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Energy sustainable systems in Terceira: global and
integrated model for the energy system
Diogo Afonso Loureiro Fernandes
Thesis to obtain the Master of Science Degree in
Mechanical Engineering
Supervisors:
Prof. Paulo Manuel Cadete Ferrão
Dr. André Alves Pina
Examination Committee
Chairperson: Prof. Mário Manuel Gonçalves da Costa
Supervisor: Dr. André Alves Pina
Member of the Committee: Prof. Tânia Alexandra dos Santos Costa e Sousa
November 2016
ii
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.
iii
Resumo
As Ilhas (designadas por sistemas isolados) possuem uma elevada dependência de combustíveis
fosseis para satisfazer as necessidades energéticas emergentes, com consequentes impactos
ambientais. Este problema levou à criação de planos de acção, com o objetivo de promover sistemas
sustentáveis de energia, caracterizados pela eficiência energética e integração de fontes de energia
endógenas e renováveis. É fundamental compreender de que forma a energia é utilizada e aproveitada
ao nível do consumidor para que seja possível criar e reconhecer os potenciais impactos que as
estratégias e políticas de eficiência energética.
O objectivo desta tese é desenvolver um modelo de procura energética integrado, focado no sector
residencial e dos transportes, com o intuito de, através da utilização de dados estatísticos, ser capaz
de avaliar o impacto da introdução de medidas e estratégias de eficiência energética e auxiliar no
desenvolvimento de exercícios de planeamento energético. O modelo apresentado combina
metodologias top-down e bottom-up, considerando taxas de penetração de equipamentos e veículos,
bem como as suas caraterísticas, consumos específicos e tecnologias. Assumindo a ilha Terceira como
caso de estudo, vários cenários foram desenvolvidos para avaliar o impacto das medidas propostas,
bem como a apresentação de uma análise de sensibilidade às emissões.
Os resultados demonstram o potencial para reduzir o consumo de energia no sector dos transportes
em 21% e 32% no sector residencial, o que se traduz na diminuição do consumo total de energia da
ilha e combustíveis fosseis em 14% e 49%, respetivamente. A análise de sensibilidade mostra que é
possível reduzir as emissões de CO2 na ilha em 20%. No entanto, a veracidade deste cenário está
dependente de uma abordagem cuidada na criação de um plano integrado que permita a introdução
de fontes de energia renováveis no sistema produtor de eletricidade, possibilitando a eletrificação dos
consumos e a integração, em larga escala, de medidas de transição energética, em especial se todos
os sectores forem considerados.
Palavras-chave: Modelos de procura energética, sistemas sustentáveis de energia, transição
energética, soluções renováveis, vetores energéticos, planeamento energético.
iv
Acknowledgments
First, I would like to thank to my thesis advisor André Pina for all the support given through the
elaboration of this thesis and its revision. His knowledge, expertise, guidance and availability steered
me in the right direction when he thought I needed it.
Also, I would like to thank to Professor Paulo Ferrão for the opportunity given and Professor Carlos Silva
for the valuable advices and comments done during this phase.
My sincere thanks to Diana Neves for having the door always open to help me when I had any question
or trouble and precious comments done through the process.
I would like to express my tremendous gratitude to my whole family, with special care to my mother
Graça and father Luis, for being so supportive, always watching over me and all the values taught; and
my amazing sister Inês, who’s always there for me. There is no one who I admired more than you.
Last, but not least, to all my friends for the years of joy, happiness and motivation to end this challenge.
I admire every single one of you, hoping that this life’s journey continues with you all here, proud of what
I did.
v
Acronyms
ACAP - Associação Automóvel de Portugal, 50
APS - Autonomous Power System, 6
ASF - Parque Automóvel Seguro, 50
ASTRA - Assessment of Transport Strategies, 14
BAU - Business as Usual, 47
BREDEM - Building Research Establishment Domestic Energy Model, 14
CO2 - Carbon Dioxide, 1
CPC - Compound parabolic collector, 56
DGEG - Direcção Geral de Energia e Geologia, 35
DWH - Domestic Hot Water, 70
EDA - Electricidade dos Açores, 35
EDP - Energias de Portugal, 77
EEI - Energy Efficiency Index, 31
ELECTRA - Empresa de electricidade e água, 6
EMVS ¬ European vehicle market statistics, 50
ERSE - Entidade Reguladora dos Serviços Energéticos, 54
ERSE – Entidade Reguladora dos Serviços Energéticos, 27
ETC - Evacuated tube collectors, 56
ETSAP - Energy Technology System Analysis Program, 12
EV - Electric Vehicles, 21
GHG - Greenhouse Gas Emissions, 1
GJ - Gigajoule, 70
ICE - Internal Combustion Engines, 21
ICESD - Inquérito ao Consumo Energético no Sector Residencial, 44
IEA - International Energy Agency, 12
INE - Instituto Nacional de Estatística, 35
IRR - Internal Rate of Return, 75
kgCO2/kWh - Kilograms of CO2 per Kilowatt hour, 74
kWh - Kilowatt per hour, 30
LCA - Life-cycle Assessment, 14
LDV - Light Duty Vehicles, 42
LEAP - Long-range Energy Alternatives Planning, 12
vi
LHV - Low Heating Value, 24
LNEG - Laboratório Nacional de Energia e Geologia, 55
LPG - Liquified Petroleum Gas, 21
LPV - Light Passenger Vehicles, 47
MoMo - IEA Mobility model, 13
MW - Megawatt, 5
NPV - Net Present Value, 75
O&M - operation and maintenance costs, 76
PATTS - Alternative Transportation Technologies Simulation tool, 13
PP - "Payback Period”, 75
REH - Regulamento do Desempenho Energético dos Edifícios de Habitação, 76
REPCV - Renewable Energy Plan for Cape Verde, 6
SREA - Serviço Regional de Estatística dos Açores, 48
VD - Vehicle Density, 18
VKT - Vehicle Kilometres Travelled, 21
ρ - fuel density, 24
vii
Table of Contents
Abstract ............................................................................................................................... ii
Resumo ................................................................................................................................iii
Acknowledgments ...............................................................................................................iv
Acronyms ............................................................................................................................. v
Table of Contents ...............................................................................................................vii
List of Figures .....................................................................................................................ix
List of Tables ......................................................................................................................xii
1. Introduction .................................................................................................................. 1
Context and motivation ............................................................................................ 1
Objectives ............................................................................................................... 2
Document Structure ................................................................................................ 2
2. State of the Art ............................................................................................................. 3
2.1 Examples of effort for reducing the dependency of isolated communities ................ 3
2.2 Energy models characterization .............................................................................. 6
2.3 Energy modelling tools ............................................................................................ 9
2.3.1 System-wide ...................................................................................................10
2.3.2 Transportation sector ......................................................................................11
2.3.3 Residential sector ............................................................................................12
2.3.4 Identified gaps .................................................................................................13
3. Energy demand models formulation ..........................................................................14
3.1 Methodology for transportation sector ....................................................................15
3.1.1 Vehicle stock evolution over time ....................................................................16
3.1.2 Mobility ............................................................................................................21
3.1.3 Fuel, energy consumption and emissions ........................................................21
3.2 Methodology for residential sector ..........................................................................23
3.2.1 Appliances Park ..............................................................................................24
3.2.2 Energy consumption .......................................................................................26
3.3 Other sectors..........................................................................................................29
4. Terceira Island .............................................................................................................31
4.1 Data sources and challenges .................................................................................31
4.2 Demand by energy source .....................................................................................32
4.2.1 Fossil Fuels .....................................................................................................32
4.2.2 Electricity .........................................................................................................33
4.2.3 Total Demand .................................................................................................35
4.3 Demand per Sector ................................................................................................36
4.3.1 Transports .......................................................................................................37
viii
4.3.2 Residential ......................................................................................................39
4.3.3 Agriculture/ Industry ........................................................................................41
4.3.4 Commerce/Services ........................................................................................42
4.4 Scenario definition ..................................................................................................43
4.4.1 Transport Sector .............................................................................................43
4.4.2 Residential Sector ...........................................................................................50
4.4.3 Other Sectors ..................................................................................................53
4.4.4 Combinations and scenarios of interest ...........................................................54
5. Results .........................................................................................................................57
5.1 Transportation sector .............................................................................................57
5.1.1 Scenarios analysis ..........................................................................................57
5.1.2 Demography and vehicle density ....................................................................59
5.1.3 Detailed Scenarios ..........................................................................................60
5.2 Residential sector ...................................................................................................63
5.2.1 Scenario compilation analysis .........................................................................63
5.2.2 Detailed Scenarios ..........................................................................................64
5.3 Agriculture/Industry and Commerce/Services (Other Sectors) ................................67
5.4 Total energy and CO2 emissions evolution ............................................................68
5.4.1 Total Energy Consumption and CO2 emissions ...............................................69
5.4.2 Sensitive analysis to CO2 emissions evolution ................................................70
5.5 DWH equipment - economic analysis .....................................................................71
5.5.1 Equipment and scenario definition...................................................................71
5.5.2 Economic results .............................................................................................73
6. Conclusions and future work .....................................................................................75
Conclusions ...........................................................................................................75
Future Work ...........................................................................................................76
7. References ...................................................................................................................77
Appendices ....................................................................................................................... A-1
Data Sources and Challenges ................................................................... A-1
Solar thermal panels characteristics .......................................................... B-5
DHW technologies economic analysis ....................................................... C-6
ix
List of Figures
Figure 1 – Top-down and bottom-up modelling methodologies (adapted from [33]) .............................. 9
Figure 2 – Hierarchical structure of LEAP demand in transportation (left) and residential (right) sectors
[37]–[39] ................................................................................................................................................. 10
Figure 3 – Model structure..................................................................................................................... 14
Figure 4 - Passenger vehicle model framework .................................................................................... 15
Figure 5 – Car density as a function of time[56] .................................................................................... 16
Figure 6 – Scrappage curves from different literature sources ............................................................. 18
Figure 7 – Household appliances model framework ............................................................................. 24
Figure 8 – Reference values for conditioning and water heating equipment [64] ................................. 26
Figure 9 – Primary Energy Consumption per fossil fuel energy source [81] ......................................... 32
Figure 10 – Petroleum derivatives consumption distribution [81] ......................................................... 33
Figure 11 – Fossil fuel consumption distribution per economic activity [81] ......................................... 33
Figure 12 – Fuel Prices in Azores per Source [82], [83] ....................................................................... 33
Figure 13 – Electricity production over the years [84] ........................................................................... 34
Figure 14 - Electricity Production 2014: Source’s Share [26]................................................................ 34
Figure 15 - Share of the total electricity consumption per sector in 2014 [85] ...................................... 35
Figure 16 - Total Energy Consumption by Economic sector [81], [85] .................................................. 35
Figure 17 - Share of the Total Energy Consumption per Sector [81], [85] ............................................ 36
Figure 18 – Total energy consumption of the main sectors .................................................................. 37
Figure 19 – Total consumption of the transportation sector per energy source[81], [85] ..................... 38
Figure 20 – Diesel consumption share per Island and Azores diesel consumption [81] ...................... 38
Figure 21 – Terceira road fleet characterization in 2014 [86]................................................................ 39
Figure 22 – Number of passenger vehicles in Terceira over the last decade [86] ................................ 39
Figure 23 - Total consumption of the residential sector per energy source [81], [85] ........................... 39
Figure 24 – Energy consumption per energy source in a typical household (Terceira) ........................ 41
Figure 25 – Energy consumption per end-use in a typical household (Azores) .................................... 41
Figure 26 - Energy consumption per energy source in the kitchen (Terceira) ..................................... 41
Figure 27 - Electricity consumption per end-use (Azores) ................................................................... 41
Figure 28 – Butane consumption per end-use (Azores) ....................................................................... 41
Figure 29 – Evolution of the agriculture/industry sector total consumption per energy source[81], [85]
(DGEG) .................................................................................................................................................. 42
Figure 30 – Commerce/services total consumption per energy source[81], [85] .................................. 42
Figure 31 – Number of inhabitants of Terceira [88]–[90] ....................................................................... 44
Figure 32 – Scenarios for the impact of Terceira in the total population of Azores .............................. 45
Figure 33 – Number of inhabitants in Terceira , based on the scenarios considered ........................... 45
Figure 34 –Vehicle density curve evolution in Terceira (left); Logistic function parameters used to
obtain the vehicle density curve (right) .................................................................................................. 45
x
Figure 35 - Vehicle density curves (vehicles per 1000 inhabitants) considered for Terceira LPV fleet
and corresponding parameters ............................................................................................................. 46
Figure 36 – Total passenger vehicle sales ............................................................................................ 46
Figure 37 – Historical diesel share in passenger vehicles sales in Portugal [62], [91] ......................... 47
Figure 38 – Market sale mix with the impact of the vehicle new sales on the total fleet characterization
............................................................................................................................................................... 48
Figure 39 - Current penetration of white appliances and respectively share of efficiency class, adapted
from ICESD [87] .................................................................................................................................... 51
Figure 40 – Electricity Consumption per capita of Terceira [74], [85], [89] ........................................... 53
Figure 41 – Residential sector electricity consumption per capita [74], [85] ......................................... 54
Figure 42 – Primary sector electricity consumption per capita [74], [85] .............................................. 54
Figure 43 – Secondary sector electricity consumption per capita [74], [85] .......................................... 54
Figure 44 – Tertiary sector electricity consumption per capita [74], [85] ............................................... 54
Figure 45 – Transportation sector electricity consumption per capita [74], [85] .................................... 54
Figure 46 - Scenario results compilation for the energy consumption and the CO2 emissions ............ 58
Figure 47 - Scenario results compilation for the increase in the electricity consumption and fuel
consumption .......................................................................................................................................... 58
Figure 48 - Total energy Consumption for an EV penetration of 25% by changing the demography
and VD(t) ............................................................................................................................................... 59
Figure 49 – CO2 Emissions for an EV penetration of 25% by changing the demography and VD(t) ... 59
Figure 50 – Energy consumption per energy source assuming BAU scenario (a) passenger fleet (b)
Transportation sector ............................................................................................................................. 60
Figure 51 - Energy consumption per energy source assuming Transport2.2.3 (a) passenger fleet (b)
Transportation sector ............................................................................................................................. 61
Figure 52 - Energy consumption per energy source assuming Transport3.3.1 (a) passenger fleet (b)
Transportation sector ............................................................................................................................. 61
Figure 53 - Energy consumption per energy source assuming Transport1.1.4 (a) passenger fleet b)
Transportation sector ............................................................................................................................. 62
Figure 54 – CO2 emissions of the detailed scenarios ........................................................................... 62
Figure 55 - Scenario compilation for the residential sector (Energy vs Emissions) .............................. 63
Figure 56 - Scenario compilation for the residential sector (Electricity vs Butane) ............................... 63
Figure 57 – Energy consumption per energy vector assuming BAU scenario (a) Appliances + DWH (b)
Residential sector .................................................................................................................................. 65
Figure 58 – Energy consumption per energy vector assuming 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙2,2,2 (a) Appliances + DWH
(b) Residential sector ............................................................................................................................. 65
Figure 59 – Energy consumption per energy vector assuming 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙3,1,1 (a) Appliances+ DHW
(b) Residential sector ............................................................................................................................. 66
Figure 60 – Energy consumption per energy vector assuming 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1,1,1 a) Appliances + DWH
b) Residential sector .............................................................................................................................. 66
Figure 61 – CO2 emissions for the detailed scenarios considered ....................................................... 67
xi
Figure 62 – Energy Consumption analysis for Other Sectors ............................................................... 68
Figure 63 – CO2 emissions analysis for Other Sectors ......................................................................... 68
Figure 64 – Energy consumption for each scenario analysed .............................................................. 69
Figure 65 – CO2 emissions for each scenario analysed ....................................................................... 69
Figure 66 – CO2 emissions sensitivity to emission factor increase ...................................................... 70
Figure 67 – CO2 emissions sensitivity to emission factor decrease ..................................................... 70
Figure 68 – Petroleum and derivatives data framework [81] ................................................................ A-1
Figure 69 – Electricity data framework [85] .......................................................................................... A-1
Figure 70 - Electricity production of Terceira in June 2014 [26] ........................................................... A-2
Figure 71 - Characteristics of different solar thermal technologies ...................................................... B-5
xii
List of Tables
Table 1 – Actions for the transportation sector [27] ................................................................................ 6
Table 2 – Literature review of values used for the Portuguese scrappage curve ................................. 18
Table 3 – Car stock matrix per vehicle technology ............................................................................... 20
Table 4 – LHV and density per type of fuel [60], [61] ............................................................................ 22
Table 5 – Appliances life-time expectancy and scrappage curve parameters [63] ............................... 25
Table 6 – Fridges characteristics assumed to obtain the standard annual consumption ..................... 28
Table 7 – Washing appliances characteristics assumed to obtain the standard annual consumption . 28
Table 8 – Annual energy consumption of the appliances park per efficiency class .............................. 29
Table 9 - Main Sectors .......................................................................................................................... 36
Table 10 – Maximum level of penetration of electric vehicles in the LDV fleet ..................................... 48
Table 11 - % of sales versus EV penetration from 2015 to 2030, for different EV penetration scenarios
............................................................................................................................................................... 48
Table 12 – Technical characteristics of diesel vehicles [91], [92] ......................................................... 49
Table 13 - Technical characteristics of Gasoline vehicles [91], [92] ..................................................... 49
Table 14 – Fuel Consumption and CO2 emissions of alternative technologies [93], [94] .................... 49
Table 15 – Terceira inhabitants and dwelling distribution [88] .............................................................. 50
Table 16 – Energy provided to each household for Terceira by solar thermal systems ....................... 52
Table 17 – Proposed scenarios for the Transport sector ...................................................................... 55
Table 18 – Proposed scenarios for the Residential sector .................................................................... 55
Table 19 – Detailed technology scenarios ............................................................................................ 56
Table 20 – Detailed technology scenarios (continuation) ..................................................................... 56
Table 21 – Scenarios proposed for the other sectors considered ........................................................ 56
Table 22 – Assumed economic characteristics of solar thermal systems ............................................. 72
Table 23 – Assumed properties of the electric heaters considered [106] ............................................ 72
Table 24 – Technical and economic properties of Heat Pumps [108], [109] ........................................ 73
Table 25 - In-depth measures profitability criteria analysis for dual-tariff scenario ............................... 74
Table 26 – Economic analysis using simple-tariff scenario ..................................................................C-6
Table 27 – Economic analysis assuming dual-tariff scenario ..............................................................C-7
Table 28 - In-depth measures profitability criteria analysis for simple-tariff scenario ..........................C-8
1
1. Introduction
Context and motivation
The European Union includes more than 500 inhabited islands occupying 6 % of its territory and
representing a population of around 14 million citizens. 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 [1].The Amsterdam Treaty recognizes in
declaration No. 30 that ‘‘insular regions suffer from structural handicaps linked to their island status, the
permanence of which impairs their economic and social development’’. These handicaps are particularly
important in energy demand and security of supply [2].
