Country residential building stock electricity demand in future climate – Portuguese case study

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Country residential building stock electricity demand in future climate – Portuguese case study ˜ , Raquel Figueiredo , Pedro Nunes , Marta J.N Oliveira Panao Miguel C. Brito PII: DOI: Reference:

S0378-7788(19)31390-8 https://doi.org/10.1016/j.enbuild.2019.109694 ENB 109694

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Energy & Buildings

Received date: Revised date: Accepted date:

8 May 2019 6 November 2019 9 December 2019

˜ , Please cite this article as: Raquel Figueiredo , Pedro Nunes , Marta J.N Oliveira Panao Miguel C. Brito , Country residential building stock electricity demand in future climate – Portuguese case study, Energy & Buildings (2019), doi: https://doi.org/10.1016/j.enbuild.2019.109694

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Highlights    

Assessment of residential demand in a changing climate. Results show potential increase of 5 to 60% of the residential demand. Space heating and cooling needs may decrease 33% and increase 20-fold, respectively. Electrification of appliances is the main driver for change in demand.

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Country residential building stock electricity demand in future climate – Portuguese case study Raquel Figueiredoa*, Pedro Nunes a, Marta J. N. Oliveira Panãoa, Miguel C. Britoa a

Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa,

Portugal *Corresponding author. E-mail: [email protected] (R. Figueiredo). Address: FCUL, DEGGE, Ed. C8 (8.3.33), Campo Grande, 1749-016 Lisboa, Portugal.

Abstract The future residential energy demand is expected to be significantly affected by increasing electrification rates and climate change. This study uses a Monte Carlo-based approach and an ensemble of climate models to address potential changes in electricity demand in this sector. The whole Portuguese residential building stock in 2050 is used as a case study. It is performed a sensitivity analysis for the retrofitting and new construction, floor area of new buildings, electrification of domestic hot water and cooking and, finally, adoption of heat pumps for space heating and cooling. Results show a potential increase of 5 to 60% of the total electricity consumption in the sector. Space heating is expected to decrease by 33% while space cooling shows a possible 20-fold increase. The electrification of domestic hot water and the development of housing stock characteristics are the factors with the largest impact on the overall electricity consumption changes.

Keywords residential energy demand; future climate; sensitivity analysis; heat pumps; electrification

Nomenclature ACH – air changes per hour [h−1] Ai – Area of internal envelope [m2] Aop – Area of opaque envelope [m2] – specific heat capacity of water [J.kg-1K-1] – Global horizontal irradiance [W/m2] ggl⊥ – Glazed surface g-value for normal incidence gshaded – Shading g-value for windows

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h – ceiling-to-floor height – Extraterrestrial irradiance [W/m2] – Clearness index [dimensionless] lѱ - length of linear thermal bridges [m] – Average number of occupants in one household – Air temperature in each hour of the year [⁰ C] Tavg – Average daily temperature [⁰ C] tday – Hour of the day – Final water temperature [⁰ C] – Initial water temperature [⁰ C] Tmax – Maximum daily temperature [⁰ C] Tmin – Minimum daily temperature [⁰ C] Ue - Overall thermal transmittance of the external envelope [Wm-2K-1] Ui - Overall thermal transmittance of the internal envelope [Wm-2K-1] – volume of hot water used per person per day [m3] – density of water [kg/m3] κ – (first) shape factor of Weibull and Burr distributions λ – scale factor of Weibull and Burr distributions ν – second shape factor of Burr distribution ѱ – Linear thermal transmittance [Wm-1K-1]

Acronyms AC – Air-conditioning ADENE – Portuguese Energy Agency APIRAC – Portuguese Association of the Refrigeration and Air Conditioning Industry BPIE – Buildings Performance Institute Europe COP – Coefficient of performance CORDEX – Coordinated Regional Climate Downscaling Experiment DHW – Domestic Hot Water ECCABS – Energy, Carbon and Cost Assessment for Building Stocks EPC – Energy Performance Certificate 3

GDP – Gross Domestic Product IPCC – Intergovernmental Panel on Climate Change NUTS – Nomenclature of Territorial Units for Statistical Purposes PDF – Probability distribution function RC – Resistance-Capacitance RCP – Representative Concentration Pathways

1. Introduction The residential electricity demand represents a significant fraction of the total final electricity demand, about 30% in the European Union [1]. It depends mainly on the electricity consumption of residential buildings, which in turn depends on location, weather, its own characteristics (construction materials), its purpose, occupation, type, etc. As buildings are longlasting infrastructures (with a long lifespan of 60 to 100 years [2]), with its major lifecycle energy consumption during their operating phase, their construction should take into consideration the changing climate. As temperature increases, the energy consumption required to keep a reasonable level of thermal comfort inside a building might change. To reduce energy consumption in buildings without compromising comfort needs, adaptation measures should be applied. Examples of some measures are: 1) improving buildings characteristics (e.g. insulation, shading, etc.); and 2) adopting more efficient technologies (e.g. heatpumps). Despite improved thermal characteristics of new buildings, the residential electricity demand is expected to increase in the future, mainly due to the adoption of electric heating and cooling devices (reducing the heating-related emissions) and to climate change. Since it represents almost a third of the final electricity demand, its increase can lead to significant challenges regarding the assurance of energy supply. For those reasons, it is very important to anticipate potential increases in residential electricity consumption due to changes in technology adoption, building characteristics, higher thermal comfort requirements, etc. Thus, the modelling of electricity demand in the residential sector is of critical importance. In the following subsection, previous work addressing this issue is summarized.

1.1. Previous studies To determine future electricity demand in the residential sector, many authors use climate variables as inputs for their models. The climate data used differ significantly across the literature. While some authors choose simple approaches such as using current climate [3] or choosing a warm past year to represent a warming climate [4], others opt to use climate models. Among the latter, the approaches also differ substantially. Some adapt current representative data to the outputs of climate models, using statistical distributions (such as Fourier‘s and Beta‘s [5]) or the morphing method [6]. This method is commonly used to downscale monthly outputs from climate models to hourly data, by adapting typical meteorological datasets. It consists mainly in applying a group of operations (shift, stretch, or both) to historical data assuring that the final data keeps the monthly averages of the 4

