1

Energy sustainable systems in Terceira: global and integrated

model for the energy system

Diogo Afonso Loureiro Fernandes1

diogo.l.fernandes@tecnico.ulisboa.pt

1Instituto Superior Técnico, Universidade de Lisboa,

Av. Rovisco Pais, 1 – 1049-001 Lisboa, Portugal

November 2016

Abstract

Small islands are highly dependent on imported fossil fuels for their energy needs, especially for transport and electricity production, which result in environmental impacts. This motivated the creation of action plans to promote sustainable energy systems, characterized by energy efficient use and integration of endogenous and renewable energy sources. To design and acknowledge the impact of energy efficiency policies and strategies, it is crucial to understand how energy is used at the consumer level.

The aim of this thesis is to develop a system-wide energy demand model, focused on the residential and transportation sectors, able to estimate the potential impacts of energy efficiency measures and polices, providing reliable results using accessible data available. This model combines top-down and bottom-up methodologies, considering equipment and vehicle ownership rates, characteristics, specific consumptions and technologies. Using Terceira Island as a case-study, a scenario impact and sensitivity analysis was performed. The scenarios range was defined based on demographic, technologic and efficiency parameters.

The results demonstrate the potential to reduce by 14 % the total energy consumption, 21 % on the transportation sector and 32 % on the residential, with 49 % fossil fuel consumption reduction. The sensitive analysis shows that is possible to further reduce CO2 emissions up to 20 %. However, this can only be achieved if an integrated planning approach to introduce RES on the electricity production mix is pursued when considering the electrification of consumption and large-scale adoption of energy transition measures, especially if all sectors are included.

KEYWORDS

Energy demand model; sustainable energy systems; energy transitions, renewable solutions; energy

vectors; energy planning.

1. Introduction

Insularity, in general, means isolation, dispersion, and small local markets, resulting in significantly higher transportation, communications and energy costs, when compared to the continental regions. From the development point of view, problems on Islands are mostly related to imported fossil fuel dependency, fresh water availability and waste management, and with the security of supply, in order to ensure living standards and economical competiveness [1].

Nowadays, small island energy systems are moving towards the status of “Renewable Islands”, through satisfying the energy demand, total or the majority, using renewable or endogenous energy sources, increasing the security of supply and job offers, without necessarily increasing the costs. Previous studies show that renewable energy technologies can have specific advantages in small-scale applications such as household electricity, street lighting, irrigation systems, village water pumps or

2

similar instruments. Technologies such as micro-hydro, biogas, wind generators and wind pumps are able to operate in locations able to satisfy the equipment requirements [2].

Although the integration of renewable energy systems, engaged by the energy supply models, are critical to consider future changes on energy production, only through the use of detailed models that consider how and when energy is used at the consumer level it will be possible design strategies to better integrate endogenous and renewable energy resources with demand. Connolly et al. [8] made a detailed review of 37 computer tools, providing the information necessary to identify a suitable tool to integrate renewable energy into various energy-systems. Some of the existing models include LEAP [9] (Long-range Energy Alternatives Planning), PATTS [10] - Alternative Transportation Technologies Simulation tool and a model developed by Ximenes [11] for the residential sector.

However, while each model has interesting capabilities, there is a lack of a model able to perform the assessment of energy policy strategies at the regional level, considering a wide range of technology choices for both the transportation and residential sectors, and that accounts for the development of future appliances or equipment’s park mix based on different energy efficiency class penetration. As such, the main goal of this work is to develop a model capable of evaluating the impact of new energy demand strategies and policies, that allow not only the decrease of carbon footprint, but also the integration of renewable and endogenous resources, the decrease in fossil fuels imports, the promotion of the system sustainability and the maximization of the added value for the region.

2. Energy demand model formulation

A modelling approach that discretizes energy consumption and emissions by energy-vector and technology (equipment) was developed. The developed model uses a bottom-up analysis of private vehicles use and main appliances in the residential sector and a top-down formulation for all other energy consumption. The electricity production sector is not included in the analysis as the model focuses only on final energy consumption.