The constant fluctuations and instability of the crude oil prices, as well as the impact of the fossil fuel
emissions, associated with increasing carbon dioxide (CO2) concentrations in the atmosphere, raises
the concern about creating alternatives that could replace the crude-oil and derived products and
mitigate GHG (Greenhouse Gas Emissions) caused by the means of transportation and equipment’s.
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 [3]. On the other hand, higher energy costs
of conventional and fuel dependent energy systems make renewable energy sources more
economically viable in small island energy systems, since their viability is less dependent on size and
fuel handling infrastructure than fossil fuel technologies [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 [4]–[8]. Many
researches showed the potential possibilities for renewable energy application in islands [4]–[6], [9],
[10]. They analysed the technical and economic feasibility to install renewable energy systems in remote
area and islands. Generally, significant progresses have been made in renewable energy technologies,
and some are available commercially. However, not all renewable energy systems are mature and cost
competitive, continuing efforts on research and demonstration are demanded [3]. Renewable energy
technologies can have specific advantages in small-scale applications such as household electricity,
street lighting, irrigation systems, village water pumps or 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 [3], [11]. Renewable energies penetration in the European Islands relies
not only on their renewable resources, but also on the politics undertaken.
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 conjugation of these models
with a detailed understanding on how and when energy is used at the consumer level it will be possible
to reach a better integration of endogenous and renewable energy resources with demand. Considering
2
detailed energy demand driven models allow us to emphasize energy efficiency and subsequently
energy conversion technologies that could satisfy the maximization of sustainable energy sources.
From this, my motivation comes from the necessity of creating a model capable of generate and evaluate
the impact of new energy demand strategies and policies, that allow not only decrease the carbon
footprint, but also integrate those with renewable and endogenous resources exploitation, justifying the
investments made by decreasing the fossil fuels importations, promoting the system sustainability and
maximizing the added value for the region.
Objectives
The main objective of this work is to assess and characterize the potential evolution of energy demand
of system through the development of a system-wide energy demand driven model. The model includes
all sectors of society, with high detailed characterization of the transportation and residential sectors.
For these sectors, the stock of equipment for the main end-uses is characterized, which includes the
passenger vehicle fleet, kitchen appliances and water heating, as well the associated energy
consumption due to their use. The evolution of demand and CO2 emissions are calculated by evolving
the equipment stock over the years, as well as the associated energy consumption based on technology
changes and efficiency improvement. To better understand how the energy system may evolve over the
years, several scenarios were created that consider future effects such as demographic and economic
development and the promotion of different energy policies focusing on technology efficiency,
renovation of equipment stocks and changes in energy vectors.
Document Structure
In chapter 2, a review is performed considering the efforts done by isolated communities to reduce fossil-
fuel dependency, followed by a structural classification of energy models and correspondent features.
Transportation and residential tools are reviewed and a few gaps existing on energy demand modelling
are highlighted.
On chapter 3, the formulation of the models developed is presented.
Chapter 4 characterizes the case study (Terceira Island), including the data set considered and detailed
energy demand analysis, and presents the scenarios considered in this work. The main results obtained
for each sector are presented in chapter 5, as well as the results for total energy consumption and CO2
emissions for four relevant scenarios. Adding to this, a sensitive analysis to the CO2 emissions is made
to account for future alternations on the electricity production system. At last, an economic analysis is
performed, taking into consideration technological options available to water heating end-use, to study
their investment viability and outcomes to householders.
Finally, some conclusions are drawn in chapter 6 and some remarks are made concerning potential that
could lead to further research.
3
2. State of the Art
This chapter presents what has been done so far in small island systems to diminish the fossil fuel
dependency, what are the main characteristics of models that can be used to study this issue and what
tools have been developed in the past.
2.1 Examples of effort for reducing the dependency of isolated
communities
Renewable energy sources, which are usually abundant on islands, are often, like wind and solar
energy, intermittent in their nature. These oscillations in the primary power supply can produce
instantaneous differences in the necessary balance between generation and demand. As a
consequence, important variations in frequency and voltage levels, which can affect the electric power
system stability, can appear. These problems are serious in small size isolated grids, and therefore a
continuous control on the instantaneous power supplied by the renewable energy sources is required
[5]. The higher penetration of renewable energy sources in islands is thus limited, and a solution to the
problem requires energy storage. The storage of electricity is feasible in various forms, like reversible
hydro, hydrogen or batteries [7], [8], [12], [13], but those solutions may not be economically viable. On
the other hand, by integrating electricity system with other energy systems, like heat, cold, or transport
fuel systems, or with other systems, like water supply system, waste treatment system or waste water
treatment system, may enable increasing the viability of the entire system, by storing what is most
appropriate in a given situation [14]. A brief characterization of the efforts made by islands similar to
Terceira to become more sustainable is presented next.
Canary Islands
In the Canary Islands, political and environmental concerns resulted in a particular energy strategy which
shows the importance of improving the indigenous resources and renewable energies towards the goal
for energy supply with stable offer, low cost and environmental friendliness [10], [15]. In this archipelago,
wind energy has been developed over the past years, with annual increase [10], [16]. In recent years
solar energy has been enhanced, and the amounts of installed solar thermal panels and photovoltaic
systems expanded. Previous studies indicated that in an isolated system, energy storage is important
for the use of the great wind potential of the islands [4], [10].
Ærø islands
In Ærø (Denmark), solar energy is used for district heating, which is the major energy source of the
island. At the time, the amount of solar panels installed, 3.7 m2 per inhabitant, covering a total of 26,800
m2, presented the most developed renewable energy penetration for a certain area [15]. In the year of
2001, the 7.2 MW wind power installation was responsible for the production of 20.5 GWh, accounting
4
for 57 % of the total electricity consumption in Ærø. The island made the decision to work continuously
to cover the islands’ energy consumption 80–100 % with renewable energy in the period of 10 years
from 1998 to 2008 [15]. Today, over 55 % of the island's total energy from solar, wind and biomass, and
ultimately, the goal is that Aero will be self-supplied with renewable energy.
Greek islands
On the Greek Islands, 50 wind parks were installed with a power of 120 MW in total, and 300 kWp of
photovoltaic power systems finished installation. According to data of the Regulatory Authority for
Energy provided on May 2008, 134.80 MW of wind energy is installed and operate in Crete, 0.17 MW
of biomass, 0.57 MW of photovoltaics and 0.48 MW of small hydroelectric plants [17]. In 2006, around
335 GWh of electricity were produced from wind energy, 0.50 GWh from biomass, 0.20 GWh from hydro
energy and 2.50 GWh from oil [17]. In Syros, studies demonstrate that interconnection among a number
of islands in the Cyclades and the mainland will eliminate the use of APS (autonomous power system),
will reinforce islands’ power network and will allow exploitation of high wind and solar potential [18]. It
has been concluded that following the interconnection, the installation of 33.5 MW of wind and solar
energy is feasible and will cover the total energy demand by 2030, allowing also exports to the Greek
mainland. Several papers presented their analytical data concerning the energy consumption in Greek
Islands and the installed RES facilities [9], [18]–[20], the results show that while there are many islands
with significant RES penetration, energy storage and management systems are required for further
development of RES in the Greek Islands [3].
Madeira
In Madeira, hydroelectric energy is well developed because of its mountainous orography and large
amount of water available. The region has hydropower plants with an installed capacity of 50 MW, an
installed wind energy capacity of 54 MW and 12 MW installed capacity of solar power [21]. In 2015, the
total production mix was 828.1 GWh, where 25.4 % came from renewable sources, which surpass the
European goal of renewable energy use for 2020 [22]. The most favourable year was 1996 because
renewable forms of energy provided 33 % of total electricity production.
Cape Verde
The electricity production in Cape Verde is based on thermal power stations running on heavy fuel or
diesel (97%); and a small percentage of wind energy (3%). ELECTRA ( “Empresa de electricidade e
água”) operates all over the country, 18 diesel power stations of different capacities (with a total capacity
of 85.08 MW), 3 wind farms (with a total capacity of 2.4 MW) [23]. The REPCV ( Renewable Energy
Plan for Cape Verde) identifies an enormous potential of RE in Cape Verde, distributed by wind energy
(220 MW), solar energy (2.600 MW - estimate annual energy production of 4.7 GWh/year), geothermal
(3 MW - estimate annual energy production of 22.3 GWh/year – Fogo island), waves and tides energy
5
(11 MW - estimate annual energy production of 14.2 GWh/year), and two Solar Parks (5 MW at Santiago
and 2.5 MW at Sal – inaugurated in November 2010) [23].
Azores Archipelago
Situated in the middle of the Atlantic Ocean, 1400 km West of the Portuguese Mainland and 3900 km
East of United States, the Azores is an archipelago of nine islands (São Miguel, Santa Maria, Terceira,
Graciosa, São Jorge, Pico, Faial, Flores and Corvo) with a population of approximately a quarter million
people. The Azores is an autonomous region of Portugal with a total land area of 2346 km2 and
commands an exclusive economic zone of 1.1 million km2 [24].
The Azores economy represents 1.7 % of the total Portuguese Gross Domestic Product, GDP [25].
Regarding the GDP per capita, the Azores reached 15.1 thousand Euros in 2014, the highest level ever,
representing 94 % of the national average. This amount is significantly higher than the Norte region (80
%), Centro region (83 %) and Alentejo (91 %) [25].
In 2014, the total electricity production of Azores was 788.9 GWh, of which 36.3 % came from renewable
sources (286.3 GWh), with São Miguel leading the renewable usage (54%) [26]. Although this renewable
penetration is considerable, the Government of the Azores, in association with other entities, has already
developed an ambitious strategy to integrate more renewables and promote sustainable behaviours
[27].
Looking beyond electricity, statistics show that the region is still very dependent on fossil fuels (63,7%
of primary energy) and that boosting the contribution of renewables to total primary energy will require
a combined effort to transform both the transportation sector and the electricity production sector [24].
In 2008, the government of Azores developed a plan, designated “Plano Energético da Região dos
Açores”, with the intention to prevent the environmental and economic consequences of using fossil
fuels on this region. This became a reference in terms of energy strategical priorities. The main
quantitative targets, defined for 2020, are:
Obtain 60% of the electricity from renewable energy sources;
Reach 20% of the total primary energy coming from renewable energy sources;
Achieve 35% of the total primary energy used as electricity by 2020;
Reduce CO2 emissions in at least 20% when compared with 2005 by 2020.
On March 2012, a new action plan was proposed by Azorina [27], called “Plano de Acção para a Energia
Sustentável”. This plan defines the actions that should be implemented in each island to reach the
stipulated premises present on the strategic plan in place. The actions proposed in the plan include:
Increment the share of primary energy coming from renewable sources – Main endogenous
sources in Azores: Wind, Hydro and Geothermal. Others are used as well (p.e Wave energy,
Pico);
6
Considerable increase on the energy efficiency in different ways of energy usage (certification
of buildings, public transportation fleet renovation, sensitization campaigns towards more
efficient equipment’s);
Change the fossil fuel utilization to electricity or renewable sources directly (Promoting electric
vehicles, solar thermal systems for heating, etc.) [27].
To promote efficiency improvement on the public transport fleet and the electric vehicles acceptation,
an action plan for transportation was created and implemented to encourage the shift to the electric
mobility. Table 1 summarizes those measures:
Table 1 – Actions for the transportation sector [27].
Sectors and areas of
intervention Actions
Responsible
for the
implementation
Implementation schedule
Starting year Year of
completion
Passenger road transport
Replacement of 90% of public transportation Replacement of 90% of the public transport fleet, with an estimate of 8% fuel consumption reduction per vehicle. SIRIART program
Regional
Government 2010 2014
Increment of the number and frequency of bus services, diminishing the use of private car.
Regional
Government 2014 2020
Private transport Promotion of the electric vehicle on all
islands
Companies
Citizens 2015 2020
Azorina predicts 2000 electric vehicles in Terceira in 2020, based on the specific need of the island in
terms of CO2 emissions reduction.
2.2 Energy models characterization
Although a model is always a simplification of reality, it provides a suitable understanding of a specific
system, which is essential to evaluate the effect of certain parameters on the system itself and to provide
reliable support on the elaboration of improvement strategies. More specifically, an energy model is
used to characterise a system and assist in projecting future energy supply and demand, assessing the
impacts of different energy systems and then appraising them [28]. The complexity of these models
depends on the outlook which they are designed for.
Through the years, energy models have grown considerably not only in number, but also in aim [28]. As
a result, there’s been the attempt to classify those models to provide insight in the differences and
similarities between them, facilitating the model suitability assessment [29]. Beeck [28] proposed a
classification based on a set of criteria, described as the following:
1) The purpose of the model, which considers general purposes reflecting how the future is
addressed in the model, in the form of forecasting (analyse relatively short-term impacts of
actions), “backcasting” (construct visions of desired futures and consequently look at what
needs to be changed to accomplish that) and exploration (scenario analysis), which complies a
set of scenarios that are compared with a “business as usual” reference scenario. These
7
scenarios rely on present and future assumptions rather than parameters observed from past
behaviour; and specific purposes, meaning the focusing aspects of the model such as energy
demand, supply, impacts and appraisal.
2) The model structure, which represents the type of assumptions that the model structure is based
on. Considering each model, a decision has to be made according to which assumptions are
restricted in the model (internal) and those left to be determined by the user (external).
3) The Analytical approach, considering the distinction between top-down and bottom-up models.
Grubb et.al [29] state that the top-down approach is associated with the economic paradigm,
while the bottom-up approach is referred to as the engineering approach. This has been a
discussion theme over the years and will be presented with more detail further on.
4) The underlying methodology, which represents the methodology to develop the energy models.