future climate data outputs without discarding the realistic profiles of the current climate. The majority of the studies based on morphing use several climate models [7–10], but there are others relying on only one model [11,12]. The most common methods used to determine the residential demand in the future use parametric, energy balance and degree day models. Parametric models are based on the relationship between demand and temperature. Both Yuan et al. [3] and Yi and Peng [13] decouple the temperature-driven demand from the total demand. The first uses the relation between historical peak demand and temperature, determining the future demand according to the development of penetration of heating/cooling technologies. The second determines the monthly historical temperature-driven demand by calculating the increase in consumption during summer, calculating the peak of cooling demand through a simple regression with average monthly temperature. The major advantages of parametric studies are simplicity and low computational requirements. However, these studies are normally based on historical data, that can easily become obsolete. Also, the consequences of building/technology improvements or behavioral changes are mostly neglected, since these studies are strongly dependent on the current society characteristics. Energy balance models (e.g. using building simulation tools such as EnergyPlus) generally consider heat gains and losses of a building. Hourly balances of energy within the building are determined according to the building design and thermal characteristics, weather variables (e.g. solar radiation and temperature), occupancy and appliances‘ usage profiles, etc. Energy balance studies usually use a low number of representative buildings [4,5,7–9,12,14]. Tettey et al. [7] address the influence on demand of changing the characteristics of a multi-storey building in Sweden, while Sabunas and Kanapickas [9] used a building simulation tool (HEED) to address the impact of climate change in a single representative residential building in Lithuania. Both Hooff et al. [4] and Dodoo and Gustavsson [12] simulated three buildings to address changes in space conditioning needs; the former focused the impact of different adaptation strategies, while the latter examined changes under different climate scenarios. Even though the chosen buildings are representative in most of these works, by simulating a few of them, the studies may lack representativeness of the overall building stock. There are studies that overcome this limitation by increasing the sample of buildings simulated. Wang et al. [10] simulated real Swiss neighborhoods of different typologies (urban, suburban and rural) to address changes on energy demand under different climate scenarios and building design improvements. Dirks et al. [15] simulated about 26 thousand representative buildings (based on statistics of building types, age, and floor area) to address the impacts of climate change until 2100 in the Eastern US. To address changes on heating demand under a changing climate in three Swedish cities, Nik et al. [16] used a bottom-up model (Energy, Carbon and Cost Assessment for Building Stocks – ECCABS, originally presented by Mata et al. [17]) that calculates hourly energy demand of a group of representative buildings (c.a. 400) and its weight on the overall building stock demand. Andrić et al. [18] present a lightweight computational resistance-capacitance model (RC model) considering buildings heat gains/losses and their characteristics to determine how heating demand of a neighborhood in Lisbon, Portugal, is impacted by climate change. Some other potential shortcomings of energy balance models are high computational complexity, site-specific and requirement for a huge amount of data (building features). However, their bottom-up approach is advantageous to study the demand in the future, because

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those models can reproduce in detail the effects of improvements in building elements or in technology, or even people behavioral changes. Alternatively, the degree day method is a simple and widely used approach to relate temperature with the heating/cooling requirements. It is usually computed by the difference between a daily temperature of reference and the average temperature on a given day [11,19]. The sum of the number of degrees that the temperature is above (cooling degree days) and below (heating degree days) the reference within a certain period (usually one year) can be used to obtain an approximation to the total needs for cooling and heating, respectively. Jakubcionis and Carlsson [19] made use of it to study the potential of residential space cooling demand in Europe, using data from the US to determine the relationship between airconditioning (AC) demand and cooling degree-days. However, some recent studies compare the results from the degree day method with the ones from energy balance models. Jylhä et al. [8] simulated a typical household in Finland and Wang and Chen [11] simulated nine residential and commercial buildings in the US to address the climate change impact on demand, both using hourly energy balance models, and compared the results with the degree day approach. They both found that the degree day method accuracy is strongly dependent on the region. It is reasonably accurate (i.e., reproduces similar results to the ones from hourly energy models) in regions with high space heating or high space cooling. However, for mild climates, its results are much more disperse when comparing to energy models, mostly for cooling energy demand. Despite its simplicity and few data requirements, it shows a huge discrepancy on results and does not consider factors such as solar radiation or building improvements. The impacts of climate change throughout the globe are expected to be different between regions. The changes in electricity consumption, for example, are dependent on how climate changes in a given region but also on its socioeconomic conditions, presently and in the future. For the sake of comprehensiveness, Table 1 shows the residential demand changes found by some studies (for mid-century horizons). The general trends are an increase in cooling demand and a decrease in heating demand.

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Table 1 – Summary of studies with climate change impacts in energy consumption for the mid-century in the residential sector (studies ordered by the average temperature, currently, on the location from higher to lower).

Climate change impact on energy consumption Model type

Region

Scale of the study

Source of impact

Impact on heating

Impact on cooling

Cumulative impact

Climate

-7 to -37%

-

-

-22 to -52%

-

-

Climate

-

-

+4 to +8%

Climate + building upgrade

-

-

+2 to +51%

Peak electricity demand

Parameters Heat demand density

Andrić et al. [18]

Energy balance

Lisbon, Portugal

Burillo et al. [20]

Energy balance

Los Angeles, USA

3 million buildings

Dirks et al. [15]

Energy balance

Eastern USA

26 thousand buildings

Climate

-

+3 to +133%

-3 to +19%

Electricity

Yi and Peng [13]

Parametric

Seoul

98 households

Climate

-

+6 to +96%

-

Increase rate on summer bill

Energy balance

Sweden

3 buildings

Climate + building upgrade

-26 to -17%

+31 to +73%

-16 to -7%

Energy demand

Switzerla 441 buildings Climate nd

-46 to -4%

-

-25 to -51%

Finland

-17 to -14%

+28 to +34%

-

-25 to -20%

+14 to +39%

-3 to -0.3%

-2 to +17%

-99 to -98%

-51 to -41%

Dodoo and Gustavsson [12] Wang et al. [10] Jylhä et al. [8]

Energy balance Energy balance

Tettey et al. Energy [7] balance

Sweden

665 buildings Climate + building upgrade

1 household

Climate

1 building

Climate (RCP4.5) + building upgrade

Energy demand Energy demand Primary energy

1.2. Research contribution This study applies a simplified large-scale hourly energy balance model to study the residential electricity demand in 2050, using Portugal as a case study. As shown above, the literature often focuses on the impacts of climate change on specific buildings or neighborhoods using a small set of possible climates. Moreover, the analyses typically discuss the aggregate results for each scenario, discarding climate variability. To overcome this lack of representativeness within the previous literature, this work uses a Monte Carlo-based approach applied to large regional datasets (totaling more than 700 thousand building energy certificates) to characterize the entire Portuguese housing stock tested for a large range of possible climate paths (more than 600). The conclusions are not confined to Portugal, they are also applicable and significant to several other geographic areas and climate conditions. Three IPCC representative concentration pathways of atmospheric greenhouse gases (RCP2.6, RCP4.5 and RCP8.5) are considered, each taking into account several climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX project [21]) for a period of eleven years (period 2045-2055). Thus, more than six hundred equivalent years are considered to account for the large range of climate pathways. This approach ensures all possible climate pathways are presented to explicitly show the range of demand that could result from different climate developments. A Monte-Carlo approach based in the work of Panão and Brito [22] was applied to the electricity demand of the residential sector with an hourly resolution. Probability 7