2.1 Methodology for transportation sector

To model the passenger fleet evolution over time, the vehicle stock (considering not only entries in the market but also the vehicle scrappage) and the fleet kilometres travelled are considered. Combining them with the vehicles fuel consumptions, according to the technology/fuel configuration and emissions, the total energy consumption and emissions are estimated for a specific fleet along time. The framework is presented on Figure 1.

Figure 1 – Passenger vehicle model framework.

To estimate the vehicle stock evolution over time, the normalized car ownership, also defined as the number of vehicles per 1000 inhabitants in a country/region (VD - Vehicle density), as a sigmoid function of time, mathematically express through a Gompertz or a Logistic function, presented by equation (1). This function rely on historical vehicle stock and population data.

𝑉𝐷i =

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑒ℎ𝑖𝑐𝑙𝑒𝑠

1000 𝑖𝑛ℎ𝑎𝑏𝑖𝑡𝑎𝑛𝑡𝑠= 𝛽 +

𝛼 − 𝛽

1 + 𝑒−𝑘(log(𝑡)−𝜙) (1)

3

where 𝛼 is the final size achieved, k is a scale parameter, 𝜙 is the x-ordinate of the inflection point of

the curve and 𝑡 is time in years. The R squared method was used to obtain the best vehicle density curve fitting to the real data. The total car stock evolution will be given by equation (2).

𝑇𝑜𝑡𝑎𝑙 𝑐𝑎𝑟 𝑠𝑡𝑜𝑐𝑘𝑖 = 𝑉𝐷𝑖 × 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖 (2)

Where 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖[𝑖𝑛ℎ𝑎𝑏𝑖𝑡𝑎𝑛𝑡𝑠] represents the number of inhabitants in the system studied in year 𝑖.

The fleet will be composed by the number of vehicles entering each year, expressed by new vehicle sales, and by their survival characteristics in the fleet. This information will define, for each vehicle type, how long the vehicles will circulate and when it will be scrapped. The annual vehicle scrappage curves may be defined as the probability of a vehicle being in circulation after k years, which can be produced by a Weibull distribution, with very good fit with real data. This distribution is expressed by equation (3).

𝜑(𝑘, 𝑐) = exp [− (𝑘+𝑏𝑐

𝑇)

𝑏𝑐

], with 𝜑(0) = 1 (3)

Where 𝑘 is the age, 𝜑(𝑘) is the presence probability of vehicles of type 𝑐 having age 𝑘, 𝑏 is the failure steepness for vehicle type 𝑐 and 𝑇 is the characteristic service lifetime for vehicle type 𝑐. In this work, a maximum life-time of 30 years was considered and regarding new technologies entering the market, it is considered they will behave in a similar way compared with the conventional technologies.

Disaggregation on different vehicle technologies is done for LV vehicles, such as diesel, gasoline, Hybrid, LPG (Liquified Petroleum Gas) and EV (Electric Vehicles). The passenger vehicle stock of technology 𝑥 for year 𝑖 is given by the following equation (4).

𝑇𝑜𝑡𝑎𝑙𝑃𝑉𝑥,𝑖

= ∑ 𝑃𝑉𝑥,𝑖,𝑦

2030

𝑦=𝑏𝑒𝑓𝑜𝑟𝑒 2005

(4)

Where 𝑇𝑜𝑡𝑎𝑙𝑃𝑉𝑥,𝑖 is the total number of passenger vehicle stock of technology 𝑥 for year 𝑖 and 𝑃𝑉𝑥,𝑖,𝑦 is

the number of vehicles of technology 𝑥 from year 𝑦 in year 𝑖.

Considering the vehicle kilometres travelled (VKT), the data was obtained based on values from EUROSTAT [12] for the Portuguese fleet, which suggest an average value 13 000 and 8 700 kilometres per year, for diesel and gasoline, respectively. The new vehicle sales and the alternative vehicle technologies follow the average trend between gasoline and diesel in terms of vehicle kilometres travelled, which corresponds to 10 850 kilometres per year. The VKT per year evolution along the vehicles lifetime was not considered in this study.