This encompasses: Econometric, which considers the application of statistical methods to
extrapolate past market behaviour into the future; Macro-economics, focusing on the global
economy and connections between sectors; Optimization, used to optimize energy investment
decisions, where the outcome denotes the best solution, giving the constraints considered;
Simulation, based on a logical representation of the system, aimed to reproduce a simplified
version of the system; Spreadsheet models, which are considered models with high flexibility
that work more as an “add-on”.
5) The mathematical approach, such as Linear Programming, which is a practical technique
subject to operative constraints that is used to find the arrangements that maximise or minimise
a defined criterion; or other methods, as Mixed Integer or Dynamic programming.
6) The Geographical and sectorial coverage, that reflects the level at which the analysis takes
place, which could be Global, Regional, local or just a simple project for geographical purposes
and multi or single sectorial models.
7) The time horizon, which determines the structure and objectives of the energy models.
According to Grubb et al. [29] , there are the short term, period of 5 years or less; medium term,
between 3 and 15 years; and long term, which includes 10 or more years.
8) Data requirements.
New classifications were considered in the past years, as EEA [30] divided the classification of the
models in terms of thematic focus, geographical scale and analytical technique.
From all the different models classification, one of the most discussed topic has been the comparison
between top-down and bottom-up approaches.
Top-down energy models try to describe the economy as a whole, on a national or regional level, and
to assess the aggregated effects of energy and/or climate change policies in monetary units. These
equation-based models take an aggregate view of the energy sectors and the economy when simulating
economic development, related energy demand and energy supply, and employment [31]. This kind of
models are usually driven by economic growth, industrial and structural change, demographic
development, and price trends (rather than energy-related technological progress or technical
innovations).
8
To model the energy demand in the residential sector, Swan and Ugursal [32] define top-down models
as those used to estimate the total residential sector energy consumption based on indicators such as
GDP, employment rates, price indices, climatic conditions, housing construction/demolition rates,
appliances ownership estimations and number of units. This approach does not separate energy
consumption in the different end-uses. The strengths are the data availability and simplicity. In terms of
shortcomings, this model relies on historical data, which makes it impossible to model advances in
technology and the lack of detail regarding the end-uses energy consumption creates the incapability to
analyse new technological developments and their future impact on energy demand, which are
extremely important for sustainable energy systems.
On the other hand, bottom-up models often use highly disaggregated data to describe energy end-uses
and technological options in detail, focusing on the energy sector exclusively [28]. The main
characteristic of a conventional bottom-up energy model is its relatively high degree of technological
detail (compared to top-down energy models) used to assess future energy demand and supply. While
this type of models is capable of describing the techniques, performances, and direct costs of all
technological options to identify possibilities of improvement, is not able to consider macroeconomic
impacts of energy prices, climate policies and related investments or transaction costs, which are
covered by top-down models. This level of detail requires that the input data requirement is greater than
those used on top-down models, as the calculation techniques are also more complex.
Considering the residential sector, according to Swan and Ugursal [32], bottom-up models have the
capability of determining the energy consumption of each end-use and in doing so can identify areas of
improvement. Based on that, models based on engineering and statistical methods exist. The first
method relies on the equipment power ratings, system/equipment usage, heat transfer/ thermodynamic
relations and dwelling properties to calculate the energy consumption. The second method depends of
historical data, such as energy bills.
Figure 1 displays a scheme that describes the general methodology behind the bottom-up and top-down
models.
9
Figure 1 – Top-down and bottom-up modelling methodologies (adapted from [33]).
To overcome the previously mentioned weaknesses and limitations of conventional top-down and
bottom-up energy models, researchers are currently developing hybrid energy system modelling,
combining at least one macroeconomic model with at least one set of bottom-up models for each final
energy and conversion sector [31]. McFarland et al. [34] used hybrid model to estimate future
anthropogenic carbon emissions considering the rate and magnitude of technological change,
concluding that the quality of a top-down economic model is enhanced when combined with bottom-up
engineering information.
The ADAM project [35], developed in 2009 in Switzerland, was an example of a hybrid approach where
a macroeconomic model (E3ME) was combined with bottom-up models from different final energy
sectors (industry, residential, services and transportation sector).
2.3 Energy modelling tools
In the last 50 years, considerable efforts by scientists and researchers have been made to formulate
and implement energy planning strategies in developing and developed countries. Such analysis require
computer tools that can create answers for those issues by modelling the energy-systems. Several tools
have been developed over the years to assist energy planning. Connolly et al. [36] 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. This paper emphasizes that choosing the most
appropriate model strongly depends on the objectives that the decision-makers want to fulfil.
10
Some of the modelling tools discussed on those reviews are presented in this section. First, system-
wide energy modelling tools are described, followed by tools specific for energy in the transportation
sector and energy in the residential sector, concluding with the gaps identified in the existing modelling
tools.
2.3.1 System-wide
LEAP [37] (Long-range Energy Alternatives Planning) is an integrated modelling tool for energy policy
analysis and climate change mitigation assessment for both energy supply and demand sides. LEAP is
used to evaluate national energy-systems, based on energy consumption, production and resource
extraction analysis in all economic sectors. It operates using an annual time-step, with an unlimited time
horizon, giving the possibility to create long-term planning horizon projections or just being used with a
purpose of a database. On the demand side, the model is disaggregated in economic sectors, such as
residential, transportation, etc. Considering the transportation sector, the hierarchical tree structure is
divided in five levels: the total passenger travel demand (level 1), the share of total travel demand
catered by road and trail (level 2), share of different types of passenger vehicles or modal split (level 3)
and the inverse of occupancy level (vehicle density) or vehicle space per passenger (level 4). Finally,
an energy intensity and emission factors for each pollutant are associated with each device at the fifth
level. As for the residential sector, it is consequently divided in subsectors, which can be income groups,
and end-uses, like water-heating, cooking and so on. At the last level, end-uses are further divided in
technologies and respective consumptions/usage.
Figure 2 – Hierarchical structure of LEAP demand in transportation (left) and residential (right) sectors [37]–[39].
MARKAL/TIMES [36] family are energy/economic/environmental tools develop by the Energy
Technology System Analysis Program (ETSAP) of the International Energy Agency (IEA). It is a bottom-
up, linear programming optimisation model, which depicts both demand and energy supply sides of the
energy system, as it provides policy makers and planners, in the public and private sectors, with
extensive details on energy producing and consuming technologies over a long period of time, usually
20-50 or 100 years, with high geographical disaggregation resolution, which gives an understanding of
the interactions between macro-economies and energy use [40]. Using the optimization routine, it
11
produces the least-cost solution from each of the sources, energy carriers and transformation
technologies, depending on a variety of constraints. As with most energy system models, energy carriers
in MARKAL interconnect the conversion and consumption of energy. Demand for energy services may
be disaggregated by sector (i.e., residential, transportation, commercial, etc.) and by specific functions
within the sector (heating, lighting, fuels, etc.). This model has been the subject in future prospects of
hydrogen, fuel cells and hydrogen vehicles [41].
2.3.2 Transportation sector
There are a number of methodological difficulties when representing the transport sector in energy
system models, such as non-cost factors, since consumers always take into consideration a variety of
features when purchasing a vehicle (size, colour, safety, features and design). However, typical
optimisation models such as energy system models account for only cost so they would always invest
in the cheapest (i.e. smallest) vehicles if given a choice [42].
A significant number of approaches have been considered to compare the prospects for, and
implications of, numerous possible future fuels and powertrains. One is to compare different vehicle
configurations in a static way, developing detailed depictions of life-cycle environmental and energy
impacts, as well as total costs of ownership [43]. Another applies system dynamics modelling to vehicle
and adoption, exploring the importance of different behavioural, technical and economic factors on the
introduction of different vehicle technologies [44].
TREMOVE [45] is a static equilibrium model, which relies on policy assessment to study the effects of
different transport and environmental policies on emissions from the transport sector. This model
estimates the transport demand, the modal shifts, the vehicle stock renewal, the emissions of air
pollutants and the welfare level, for policies as road and public transport pricing, emission standards,
subsidies. For passenger and freight transport, the model simulates the changes in volume of transport,
modal choice, vehicle choice (size and technology) relative to a transport and emissions baseline.
Covering 31 country models, each of them consists in three inter-linked “core” modules: a transport
demand module, the vehicle stock turnover module, an emission and fuel consumption module, welfare
cost module and a well-to-tank emissions module. The majority of the information necessary to develop
a baseline are extracted from SCENES transport model and further calibrated towards national statistics.
VISION [46] is a forecast model, developed by the Argonne National Laboratory, capable of estimate,
considering the US based vehicle survival and age dependent usage characteristics, the total – light
and heavy – vehicle stock, total energy use by technology and fuel type per year and carbon emissions
up to 2050. This model does not consider plug in technologies and the vehicle to grid approach.
The IEA Mobility Model (MoMo) [47] is a software developed by IEA that uses a technical-economic
spreadsheet model that allows detailed projections of transport activity, vehicle activity, energy demand,
as well as CO2 and pollutant emissions in different policy scenarios to 2050. The inputs are given
through a Microsoft Excel spreadsheet, allowing the user to elaborate scenarios based on vehicle type,
fuels, efficiency and travelling levels, and then estimating/projecting energy consumption, emissions of
12
air pollutants and greenhouse gases for worldwide mobility. The model gives the possibility to create
“what-if” scenarios, where the user can analyse the impact of different trends on different outputs.
PATTS - Alternative Transportation Technologies Simulation tool – was develop by Baptista [48] to
perform an integrated analysis on the energy and environmental impacts of different assumptions for
the transportation sector, in order to assess the possible future evolution pathways. The model resorts
to linear programming modules using Microsoft Excel to track variables and includes a full life-cycle
assessment (LCA) approach, at a national and total scale, for all vehicle segments (Light-duty vehicles,
Heavy-duty vehicles and buses), in order to estimate yearly evolution for the transportation segment
and the impacts in terms of energy consumption and local and global emissions. The historic data from
around 1973 was used to calibrate the model and has a time frame until 2050.
ASTRA [49], which means Assessment of Transport Strategies, is an integrated assessment model
applied for strategic policy assessment in the transport and energy field. It covers EU27+2 countries
and integrates a vehicle fleet model, transport model, emission and accident models, population model,
foreign trade and economic model with input-output tables, government, employment and investment
models Policy assessment capabilities in ASTRA cover a wide range of policies, like infrastructure
pricing, fuel and carbon taxation or speed limits, with flexible timing and levels of the policy
implementation. The model builds on recursive simulations following the system dynamics concept and
enables to run scenarios until 2050. It relies on Vensim system dynamic software to perform sensitivity
analysis. A strong feature of ASTRA is the ability to simulate and test integrated policy packages and to
provide indicators for the indirect effects of transport on the economic system.
2.3.3 Residential sector
In the United Kingdom, numerous bottom-up models have been developed to estimate the residential
demand and all of them use the Building Research Establishment Domestic Energy Model (BREDEM)
as the main calculation tool. The following modes resort to this algorithm. BREHOMES is physical based
bottom-up residential energy model that uses weighted average stock transformation method to
calculate energy use for dwellings. To perform the calculations, it requires dwelling areas, thermal
characteristics, thermal properties, internal and external temperatures, heating patterns, solar gains and
occupancy. The energy use for lights and appliances are at an aggregated level. UKDCM uses weighted
stock transformation method as well to calculate monthly demand for space heating. In this model,
dwellings are classified by age, dwelling and construction type, number of floors and respective area.
Using the same bottom-up model, DECarb uses a highly disaggregated housing stock approach,
considering 8064 unique combinations for each age class available.
The Huang and Brodrick [50] model was developed to estimate potential upgrading in the United
States of America buildings energy efficiency. This model includes single-family, multi-family, and
commercial buildings, with information regarding the buildings age, dwelling type and total building stock
in each region. It produces aggregated estimates of residential and commercial building energy use
based on combined cooling and heating loads from building envelope components, such as windows,
13
roofs or walls. Some of the shortcomings of this model are the fact that only gas was considered as the
primary fuel source for space and water heating and the totals for the non-space conditioning end-use,
such as water heating and lightning, were modelled very simply.
The North Karelia Finland model [51] is a non-dynamic, bottom-up numerical tool for producing annual
energy and CO2 emissions estimates as well as the associated heating costs assessment, giving
assistance to the local decision making authorities. The model comprises of calculation units that
represent municipality clusters of buildings in the area. This aggregation is done according to the type
of building and utilisation type, heat technologies and sources, as well as the buildings age. One of the
major downsides of the model is the incapability to address the temporal changes in demand, due to
the steady-state physics, that result from heat loads due to occupants, appliances usage or solar gains.
Ximenes [52] developed a residential energy demand hybrid simulation model with the intent of
supporting energy technology policy options assessment at a regional and national level. This includes
both bottom-up formulation to estimate the energy demand for space heating, space cooling and water
heating, and top-down approach to estimate the baseline lighting end-use energy consumption. It
considers building’s geometric and thermodynamic characteristics, climate data and technology
penetration information. To determine the cooking and appliances end-uses consumption, a white goods
ownership and power rating based methodology was considered. The technology parameterization
associated with different energy vectors that they may convert, enables the development of different
energy planning scenarios.
2.3.4 Identified gaps
Considering the tools and models developed throughout the years, much of them give more emphasis
to the supply side rather than demand. This part is often integrated as external input or modelled in a
form of simplified top-down approaches. Besides, most of them work with broad geographic resolutions
instead of smaller regional or even municipality scales. Another important characteristic is the availability
of the model for application in other regions besides the ones they were intended to. Some of the models
mentioned have the problem of being of difficult access, either because they are payed or not available
for public usage. Among the tools discussed and reviewed, some like PATTS and the hybrid simulation
tool developed by Ximenes could be applied to the case study of this work. However, the lack of regional
geographic resolution detail (PATTS) and the incapacity to perform the assessment of energy policy
strategies, through the development of future appliances or equipment’s park mix based on different
energy efficiency class penetration (hybrid simulation tool), showed that there is the need for a model
capable of design energy demand scenarios at a regional scale, with a wide range of technology
choices, both for transportation and residential, in order to present future scenarios to support the
analysis and decision making of those sectors.
14
3. Energy demand models formulation
To address the potential impact of different energy measures and polices that promote the integration
of more renewables and sustainable behaviours, 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. Evolution
of costs are not considered due to the uncertainties associated with their evolution. As such, all cost
analysis consider only current fuel costs.
Regarding private vehicles, the bottom-up approach was used was used to characterize the fleet and
its use over the years, based on the percentage of sales per technology assumed for each year, the
energy consumption and emissions, total and per vehicle technology, the estimated average distance
travelled and lifetime of the vehicles. As for the residential sector, the bottom-up methodology was used
to estimate the future energy demand on kitchen related with kitchen and water-heating end-uses,
assessing the impact of the efficiency improvement and technology changes. For this, the appliances
and equipment’s ownership rates, as well as the specific consumptions of each equipment, were used.
The cooling, heating and lightning needs are kept constant through the scenario development. For the
non-considered end-uses, their energy needs are maintain constant through the scenario development.
While the model enables a long-term analysis, it has a temporal resolution of one year and is therefore
not appropriate to model hourly or seasonal variations in demand.
The outputs of the model are the energy demand and emissions by energy vector and by sector. For
the transportation sector and residential sectors, higher disaggregation is also available for the main
end-uses. The Figure 3 outlines the inputs and outputs structure of the model proposed on the present
work.
Figure 3 – Model structure.
The major contributions of the developed model consist in the possibility of altering the influence of each
technology on the sales for a specific year, based on the creation of new transportation, equipment and
15
technology policies and/or incentives, as well as the fact that, with this model, the yearly equipment’s
and vehicles park age stratification is highly detailed, giving a better representation of reality, without
being necessary to assume typical average values for specific considerations, such as average
consumption and emissions.
3.1 Methodology for transportation sector
According to Baptista [48], there are two possible ways of addressing the impacts of introducing
alternative technologies or energy sources in the transportation sector, depending on the time resolution
and dimension of the fleet in study. A substitution methodology could be used if the analyses focus on
small fleets or what-if scenarios, where the investment constraint can be disaggregated and the
renovation of the fleet may be obtained almost instantaneous [53]–[55].
However, in order to better represent the reality of the road transportation sector for a large fleet, the
vehicle stock must be represented in a year-by-year model that tracks sales, scrappage, vehicle lifetime
among others, in order to estimate past and future trends of the road transportation sector reflecting the
inertia associated to the fleet renewal [48]. The model developed on this work uses this characterization
for assessing the evolution of energy consumption in passenger vehicles. Other vehicle categories are
not modelled in detail and are kept constant over the years.