distribution functions are used to describe the country‘s future building stock and other related parameters (e.g. occupancy patterns) for three society development scenarios considering different levels of electrification. Sensitivity analyses on the building stock development and level of electrification are conducted. A significant number of improvements has been made to the Monte Carlo-based approach previously presented by Panão and Brito in Ref. [22]. Panão and Brito validated the model for the region of Lisbon using smart-metering data, the current housing stock characteristics and climate data. In the present work, the authors improved some of the model features and, more importantly, the model was used to calculate future scenarios for all NUTSIII regions including future climate models, future housing stock characteristics and changes in heating/cooling technologies. The main changes to the model were the introduction of a dynamic cooling season (the one in the original model was static), the modelling of the cooking and domestic hot water demand and the ability to run it both for every NUTSIII region and climate model. However, the most important novelty of this work is its application. While Panão and Brito developed and validated the method for the present context, here we apply it to Portugal in 2050 considering all regions, several climate models, the future housing stock, different cooling/heating appliances, etc. The paper is organized as follows: Section 2 presents in detail the methods; Section 3 describes the demand scenarios and the corresponding results are shown; Section 4 presents the sensitivity analysis and its results; Section 5 discusses the main results compared to recent literature; finally, Section 6 summarizes the conclusions.

2. Methods This section presents the methods used to determine the electricity demand in the residential sector. Subsection 2.1 explains briefly the Monte Carlo approach adopted, subsection 2.2 summarizes the approach to estimate the residential demand, subsection 2.3 describes the climate models, and subsection 2.4 lists some limitations of the chosen method.

2.1. Monte Carlo approach The hourly residential electric demand was determined by adapting the method presented and validated by Panão and Brito [22]. Here, a brief description of the method is presented; for more information, please refer to Ref. [22]. The Portuguese housing stock was firstly characterized using the Monte Carlo approach described in Ref. [23], based on data from Energy Performance Certificates (EPCs) provided by ADENE (the Portuguese Energy Agency) [24], creating probability distribution functions (PDFs) for several characteristics of the buildings – e.g. building year, building thermal characteristics, floor area, glazing, etc. The behavior of the users of electric, heating and cooling devices was modelled based on a survey made in Portugal [25].

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First, the model randomly generates one batch1 with 100 dwellings, characterized by random combinations of the different parameters of the building features (e.g. heating/cooling areas, floor area, type of heating system, etc.) according to their statistical distributions and user profiles (considering the probability of occupants being at home and of using space heating/cooling appliances). Then, it calculates the average hourly profile for the total electricity and heating demand for that batch. The model generates successive random batches until the total electricity and heating demand of the new batch does not change the average of the previous batches, i.e., when it is aligned, within a given tolerance (<0.5%) and assuring a minimum number of iterations (N=20), with the average demand of the previous batches. Figure 1 exemplifies the application of the process.

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One batch is a set of dwellings with detailed information regarding its characteristics, the behavior profile of the occupants and the type of domestic appliances for cooking, space heating and cooling and domestic hot water.

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STATISTICAL ANALYSIS sample

surveys

Probability distribution functions Building profile…

features,

CREATION BATCHES BATCH 1 N dwellings User profile

devices

ownership,

users‘

OF

…

Average anual electricity consumption, Q1

Adds BATCH n N dwellings User profile Average anual electricity consumption, Qn

Calculate the average electricity consumption of all n batches,Q1 to n CRITERIA EVALUATION

n > minimum iterations

NO

YES 𝑄1 𝑡𝑜 𝑛 − 𝑄1 𝑡𝑜 (𝑛−1) 𝑄1𝑡𝑜 (𝑛−1)

NO < convergence value

YES MODEL STOPS

Data from (n-1) batches Figure 1 – Schematic of the Monte Carlo approach used (some elements adapted from [23]).

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Space heating and cooling demand are based on hourly energy balances considering losses and heat gains. It uses as inputs hourly air temperature, solar irradiation, electric devices, occupancy and building features to determine both the heat gains and losses. The efficiency of space heating and cooling was assumed constant over time: 1) electric resistance has a 100% efficiency; and 2) the heat pumps‘ efficiencies are described by Weibull probability distribution functions (describing the present situation [23]: for space heating the parameters are λ = 2.76; κ = 2.20, while for space cooling the Weibull parameters are λ = 2.80; κ = 3.83). The heating and cooling seasons are dynamic, i.e., their first and last days change according to the climate data. The heating season starts on the day that precedes a period of more than 10 consecutive days with an average daily temperature below 15°C and ends on the day preceding a correspondent period warmer than 15°C. The cooling season is determined analogously, considering a temperature above or below 20°C. The original Monte Carlo model did not include electric domestic hot water (DHW) needs, which was added in this work. This is of major importance for this study, given the expected increase in this type of energy consumption. Equation 1 describes the calculation of the DHW energy needs per day per household. It considers 40 litres of hot water per person per day ( in m3), a final temperature of 60ºC ( , in ⁰ C) and an initial temperature of 15ºC ( , in ⁰ C) [26,27]. It also depends on the number of occupants ( ). The assumed water densi3 -1 -1 ty ( ) is 1,000 kg/m and thermal capacity ( ) is 4,186 J.kg K . (

−

)

(1)

A typical hourly profile for domestic hot water consumption was also assumed, which considers the larger peak in the morning, a small hot water usage at lunch and a second peak of hot water usage in the late afternoon and dinner time [28]. Houses with electric DHW feature an equal distribution between electric hot water tanks (efficiency of 93% [29]) and heat pumps with a coefficient of performance (COP) of 3 [30] since these are well established technologies its efficiency were considered constant over time. Regarding cooking electricity demand, the model considers that cooking appliances usage follows the same typical profile of hourly lighting use in kitchens in Portugal [31], and that the cooking electric demand corresponds to 7.6% of the total electricity consumption in households having electric cooking appliances (excluding electric DHW) [32],[33]. Besides space heating/cooling and DHW/cooking electric loads, the model also assumes a baseload profile dependent on the floor area of the dwelling, representing other typical electric devices in use, mentioned below as ‗sockets‘, e.g. television, fridge, washing machine, etc. The ‗sockets‘ profile is the same for all dwellings and corresponds to the average mid-season electric profile (since the use of heating and cooling devices is unlikely at this time of the year); the data was obtained from a smart metering project [34], and used in the validation of the original model by Panão and Brito [22]. Since lighting needs are dependent on natural light, which changes according to the time of day and year, following Ref. [22] it is considered that when the global solar radiation is below 100 W/m2, during the early morning and late afternoon, the ‗sockets‘ electric profile increases 10%. This profile is also adapted according to the occupancy of the dwelling. The space heating/cooling, DHW and cooking loads are summed to this typical profile to calculate the aggregate one. All electric loads (including space heating and cooling, DHW and cooking) consider 11

the occupancy profile of the houses, i.e., at a given hour the loads are adapted according to the occupancy and probability of using the appliances.