The yearly fleet fuel consumption for each technology is obtained using equation (5).

𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦 = 𝑃𝑉𝑥,𝑖,𝑦 ×

𝐶𝑎𝑟𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦

100× 𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝑠𝑥 (5)

𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦[𝑙𝑖𝑡𝑟𝑒𝑠] is the fuel consumption of a vehicle from year 𝑦 ,with technology 𝑥 in year

𝑖, 𝑃𝑉𝑥,𝑖,𝑦 is the number of vehicles of technology 𝑥 from year 𝑦 in year 𝑖, the 𝐶𝑎𝑟𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦[𝑙𝑖𝑡𝑟𝑒𝑠]

is the fuel consumption taken from the technical data presented by the brands, of a vehicle from year 𝑦, in year 𝑖, using technology 𝑥, and 𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝑠𝑥[𝑘𝑚] is the total number of kilometres assumed for

technology 𝑥.

2.2 Methodology for the residential sector

In order to model the appliances park evolution over time, the equipment’s stock is considered. Combining this with specific consumption, according to the technology and efficiency class, based on the international regulations, the energy consumption and future energy demand is obtained for a specific set of end-uses. For the future equipment’s park, only appliances with an efficiency classification equal or higher than A were considered to the stock as sales. The framework is presented on Figure 2.

The appliances for which an appliances stock was calculated were: refrigerators, freezers, washing machines, tumble dryers, dishwashers, stoves with oven, inductive ovens and hobs. On the other hand, while water heating systems are considered in the model, they are not modelled using an appliances stock approach due to the lack of data.

4

Figure 2 – Household appliances model framework.

The number of equipment is calculated using equation (6).

𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝑖,𝑥 =

%𝑒𝑞𝑢𝑖𝑝𝑖,𝑥 × 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖 × 𝐸𝑞𝑢𝑖𝑝𝑝𝑒𝑟𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔𝑥

𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑜𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 (6)

Where 𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝑖,𝑥 is the number of equipment’s per technology 𝑥 in year 𝑖, %𝑒𝑞𝑢𝑖𝑝𝑖,𝑥 [%] denotes the

equipment household penetration percentage, 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖 is the number of inhabitants in year 𝑖 ,

𝐸𝑞𝑢𝑖𝑝𝑝𝑒𝑟𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔𝑥 [

𝐸𝑞𝑢𝑖𝑝

𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔] is the number of equipment’s of technology 𝑥 per dwelling and

𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑜𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 [𝑃𝑒𝑟𝑠𝑜𝑛

𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔] is the average number of inhabitants per dwelling. To simulate the appliances

scrappage curves, a behaviour similar to the vehicles was assumed, based on a Weibull distribution and applying equation (3). The maximum life-time expectancy assumed is 18 years.

A combination between the yearly new equipment’s sales per efficiency class, the scrappage curves and the assumed total number of equipment’s per efficiency class, adapted from ICESD [13], gives the year appliance’s park composition per efficiency technology. The estimation of the number of future appliances for future years is then calculated in a similar way to what was presented in section 2.1.

The energy consumption of the appliances park is calculated depending on the equipment purpose of use. For water heating equipment, the energy demand is computed using equation (7) .

𝐶𝑊𝐻𝑖,𝑥,𝑦 =

𝑄𝑊𝐻𝑖. 𝑃𝑖,𝑥,𝑦

𝜂𝑥,𝑦 (7)

Where, for an equipment of technology 𝑥, in year 𝑖 using energy source 𝑦, 𝐶𝑊𝐻𝑥,𝑦,𝑧 [𝑘𝑊ℎ] is the annual

energy consumption, 𝑄𝑊𝐻𝑖 [𝑘𝑊ℎ] is the annual energy needed for water heating, 𝑃𝑖,𝑥,𝑦 is que equipment

penetration and 𝜂𝑥,𝑦 [−] is the water heating technology efficiency.