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
presented is detailed on Figure 4. This model is implemented using Microsoft Excel with a spreadsheet
methodology and some linear programming modules that track numerous variables such as the
percentage and value of new vehicle sales, vehicle stock and scrappage, their fuel consumption, annual
kilometres travelled, demographical variations and fuel mixes. Historical data is used to calibrate the
model.
Figure 4 - Passenger vehicle model framework.
16
3.1.1 Vehicle stock evolution over time
Car ownership is partly related with the standard of living in a country, therefore economic parameters
may sometimes be insufficient to explain the fleet’s evolution [48], [56]. A widely used approach it’s to
express normalized car ownership, also defined as the number of vehicles per 1000 inhabitants in a
country (vehicle density – VD), as a sigmoid function of time. This function can fit in the fleet evolution
in different cases, such as from the “virgin” car markets (Part A), to the booming cark market (Part B) as
well as nearly saturated markets (Part C). This situation is illustrated in Figure 5.
Figure 5 – Car density as a function of time[56]
Based on this, the vehicle density can be expressed mathematically through a Gompertz or a Logistic
function, the latter presented by equation (1). This function rely on historical vehicle stock and population
data to create a Logistic function capable of estimating the evolution of passenger-vehicle fleet.
𝑉𝐷i =
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑒ℎ𝑖𝑐𝑙𝑒𝑠
1000 𝑖𝑛ℎ𝑎𝑏𝑖𝑡𝑎𝑛𝑡𝑠= 𝛽 +
𝛼 − 𝛽
1 + 𝑒−𝑘(log(𝑡)−𝜙) (1)
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.
In order to obtain the best fitting of the vehicle density curve to the real data, the R squared method
(coefficient of determination) was used, using equation (2).
𝑅2 = 1 −𝑆𝑆𝑟𝑒𝑠
𝑆𝑆𝑡𝑜𝑡
(2)
with 𝑆𝑆𝑟𝑒𝑠 being the sum of squares of residuals and 𝑆𝑆𝑡𝑜𝑡 the total sum of squares. Both parameters
are obtained from (3) and (4).
𝑆𝑆𝑟𝑒𝑠 = ∑(𝑦𝑖 − 𝑓𝑖)
2
𝑖
(3)
𝑆𝑆𝑡𝑜𝑡 = ∑(𝑦𝑖 − �̅�)2
𝑖
(4)
17
where 𝑦𝑖 the real vehicle density in year 𝑖, 𝑓𝑖 the vehicle density estimated in year 𝑖 and �̅� average vehicle
density for the total number of years considered.
The total car stock evolution will be given by equation (5).
𝑇𝑜𝑡𝑎𝑙 𝑐𝑎𝑟 𝑠𝑡𝑜𝑐𝑘𝑖 = 𝑉𝐷𝑖 × 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖 (5)
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.
For the vehicle sales, information can be obtained from historical data as the correspondent value or
based on the total vehicles fleet year of construction. For the latter, equation (6) has to be applied to
obtain the correspond value of vehicle sales:
𝑇𝑠𝑎𝑙𝑒𝑠𝑖 = 𝑇𝑝𝑣𝑖
× %𝑇−1𝑦𝑒𝑎𝑟𝑖 (6)
𝑇𝑠𝑎𝑙𝑒𝑠𝑖 is the total number of passenger vehicle sales in year 𝑖, 𝑇𝑝𝑣𝑖 is the total passenger vehicles fleet
in year 𝑖 and %𝑇−1𝑦𝑒𝑎𝑟𝑖 is the percentage of vehicles present on the total fleet which are less than year
old, in year 𝑖.
Knowing the total vehicle sales of a specific year, the total end-of-life vehicles for the past years can be
obtain. The number of end-of-life vehicles is the total number of vehicles that disappear from the market
in a specific year. This can be obtained using equation (7):
𝑇𝑒𝑛𝑑−𝑜𝑓−𝑙𝑖𝑓𝑒_𝑖 = 𝑇𝑝𝑣𝑖−1+ 𝑇𝑠𝑎𝑙𝑒𝑠𝑖 − 𝑇𝑝𝑣𝑖
(7)
Where 𝑇𝑒𝑛𝑑−𝑜𝑓−𝑙𝑖𝑓𝑒𝑖 is the total end-of-life vehicles in year 𝑖, 𝑇𝑝𝑣𝑖
is the total passenger vehicles fleet in
year 𝑖, 𝑇𝑠𝑎𝑙𝑒𝑠𝑖 is the total number of passenger vehicle sales in year 𝑖 and 𝑇𝑝𝑣𝑖−1 is the total passenger
vehicle stock of the previous year. Equations (6) and (7) allow the characterization of the historical sales
and end-of-life of vehicles.
After characterizing the years for which historical data is available, the following step is to define the
vehicle survival curve in the car stock. Zachariadis et al. [56] considers that this parameter can be crucial
in a detailed analysis as the emission legislation for motor vehicles has become increasingly strict in the
last two to three decades, new cars are considerably cleaner than old ones. Moreover, older cars are
often badly maintained and therefore have higher emissions than new ones of the same technological
level. The combined effect of these two factors makes the overall emissions performance of the vehicle
fleet very sensitive to its turnover. Accelerated replacement of old cars can therefore serve as a valuable
tool toward future emission reductions in some countries.
Vehicle scrappage is a function of the technical lifetime of the vehicle, so it represents the probability of
breakdown before the planned technical life-time, of car wreckage (for example, after an accident) and
18
the probability of a car being replaced by a new or used car. The latter depends mainly on the costs of
cars and on policies that may affect those costs (such as purchase premiums or cash-for-clunker similar
policies).
The annual vehicle scrappage curves may be defined as the probability of a vehicle being in circulation
after k years. Zachariadis et al. [56] did an analysis of annual scrappage rates for cars and was verified
that the Weibull distribution produced a very good fit with real data, with the correlation coefficient being
approximately 0.95. Based on this, for this work a Weibull distribution was used, as is expressed by
equation (8):
𝜑(𝑘, 𝑐) = exp [− (
𝑘 + 𝑏𝑐
𝑇)
𝑏𝑐
]
With 𝜑(0) = 1
(8)
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 (𝑏𝑐 > 1, so failure steepness increases with age) and 𝑇 is the characteristic
service lifetime for vehicle type 𝑐. There have been some previous studies for the Portuguese fleet
vehicle scrappage curve. The Table 2 enumerates the results obtained by some of them.
Table 2 – Literature review of values used for the Portuguese scrappage curve.
𝑻 𝒃𝒄
MIT (U.S. L.D.V Fleet) [57] 30 8
Zachariadis et al. [56] 30 8
Moura [58]
1995 31 11
2000 35 13
2005 34 11
The representation of these scrappage curves demonstrates the survival behaviour based on the age.
As the vehicle age increases, the probability of the vehicle survival on the car stock decreases and after
around 27 years this probability is approximately zero. In this work, a maximum life-time of 30 years was
considered.
Figure 6 – Scrappage curves from different literature sources.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Vehic
le P
robabili
ty o
f S
urv
ival
Vehicle Age (years)
MIT
Moura 1995
Moura 2000
Moura 2005
19
The chosen scrappage curve is then applied to all vehicles entering the market. This means that for
each year, the life-time of the cars entering the market in that year will be distributed in the next 30 years
into the future, according to its probability of survival. Regarding new technologies entering the market,
it is considered they will behave in a similar way compared with the conventional technologies. This is
assumed since, in one hand, the alternative vehicle technologies behaviour on the market is still
unknown and, on the other hand, it is considered that the user will shift to alternative vehicle technologies
if the vehicle behaves in a similar way than their current vehicles.
The combination of the estimated total vehicle stock (resulting from the population and vehicle density)
with the scrappage curves, allows the estimation of the yearly future sales of vehicles through the
reorganization of equation (7) to evidence the number of vehicles sold.
The introduction of alternative vehicle technologies is also considered. To apply this, their share in new
vehicle sales has to be assumed. This shift in new vehicle sales from conventional vehicle technologies,
in this case gasoline and diesel ICE (internal combustion engines), to alternative ones is defined by:
Availability – defines when most alternative vehicle technologies will be readily available and will start
entering the market;
Aggressiveness – when alternative vehicle technologies are available, this parameter defines how fast
they will enter the market.
Maximum penetration level – defines the maximum penetration level in 2030 for the different vehicle
technologies in new vehicle sales.
According to the alternative technology share, the conventional technologies (gasoline and diesel ICE)
share reduces correspondingly. The same methodology in terms of survival in the fleet, VKT (vehicle
kilometres travelled) per year, vehicle.kilometers of that technology and consequently fuel consumption
and emissions is applied. Using historical data by vehicle technology and assuming the share of each
technology in the sales of each year the stock of vehicles by technology can be estimated. In this work,
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 (9) and based
on Table 3.
𝑇𝑜𝑡𝑎𝑙𝑃𝑉𝑥,𝑖
= ∑ 𝑃𝑉𝑥,𝑖,𝑦
2030
𝑦=𝑏𝑒𝑓𝑜𝑟𝑒 2005
(9)
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.
20
Table 3 – Car stock matrix per vehicle technology.
Vehicles 2005 2006 2007 … 2029 2030
Before 2005 𝑃𝑉𝑥,2005,𝑏𝑒𝑓𝑜𝑟𝑒2005 … … …
2005 𝑃𝑉𝑥,2005,2005 𝑃𝑉𝑥,2006,2005 𝑃𝑉𝑥,2007,2005 … … …
2006 0 𝑃𝑉𝑥,2006,2006 𝑃𝑉𝑥,2007,2006 … … …
2007 0 0 𝑃𝑉𝑥,2007,2007 … … …
… 0 0 0 … 𝑃𝑉𝑥,2029,2027 …
…. 0 0 0 … 𝑃𝑉𝑥,2029,2028 𝑃𝑉𝑥,2030,2028
2029 0 0 0 … 𝑃𝑉𝑥,2029,2029 𝑃𝑉𝑥,2030,2029
2030 0 0 0 … 0 𝑃𝑉𝑥,2030,2030
Total Car
Stock = Column sum
= Columm
sum
= Columm
sum
= Columm
sum
= Columm
sum
= Columm
sum
With this formulation, the end-of-life vehicles per technology in a specific year are obtained using
equation (10).
𝑃𝑉𝑒𝑛𝑑−𝑜𝑓−𝑙𝑖𝑓𝑒𝑥,𝑖
= ∑ 𝑃𝑉𝑥,𝑖,𝑦−1
2030
𝑦=𝑏𝑒𝑓𝑜𝑟𝑒 2005
− ∑ 𝑃𝑉𝑥,𝑖,𝑦
2030
𝑦=𝑏𝑒𝑓𝑜𝑟𝑒 2005
(10)
𝑃𝑉𝑒𝑛𝑑−𝑜𝑓−𝑙𝑖𝑓𝑒𝑥,𝑖 is the number of end-use vehicles per technology 𝑥 in year 𝑖, 𝑃𝑉𝑥,𝑖,𝑦−1 is the number of
vehicles of technology 𝑥 from year 𝑦 − 1 (which means the sales from the year considered are not
accounted for) in year 𝑖 and 𝑃𝑉𝑥,𝑖,𝑦 is the number of vehicles of technology 𝑥 from year 𝑦 in year 𝑖.
The number of vehicles before 2005 is given by equation (11).
𝑃𝑉𝑏𝑒𝑓𝑜𝑟𝑒2005𝑥,𝑖
= 𝑇𝑜𝑡𝑎𝑙𝑃𝑉𝑥,𝑖− ∑ 𝑃𝑉𝑥,𝑖,𝑦
2030
𝑦=2005
(11)
Where 𝑃𝑉𝑏𝑒𝑓𝑜𝑟𝑒2005𝑥,𝑖 is the number of vehicles that appear on the fleet previous to 2005, per technology
𝑥 in year 𝑖, 𝑇𝑜𝑡𝑎𝑙𝑃𝑉𝑥,𝑖 is the total number of passenger vehicle stock of technology 𝑥 for year 𝑖 and
𝑃𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠𝑥,𝑖,𝑦 is the number of vehicles of technology 𝑥 from year 𝑦 in year 𝑖.
In order to have a complete characterization of the vehicle stock over the years, there is often the need
to characterize the sales and scrappage rates of year previous to those for which historical data is
available. This was computed by estimating the sales for past years that, when considering the chosen
scrappage rates, allow the approximate estimation of the total number of vehicles in each year. The
solution was developed combining the linear programming approach in Microsoft Excel with the R2
methodology to optimise the sales and obtain the best approximation to the real fleet evolution curve.
The same methodology can be applied for each individual technology. Some constraints need to be
considered to give physical and historical meaning to the results, such as, for example, the minimum
21
number of sales per year, for the total fleet, or the introduction and increase of a vehicle technology on
the market.
3.1.2 Mobility
The annual vehicle kilometres travelled (VKT) are obtained from the literature, such as statistical data
or vehicle inspections. For Terceira, the data was obtained based on the values from EUROSTAT [59]
for the Portuguese fleet. The values obtained suggest an average value of 13 000 and 8 700 kilometres
per year, for diesel and gasoline, respectively. The VKT per year evolution along the vehicles lifetime
was not considered in this study.
Generally, ICE diesel vehicles travel more kilometres than ICE gasoline vehicles. However, since the
kilometres travelled is dependent on the use given by the vehicle owner and not the technology itself, it
cannot be assumed that if all vehicles sold were diesel the number of kilometres travelled would
increase. As such, it is assumed that the difference between the VKT of the two technologies will
disappear in the future. According to this, ICE diesel VKT for passenger cars was considered to
converge in the next 40 years to the ICE gasoline VKT [48]. 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.
3.1.3 Fuel, energy consumption and emissions
With the fleet characterization defined, it is necessary to estimate the impact of the penetration of new
technologies, in terms of energy consumption and emissions. The fleet’s composition is matched with
the number of kilometres travelled by each vehicle technology, giving the total number of kilometres
travelled by technology. Then fuel consumption, energy consumption or emissions factors are applied
to obtain the resulting yearly fleet’s energy and fuel consumption, as well as the emissions for each
vehicle technology. The fuel consumption analysis is based on Equations (12), (13), and (14). The most
recent vehicle technology characteristics presented on the data available were used for the new vehicles
entering the fleet.
𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦 = 𝑃𝑉𝑥,𝑖,𝑦 ×
𝐶𝑎𝑟𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦
100× 𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝑠𝑥 (12)
where 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.
𝑌𝑒𝑎𝑟𝑙𝑦𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑥,𝑖
= ∑ 𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦
2030
𝑦=𝑏𝑒𝑓𝑜𝑟𝑒 2005
(13)
22
𝑌𝑒𝑎𝑟𝑙𝑦𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑥,𝑖[𝑙𝑖𝑡𝑟𝑒𝑠] is the annual fuel consumption of the vehicles with of technology 𝑥,in year 𝑖.
𝑇𝑜𝑡𝑎𝑙𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑖= ∑ 𝑌𝑒𝑎𝑟𝑙𝑦𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑥,𝑖
#𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦
𝑥=1
(14)
𝑇𝑜𝑡𝑎𝑙𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑖[𝑙𝑖𝑡𝑟𝑒𝑠] is the total fuel consumption of the vehicle fleet in year 𝑖.
Using the Low Heating Value [LHV] and fuel density (ρ), it is possible to retrieve the amount of energy
consumed by the fleet in each year. The values for the fuel’s LHV and density were obtain from DGEG
[60], [61] and presented in the Table 4.
Table 4 – LHV and density per type of fuel [60], [61].
Fuel Low Heating Value
[MJ/kg]
Density
[kg/m3]
Diesel 45 0.83
Gasoline 43.5 0.75
LPG 46 0.51
To calculate the energy consumption of Hybrid vehicles, the gasoline LHV and density can be assumed,
since most of the hybrid vehicles sold rely on gasoline engines [62]. Using the LHV from Table 4 and
considering that 1 kWh are 3.6 MJ, it is possible to calculate the energy consumed.
The energy consumption is obtain using equations (15), (16) and (17).