2.2. Residential demand To estimate the hourly residential electricity demand in the future, several assumptions were made. As it changes with regional factors such as the climate and socio-economic conditions, its computation was done separately for each NUTS III2 region (2002 division), excluding the archipelagos. Thus, not only climate data (derived from the climate models – see subsection 2.3) but also several other indicators required for the calculations were gathered for each of the regions (e.g. number of households, percentage of apartments, average area per dwelling, average number of inhabitants per dwelling, existence and type of heating and cooling systems and its distribution, etc. [35]). After calculating the electricity demand in the residential sector for each region, a weighted sum according to the number of houses in each region was done to obtain the total residential electricity demand of the country. Due to an increasing elderly population and a low birth rate, the resident Portuguese population is expected to decrease in the future from 10.2 million in 2019 to 9.2 million in 2050 [36].

2.3.Climate models The climate data required by the Monte Carlo-based model includes hourly data for the air temperature and the global solar irradiance incident on a vertical surface facing each of the eight main orientations (North, Northeast, East, Southeast, South, Southwest, West, Northwest). In this work, the climate data for the future is provided by the Coordinated Regional Climate Downscaling Experiment (CORDEX). It addressed three IPCC Representative Concentration Pathways (RCPs), defined by the corresponding radiative forcing considered for 2100: RCP2.6 (2.6 W/m2), RCP4.5 (4.5 W/m2) and RCP8.5 (8.5 W/m2). For RCP2.6, RCP4.5 and RCP8.5, the number of climate models considered was of 14, 21 and 22, respectively, resulting from the downscaling of several global climate models using different regional models, with a final spatial resolution of 0.11° (approximately 12 km). The gathered data either has a three-hourly time resolution (three models in RCP2.6 and five models in RCP4.5 and RCP8.5) or a 24h resolution (11, 16 and 17 models in RCP2.6, RCP4.5 and 8.5, respectively). As specified in subsection 2.2, the residential demand is estimated for each NUTSIII region, and so the model requires regional climate data. To calculate it, each parameter is averaged spatially (without considering the effect of urban heat islands) and then downscaled to an hourly resolution. Since the data required by the model consists of hourly time-series, the datasets were downscaled to 1h resolution. An adaptation of Erbs‘ method [37] was applied to air temperature for both the 3 and 24h resolution data, as in Equation 2 and Equation 3. (

−

)

( −

)

(2)

2

Nomenclature of Territorial Units for Statistical Purposes (NUTS) is a system developed by Eurostat that divides the territory hierarchically for the purpose of statistical studies. NUTS III defines ―small regions for specific diagnosis‖ and include, in the case of Portugal, 30 regions [54,55].

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with (

− 1)

(3)

where is the air temperature in each hour of the year [⁰ C]; Tavg is the average daily temperature [⁰ C]; Tmax is the maximum daily temperature [⁰ C]; Tmin is the minimum daily temperature [⁰ C]; and tday is the hour of the day. The adaptation from the method consists of, instead of using the monthly-average day (as in the original Erbs‘ method), using data for each day of the year. It considers directly the daily data (24h resolution) or the daily mean/maximum/minimum temperature determined with threehourly resolution data. Regarding the global solar irradiance ( , in W/m2), the hourly data was obtained by determining the hourly extraterrestrial irradiance ( , in W/m2) and applying to it the 3/24h-resolution clearness index ( ), as in Equation 4. In the case of three-hourly data, a linear interpolation of the clearness index was done before applying it to the hourly extraterrestrial irradiance. (4) Finally, the hourly global solar irradiance incident in a horizontal surface was converted to the one incident on vertical plans facing the eight key orientations. The global irradiance on each plane considers the beam and diffuse irradiance. Following the method CLIMED2 described in [38], the diffuse irradiance was estimated using the clearness index.

2.4. Limitations Residential electricity demand depends on socio-economic context, signal prices, user‘s behavior, climate, etc. The projection of its development is strongly dependent on the assumptions and on the chosen approach to model it. Thus, as for any complex framework, the method proposed to model the residential electricity demand in the future has some limitations, which are discussed below. In this work, to determine the residential electricity demand, several sets of dwellings are created and characterized by their building characteristics, the existence of space heating and cooling systems, etc. Each parameter of a dwelling is selected according to its probability distribution function. One of the limitations of this method is not considering possible correlations among variables. For example, more efficient equipment is expected in houses with improved thermal performance. A potential future energy efficiency improvement of electrical appliances was not considered (see subsection 2.2) since efficiency (e.g. of the electric resistances or heat pumps) was assumed already stable. The urban heat island effect is not considered in this study, which, for example, may be aggravated by the use of heat pumps, which could result in higher cooling needs. Despite the calculation of heating and cooling needs considers several internal gains sources (e.g. occupants, electric appliances, lighting, etc.), it does not take into account the contribution of non-electric appliances. Regarding the socio-economic features of the presented model, one significant limitation is the lack of considering economic factors such as price-driven mechanisms that may change the consumer‘s behavior and economic growth that may affect ownership of electric equipment. 13

3. Demand scenarios The different building stock demand scenarios are described by a variety of factors, from the development of the housing stock to the electrification rate of the energy demand. For simplicity, the three main scenarios were classified according to the demand level: 1) Low; 2) Central; and 3) High. The assumptions made regarding each one are presented in subsection 3.1, while the results are shown in subsection 3.2.

3.1. Assumptions The differentiation between scenarios is mainly influenced by 1) the development of the housing stock; 2) the floor area of new buildings; 3) the electrification of domestic hot water and cooking; and 4) the electrification of space heating/cooling. Below, each of these parameters is explained in detail, along with assumptions made.