For white appliances that are governed by international regulations, the energy efficiency class shall be determined on the basis of its Energy Efficiency Index (EII) [14]. This is calculated as presented in equation (8) and rounded to one decimal place.

𝐸𝐸𝐼 =

𝐴𝐸𝑐

𝑆𝐴𝐸𝑐× 100 (8)

Where 𝐸𝐸𝐼 is the energy efficiency index, 𝐴𝐸𝑐 [𝑘𝑊ℎ

𝑦𝑒𝑎𝑟] is the annual energy consumption of the household

appliance and 𝑆𝐴𝐸𝑐 [𝑘𝑊ℎ

𝑦𝑒𝑎𝑟] is the standard annual consumption of the household appliance. The

equipment’s characteristics have to be assumed to calculate 𝑆𝐴𝐸𝑐. Combining the values of IEE per technology and per efficiency class assumed on the regulations and the standard annual consumption (equation (8)), the annual energy consumption [𝐴𝐸𝑐] per technology and per efficiency class is obtained.

2.3 Other sectors

The Agriculture, Industry, Commerce and Services analysis was only possible using a simplified top-down approach and focusing only on electricity due to lack of reliable data. It was assumed that a Logistic or Gompertz function (equation (1)), provides a good approximation to estimate the future electricity evolution per capita, applied in the same way that was done on the passenger vehicle fleet

5

characterization. Following this, the electricity consumption per sector over the years is given using equation (9).

𝑆𝑒𝑐𝑡𝑜𝑟𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖,𝑗

=𝐶𝑆𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎𝑖,𝑗

× 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖

1000 (9)

𝑆𝑒𝑐𝑡𝑜𝑟𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖,𝑗[𝑀𝑊ℎ] is the total electricity consumption of sector 𝑗 in year 𝑖 and

𝐶𝑆𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎𝑖,𝑗[

𝑘𝑊ℎ

𝑖𝑛ℎ𝑎𝑏𝑖𝑡𝑎𝑛𝑡] is the electricity consumption per capita of sector 𝑗 in year 𝑖.

3. Terceira Island

Terceira is one of nine islands that belong to Azores Archipelago. With 402 km2 of area, is the second most populated island on Azores, with 56 437 inhabitants, according to Census 2011 [15]. Terceira's main economic activity is rising of livestock and the production of dairy-based products. It also benefits greatly from the leasing agreement for the air force base with the United States which brings a tremendous amount of indirect revenues to the its population. In 2014, 75 thousand tonnes of petroleum products were consumed, divided by a 53 % share to fuel oil, 28 % to diesel, 10% do gasoline and 9% to butane. From this, 86 % of the fuel oil is used to produce electricity. According to “Electricidade dos Açores” (EDA), 203.25 GWh of electric energy were produced in 2014,with 17.3% coming from renewables [16].

3.1 Energy demand by sector

Road transportation is characterized by a large number of light duty vehicles, with the majority of these being private passenger vehicles. Figure 3 illustrates the total consumption of the transportation sector per energy source. This is the most critical sector in terms of energy expenditure, contemplating almost 50 % of the total energy consumption, divided between Diesel and Gasoline. For 2014, this island included 31 916 vehicles, where 73.4 % were light-passenger vehicles. On the other hand, mixed and heady-duty vehicles have few relevance on the fleet, corresponding to approximately 0.2 % and 0.3 %, respectively.

Figure 4 represents the total consumption of the domestic sector over the years. After transportation, this is the sector which contributes more to the total energy consumption of the island, with approximately 20 %, mainly due to the huge butane consumption associated with the cooking and heating necessities. The consumption of electricity is associated with all the electric equipment.

Figure 3 – Total consumption of the transportation sector per energy source [17], [18].

Figure 4 – Total consumption of the residential sector per energy source [17], [18].

