European locations

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Cost-effective NZEBs are achieved with 90% and beyond energy source savings

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The importance of efficient appliances and lighting is highlighted

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The selection of efficiency measures and renewables strongly depends on climate

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