3.1.1. Household market The characteristics of a building are very relevant in its energy performance. The energy performance of a building is generally determined through its heat balances. Heat gains and losses both change significantly with the building insulation, solar exposure, density of people/appliances, outside air infiltration, etc. In this work, to better describe the future housing stock, different approaches for the development of the building stock characteristics were taken depending on the nature of the parameter: 

Distributions without significant changes in the future housing stock: overall thermal transmittance of the internal envelope (i.e., envelope in contact with non-usable areas), linear thermal bridges, normalized overall opaque area, ceiling-to-floor height, etc. Their distributions were kept constant and equal to the values assumed in Ref. [23], Table 2;

Table 2 – Probability distribution functions considered and their parameters (the functions are assumed to be the same in the future housing stock)[23].

Probability distribution functions considered and their parameters Type

Parameters λ = 1.411 Wm-2K-1; κ = 2.914

Overall thermal transmittance of the internal envelope, Ui

Weibull

Length of linear thermal bridges, lѱ/Aop

Weibull

λ = 1.169 m-1; κ =1.756

Internal-to-opaque envelope area, Ai/Aop

Weibull

λ = 0.343; κ =1.835

Ceiling-to-floor height, h

Burr

λ =2.567 m; κ =36.864; ν = 0.560



No further changes from today‘s standards: external envelope thermal transmittance, linear thermal transmittance, normalized window area, shading g-value for windows, air infiltration rate and glazed surface g-value for normal incidence. It considers that future 14

buildings keep the average values of modern buildings, i.e., buildings built in the present (taken from ADENE‘s updated database [24], Table 3); Table 3 – Average parameters of new dwellings as of 2017 [24].

Characteristics of modern buildings in 2017 Average value External envelope thermal transmittance, Ue

0.45 Wm-2K-1

Linear thermal transmittance, ѱ

0.4 Wm-1K-1

Window-to-floor area, Awindow/Afloor

0.25

Shading g-value for windows, gshaded

0.1

Glazed surface g-value for normal incidence, ggl⊥

0.56

Air infiltration rate, ACH

0.65 h-1



Linear development towards the projected values in 2050: windows thermal transmittance (from an average of 2.2 Wm-2K-1 in 2017 to 1.2 Wm-2K-1 in 2050) 3.

A realistic housing stock for the future should consider not only the replacement of old buildings by new ones but also a fraction of existing buildings that may undergo energy retrofit. According to the Buildings Performance Institute Europe (BPIE), European countries should increase their renovation rate of old constructions up to 3% per year in order to meet international commitments [39]. However, Europe is still far from this goal, with a current yearly average rate of 1.2% [39], whilst in Portugal it is lower, 0.06% [40,41]. Regarding new residential buildings per year, Europe presents a rate of 0.5% according to the EU Buildings Database (2014) [42], while in Portugal is 0.13% (2016) [40,41]. The different paths for the development of the housing stock, including the rate of new buildings‘ construction and rate of major renovations, are based on the number of dwellings being built or renovated annually, assuming that dwellings built or renovated in the same 5-year period show the same main design characteristics (e.g. windows thermal transmittance). The possible development of the housing stock is described by assuming three paths, focusing on two main factors: 1) level of renovation of current and old dwellings; and 2) people living in one dwelling. Each development path for the housing stock is attributed to each of the demand scenarios, according to the expected demand level (e.g. newer housing stock with better energy performance is expected to lead to less energy demand; thus, such development is considered for the ‗Low‘ energy demand scenario). The retrofitting and new buildings‘ rates were assumed to have a linear development until 2050, assuming the previously mentioned values for Portugal

3

Even though windows with lower thermal transmittance may be available (e.g. triple glazing windows with 0.75 Wm-2K-1 [56]), the authors believe that, given the expected future climate in Portugal, such windows would not be required or economically feasible to install.

15

in 2016 as their starting point. Taking into consideration the European context, the three paths are: 

New: Since major renovation rates in Portugal are still behind the European average, and even more from the European goal, it is assumed that the country will meet this goal in 2050 – 3% per year. As for new buildings, based on several European countries with a rate of about 1% of new residential buildings per year (Austria, Belgium, France, Finland, etc. [42]), it is assumed that Portugal will achieve this value in 2050. Furthermore, it is considered that new buildings are replacing the older ones first. It is also assumed an average of 2.5 people/house – the Low demand scenario.



Middle: In this case, in 2050 it is assumed a retrofitting rate of 2%, as well as a new buildings rate of 0.75%. It is also assumed the replacement of older buildings first and an average of 2.5 people/household – the Central demand scenario.



Old: To consider an older housing stock, it is assumed in 2050 a retrofitting rate of 1% and a new buildings rate of 0.5%. It is considered 2 people/household and that new buildings replace equally existing buildings from all ages– the High demand scenario.

Table 4 summarizes the three paths above. Given the different assumptions for the new construction/retrofitting rates, the above paths lead to a different number of existing houses in the year 2050. Even though the population is kept the same for all paths, by assuming a different number of people per house, the number of occupied houses also varies. The occupation factor (see Table 5) can be determined through the existing and inhabited houses.

3.1.2. Floor area of new dwellings The floor area of a dwelling impacts its consumption, influencing the lighting needs and the space heating/cooling needs. In general, bigger dwellings would demand higher energy consumption. For this reason, different floor area developments were considered for the main scenarios. Gross domestic product (GDP) is commonly used to project future socio-economic factors, such as dwellings‘ floor area. In the past, Portuguese size of dwellings has increased with GDP per capita. Relying on the same future relationship between floor area and GDP would be believing that: 1) the society lifestyle will not change and people will always live in bigger houses; 2) there are no limits in the area available to build increasingly bigger buildings; and 3) the real estate market dynamics would not have a role in this trend. In this regard, the creation of the scenarios aims at covering very different trends for the future of dwellings‘ size. For 2050, Gouveia et al. [43] considered a 20% increase in floor area of dwellings, while the Portuguese ‗Roadmap For Carbon Neutrality‘ [44] considers it constant or smaller. Following this, the three assumptions made were: 1) new dwellings will be 20% smaller in 2050 compared to the present average of new dwellings (it is assumed a linear decrease); 2) dwellings built in 2050 will keep the average area observed in current new dwellings; and 3) new dwellings will be 20% bigger in 2050 (assuming also a linear increase). The floor area of other dwellings, undergone or not major renovations, was considered to stay the same.

16

3.1.3. Electrification of domestic hot water and cooking The use of electricity to supply domestic hot water (DHW) and cooking needs has been increasing in the residential sector. According to the studies, in Portugal, electric domestic hot water was present in 6% of households in 2008 (EcoFamílias project [45]), in 14% in 2010 (EcoFamílias II project [46]), and in about 26% in 2015 (FROnT project [47,48]). As for electric ovens and cooking plates, in 2008 about 69 and 18% of households had them, respectively (EcoFamílias project [45]), which is about 6% more than in 2006 (EcoFamílias 30 project [49]). To show the electrification tendency of the domestic hot water and cooking needs, three levels of electrification were assumed: 50%, 75%, and 100%, for the Low, Central and High scenarios, respectively.