From Figure 5, it is possible to observe that agriculture/industry sector relies in three major energy sources: fuel oil, electricity, and diesel. The fuel oil and the electricity are used to feed the equipment’s necessary to execute the vital tasks, such as the rising of livestock or the production of dairy-basic products, as diesel is mostly used on off-road vehicles for transportation. For commerce/services, Figure 6 shows that the main energy source used to satisfy the demand of this sector is electricity, used to supply the electric daily basis electric systems, necessary to develop the activities associated with this sector. As for the butane, it’s mainly used on the restaurants and hotels to cook the meals and, in some cases, to heat the rooms.

6

Figure 5 – Agriculture/Industry energy consumption per energy source [17], [18].

Figure 6 – Commerce/services total energy consumption per energy source [17], [18].

3.2 Scenario definition

The scenarios are designed based on four key issues: (1) demography, based on the expected evolution of the population; (2) technology penetration, related with the level of introduction of specific technologies, such as vehicle density or appliances penetration rates; (3) technology choice, related with the choice of technologies to fulfil a specific energy service, such as the introduction of electric vehicles or other unconventional technologies; and (4) technology efficiency, related with the efficiency of appliances and vehicles. The scenarios designed are compared with the BAU (business as usual) scenario, also assumed as the reference scenario, which considers that no actions (measures and policies) are developed to improve energy efficiency, shift uses of fossil fuels to electricity and exploit renewables and endogenous energy sources.

Considering statistical data provided from the report of ACAP [19], a vehicle model was assumed, based on the most sold brand in Portugal over the years, as well as the engine capacity. The vehicle age was assumed based on the fleet park characterization from ASF [20]. The technical information for diesel and gasoline is presented on Table 1. For the alternative technologies, hybrid and LPG vehicles were assumed to have a fuel consumption of 3.31 and 11.30 l/100km, respectively, and a CO2 emissions factor of 76.99 and 47.23 g/km, respectively.

Table 1 - Technical characteristics of diesel and gasoline vehicles [21].

As for electric vehicles, the values assumed were an EV battery capacity of 19.2 kWh and a consumption of 0.2 kWh/km, which are kept constant through the years due to its high efficiency. As for the standard CO2 emissions per kWh produced by the Terceira electricity generation systems, the assumed value was 564 gCO2/kWh, obtained from ERSE report [22], and it’s kept constant through the years.

For the residential sector, the base-line scenario of the appliances park was developed based on the statistical data from ICESD [13]. Due to the lack of data, the total appliance stock and respective penetration today will be assumed as the same of 2010. For the development of future scenarios, the A+++ efficiency class will be introduced and will have preponderance on the future equipment sales.

As for water heating, assuming a 3 m2 minimum per solar system and a reservoir of 200 litres for 40 litres/person/day, the thermosiphon solar system, known as the “domestic kit”, was considered as the solar thermal equipment, satisfying 84.36 % of the heating demand, leaving the other 15.64 % to the electric back-up system. Considering the electric heaters, the calculations were performed using equation (7).

Fuel Consumption

[l/100 km]

CO2 emissions

( g/km)

Fuel Consumption

[l/100 km]

CO2 emissions

( g/km)

Less than 1 year Renault 2015 3,7 95 n/d n/d

1 year Renault 2014 4,2 110 6,9 159

2 years Renault 2013 4,2 110 6,9 n/d

3 years Renault 2012 4,21 110 6,9 159

4 years Renault 2008 4,55 120 6,9 163

5 to 10 years Renault 2008 4,55 120 6,9 163

plus than 10 years Renault 2006 4,66 124 6,9 164

GasolineDiesel

Vehicles Brand Year

7

The scenario combinations for the transportation and residential sector are defined according to the following divisions and nomenclature presented on Table 2 and Table 3.

𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝑤,𝑥,𝑦, where {

𝑤 − 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑥 − 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑦 − 𝐸𝑉 𝑝𝑒𝑛𝑒𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠

𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙𝑤,𝑥,𝑦, where {

𝑤 − 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑥 − 𝑇𝑒𝑐𝑛𝑜𝑙𝑜𝑔𝑦 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑦 − 𝐸𝑓𝑓𝑖𝑐𝑒𝑛𝑐𝑦 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠

Table 2 – Proposed scenarios for the transportation sector.