𝐸𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖,𝑥,𝑦 =
𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦 × 𝐿𝐻𝑉𝑥 × 𝜌𝑥
3.6 × 10−6 (15)
Where 𝐸𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦[𝐺𝑊ℎ] is the energy consumption of a vehicle from year 𝑦, with technology 𝑥
in year 𝑖, 𝐹𝑢𝑒𝑙𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦 is the fuel consumption of a vehicle from year 𝑦, with technology 𝑥 in
year 𝑖, 𝐿𝐻𝑉𝑥[𝑀𝐽 𝑘𝑔]⁄ is the low heating value of fuel/technology 𝑥, 𝜌𝑥[𝐾𝑔 𝑚3]⁄ is the fuel density.
𝑌𝑒𝑎𝑟𝑙𝑦𝐸𝑛𝑒𝑟𝑔𝐶𝑜𝑛𝑠𝑥,𝑖
= ∑ 𝐸𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑥,𝑖,𝑦
2030
𝑦=𝑏𝑒𝑓𝑜𝑟𝑒 2005
(16)
Where 𝑌𝑒𝑎𝑟𝑙𝑦𝐸𝑛𝑒𝑟𝑔𝐶𝑜𝑛𝑠𝑥,𝑖[𝐺𝑊ℎ] is the yearly energy consumption per technology 𝑥, in year 𝑖.
𝑇𝑜𝑡𝑎𝑙𝐸𝑛𝑒𝑟𝑔𝐶𝑜𝑛𝑠𝑖= ∑ 𝑌𝑒𝑎𝑟𝑙𝑦𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑥,𝑖
#𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦
𝑥=1
(17)
𝑇𝑜𝑡𝑎𝑙𝐸𝑛𝑒𝑟𝑔𝐶𝑜𝑛𝑠𝑖[𝐺𝑊ℎ] is the total energy consumed in year 𝑖
The same methodology was applied to obtain the vehicle emissions, using equations (18), (19) and (20).
23
𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖,𝑦 =
𝑃𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠𝑥,𝑖,𝑦 × 𝐶𝑎𝑟𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖,𝑦 × 𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑟𝑒𝑠𝑥
106 (18)
Where 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖,𝑦[𝑇𝑜𝑛𝑛𝑒𝑠 𝑜𝑓 𝐶𝑂2] is the amount of CO2 emissions of a vehicle from year 𝑦 ,with
technology 𝑥 in year 𝑖, 𝑃𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠𝑥,𝑖,𝑦 is the number of vehicles of technology 𝑥 ,from year 𝑦 in year 𝑖,
𝐶𝑎𝑟𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖,𝑦 is the car emissions taken from the technical data presented by the brands, per
technology 𝑥 ,from year 𝑦 in year 𝑖 and 𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑟𝑒𝑠𝑥 is the total number of kilometres assumed for
technology 𝑥.
𝑌𝑒𝑎𝑟𝑙𝑦𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖
= ∑ 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖,𝑦
2030
𝑦=𝑏𝑒𝑓𝑜𝑟𝑒 2005
(19)
𝑌𝑒𝑎𝑟𝑙𝑦𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖[𝑇𝑜𝑛𝑛𝑒𝑠 𝑜𝑓 𝐶𝑂2] is the yearly CO2 emissions released by technology 𝑥, in year 𝑖.
𝑇𝑜𝑡𝑎𝑙𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖= ∑ 𝑌𝑒𝑎𝑟𝑙𝑦𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑥,𝑖
#𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦
𝑥=1
(20)
𝑇𝑜𝑡𝑎𝑙𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖[𝑇𝑜𝑛𝑛𝑒𝑠 𝑜𝑓 𝐶𝑂2] is the total CO2 emissions released in year 𝑖.
3.2 Methodology for residential sector
Ideally, the implementation of the model for the residential sector would be quite similar to the vehicle
to the one developed for the transportation sector (presented on section 3.1). However, due to the large
number of appliances existing in the households and the lack of data for some of them, an approach
similar to the previously explained methodology was only applied to some of the most relevant
appliances. The main difference is that for each appliance, a disaggregation by energy efficiency class
is made. In order to model the appliances park evolution over time, the equipment’s stock (considering
not only the new appliances entering in the market, but also the ones that disappear due to equipment
inoperativeness) is considered. Combining this with specific consumption, according to the technology
and efficiency class, based on the international regulations, the energy consumption, total and per
energy vector, and future energy demand is obtained for a specific set of end-uses (Figure 7).
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 yearly characterization of sales per efficiency class will be
the same for technologies that possess this feature.
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.
24
First, the appliances park definition is introduced, followed by the formulation used to model energy
demand for water heating and cooking/white appliances, ending with the energy consumption.
Figure 7 – Household appliances model framework.
3.2.1 Appliances Park
The white goods park is comprised by equipment’s that belong to the kitchen, such as refrigerators,
freezers, washing machines, drying machines, dishwashers, individual ovens, stoves and hobs,
including the respective variations and combinations, such as fridges with freezers or combined washing
and tumbling machines, but also the water heating equipment, like heaters, boilers or solar thermal.
The number of equipment is then calculated using the following equation (21) :
𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝑖,𝑥 =
%𝑒𝑞𝑢𝑖𝑝𝑖,𝑥 × 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖 × 𝐸𝑞𝑢𝑖𝑝𝑝𝑒𝑟𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔𝑥
𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑜𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛
(21)
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.
As done previously for the car fleet, it is necessary to define the equipment’s survival in the equipment’s
park. The appliance’s scrappage is a function of the technical lifetime of the equipment, so it represents
the probability of breakdown/ inoperable before the planned technical life-time.
The annual appliances scrappage curves may be defined as the probability of an equipment being in
operation after k years. To simulate this, a behaviour similar to the vehicles was assumed, based on a
Weibull distribution and applying equation (8).
Since the usage characteristic and solicitations differ throughout various appliances, they life-time
expectancy different. Table 5 enumerates the appliance’s life-time expectancy and the respective
parameters.
25
Table 5 – Appliances life-time expectancy and scrappage curve parameters [63].
Appliances Life-time expectancy 𝑇 𝑏𝑖
Refrigerators 18 18 7
Freezer 16 16 7
Washing machines 18 18 7
Tumble dryers 16 16 7
Dishwashers 16 16 7
Stoves with oven Gas 17 19 7
Electric 16 17 7
Ind. Oven Gas 17 19 7
Electric 16 17 7
Hobs Gas 15 16 7
Electric 13 15 7
With the yearly number of appliances, per technology, for specific historic years and respective
scrappage curves, yearly past sales can be obtained using the linear programming of Microsoft Excel,
to find a solution, in this case, the sales, that satisfies all of the constraints and maximizes (optimise)
this values using an iterative process and validate using the R2 methodology, fitting the curve of the new
appliances park with the real historical data. Constraints are applied to induce some physical and
historical meaning to the results, such has relations between the sales and end-use to guarantee the
respective appliance’s technology penetration.
With the new sales per technology obtained and using the assumed yearly efficiency percentage
penetration on the new sales, the yearly efficiency class sales per technology is obtained by the following
equation (22) :
𝐸𝑞𝑢𝑖𝑝𝑠𝑎𝑙𝑒𝑠𝑖,𝑥,𝑒 = 𝐸𝑞𝑢𝑖𝑝𝑠𝑎𝑙𝑒𝑠𝑖,𝑥 × %𝑠𝑎𝑙𝑒𝑠𝑖,𝑒 (22)
Where 𝐸𝑞𝑢𝑖𝑝𝑠𝑎𝑙𝑒𝑠𝑥,𝑒 is the number of sales from technology 𝑥 with efficiency class 𝑒 in year 𝑖,
𝐸𝑞𝑢𝑖𝑝𝑠𝑎𝑙𝑒𝑠𝑖,𝑥 represents the total sales of technology 𝑥 in year 𝑖 and %𝑠𝑎𝑙𝑒𝑠𝑖,𝑒 [%] is the yearly
percentage of efficiency class 𝑒 assumed in sales in year 𝑖.
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, gives the year
appliance’s park composition per efficiency technology from 2015 to 2010, with the same conf iguration
of Table 3. The estimation of the number of appliances for future years is then calculated in a similar
way to what was presented in section 3.1.1.
26
3.2.2 Energy consumption
The energy consumption for the appliances park is calculated depending on the equipment purpose of
use.
Water Heating
The water heating energy needs include all the energy required to heat water for residential usage. The
appliances which use their own heating system are not included. Considering the Portuguese regulation
[64], the annual energy required for residential water heating purposes in a household is given by
equation (33).
𝑄𝑊𝐻 =
𝑉𝑤 . 𝑛ℎ. 𝑓ℎ. 𝜌𝑤. 𝑐𝑝𝑤. ∆𝑇. 365
3600 (23)
𝑄𝑊𝐻 [𝑘𝑊ℎ] is the annual energy needed for water heating, 𝑉𝑤 [𝑙
𝑝𝑒𝑟𝑠𝑜𝑛.𝑑𝑎𝑦] is the daily water volume
consumed per person, 𝑛ℎ[𝑝𝑒𝑟𝑠𝑜𝑛] is the number of persons in the household, 𝑓ℎ [%] is a factor that
counts for hydraulic systems, 𝜌𝑤 [𝑘𝑔
𝑙] is the water density, 𝑐𝑝𝑤
[𝐾𝐽
𝑘𝑔.𝐾] is water specific heat and ∆𝑇 [𝐾] is
the water temperature increase by the heating system.
For water heating equipment, the energy demand is computed relying on the energy required for heating
water, equipment technology efficiency, penetration and energy source, as described on equation (24):
𝐶𝑊𝐻𝑖,𝑥,𝑦
=𝑄𝑊𝐻𝑖
. 𝑃𝑖,𝑥,𝑦
𝜂𝑥,𝑦
(24)
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. Those efficiencies are obtained from
REH [64], illustrated on Figure 8.
Figure 8 – Reference values for conditioning and water heating equipment [64].
27
Cooking/White appliances
This category includes all the energy demand from the usual equipment used for meal time preparation
as well as the appliances with exclusive or common usage on the kitchen. The total energy demand
results from the sum of all appliances consumptions included in this category, given by equation (25):
𝐶𝑡 = ∑ 𝐶𝑡,𝑎 (25)
Where 𝐶𝑡 [𝑘𝑊ℎ] is the total energy demand and 𝐶𝑡,𝑎 [𝑘𝑊ℎ] is the energy demand required for appliance
𝑎. The latter depends on the power rating, usage and equipment penetration. The power and usage can
be combined in the form of appliance specific consumption and used to calculate the energy demand
for a certain appliance. This formulation is specified by equation (26):
𝐶𝑡,𝑎 = 𝑁𝑎. 𝑆𝑐𝑎 (26)
Where, for an appliance “𝑎”, 𝑁𝑎 [𝑢𝑛𝑖𝑡𝑠] is the number of appliances and 𝑆𝑐𝑎 [𝑘𝑊ℎ
𝑦𝑒𝑎𝑟] is the specific energy
consumption. The sum of the total energy demand may also be calculated by energy vector, since not
all appliances have the same energy vector source. For cooking appliances not affected by international
regulations, such as stoves and hobs, the energy consumption is calculated combining equations (25)
and (26). Those governed by regulations, the formulation to perform the calculations is presented
hereafter.
After more than 10 years from the enforcement of the EU energy labelling scheme there is evidence
that, at least for a number of appliances, the label has had a considerable impact in persuading
consumers to buy more energy-efficient models [65].The energetic labels were created to inform the
consumer about appliance’s performance and characteristics, using a rating scale to identify the
equipment efficiency. In addition to the energy consumption, information about water consumption (if
used) and noise is available.
In order to follow up and introduce technological advances by producers and meet the growing
consumers demand, the old directives on energy labelling and product eco-design were reviewed,
resulting in the adaptation and implementation of a new standard, called Directiva 2010/30/CE” [66] ,
for the energy consumption and “Directiva 2009/125/ICE” [67], for the ecologic conception . Within these
reviews, the new energy label was introduced with new energy classes and some criteria amendment,
regarding energy efficiency acknowledgement [68].
Based on the new regulations, the energy efficiency class of an appliance shall be determined on the
basis of its Energy Efficiency Index (EII) [66]. The respective value for each appliance class efficiency
is defined in the related regulations. The Energy Efficiency Index (EEI) is calculated as presented in
equation (27) and rounded to one decimal place.
𝐸𝐸𝐼 =
𝐴𝐸𝑐
𝑆𝐴𝐸𝑐
× 100 (27)
28
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.
Since the formulation to calculate the standard annual energy consumption of a household appliance
and energy efficiency indexes are well defined on the regulations, it is possible to compute the annual
energy consumption of the equipment park. For this, the equipment’s characteristics have to be
assumed, as presented on Table 6 for fridges and Table 7 for washing appliances, which combined with
the formulas described on the regulations, allows the standard annual consumption calculation [𝑆𝐴𝐸𝑐].
Table 6 – Fridges characteristics assumed to obtain the standard annual consumption.
Fridges Category
Useful
Refrigeration
Volume [l]
Useful
Freezer
volume[l]
Equivalent
Volume [l] M N
𝑺𝑨𝑬𝒄
[kWh]
Fridge without
Freezer 1 342 0 342 0.233 245 324.69
Fridge with
Freezer 5 230 72 415.76 0.777 303 626.05
Combined
Fridge 6 252 91 486.78 0.777 303 681.23
Freezer 9 0 250 645 0.472 286 590.44
Each category is defined by the specific compartment composition and is independent of the number of
doors and/or drawers; the ‘equivalent volume’ of a household refrigerating appliance is the sum of the
equivalent volumes of all compartments, the M and N values are given in tables for each household
refrigerating appliance category [69]. For washing appliances and independent ovens, the
characteristics are illustrated on Table 7.
Table 7 – Washing appliances characteristics assumed to obtain the standard annual consumption.
Appliances Rated Capacity
[kg]
Number of
place settings
Volume
Capacity
[l]
Nº of cycles 𝑺𝑨𝑬𝒄
[kWh]
Washing Machine 8 - - - 427.70
Tumble Dryer 8 - - - 738.90
Dishwasher - 12 - - 352.80
Ind. Oven (gas) - - 59 153 260.40
Ind. Oven (elec.) - - 59 153 122.10
Where ‘Place settings’ means a defined set of crockery, glass and cutlery for use by one person [70]
and ‘Rated capacity’ means the maximum mass in kilograms stated by the supplier at 0.5 kg intervals
of dry textiles of a particular type, which can be treated in a household washing machine on the selected
programme, when loaded in accordance with the supplier’s instructions [71]. The ‘Volume Capacity’ is
the volume of the cavity of the domestic oven, in litres, and the ‘number of cycles’ is the period of heating
a standardised load in a cavity of an oven under defined conditions [72].Combining the values of IEE
29
per technology and per efficiency class assumed on the regulations and the standard annual
consumption (equation (27)), the annual energy consumption [𝐴𝐸𝑐] per technology and per efficiency
class is obtained. Since the regulation for washing and tumble combined does not define the specific
consumptions, an average energy consumption between washing machine and tumble dryer was
assumed for each efficiency class. The annual consumptions values are presented on Table 8 and
assumed to remain constant through the years.
Table 8 – Annual energy consumption of the appliances park per efficiency class.
Appliances Annual Energy Consumption - 𝑨𝑬𝒄 [kWh]
A+++ A++ A+ A B C D-G
Fridge without Freezer
68.18 90.91 123.38 159.10 211.05 275.98 334.43
Fridge with Freezer
131.47 175.29 237.90 644.83 406.93 532.14 644.83
Combined Fridge
143.06 190.74 258.87 333.80 442.80 579.04 701.66
Freezer 123.99 165.32 224.37 289.32 383.79 501.87 608.15
Washing Machine
192.47 209.57 239.51 273.73 312.22 350.71 376.38
Tumble Dryer
169.95 206.90 273.40 399.02 524.64 598.53 635.48
Washing and Tumble
181.21 208.24 256.73 336.37 418.43 474.62 505.93
Dishwasher 172.87 186.98 211.68 236.38 268.13 299.88 321.05
Ind. Oven (gas)
114.56 140.59 187.46 247.34 312.43 380.12 416.57
Ind. Oven (elec.)
53.71 65.91 87.89 115.96 146.48 178.21 195.30
3.3 Other sectors
The introduction of the strategies promoted, through the development of energy policies and measures
to promote energy and environmental sustainability, will have consequences on the overall energy
consumption of the different economic sectors. Although bottom-up approach is used for the
transportation and residential sectors, the analysis of the other sectors, which are Agriculture, Industry,
Commerce and Services, was only possible using a simplified top-down approach and focusing only on
electricity due to lack of reliable data.