3.1.4. Space heating and cooling Based on past records of sales of heat pumps for the residential sector [1], current penetration of heat pumps was estimated to be about 7 to 9% in the dwellings existing in 2017, following a linear trend since 1999 [50]. Maintaining the tendency, it is to expect heat pumps in 27% of all the existing houses by 2050 – this is the ‗Central‘ scenario. The High and Low demand scenarios assume more and less 10 percentual points (37% and 17%), respectively.

It is considered that heat pumps are mainly installed in occupied houses; thus, the number of heat pumps given by the previous percentages is adjusted to the number of occupied houses in each scenario. It is assumed that households without heat pumps in 2050 have the same distribution of electric resistance heating and other heating means as in the present.

3.1.5. Summary Table 4 summarizes the main assumptions for each of the main scenarios. Table 5 summarizes second order assumptions about the scenarios resulting from the previous assumptions. Figure 2 and Figure 3 show some ilustrations of the parameters described in the previous tables.

17

Table 4 – Summary of assumptions for the demand scenarios.

Main assumptions Low

Central

High

New

Middle

Old

Retrofitting rate

3%/year

2%/year

1%/year

New buildings rate

1%/year

0.75%/year

0.5%/year

Replace older buildings

Replace older buildings

Replace all buildings

2.5

2.5

2

-20% than current average

current average

+20% than current average

% electrification

50%

75%

100%

% heat pumps

17%

27%

37%

Housing stock development

Household market

Replacement strategy People/household Floor area of new dwellings Electrification of DHW and cooking Space heating/cooling

Dwellings built in 2050

Table 5 – Second order assumptions for the demand scenarios.

Additional assumptions Low

Central

High

Houses in 2050 [#]

7,230,004

6,952,410

6,627,432

Occupied houses in 2050 [#]

3,687,200

3,687,200

4,609,000

% old houses

34%

52%

72%

% retrofitted houses

49%

33%

18%

% new houses

18%

14%

10%

Occupation factor

51%

53%

70%

New buildings in 2050 [m2]

108.5

135.6

162.7

Average of housing stock in 2050 [m2]

88.1

96.1

118

Heat pump

26.1%

39.8%

41.3%

Electric resistance

33.5%

27.3%

26.6%

Others

31.6%

25.8%

25.1%

No system

8.2%

7.1%

7.0%

Heat pump

36.7%

50.7%

52.0%

No system

63.3%

49.2%

48%

Housing stock

Floor area

Space heating system (occupied houses)

Space cooling system (occupied houses)

18

150

4.0

120

3.0

90

2.0

Floor area [m2]

# x106 houses

5.0

60

1.0

new houses

retrofitted houses old houses floor area

30

0.0

0

Current

Low

Central

High

100% 80% 60%

heat pump (cooling) heat pump (heating)

40%

electric resistance others

20%

no system

Current

Low

Central

cooling

heating

cooling

heating

cooling

heating

cooling

0% heating

distribution of space heating and cooling systems [%]

Figure 2 – Distribution of the occupied housing stock for each scenario according to the construction type (old, retrofitted or new)(left axis) and the average area of each dwelling (right axis).

High

Figure 3 – Distribution of space heating and cooling devices in the present and in each scenario.

19

3.2. Results The average load profiles of the total electricity demand in households are shown by the lines in Figure 4. Their ranges, due to different climate models, are represented by the shaded areas. As expected, the Low demand scenario is the one with the lowest load profile, while the High demand scenario shows the highest, resulting from the high electrification levels assumed but also from the older housing stock, which, being less efficient, contributes to higher needs of space heating and cooling. The High demand scenario shows also a higher dispersion of results, due to its higher electrification of space heating/cooling, making electricity consumption more sensitive to climate. Scenarios with lower electrification of space heating/cooling, or higher energy performance buildings (e.g. higher insulation), or both, are much less sensitive to the climate models used. In summer, the pronounced peaks at times of leaving/arriving home are observed for future consumption but not for present consumption. This is mainly due to higher usage of electric appliances (for cooking, DHW and space cooling) compared to the present.

Figure 4 – Average load diagram for a summer day (left) and a winter day (right). The shaded area represents the range of the results (between the maximum and minimum observed).

In Figure 5 the discriminated histograms for the consumption in each scenario are presented for the climate paths RCP4.5. The present level of demand is also shown in dashed. It may be noted that climate impacts only heating and cooling demands. The remaining loads are mainly dependent on the society development considered for each scenario, such as the floor area of the dwellings and the electrification of cooking and DHW loads.

20

The High scenario shows always higher levels for the different types of demands. As previously mentioned, the high electrification rates for space heating/cooling makes this scenario highly dependent on climate, increasing significantly its space heating/cooling needs. Bigger new

dwellings also help to increase the plug loads and space heating/cooling. Figure 5 – Histograms for the electricity consumption of the different types of demand (total, heat, cool, sockets, cooking and DHW) in the mid-century for RCP4.5 according to the Low, Central and High demand scenarios. The black dashed line shows the present electricity consumption.

Since the results for the three climate paths (RCP2.6, RCP4.5 and RCP8.5) are qualitatively similar, those for RCP2.6 and RCP8.5 are only shown in the Error! Reference source not found. - Error! Reference source not found.. Due to the lower temperature expected in RCP2.6, this path shows slightly lower cooling needs and higher heating needs. On the contrary, RCP8.5 shows slightly higher cooling needs and slightly lower heating needs, both explained by the temperature increase considered in this climate path. Table 6 shows the average electricity consumption per type of consumption and per scenario, for the three RCP paths considered. Table 6 – Average electricity consumption for every scenario considered (for RCP2.6, RCP4.5 and RCP8.5). The minimum and maximum values obtained in the simulation are shown below the average value as: ‘(minimum/maximum)’.