Theme Description

Demography 𝑤 -

Population

2. Optimistic (30%)

1. Medium (23%)

3. Pessimistic (19%)

Car stock 𝑥 - Vehicle

density

2. Optimistic (507 vehicles per 1000 inhabitants)

1. Medium (461 vehicles per 1000 inhabitants)

3. Pessimistic (415 vehicles per 1000 inhabitants)

Vehicle

Technology

𝑦 - EV

penetration

1.Optimistic (50% EV penetration)

2.Medium (25% EV penetration)

3.Pessimistic (10% EV penetration)

4.Azorina (30 % EV Penetration)

Table 3 – Proposed scenarios for the residential sector.

Theme Description

Demography 𝑤 - Population

2. Optimistic (30%)

1. Medium (23%)

3. Pessimistic (19%)

Technology 𝑥 – Equipment’s

Technology

1. New Technologies

2. Same Technologies

Efficiency 𝑦 – Appliances

efficiency Class

1.Optimistic ( 75 % A+++

efficiency class)

2.Pessimistic (95 % A

efficiency class)

For the other sectors, the analysis is done in a simple way, where only demography changes and positive consumption per capita evolution have impact on the future electricity consumption. Agriculture and Industry is defined as 𝐴. 𝐶𝑤, while Commerce and Services is 𝐶. 𝑆𝑤. Considering this, the following table illustrates the scenarios proposed for the other sectors.

Table 4 – Scenarios proposed for the other sectors considered.

Theme Agriculture/Industry (𝐴. 𝐼𝑤) Commerce/Services (𝐶. 𝑆𝑤)

Demography w – Population

2. Optimistic (30%)

1. Medium (23%)

3. Pessimistic (19%)

For the future total energy consumption and emissions of the island (section 4.3), four scenarios were analysed, which are: i) BAU scenario, assumed as the reference; ii) 𝑃𝑒𝑠𝑠𝑖𝑚𝑖𝑠𝑡𝑖𝑐 scenario, which considers a migration increase and no interest in promoting efficient energy solutions. Results from the combination of 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡3.3.3, 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙3.2.2, 𝐴. 𝐼3 and 𝐶. 𝑆3; iii) 𝑂𝑝𝑡𝑖𝑚𝑖𝑠𝑡𝑖𝑐 scenario, which considers an increase in population associated with the promotion of sustainable actions, combining 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡2.2.1, 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙2.1.1, 𝐴. 𝐼2 and 𝐶. 𝑆2 scenarios; and iv) 𝑀𝑒𝑑𝑃𝑜𝑝𝑐ℎ𝑎𝑛𝑔𝑒𝑠, which considers the

baseline demographic scenario but assumes all technological changes and the adoption of energy efficiency measures. This combines 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡1.1.1, 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1.1.1, 𝐴. 𝐼1 and 𝐶. 𝑆1 scenarios.

4. Results

In this chapter, the global and detailed results presented, including the assessment of the measures introduced and future outcomes in terms of energy consumption and CO2 emissions. These results are evaluated according with the reference scenario considered. Due to the lack of reliable data, the main focus will be given to the Transportation and Residential sectors.

4.1 Transportation sector

A general compilation of the results obtained from the scenarios developed for the passenger fleet is presented, considering some of the most relevant features, followed by one of the four representative scenarios analysed. Using the BAU scenario as a reference, Figure 7 shows the results obtained for each scenario in terms of energy consumption and CO2 emissions savings in 2030, considering light-passenger fleet only, while Figure 8 presents the future energy demand for scenario 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡1.1.4.

8

Figure 7 - Scenario results compilation for the energy consumption and the CO2 emissions.

Figure 8 - Total energy consumption per energy source assuming Transport1.1.4 .

When considering all scenarios (Figure 7), it can be seen that energy consumption savings range between 36.2% and -38.8%, CO2 emissions savings range between 18.6% and -54.1%. This analysis shows a particular feature, higher EV penetration corresponds to higher CO2 emissions. First, Terceira still has a high fossil fuel dependency on electricity production, which results in high emissions per kWh produced. Second, new diesel vehicles emit 95 gCO2/km, which is lower than the resulting CO2 emission factors for EVs due to the actual electricity generation mix (approximately 113 gCO2/km).