The number of inhabitants, based on technologies and behavioural patterns, has a huge influence on
the overall electricity consumption of a specific location. To model the future electricity consumption,
first it is necessary to calculate the electricity consumption per capita, which represents the average
personal electricity consumption in a certain place. This is calculated by the following equation (28):
𝐶𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎𝑖
=𝐶𝑖
𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖
(28)
30
with 𝐶𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎𝑖[𝑘𝑊ℎ] representing the electricity consumption per capita for year 𝑖, 𝐶𝑖 the total electricity
consumption for year 𝑖 and 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖 the number of inhabitants of a specific location in year 𝑖.
It was assumed that a Logistic or Gompertz function (equation (1)), once it as a sigmoid curve shape,
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 characterization. Following this and using these
results, it is possible to compute the electricity consumption per sector over the years, using the following
equation (29).
𝑆𝑒𝑐𝑡𝑜𝑟𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖,𝑗
=𝐶𝑆𝑝𝑒𝑟𝑐𝑎𝑝𝑖𝑡𝑎𝑖,𝑗
× 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖
1000 (29)
where 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.The total electricity
consumption is given by equation (30) :
𝐸𝑙𝑒𝑐𝑡𝐶𝑜𝑛𝑠𝑡𝑜𝑡𝑎𝑙𝑖
= ∑ 𝑆𝑒𝑐𝑡𝑜𝑟𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝑖,𝑗
#𝑠𝑒𝑐𝑡𝑜𝑟
𝑗=1
(30)
𝐸𝑙𝑒𝑐𝑡𝐶𝑜𝑛𝑠𝑡𝑜𝑡𝑎𝑙𝑖[𝑀𝑊ℎ] is the total electricity consumption in year 𝑖.The implications of the changes
provided by the measures considered on the future scenarios development for the electricity
consumption will then be added to the respective sectors, which in this case, are those referred on the
beginning of this section, and make part of the future electricity consumption.
31
4. Terceira Island
Terceira is the eastern island of the five that form the central group and is the nearest one from São
Jorge Island, about 38 km away. The highest point of the island, at 1021 m altitude, is located in the
Serra de Santa Bárbara, at 38°43’47’’ latitude north and 27°19’11’’ longitude west. The island’s area is
about 402 km2, with 30.1 km long and 17.6 km at its maximum width [73]. According to Census 2011,
Terceira is the second most populated island of Azores, with 56 437 inhabitants [74]. Terceira's main
economic activity is rising of livestock and the production of dairy-based products. It has two main sea
ports, one at Angra do Heroísmo and the other at Praia da Vitoria, and a commercial airport integrated
with the flight operations of the air force in Lajes. Terceira's economy 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 [75]. According to Electricidade dos Açores (EDA), 203.25 GWh
of electric energy were produced in 2014, with 17.3 % coming from renewables, much less than the
average of Azores [76]. Although, there’s been a commitment to change this situation through research
and developing new solutions to take advantage of the endogenous and renewable sources of energy
[77]–[80]. As Azores, Terceira is also under the Pact of Islands to develop energy policies, to promote
energetic and environmental sustainability, economic development and creation of jobs.
In this study, the main focus will be given to the promotion of electric vehicles, through changes on the
fleet characterization over the years, follow by an EV penetration increase, and the electrification of all
equipment’s presented on the kitchen, including end-use alterations and increase of the energy
efficiency in some forms of energy use, mostly electrical appliances.
4.1 Data sources and challenges
When collecting and treating data, there are some major concerns about the needed information to
develop an accurate project. Some of the concerns are related with the existence of data with proper
detail in terms of resolution, to guarantee accurate results from the available information, as well as data
dimensions to ensure that the system is well characterized, in order to define trends over the years.
Other important points are the ease in finding detailed data for this specific region and further treatment.
This treatment comprehends the adaptation of the initial information into a data set appropriate for the
following studies and consequent calculations. Before any analysis it is important to have an idea of the
behaviour and development of the system over the last years. To do so, the information should be
organized in a way that allows the perception of the influence and impact of each energetic vector on
the different activities and sectors presented in Terceira Island. For that, information related with the
demand, consumption and production over the years was gathered. This data was collected from
different sources, such as DGEG (Direcção Geral de Energia e Geologia), EDA (Eletricidade dos
Açores) and INE (Instituto Nacional de Estatística). Detailed information about the data sources used to
perform this study are presented in Appendix A.
32
4.2 Demand by energy source
4.2.1 Fossil Fuels
As mentioned before, Terceira Island is highly dependable on fossil fuels to develop their daily basis
activities. Not only on the energy supply systems, such as the Thermoelectric Power Plant of Belo
Jardim, but also in the mobility and lifestyle associated with the residential and transportation sector.
The evolution of the fossil fuel consumption is presented on Figure 9, which shows a growing tendency,
with some oscillations, until 2009, having been decreasing since then. Fuels like special diesel, gas auto
and propane were omitted from this evolution since their consumption is negligible when compared with
the other sources.
Figure 9 – Primary Energy Consumption per fossil fuel energy source [81].
The collected data shows, in 2014, a petroleum products consumption close to 75 thousand tonnes,
which represents a decrease of 2.4 % and 7.4 % when compared with 2013 and 2012, respectively.
This values reflect the influence of some policies implemented by the Regional Government of Azores
towards a sustainable development, including the penetration of renewable resources or energy
efficiency increase for the infrastructures. The share of the different oil products is presented in the
Figure 9, with fuel oil representing more than half of the total consumption. In 2014, 86.7% of the fuel
oil was used to produce electricity, while the remaining was applied in the food industry and construction.
Diesel and Gasoline represent 28 % and 10 % of the total petroleum derivatives consumption,
respectively. Gasoline is mainly used on the transport sector, while Diesel has more extensive
application. Following this, Figure 10 presents disaggregation on fossil fuel sources demand by
economic activity. Those activities are aggregated on sectors and then fully characterized hereafter.
0
1 000 000
2 000 000
3 000 000
4 000 000
5 000 000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Ener
gy (
GJ)
YearButane Gasoline Diesel FuelOil
33
Figure 10 – Petroleum derivatives consumption distribution [81].
Figure 11 – Fossil fuel consumption distribution per economic activity [81].
Since the accessibility to fossil fuel sources in isolated communities, such as Azores Archipelago, are
limited and restricted when compared with the Portugal mainland, the government developed new
policies to reduce the discrepancy between prices, enabling a more egalitarian and competitive system.
The prices used in the Autonomous Region of Azores are limited by their own government, where they
published that the maximum selling price to the public from fuels like gasoline and diesel has to be less
than 10 % when compared with the reference price stipulated in Portugal, an 18 % for fuel oil [82]. The
data available from the website of Azores government starts from 2007 until 2015. To have a clear idea
of the behaviour/ fluctuation of the fuel prices, an estimation was created from 2000 and 2006, taking
into account the reference price predicated on the continent and the maximum price verified on selling
fuels in Azores. The results are present on Figure 12.
Figure 12 – Fuel prices in Azores per source [82], [83].
4.2.2 Electricity
Due to technological advances over the years, there has been a change in the consumption paradigm,
especially with the increase of appliances and basic equipment necessary to carry out the daily activities,
whether at work or at home. The changes verified over the years have repercussions at the
Butane9%
Gasoline10%
Diesel28%
Fuel Oil53%
Butane Gasoline Diesel Fuel Oil
0
300000
600000
900000
1200000
1500000
1800000
Butane Gasoline98
Gasoline95
Diesel Fuel Oil
Ener
gy (
GJ)
Agriculture Food, Drinks and TobaccoFuels Public Works and ConstructionDomestic EducationExtraction FishingProduction/Distribution AccomodationHealth ServicesTransports
0,00
0,50
1,00
1,50
2,00
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Pri
ce (
€/L
)
Year
Gasoline 98 (€/L) Gasoline 95 (€/L) Diesel (€/L) Fuel Oil (€/kg)
34
consumption, possible to observe on Figure 13, which represents the electricity production from 2010
to 2014.
Figure 13 – Electricity production over the years [84].
Regarding the year of 2014, the total electricity production corresponds to 203 254 MWh, which
represents a reduction of 2.7 % and 4.5 % when compared with 2013 and 2012, respectively. The
electricity produced by each energy source is shown on Figure 14, with fuel oil being dominant. In 2014,
82.7 % of the total electricity production came from fossil fuels – fuel oil and diesel – used on the thermal
power plants, where the rest 17.3 % of share corresponds mostly to the exploitation of renewables
sources available (17 % wind and 0.24% hydro).
Figure 14 - Electricity production by energy source 2014 [26].
Based on the electricity produced in 2014, the most critical consumers are the Residential and Services
sectors, with 32.9 % and 22.9 % each, due to severe dependency on electronic equipment’s necessary
to produce the daily activities and responsible for present quality of life. The Commerce sector has also
an important role, contributing with 12.3 % of the total electricity consumption. Although the other sectors
have their contribution to the final consumption, most of them are small when compared with those
mentioned before. All the detailed data related with the consumption of electricity per sector is presented
in Figure 15.
15 000
16 000
17 000
18 000
19 000
20 000EN
ERG
Y (
MW
H)
MONTH
2014 2013 2012 2011 2010
Fuel Oil81,54%
Diesel1,17%
Hydro0,24%
Wind17,01% Micro
Generation0,03%
Fuel Oil
Diesel
Hydro
Wind
Micro Generation
35
Figure 15 - Share of the total electricity consumption per sector in 2014 [85].
4.2.3 Total Demand
Gathering the data regarding the fossil fuels with the electricity consumption, it is possible to highlight
the economic sectors that have more impact on the total energy consumption. This evidence is exposed
in Figure 16. In terms of the total energy, this figure shows the same behaviour as Figure 9, with a
growing tendency until 2009 and decreasing from there on. Since 2007, the increasing contribution of
the transportation sector on the energy consumption was the segment with the largest impact on
Terceira Island. This happens due to the composition of the vehicle fleet that exists on the island, which
consumes a huge amount of fuel. Although it is visible that the transport sector has the biggest influence
in the total energy consumption of the island, with approximately 49%, the re sector has also a significant
contribution, with 20 %, followed by the Food, drinks and Tobacco, and the Services sectors, with 11.4
% and 7.4 % respectively (Figure 17). In this graph, the Production and Distribution sector was not taken
into account, since the energy associated to the electricity used on the different sectors results from the
transformation of the fossil fuels presented on referred segment.
Figure 16 - Total energy consumption by economic sector [81], [85].
Agriculture1%Food, Drinks and
Tobacco7%
Commerce12%
Public Works and Construction
5%
Self Consumption0%
Domestics33%
Education1%
Extraction0%
Fabrication0%
Street Lighting3%
Industry0%
Electro-Metal-Mechanics
0%
Fishing1%
Production/Distribution
1%
Accomodation5% Health
0%
Services23%
Telecomunications2%
Transports4%
Other11%
0500 000
1 000 0001 500 0002 000 0002 500 0003 000 0003 500 000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Fin
al E
ne
rgy
(GJ)
YearFood, Drinks and Tobacco Ceramics Fuels Commerce
Public Works and Construction Self Consumption Domestics Education
Extraction Fabrication Street Lighting Industry
Electro-Metal-Mechanics Fishing Accomodation Health
Services Telecomunications Transports Agriculture
36
Figure 17 - Share of the total energy consumption per sector [81], [85].
4.3 Demand per Sector
From the detailed information of the previous chapters, it was possible to recognise the contribution of
some specific sectors on the energy consumption was irrelevant when compared with others. Since this
study does not require such fine detail for the results, four main sectors were created, resulting from the
aggregation of the previous ones. This main sectors are Agriculture/Industry, Commerce/Services,
Residential and Transportation. Table 9 illustrates the aggregation of the specific sectors created before
to create the new ones, which are going to be used from now on.
Table 9 - Main sectors.
Main Sector Specific Sector
Agriculture/ Industry
Agriculture, Fishing, Food, Drinks and Tobacco,
Ceramics, Fuels, Construction and Public Works,
Extraction, Fabrication, Industry, Electro-Metal-
Mechanics, Production
Commerce/Services
Commerce, Education, Public Lighting,
Accommodation, Health, Services,
Telecommunications
Residential Domestic
Transportation Transportation
With all those specific sectors combined in just four, the new final energy consumption is then
represented in Figure 18.
37
Figure 18 – Total energy consumption of the main sectors.
4.3.1 Transports
This sector contemplates all the activities related with the road transportation used on Terceira Island,
from the light to the heavy categories. Although there are other means of transportation on the island,
such as planes and boats, responsible for the interconnection of the archipelago and creating
communication and trading routes with inland regions, they were not accounted on the fossil fuel
consumption reports from DGEG for Terceira. Road transportation is characterized by a large number
of light duty vehicles, with the majority of these being private passenger vehicles. The low number of
heavy-duty vehicles (trucks, buses, etc.) is due to the small size of the island, when compared with
inland countries [24].
The Figure 19 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. From 2007 to 2008, there is a significant increase
on the diesel consumption, due to the aggregation of this fuel source from the other sectors to
Transportation. Although, this value almost triples and, based on the previous trends, the increase on
the energy consumption should have been lower. To identify the problem, a thorough diesel
consumption analysis was made to the other islands. Considering this, the fluctuations on the diesel
consumption percentage of each island and their influence on the total consumption of the archipelago
are notorious, presenting evidence to the data inconsistency associated with this fuel. Despite the
oscillations, the total consumption of the archipelago remains unchanged, which enhances the problem
stated. The results for the variations on diesel consumption from the different islands and archipelago
total consumption are presented on Figure 20. This fossil fuel consumption levels arise from the
necessity of the local people to have their own transport, usually light passenger vehicles, in order to
make their daily commute. Although alternatives are presented, like the public transportation fleet,
including the buses, the locals prefer their own transportation, due to the lower use constraints and
inefficiency of the public transport grid [27].
0
1 000 000
2 000 000
3 000 000
4 000 000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Ener
gy C
on
sum
pti
on
[G
J]
Year
Agriculture/Industry Domestic Commerce/Services Transportation
38
Figure 19 – Total consumption of the transportation sector per energy source [81], [85].
Figure 20 – Diesel consumption share per island and Azores diesel consumption [81].
Giving focus to the road transportation, the available data from insurances reports, accessible at ISP
[86], provide detailed information regarding yearly Portuguese road transportation fleet characterization,
with a considerable geographical resolution, including smaller municipality scales. Following this, the
Terceira vehicle park in 2014 per category is illustrated on Figure 21. For that year, this island included
31 916 vehicles, where 73.4 % were light-passenger vehicles, which represents a total number of 23 417
vehicles, followed by the light-duty category, with 8 %. On the other hand, mixed and heady-duty
vehicles have few relevance on the fleet, corresponding to approximately 0.2 % and 0.3 %, respectively.
From 2005 to 2014, the motorcycles and tractors were the segments with larger increase, corresponding
to 68 % and 75 %, respectively. As for the LDV, the total number of vehicles increased approximately
23 %. Considering just the light-passenger vehicles, Figure 20 represents the evolution of the number
of passenger vehicles over the last ten years. Although the economic situation as not been favourable
since the beginning of the new millennium, the number of passenger vehicle as increased almost 27 %
in the last decade.
0
500 000
1 000 000
1 500 000
2 000 000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Ener
gy C
on
sum
pti
on
(G
J)
Year
Butane Gasoline Electricity Fuel Oil Propane Diesel
0
50 000
100 000
150 000
0%
20%
40%
60%
80%
100%
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
TON
NES
OF
DIE
SEL
(TO
N)
DIE
SEL
SHA
RE
(%)
YEAR
Azores Graciosa Pico Faial Corvo Santa Maria São Jorge Flores Terceira S.Miguel
39
Figure 21 – Terceira road fleet characterization in 2014 [86].
Figure 22 – Number of passenger vehicles in Terceira over the last decade [86].
4.3.2 Residential
This sector only contemplates the domestic daily basis activities such as cleaning, washing the dishes,
taking a bath, cooking and so on. Using the same approach as before, the Figure 23 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. On the daily basis
people spend most of time at work and return almost at night. During the period that they are at home,
most of the fundamental activities necessary to achieve a certain quality of life, require the usage of an
electronic device or equipment. The consumption of electricity is associated with all the electric
equipment’s, from those who are always working, like the refrigerator or the freezer, to the ones used
only in short periods of time, as the microwave.