Average, minimum and maximum electricity consumption [TWh/year]

Current Low Central

Total

Heat

Cool

Sockets

Kitchen

DHW

11.56

1.80

0.06

9.42

0.21

0.08

12.18

1.21

0.39

9.11

0.31

1.17

(11.49/13.03)

(0.57/1.77)

(0.09/0.73)

(9.03/9.17)

(0.27/0.32)

(1.08/1.48)

13.94

1.33

0.89

9.47

0.40

1.85

(13.00/14.64)

(0.64/2.01)

(0.21/1.65)

(9.38/9.55)

(0.39/0.42)

(1.77/1.92)

21

High

18.50

1.85

1.29

12.06

0.73

2.58

(17.13/19.64)

(0.90/2.80)

(0.30/2.42)

(11.95/12.16)

(0.73/0.74)

(2.57/2.60)

In the future, the residential electricity demand may increase from 5 to 60% on average for the Low, Central, and High demand scenarios, respectively. The heating demand tendency differs between the High demand scenario and the remaining ones: in the High scenario, the heating demand remains at the present value, while in the Low and Central scenarios it may decrease 33 and 26%, respectively. For the cooling needs, it is expected an increase from 5 to 20-fold. The decrease in heating electricity consumption is the result of three main factors: 1) increase on average temperature; 2) better insulated houses; and 3) wider adoption of more efficient heating devices (heat pumps). The High scenario maintains the magnitude of current consumption mainly because it considers not only more occupied houses but also an aged housing stock. As for the increase in cooling needs, it results from higher temperatures and from wider adoption of cooling electric devices. Electrification rate for domestic hot water and cooking is also a driver for the increase in demand. Demand attributed to socket loads is mainly determined by assumptions on the dwelling floor areas. This work assumes that the final electricity consumption is directly dependent on the assumptions made for each scenario, ignoring two way dynamics such as 1) price-driven mechanisms conditioning end-user behavior, such as the use of air-conditioning, which may affect and be affected by the investment need in the electrification of buildings; 2) societal factors, such as economic growth, migration fluxes, and changes in the age structure of the population, which may affect investment in buildings. Regarding the impact of different climate conditions on the variability of demand, it is clearly observed in the spread of the histograms presented in Figure 5. One can observe that changes in building stock, electrification rates of cooking and DHW and adoption of heat pumps have a stronger effect on residential demand than climate variability, i.e., the range of demand for each scenario driven by the different climate realizations is smaller than the range of demand across different scenarios. Hence, future residential demand is primarily driven by political and market choices regarding the development of the building stock and technologies used. Increasing electrification (High scenario) leads to a higher sensitivity to climate. That is, the variability on the demand (i.e., the range between minimum and maximum values) increases with the electrification of space heating/cooling devices, as shown in Table 6. For heating, the range goes from Low electrification with 1.2 TWh to 1.9 TWh for High electrification – which corresponds to 2.4% and 3.8% of the overall Portuguese consumption in 2017 [51], respectively. For cooling, the variation between maximum and minimum observations is wider, starting at 0.6 TWh (Low) to 2.1 TWh (High). Variability of the total demand hence ranges between 1.5 and 2.5 TWh, for the Low and High demand scenarios, respectively. By observing the extreme values of demand, total demand may change from -0.6% to +70.0%, compared to current levels. The range of change for heating electricity demand goes from an increase of +55.4% to a decrease down to -68.5%. Cooling electricity demand is expected to increase in every case, from a minimum of +42.9% to a maximum of +3935.0%. For the remaining loads, the development of demand depends mainly on the future electrification levels

22

(e.g. DHW demand can increase 31-fold due to the low level of DHW electrification existing today).

4. Sensitivity analysis The residential electricity demand is sensitive to a variety of different assumptions, as considered in the previously described scenarios. In order to have a broader perspective on future demand, a sensitivity analysis on several of those factors was performed to the Central demand scenario.

4.1. Assumptions For all the sensitivity analyses, the approach was the same. The Central scenario was taken as the base scenario, and then, the parameter to be evaluated was changed to its value in the High and Low demand scenarios, without changing any other parameters. The sensitivity analyses performed are presented below: 

Household market: The housing stock of the base scenario was altered to the newer and aged housing stock alternatives, corresponding to the housing stocks assumed for the Low and High demand scenarios, respectively. Table 7 shows the parameters that were changed to perform this analysis. Resulting from the changed parameters shown in Table 7, the age distribution of dwellings changes and, therefore, the distribution of buildings having certain design parameters also changes.

Table 7 – Assumptions made in the sensitivity analysis performed to the household market.

Sensitivity analysis – Household market Central

New

Old

Housing stock development

Middle

New

Old

Retrofitting rate

2%/year

3%/year

1%/year

0.75%/year

1%/year

0.5%/year

Replace older buildings

Replace older buildings

Replace all buildings equally

2.5

2.5

2

Houses in 2050 [#]

6,952,410

7,230,004

6,627,432

Occupied houses in 2050 [#]

3,687,200

3,687,200

4,609,000

53%

51%

70%

New buildings rate Replacement strategy People/house

Occupation factor

23



Floor area of new dwellings: The floor space of new houses are considered to decrease or increase by 20% (compared to the Central scenario) in the Low and High demand scenarios respectively, as Table 8 shows. The distribution of dwellings according to their age is the same as in the Central (i.e., the housing stock market is the same).

Table 8 - Assumptions made in the sensitivity analysis performed to the floor area of new dwellings.

Sensitivity analysis – Floor area of new dwellings Central

Smaller houses

Bigger houses

Current average

-20% than current average

+20% than current average

New buildings in 2050 [m2]

135.6

108.5

162.7

Average of housing stock in 2050 [m2]

96.1

88.7

103.6

New buildings in 2050



Electrification of DHW and cooking: The penetration of electrified devices for DHW and cooking is altered to 50% and 100% (75% in the Central scenario), mentioned as ‗Less electrification (DHW and cooking)‘ and ‗More electrification (DHW and cooking)‘, respectively.



Space heating and cooling: The penetration of heat pumps for space heating and cooling was changed to 17 and 37%, compared to the 27% of the Central demand scenario. These cases are aforementioned as ‗Less heat pumps‘ and ‗More heat pumps‘, respectively.

4.2. Results

Figure 6 shows the effect of the household market, floor area of new dwellings, electrification of DHW and cooking and space heating and cooling on the total residential demand for RCP 4.5 24

pathway. Results for the RCP2.6 and RCP8.5 can be seen in the Error! Reference source not found. - Error! Reference source not found.. For the household market, the changes to the Central scenario are associated with energy demand for space heating and cooling. A newer housing stock results in less heating but more cooling, due to better thermal insulation. Also, with less inhabited houses, penetration of heat pumps in occupied buildings is higher (to keep the level of penetration on the overall existing houses, see subsection 3.1.4). This higher adoption of heat pumps can contribute to lower heating needs (due to higher efficiency of heat pumps compared to electric resistances) while also contributing for more cooling energy consumption due to the availability of heat pumps (heat pumps are the only cooling devices considered in this work). An older housing stock with more houses, and thus fewer people per house, lead to more heating but less cooling, due to less efficient houses and fewer people per house, respectively. Even though the average dwellings‘ floor area is smaller in older houses, more dwellings lead to demand associated with general electric appliances and cooking, which are related to the floor area. The existence of fewer heat pumps, given the higher number of occupied houses (less concentrated heat pumps), also contribute to the heating/cooling results obtained.

a.

b.

c.

d.