For the global transportation sector, Transport1.1.4, (Figure 8) demonstrates a total energy consumption reduction of 5% and 11% penetration of electricity at final demand. The reductions are obtained due to the high efficiency of electric vehicles.

4.2 Residential sector

The Figure 9 presents the reduction of energy consumption and CO2 emissions, when compared to the BAU scenario, while Figure 10 shows the future energy demand for 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1.1.1 scenario.

Figure 9 - Scenario compilation for the residential sector (Energy vs Emissions).

Figure 10 - Energy consumption per energy vector assuming 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1,1,1.

Considering all scenarios (Figure 9), it can be seen that energy consumption savings range between 36.7% and -16.6%, CO2 emissions savings between 41.1% and 11.0%. From all parameters used to define the scenarios for the residential sector, the technologies considered as part of the future equipment’s park was found to be extremely relevant. This is a consequence of changing not only from butane-based equipment to electric and solar energy devices (for water heating purposes), which are more efficient, but also the preference for multifunction instead of stand-alone equipment, resulting in the equipment usage optimization.

9

The 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1.1.1 scenario (Figure 10) is presented as technological and appliances efficiency shift from the current situation, due to strategies and policies develop by the government to promote energy efficiency, fossil fuel mitigation and rationalization of resources. This shows a total energy consumption reduction of 29.4% and 39.7% reduction on CO2 emissions.

4.3 Total energy consumption and CO2 emissions evolution

The evolutions of total energy consumption and CO2 emissions for each scenario considered are presented on Figure 11 and Figure 12, respectively.

Figure 11 – Energy consumption for each scenario analysed.

Figure 12 – CO2 emissions for each scenario analysed.

Figure 11 shows that 𝑂𝑝𝑡𝑖𝑚𝑖𝑠𝑡𝑖𝑐 scenario has the highest energy consumption, 4 % and 13.8 % greater

than the BAU and 𝑀𝑒𝑑𝑃𝑜𝑝𝑐ℎ𝑎𝑛𝑔𝑒𝑠 scenario, respectively. According to the results, in 2025, the

𝑀𝑒𝑑𝑃𝑜𝑝𝑐ℎ𝑎𝑛𝑔𝑒𝑠 scenario would lead to a lower energy consumption than the 𝑃𝑒𝑠𝑠𝑖𝑚𝑖𝑠𝑡𝑖𝑐 scenario.

Although the 𝑀𝑒𝑑𝑃𝑜𝑝𝑐ℎ𝑎𝑛𝑔𝑒𝑠 scenario considers a much larger light-passenger vehicles fleet and

equipment park, the introduction of electric vehicles and appliances with higher efficiency, combined with technological changes on DWH, results in important reductions in terms of energy consumed.

Regarding the CO2 emissions (Figure 12), even considering high efficiency equipment and vehicles penetration, the high emission factor that result from the electricity production mix doesn’t allow to obtain the results that could lead towards a more sustainable and environmental friendly path. Assuming a yearly 2% decrease on CO2 emissions, the emission factor will decrease from 0.58 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ in 2015

to 0.43 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ in 2030, which represents an electric vehicle emission factor of 83 gCO2/km, much lower than the values presented for the diesel and gasoline (95 and 159 gCO2/km, respectively).

5. Conclusions

A system-wide energy demand model was developed to assess the potential impact of energy saving measures and polices with renewable energy penetration at a consumer level, promoting not only renewable and endogenous energy sources integration, but also sustainable behaviours. The model relies on a bottom-up approach to define the transportation and residential sectors, considering technical and usage characteristics, as well as demographic, technology, efficiency and ownership penetration information, suited to be integrated on energy planning exercises.