Figure 23 - Total consumption of the residential sector per energy source [81], [85].
Mopeds2,11%
Light-duty 8,01%
Light-passenger73,33%
Mixed0,21%
Motorcycle4,14%
Others0,85%
Heavy0,03%
Heavy-Duty 2,87%
Heavy- Passenger0,28%
Trailer4,19%
Semi-Trailer0,44%
Tractor3,50%
Other12,20%
0
5000
10000
15000
20000
25000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Nu
mb
er
of
Passen
ger
Veh
icle
s
Year
Angra do Heroísmo Vila da Praia de Vitória Total
0
200 000
400 000
600 000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Consum
ptio
n (
GJ)
YearElectricity Gasoline Diesel Fuel Oil Propane Butane
40
For a more detailed analysis, ICESD [87] survey focused on gathering information related with energy
consumption and expenses on households , per end-use and energy vector. Although the data available
on the survey is linked to Azores, the same energy, end-use and energy vector’s distribution was
assumed for Terceira, with the results demonstrated on the following paragraphs.
The Figure 24 gives the energy consumption distribution on a typical household of Azores, per energy
vector. Butane is the main energy source consumed on the residential sector, corresponding to 50 % of
the total energy consumption (6 771 toe), used on 97 % of the households, representing 40 % of the
energetic bill costs. The second is electricity, with 43 % share on the total energy consumption (5866
toe), with 100 % household penetration. Even though electricity isn’t the predominant energy source
consumed, it is the most representative on the energetic bill, corresponding to 59 % of the energy
expenses. It should be enhanced that butane and electricity combined represent around 93 % of the
total energy consumption per source. Other sources, like biomass and propane, only represent 5.9 %
and 0.9 %, respectively. In terms of renewables, the solar thermal still has a reduced influence on the
households, with only 0.2 % of the total energy consumption. Information regarding diesel for heating
and coal was not available and natural gas is not applicable.
In terms of energy end-use, the Figure 25 gives the distribution for a typical house. For this analysis,
the share related with the energy consumed by vehicles related with the residents was excluded and
were considered six types of energy usage, such as: water heating, heating, cooling, kitchen1, lights and
electric equipment2. The biggest contribution to the energy consumption comes from the kitchen, which
corresponds to 46 % of the total, in the reference period, followed by water heating, with 33 % share.
In opposition, cooling and heating are the end-uses with less influence on the energy consumption,
representing 0.1 % and 2 %, respectively, as a result of the temperate climate present in Azores.
In the kitchen (Figure 26), butane (for cooking) and electricity (appliances) are the main energy sources
used, corresponding with 47 % and 43 %, respectively. After those, only biomass has some importance,
with 8 %. The rest are vestigial (propane) or inexistent (Natural Gas).For water heating, almost all the
energy spend is provided by butane (96 %), as the other 4 % are divided by propane, LPG and electricity.
Considering the energy sources per end-use, the data shows that electricity is the only energy vector
that has a contribution in every end-use, with the kitchen and electric appliances being the main ways
of using electricity (79 %) (Figure 27).This reflects the growing household electrification tendency. In
terms of butane consumption, this is divided in two main uses: cooking (44 %) and water heating (55 %)
(Figure 28).
1 Includes ovens, hobs, fireplace, microwave, extractor, fridges, freezer, Dishwasher, washing machine,
tumble dryer and washing and tumble. 2 Includes vacuum cleaner, central cleaner, iron, dehumidifier, TV, radio, sound system, DVD player,
Computer, fax and printer.
41
Figure 28 – Butane consumption per end-use (Azores).
4.3.3 Agriculture/ Industry
The evolution of the total consumption per energy source of this sector is presented on the Figure 29.
From the analysis of the graph it is possible to observe that this 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 of the different activities presented before, such as the rising of
livestock or the production of dairy-basic products, as diesel is mostly used on off-road vehicles for
Water heating 56%
Kitchen 44%
BUTANE CONSUMPTION
Figure 24 – Energy consumption per energy source in a typical household (Terceira).
Figure 25 – Energy consumption per end-use in a typical household (Azores).
Figure 26 - Energy consumption per energy source in the kitchen (Terceira).
Figure 27 - Electricity consumption per end-use (Azores).
Electricity43%
Biomass5%
Butane 50%
Propane 1%
ENERGY SOURCEHeating
2%Cooling 0,10%
Water heating
33%
Kitchen 46%
Electric Equipment
12%
Lights7%
ENERGY CONSUMPTION
Electricity43%
Biomass8%
Butane47%
Propane1%
GPL1%
KITCHENHeating 1,15%
Cooling 0,29%
Water heating 0,43%
Kitchen 47%
Electric Equipment
33%
Lights19%
Electricity consumption
42
transportation. The reduction verified on que diesel consumption is due to the fact that since 2008,
DGEG reports associate all the consumption of diesel and gasoline to the activities related with
transportation. The peak verified on 2013 results from a dissociation from the previous assumption made
by DGEG, where the diesel for the vehicle mobility on the agriculture sector is again accounted and
separated from the transportation sector.
Figure 29 – Evolution of the agriculture/industry sector total consumption per energy source [81], [85].
4.3.4 Commerce/Services
The Figure 30 contains the total consumption associated to the commerce/services sector, with the
emphasis on the evolution over the years. The main energy source used to satisfy the demand of this
sector is electricity, since it is used to supply the electric systems, equipment’s and appliances, such as
lighting, air conditioning, or electronical devices, which are 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.
Figure 30 – Commerce/services total consumption per energy source [81], [85].
From all the information presented along this chapter, the fossil fuel dependency on energy demand,
regarding the different sectors is notorious, resulting from the lack of alternative energy sources to satisfy
the existent demand. Adding to this, policies and incentives given to the local population to adopt more
sustainable behaviours/lifestyles are relatively low. For the residential sector, there is no awareness or
incentive to change the equipment’s that rely on fossil fuels to produce heat. Also, it is clear that public
transportation grid does not possess the necessary conditions to be a reasonable alternative to the
passenger vehicle in Terceira. Taking the dimension of the island into account, private vehicles does
not have the same constraints as in the inland cities, like parking or car density, which makes the public
transportation less appealing.
0
200 000
400 000
600 000
800 000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Co
nsu
mp
tio
n (
GJ)
Year
Butane Gasoline Electricity Propane Fuel Oil Diesel
0
100 000
200 000
300 000
400 000
500 000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Co
nsu
mp
tio
n (
GJ)
YearElectricity Fuel Oil Propane Butane
43
The application of the measures proposed in the “Plano de Acção para o Desenvolvimento Sustentável”,
in terms of renewable penetration on primary and final consumption, energy efficiency policies and fossil
fuel migration to electricity or other renewable sources, such as the promotion of the electric vehicles,
residential consumption awareness campaigns or the application of solar thermal to heating purposes,
will contribute not only to reduce the fossil fuels importation dependency, associated with carbon
footprint reduction, but also to raise concern about the importance of adopting sustainable behaviours.
4.4 Scenario definition
To study the implementation of energy efficiency measures and policies, future demand scenarios will
be assessed using the developed model. These scenarios are designed based on four key issues:
1. Demography: which is based on the expected evolution of the population;
2. Technology penetration: related with the level of introduction of specific technologies, such as
vehicle density for the transportation sector or penetration rates for household appliances;
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;
4. Technology efficiency: related with the efficiency of appliances and vehicles.
The following sections describe the scenarios designed. All scenarios are compared with a BAU
(Business as usual) scenario, which is considered as the reference scenario. The Business as usual
scenario (BAU) 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.
Using the year 2008 as baseline, growth rates were estimated for the final energy needs for each activity
sector. Efficiencies of the electricity generation systems and for the electricity use devices were kept
constant and no new renewable sources projects were considered up to 2030. As for the fleet
characterization, it was considered that the sales share by technology were identical to the Portuguese
fleet but with a delay of seven years. This assumption assumes that light passenger vehicle fleet will be
constituted, in is majority, by conventional technologies, such as diesel and gasoline, with a minimal
contribution from non-conventional technologies.
For all scenarios, the energy demand of energy services not included in the model but necessary to
estimate the total energy consumption and CO2 emissions for each sector were calculated as the
difference between the sector total consumption and the considered energy services as estimated in
the BAU scenario. These values were kept constant over the years for all scenarios.
4.4.1 Transport Sector
The inputs required for the application of the model that were considered to design the different
scenarios for the transportation sector are detailed next.
44
Population
The population, as GDP, influences directly the car fleet stock. To provide the evolution of the population
along time, data has to be retrieved from the statistical institutions responsible for collecting the
information and dispose quality statistical data.
For Terceira, the population data are obtained from the Portuguese Census [88], elaborated by INE
(Instituto Nacional de Estatística), and SREA [89]. Since there are no predictions for the future number
of inhabitants on Terceira, it was necessary to consider the future forecasts available for Azores
archipelago, which can be obtain from Eurostat [90], and then extrapolate to Terceira. For this, the
percentage of inhabitants that Terceira represents on the total population of Azores is calculated, based
on the statistical data reviewed, for each year and, considering this results, an average value is obtained,
expressed by Equation (31). Then, the values for the future number of inhabitants are extrapolated from
the predictions done for Azores. The population growth of Terceira is represented on Figure 31, which
combines the statistical data with the future predictions. The abrupt reduction of population from 1999
to 2000 result from data inconsistency, once the statistic series presented by SREA between the years
that surveys are made by INE are based on predictions.
Figure 31 – Number of inhabitants of Terceira [88]–[90].
%𝐻 = ∑
𝐻𝑇𝑡𝐻𝐴𝑡
⁄
𝑛
2014
𝑡=1991
(31)
In equation (31), the %H represents the average percentage of Terceira inhabitants in relation to the
Azores archipelago, 𝑡 is the year, 𝐻𝑇𝑡 the total number of inhabitants of Terceira in year t, 𝐻𝐴𝑡
is the total
number of inhabitants in Azores in year t and 𝑛 represents the number of considered years. Performing
this calculation, the average percentage of Terceira inhabitants, in relation to the Azores Archipelago,
corresponds to 22.93 %. This percentage will be assumed as the medium scenario value for
demography scenarios.
In terms of Terceira population, three possible scenarios were considered based on Figure 32 and
Figure 33, starting with the present situation of Terceira representing around 23% of the total population
of Azores (medium), which corresponds to 56 091 inhabitants . The optimistic considers that Terceira
will continue to grow and have a bigger impact on the total population of Azores, representing 30% of
the total number of inhabitants on the archipelago, which corresponds having 72 682 persons in 2030.
This reflects a low migration and high fertility levels. The pessimistic scenario considers a population
54500
55000
55500
56000
56500
57000
57500
58000
1990 1995 2000 2005 2010 2015 2020 2025 2030
Nº
of in
habitants
Year
Number of inhabitants
45
decrease since 2015 until 2030, reaching 46 879 inhabitants in Terceira, which represents 19% of the
total population of the Archipelago. This is a reflection of high migration and low fertility.
Figure 32 – Scenarios for the impact of Terceira in the total population of Azores.
Figure 33 – Number of inhabitants in Terceira , based on the scenarios considered.
Vehicle density curves
Considering the vehicle density curves, the Logistic function gives a better correlation of the Portuguese
fleet and since the characterization of Terceira fleet will be given using the Portuguese vehicle sales
percentage, the formulation presented in equation (1) be considered from now on and used to estimate
the vehicle density of Terceira. Using the historical data provided by SREA, INE and ASF [86], [88], [89]
the vehicle density is obtain between 2005 and 2014, using equation (32). This results are used to create
the Logistic function capable of characterize the passenger vehicles estimations. This outcome is
expressed on Figure 34, together with the logistic function parameters used.
𝑓𝑖 =
𝑁𝑐𝑖
𝑁ℎ𝑖
× 1000 (32)
𝑓𝑖 is the vehicle density in year 𝑖, 𝑁𝑐𝑖 is the number of passenger vehicles for year 𝑖 and 𝑁ℎ𝑖
the number
of inhabitants for year 𝑖.
Logistic function
parameters
𝜶 461
𝒌 0.139
𝜷 0
𝝓 1998.3
𝑹𝟐 0.94
Figure 34 –Vehicle density curve evolution in Terceira (left); Logistic function parameters used to obtain the vehicle density curve (right).
For the vehicle density curves, the LPV best fleet was considering by using the logistic curve, with the
best fitting results were obtained for 456 vehicles by 1000 inhabitants, which was considered the
medium scenario. Other two options were taking into account: an optimistic one, stabilizing at 507
0%
5%
10%
15%
20%
25%
30%
35%
40%
1990 2000 2010 2020 2030
% o
f A
zore
s P
opula
tion
YearHistorical data Reference Optimistic Pessimistic
0
10000
20000
30000
40000
50000
60000
70000
80000
1990 2000 2010 2020 2030
NU
MB
ER
OF
IN
HA
BIT
AN
TS
YEARHistorical Data Reference
optimistic pessimistic
300
350
400
450
500
2005 2010 2015 2020 2025 2030
VD
(t)
YearVD(t) Real VD(t) Estimation
46
vehicles per 1000 inhabitants; and a pessimistic scenario, converging to 415 vehicles per 1000
inhabitants. Figure 35 presents the LPV scenarios considered the previous assumptions.
Figure 35 - Vehicle density curves (vehicles per 1000 inhabitants) considered for Terceira LPV fleet and corresponding parameters.
Vehicle stock
For the case study of Terceira, there is few data available regarding the light-vehicle stock and
respectively characterization. The necessary information was gathered from several sources, where the
total vehicle stock came from ASF [86], that contains information at a regional level, as the vehicle sales
per technology from EMVS [62] and ACAP [91], which the data is organised at a national level. To raise
the concern, the data available has different time-frames, in which the data from the sales came from
2001, and the total vehicle stock only relies on the past 10 years.
Vehicle sales
To compute the passenger vehicle sales in each year for Terceira, it was assumed, from the reports of
ASF [86] regarding “Parque Automóvel Seguro”, that the percentage of the vehicles, for Azores, with
less than 1 year of construction was the percentage of the total passenger vehicles sales for Terceira,
which multiplied by the total passenger vehicles of the year respectively, gives the total passenger
vehicle sales for the year considered. This is expressed by Equation (6) and illustrated on Figure 36.
The passenger vehicle sales of Terceira was directly affected by the economic crisis that started in 2010,
with a reduction around 50%, and then stabilized in values lower than those recorded before the crisis.
According to the different population and vehicle density scenario, vehicle sales were adjusted in order
to follow the car stock curve resulting of the scenarios chosen.
Figure 36 – Total passenger vehicle sales.
0
100
200
300
400
500
600
2004 2007 2010 2013 2016 2019 2022 2025 2028 2031
Ve
hic
le d
ensi
ty
YearHistorical Data ReferenceOptimistic Pessimistic
1019 1044 10281164 1084 1030
782
488 504 563
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Passen
ger
Veh
icle
s
Sale
s
Year
Total sales
Vehicle
Density
Parameters
Scenarios
Pessimistic Medium Optimistic
𝜶 415 461 507
𝑘 0.800 0.139 0.250
𝛽 315 0 0
𝜑 2008 1998.3 2007.5
47
Since there is no information about the vehicle sales, total and per technology, on Terceira, they have
to be considered based on the values for the Portuguese market. As the formulation for the total sales
was presented before, the sales percentage per technology will be assumed as the same of the
Portuguese market, retrieved from reports of European Market Pocket Statistics [62] and ACAP [91].
Analysing the data presented on the previous reports, an important aspect concerning the vehicle sales
of passenger and commercial light duty vehicles in Portugal has been revealed: the increasing
penetration of diesel vehicles, presented on Figure 37. The fact is that in the last year’s sales of vehicles
have shown a considerable shift to diesel light-duty passenger vehicles. This is a result of a lower diesel
fuel price and different vehicle characteristics perceived as better for the public in general (in spite of
the higher vehicle purchase cost). This trend may be overturned if technology improvements occur
and/or due to a revision in the fuel taxation. It was assumed that this consumer preference towards
diesel vs gasoline affects the conventional technologies.
Figure 37 – Historical diesel share in passenger vehicles sales in Portugal [62], [91].
Vehicle scrappage curve
For Terceira , the past values obtained through surveys by Moura [58] were assumed, defined on section
3.1.1.