Figure 6 – Histograms for the sensitivity analysis performed for the climate path RCP4.5: a. Household market; b. Floor area of new dwellings; c. Electrification of cooking and DHW; and d. Space heating and cooling.

As for the floor area of new dwellings, as expected, smaller homes lead to lower electricity consumption due to lower heating, cooling, cooking and general electric appliances needs (all considered area dependent). In contrast, larger homes lead to higher electricity needs mainly for heating, cooling and general appliances. Since the use of general electric appliances represents heat gains, they may also contribute to increasing cooling needs in dwellings. The cooking and DHW rates of electrification correlate positively with electricity consumption. As a second order effect, lower electrification rates lead to lower internal heat gains (less electric DHW and cooking devices), which leads to higher needs of heating and lower needs of 25

cooling. The opposite happens in the case of higher electrification rates. It is noteworthy mentioning that the use of non-electric appliances may also contribute to the increase of internal gains. However, non-electric appliances energy demand or their contribution to internal gains are not taken into account in this study. Finally, regarding the adoption of heat pumps, one may observe a positive correlation with electricity consumption but with a very slight expression of changes compared to the Central scenario. Fewer heat pumps available result in an increase of non-electric heating, although there is only a small decrease in heating demand because there is also an increase in electric resistance usage. The decrease in cooling energy consumption is explained by the fact that cooling is only provided by heat pumps. More heat pumps result only on a slightly higher heating electricity demand because of their high efficiency. The increase in cooling consumption is directly due to the increase in heat pumps availability. Even though it is not considered in this study, the operation of heat pumps may aggravate the urban heat island effect, contributing potentially to increase cooling needs. Figure 7 summarizes the averaged numerical effect of the changing parameters on the demand per sector. One can observe that total electricity demand is more sensitive to an old housing stock (+19%), due to increasing heating and cooking demand, followed by electrification rate of DHW and cooking (+8%), which has an obvious strong effect on cooking and DHW needs but also cooling demand. Floor area and penetration of heat pumps are shown to have a lower impact on residential demand.

Figure 7 – Change in the average electricity consumption relative to the Central scenario for each type of consumption (RCP4.5).

5. Discussion Even though the scientific community has done an effort to uniformize the assumptions made in climate impact studies (for example, by creating the RCPs), there are still several issues that make the studies hard to compare. In the specific case of the impacts of climate change on energy demand, those are strongly dependent on the region and socio-economic context. Also, 26

the type of parameter that is used to measure such impacts varies significantly among the current literature. For these reasons, the comparison of results among climate impact studies might be challenging. For these reasons, the comparisons here are made only with recent studies focusing the future Portuguese demand. Four works were chosen: 1) Anjo et al. [52] – without considering climate changes, this study addresses the importance of demand response in the Portuguese power system in 2050; 2) Fortes et al. [53] – addressing the impact of different emission caps on the socio-economic sectors in Portugal in 2050 using TIMES-PT model; 3) Jakubcionis and Carlsson [19] – using the cooling degree-days method to study the potential of space cooling demand in Europe; and 4) Andrić et al. [18] – addressing the impact of climate change on the heating needs of a neighborhood in Lisbon, using a Resistance-Capacitance model that considers the buildings‘ characteristics. In this study, the average residential electricity demand in the future is expected to increase more or less, depending on the scenario (5, 21 and 60%, for the Low, Central, and High demand scenarios, respectively). Such results are in line with the studies above: Anjo et al. [52] points to an increase of about 30%; Jakubcionis and Carlsson [19] expects an increase of 35%; and Fortes et al. [53] projects an increase up to 60%, depending on the emission cap policy. The heating needs tend to decrease by 33 and 26% in the Low and Central scenarios, respectively, remaining at the present level in the High scenario, which compares to the decrease of 7 to 52% expected by Andrić et al. [18]. Cooling needs increase in every scenario: 20-fold in the most extreme case, which aligns with the potential 14 to 36-fold increase found by Jakubcionis and Carlsson [19].

6. Conclusions The future of electricity consumption depends on a wide range of factors, such as socioeconomic and environmental. Residential demand will be impacted by climate change due to the increase of average temperature, weather extremes and the consequent change on space heating and cooling needs. This study explores the impact of climate change in the Portuguese residential electricity demand in the middle of this century. A Monte Carlo model is used to determine the hourly electricity consumption considering the housing stock characteristics, occupancy profiles, adoption of space heating and cooling devices, and others. The model uses temperature and solar irradiation data given by various climate models considering three IPCC representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5), featuring a wide range of possible climate conditions for the future. Three main scenarios of different levels of electricity consumption are tested, and a sensitivity analysis is performed for the parameters retrofitting and new construction rates, floor area of new dwellings, electrification of domestic hot water and cooking, and space heating and cooling devices adoption. The results show that residential electricity demand in Portugal may increase from 5 to 60%, mostly due to increased electrification and an increase in cooling needs. Due to the warmer climate, heating demand might decrease down to 33%, while cooling demand might increase 20fold. Even though averages are useful to understand the trend of change, the distribution of the results within each scenario and its variability is important to understand the possible extremes. It was concluded that higher electrification leads to higher sensitivity of the residential electrici27

ty demand to climate conditions – in the scenarios with high electrification, demand variability may be as high as 13.5%, or 2.5 TWh, almost the double than with less electrification. The daily load profiles are affected mainly by the electrification of appliances, which contributes to the appearance of pronounced peaks at times of arriving/leaving home, specially in summer. The sensitivity analysis shows that the electrification of domestic hot water and cooking are the main drivers for the changes, followed by retrofitting and new construction rates. The results are for Portugal, but they may well be indicative to many other regions climate- and socioeconomically alike. Future work may focus on applying the proposed method to other regions in order to have a better overview of the expected electricity demand and its variability in different contexts.

28

Declaration of interest: none.

Acknowledgments The authors gratefully acknowledge the financial support by the MIT Portugal Program on Sustainable Energy Systems, the Portuguese Science and Technology Foundation (FCT), grant PD/BD/114174/2016 and FCT-project UID/GEO/50019/2019 - Instituto Dom Luiz. The authors would like to acknowledge the help provided by Manuel Nascimento, João Careto and Dr. Pedro Soares in the acquisition of climate data and the assistance provided. Finally, the authors thank the Portuguese Energy Agency (ADENE) for providing access to the EPC database.

29

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