The combination of all the measures proposed demonstrates that there is the potential to reduce by 13.7 % the total energy consumption, with a 49% reduction in fossil fuel consumption. In terms of emissions, the sensitive analysis showed that if a yearly electricity emission factor reduction of 2% is achieved, it is possible to reduce CO2 emissions by up to 20%, improving the benefits of EVs.

It is important to note that, to maximize the impacts and achieve the best results obtained in this work, it is necessary to design an integrated planning approach to introduce RES on the electricity production mix when considering the electrification of consumption and large-scale adoption of energy transition measures, especially if all sectors are included.

10

6. References

[1] N. Duić and M. da Graça Carvalho, “Increasing renewable energy sources in island energy supply: case

study Porto Santo,” Renew. Sustain. Energy Rev., vol. 8, no. 4, pp. 383–399, 2004. [2] F. Chen, N. Duic, L. Manuel Alves, and M. da Graça Carvalho, “Renewislands-Renewable energy

solutions for islands,” Renewable and Sustainable Energy Reviews, vol. 11, no. 8. pp. 1888–1902, 2007. [3] C. Bueno and J. A. Carta, “Wind powered pumped hydro storage systems, a means of increasing the

penetration of renewable energy in the Canary Islands,” Renew. Sustain. Energy Rev., vol. 10, no. 4, pp. 312–340, 2006.

[4] E. Michalena and V. Angeon, “Local challenges in the promotion of renewable energy sources: The case of Crete,” Energy Policy, vol. 37, no. 5, pp. 2018–2026, 2009.

[5] A. Figueira, “Energias Renováveis na Região Autónoma da Madeira,” 2011. [6] G. I. Project, “Green Islands Project: Towards sustanable energy systems - Azores Energy Outlook:

Challenges and Opportunities for 2018,” 2010. [7] Azorina, “Plano de Acção para a Energia Sustentável,” 2012. [8] D. Connolly, H. Lund, B. V. Mathiesen, and M. Leahy, “A review of computer tools for analysing the

integration of renewable energy into various energy systems,” Appl. Energy, vol. 87, no. 4, pp. 1059–

1082, 2010. [9] C. Heaps, “An introduction to LEAP,” 2008. [10] P. D. B. M. Carvalho, “Evaluation of the impacts of the introduction of alternative fuelled vehicles in the

road transportation sector,” Universidade Técnica de Lisboa - Instituto Superior Técnico, 2011. [11] R. Ximenes, “Energy Demand Model To Support the Definition of Sustainable Energy Systems:

Residential Sector,” 2015. [12] “Passenger Transport Statitistics,” Eurostat. [Online]. Available: http://ec.europa.eu/eurostat/statistics-

explained/index.php/Passenger_transport_statistics. [13] “Inquérito ao Consumo de Energia no sector doméstico 2010,” Direcção Geral de Energia e Geologia

(DGEG), 2011. . [14] U. Europeia, “Directiva 2010/30/UE,” no. 4, pp. 1–12, 2010. [15] “Census 2011 - Resultados Definitivos Açores,” INE - Instituto Nacional de Estatística, 2011. [Online].

Available: www.ine.pt. [Accessed: 10-Nov-2015]. [16] EDA - Electricidade dos Açores, “Relatório de Contas,” 2014. [17] “Venda de Combustíveis- Por municipio e actividade económica,” DGEG - Direcção Geral de Energia e

Geologia. [Online]. Available: http://www.dgeg.pt/. [18] “Consumo de electricidade - Por sector de Actividade,” DGEG. [Online]. Available: http://www.dgeg.pt/. [19] ACAP, “Estatísticas do Sector Automóvel,” Associação Automóvel de Portugal, 2013. . [20] “Parque Automóvel Seguro,” ASF - Autoridade de Supervisão de Seguros e Fundos de Pensões.

[Online]. Available: http://www.asf.com.pt/NR/exeres/7D383D46-9431-416E-98C7-395B0A9E7080.htm. [21] “Renault Mégane,” Carinf - focused on data. [Online]. Available: http://www.carinf.com/.

[22] ERSE, “ERSE - Dados rotulagem.” .