Vehicle Technology
The alternative vehicle technology parameters must be defined for the scenarios considered:
Availability – Only the short-term scenario will be considered, with introduction of new technologies
starting right away (2015)
Aggressiveness – two introduction rates will be considered: medium pace scenario, where the vehicles
technologies enter at a medium rates and reach the maximum after 15 years (2030), and high pace
scenario, where vehicles technologies enter at high pace and reach a maximum in 5 – 10 years ( to
reach the objectives of Azorina). The parameter will depend on the maximum penetration level.
Maximum penetration level – Three maximum level of penetration of alternative vehicle technologies
were considered. For now, only the electric vehicle penetration was considered. The information is
presented in Table 10.
0%
10%
20%
30%
40%
50%
60%
70%
80%
2001 2003 2005 2007 2009 2011 2013 2015
Die
sel share
on v
ehic
le s
ale
s
(%)
Year
HistoricalDieselShare
48
Table 10 – Maximum level of penetration of electric vehicles in the LDV fleet.
Technologies LDV scenarios
Pessimistic Reference Optimistic
Electric Vehicles 10 % 25 % 50%
Table 11 - % of sales versus EV penetration from 2015 to 2030, for different EV penetration scenarios.
Shift from
conventional to EV
vehicles
2015 2020 2025 2030
% Sales
%
penetrati
on
% Sales
%
penetrati
on
% Sales
%
penetrati
on
% Sales
%
penetrati
on
10% EV 1% 0,02% 10% 1,65% 15% 5,95% 18% 10%
25% EV 10% 0,41% 28% 6,56% 34% 16,48% 37% 25%
50% EV 25% 1,02% 55% 12,75% 68% 33,49% 71% 50%
The real effects of an alternative technology entering the car stock are delayed for almost a decade.
That can be seen on Table 11 and compared on Figure 38, where, for instance, 28% of the total new
sales are electric vehicles in 2020 and the reflection of this on the total vehicle stock, in other words, the
EV represents 28% of the total vehicle fleet, will only occur after 2030, more than 10 years delayed.
10% EV 25% EV 50% EV
Figure 38 – Market sale mix with the impact of the vehicle new sales on the total fleet characterization.
Fuel Consumption and emissions
For Terceira, since yearly vehicle fleet characterization for conventional technologies is well discretized,
instead of assuming an average consumption and emissions, what was done was a research and collect
technical data from different vehicle technologies, with different years, and assume those as the
reference vehicles for the emissions and consumptions. Considering this and using statistical data,
provided from the report of ACAP [91], it was possible to assume a vehicle model, based on the brand
most sold in Portugal over the years, as well as the engine capacity. The vehicle age was assumed
based on the fleet park characterization from ASF [86]. The technical information for diesel and gasoline
vehicles is organised on Table 12 and Table 13. The reference year is 2014.
Since the penetration of alternative technologies on the Portuguese vehicle fleet is almost inexistent,
the fuel consumption and emissions were assumed as fixed. This values are exposed on Table 14.
0%
50%
100%
20
05
20
07
20
09
20
11
20
13
20
15
20
17
20
19
20
21
20
23
20
25
20
27
20
29
% v
eh
icle
ne
w s
ale
s
Year
Diesel Gasoline Hybrid GPL Electric
0%
50%
100%
20
05
20
07
20
09
20
11
20
13
20
15
20
17
20
19
20
21
20
23
20
25
20
27
20
29% n
ew
sa
les
Year
Diesel Gasoline Hybrid GPL Electric
0%
50%
100%
20
05
20
07
20
09
20
11
20
13
20
15
20
17
20
19
20
21
20
23
20
25
20
27
20
29%
ne
w s
ale
s
Year
Diesel Gasoline Hybrid GPL Electric
49
Table 12 – Technical characteristics of diesel vehicles [91], [92].
Diesel Brand Model Year Engine
Capacity (l)
Power(Hp) Fuel Consumption
[l/100 km] CO2 emissions
( g/km)
Less than 1 year
Renault Megane IV Energy dCi
110 2015 1,5 110 3,7 95
1 year Renault Megane III dCi 110 EDC 2014 1,5 110 4,2 110
2 years Renault Megane III dCi 110 EDC 2013 1,5 110 4,2 110
3 years Renault Megane III dCi 110 EDC 2012 1,5 110 4,21 110
4 years Renault Megane III dCi 110 DPF 2011 1,5 110 4,55 120
Between 5 a 10 years
Renault Megane III dCi 110 DPF 2008 1,5 110 4,55 120
More than 10 years
Renault Megane II Hatch 1.5 dCi 2006 1,5 105 4,66 124
Table 13 - Technical characteristics of Gasoline vehicles [91], [92].
Gasoline Brand Model Year Engine
Capacity [l]
Power[hp] Fuel
Consumption [l/100 km]
CO2 emissions
( g/km)
Less than 1 year Renault Megane IV 1.6 SCe 115 Hp 2015 1,6 115 n/d 159
1 year Renault Megane III 1.6 16V 110 2014 1,6 110 6,9 159
2 years Renault Megane III 1.6 16V (110 Hp) 2013 1,6 110 6,9 159
3 years Renault Megane 1.6 16v 110 2012 1,6 109 6,9 159
4 years Renault Megane 1.6 16v 2008 1,6 109 6,9 163
5 to 10 years Renault Megane 1.6 16v 2008 1,6 109 6,9 163 plus than 10
years Renault Megane II Hatch 1.6 16V 2006 1,6 109 6,9 164
Table 14 – Fuel Consumption and CO2 emissions of alternative technologies [93], [94].
Technology Fuel consumption
[l/100 km]
CO2 Emissions
( g/km)
Hybrid 3.31 76.99
LPG 11.30 47.23
To calculate the average daily energy necessary to charge the electric vehicles, information related with
the EV battery capacity and consumption is needed. For the calculations present in his thesis, Martins
[95] considered a EV battery capacity of 19,2 kWh and a consumption of 0,2 kWh/km. The same values
were assumed in this work. The value considered for the EV consumption will be kept constant, due to
the high efficiency of the electric motor. For the electric vehicles, the amount of CO2 emitted are obtained
from the ERSE report [96] , which characterizes the standard CO2 emissions per kWh produced by the
Terceira electricity generation systems, based on monthly fuel mix. In this case, the assumed value was
564 gCO2/kWh and it’s kept constant through the years.
50
4.4.2 Residential Sector
As referred on section 4.4, the measures consider affect almost all end-uses services and are related
to technologies and equipment properties. Regarding the baseline scenarios technologies, information
came from the national-based share, such as the energetic class disaggregation of the equipment’s,
and from regional-based share, as the household equipment’s penetration (SREA – Conforto das
familias).
The base-line scenario of the appliances park was developed based on the statistical data from ICESD
[87], which provides technology and dwellings description, with national and, for some technologies,
regional resolution, as the dwelling equipment’s penetration for Azores from SREA report called
“Conforto das familias” [97], which results from surveys done in specific years (through the nineties until
2010). The data available in ICESD goes back to 2010.
Since there’s no update that considers more recent years, the total appliance stock and respective
penetration today will be assumed as the same of 2010.
Since there is no specific data regarding Terceira, the Azores appliances park was assumed and then
extrapolated to Terceira, using dwelling and population ratios (equation (33)):
𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝑡𝑒𝑟𝑐𝑒𝑖𝑟𝑎𝑥
= 𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝐴𝑧𝑜𝑟𝑒𝑠𝑥×
𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔𝑠𝑇𝑒𝑟𝑐𝑒𝑖𝑟𝑎
𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔𝑠𝐴𝑧𝑜𝑟𝑒𝑠
(33)
𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝑡𝑒𝑟𝑐𝑒𝑖𝑟𝑎𝑥 is the number of equipment from technology 𝑥 extrapolated, 𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝐴𝑧𝑜𝑟𝑒𝑠𝑥
is the
number of equipment from technology 𝑥 in Azores for 2010, 𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔𝑠𝑇𝑒𝑟𝑐𝑒𝑖𝑟𝑎 is the number of dwellings
registered in Terceira in 2011 [88] and 𝑑𝑤𝑒𝑙𝑙𝑖𝑛𝑔𝑠𝐴𝑧𝑜𝑟𝑒𝑠 is the number of dwellings registered in Azores
in 2011 [88]. As described on equation (21), the future appliances park depends on number of
inhabitants and average occupation. The latter is kept constant through the scenarios development and
was obtained from CENSUS 2011.
Table 15 – Terceira inhabitants and dwelling distribution [88].
Number of dwellings [hh]
Number of inhabitants [inh]
Average occupation (inh/hh)
Terceira 24 569 56 437 2.3
For the appliances efficiency class, the national characterization presented on ICESD [87] was assumed
to define the Terceira base-line scenario. In Figure 39, the equipment included by the scenario are
presented by their penetration in dwellings and current share of efficiency classes by technology.
51
Figure 39 - Current penetration of white appliances and respectively share of efficiency class, adapted from ICESD [87].
For the development of future scenarios, the A+++ efficiency class will be introduced and will have
preponderance on the future equipment sales.
An analysis to the introduction of new technological options for water heating for Terceira households
will be considered, compared and discussed afterwards in detail manner, in terms of energy and
emissions. This technological changes are related with the implementation of electric heaters and solar
panels for domestic hot water.
Solar Thermal
The implementation of solar panels for water heating is the change of a technology that is used to satisfy
the human needs of a specific energy service, the heated water, changing the final energy vector for
solar energy. In Portugal, the number of houses relying on solar panels for water heating is significantly
growing. This technology, combined with Portuguese weather characteristics, enables that household
needs for hot water can be mostly or completely fulfilled, without the support of another heating water
technology. The electric bill savings and considerable ecological benefits make this technology a viable
choice to substitute typical fossil fuel alternatives, with a positive income for families. Normally the
installation of solar panels is complemented with a backup system, for circumstances when the solar
energy is not enough to cover the water heating needs. Taking into account the fossil fuel dependency
reduction objective on Terceira, only electrical backup systems are considered.
A technical assessment was develop to maximize the amount of energy for water heating that can be
covered by the solar thermal for different technologies. This investigation was performed using the
program Solterm 5.1 [98], develop by LNEG (“Laboratório Nacional de Energia e Geologia”), which
computes technical and economic analysis for different water heating solutions, as well as energy
needs. This program is recommended by the Portuguese regulation to estimate the energy produced
by solar thermal and considers two major suppositions, where installation conditions are optimal (panel
tilt, reservoir location and back-up system usage) and estimated values for radiation, based on
international weather models. The first create an overestimation of the energy produced by the solar
systems but the models used for the weather forecast seem to underestimate the diffuse radiation on
Azores archipelago, which can be very important for the estimation of solar thermal energy generated
16,70%
4,07%
83,80%
11,63%
66,30%
26,50%
0,30%
56,40%
96,70%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Independentoven
FridgewithoutFreezer
Fridge withFreezer
CombinedFridge
Freezer Dishwashingmachine
Washing andtuble dryer
machine
Trumbledryer
Washingmachine
Shar
e o
f eq
uim
ent
efic
ien
cy c
lass
/ Eq
up
men
t p
enet
rati
on
[%
]
Equipment
D-G
C
B
A
A+
A++
Penetration
52
[99]. For a typical household in Terceira, it was assumed a 3 m2 panel minimum per solar system and a
reservoir with 200 litres minimum for 40 litres/person/day. The following technologies were considered,
which are: Flat plate with selective coating, most common for water heating (up to 60ºC); compound
parabolic collector (CPC), makes better use of diffuse radiation; evacuated tube collectors (ETC), lower
thermal losses, with better efficiency for high temperatures (>70ºC); Domestic Kit (thermosiphon), relies
on natural convection to promote heat exchange and it is constituted by a flat-plate collector with a
coupled reservoir above the panel. The equipment characteristics are presented on Appendix B.
The results obtained for energy produced, exploitation and back-up needs for a typical household in
Terceira using solar thermal are detailed in Table 16.
Table 16 – Energy provided to each household for Terceira by solar thermal systems.
Looking at Table 16, one can notice that evacuated tubes and thermosiphon are the technologies that
obtain better energy performances, considering the local conditions (1 195 kWh/year and 1 203
kWh/year, respectively). With this, higher water heating demand is covered by solar power, without
using the back-up system. In comparison, the flat plate and CPC systems required more energy from
the back-up systems to satisfy the energy needs for water heating.
Some details should be considered before choosing the technology. Although the “domestic kit” seems
to be the most attractive, it should be taking into consideration that the water reservoirs are installed on
the top of the roofs, coupled with the panel, which will have some impact on the architectural style that
could lead to a difficult public acceptance. On the other hand, the local household characteristics (i.e.
lack of space) may not be compatible with the installation of the inside water tanks, leaving just the
domestic kit option.
For the scenarios development, 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.
Storage water heaters
The storage water heater is a domestic water heating appliance that uses a water storage tank to
maximize heating capacity and provide instantaneous delivery of water. The water is heated and then
stored in a reservoir, able to be spent to perform multiple tasks simultaneously, provided that there is
enough pressure from the grid, without compromising the effectiveness of the processes. It can use a
System Solar Fraction [%]Demand
[kWh/year]
Energy produced
[kWh/year]
Support
[kWh/year]
Demand covered by
solar
[%]
Flat-Plate 74,4 1426 1061 365 74,40%
CPC 74,5 1426 1062 364 74,47%
Evacuated tubes 83,8 1426 1195 231 83,80%
thermosiphon solar
water heater84,4 1426 1203 223 84,36%
53
variety of energy sources to heat the water, such as natural gas, butane or electricity. The preference
for this technology, especially using electricity as the fuel source, has benefits for the consumers (energy
bill reduction) due to the high efficiency, as for the environment. Recently, EDA began subsidising the
substitution of the usual fossil-fuel water heaters for electric storage heaters, giving financial help in the
equipment installation, with the condition of shifting to dual or tri tariff plan [100]. This initiative intents
take advantage of the non-used renewable energy obtained at night and to reduce government
expenses with gas subsidies. From here on the storage water heaters will be called by “electric heaters”.
The calculations will be performed using equation (24), presented on section 3.2.2.
Considering the previous paragraphs, priority will be given to solar panels and electric heaters for water
heating purposes.
All this measures, from the technological changes to the equipment modifications, based on energy-
vector substitution and promotion of better energy efficiency classes for white goods, suggest a possible
path towards a sustainable island.
4.4.3 Other Sectors
For Terceira, the calculations were performed based on population and consumption statistical data,
resulting in the following Figure 40 :
Figure 40 – Electricity Consumption per capita of Terceira [74], [85], [89].
Since the statistical data provided by DGEG has detailed information about the expenditure of electricity
in the different economical activities, it is interesting to consider not only a total electricity consumption
per capita, but also a sectorial electricity consumption per capita. The following figures demonstrate the
evolution electricity consumption per capita in each sector over the years:
0
1 000
2 000
3 000
4 000
ELE
CT
RIC
ITY
C
ON
SU
MP
TIO
N P
ER
C
AP
ITA
[K
WH
]
YEAR
Electricity consumption per capita
54
Figure 41 – Residential sector electricity consumption per
capita [74], [85].
Figure 42 – Primary sector electricity consumption per
capita [74], [85].
Figure 43 – Secondary sector electricity consumption per capita [74], [85].
Figure 44 – Tertiary sector electricity consumption per capita [74], [85].
Figure 45 – Transportation sector electricity consumption per capita [74], [85].
The residential and tertiary (services) have the major contribution per capita for the electricity
consumption. Although there are some fluctuations on the data for the electricity consumption on the
transportation sector, is contribution to the overall electricity consumption is almost none.
4.4.4 Combinations and scenarios of interest
After establishing the previous options for the future scenarios, a plausible number of combinations is
possible to assess.
The outcomes provided by those scenarios will be compared with the business-as-usual scenario (BAU),
referred on section 4.4, considering that everything (technologies and efficiencies) remain unchanged
to all sectors, assuming demography and vehicle density medium scenario.
0
200
400
600
800
1000
1200
1400
1993 1998 2003 2008 2013Do
mesti
c E
lectr
icit
y C
on
su
mp
tio
n
per
Cap
ita [
kW
h]
Year
Residential Sector
Domestic sector
0
10
20
30
40
50
60
1994 1999 2004 2009 2014Pri
ma
ry s
ec
tor
ele
ctr
icit
y
co
ns
um
pti
on
pe
r c
ap
ita
[k
Wh
]
Year
Primary Sector
Primary sector
0
100
200
300
400
500
600
1990 1995 2000 2005 2010 2015Se
co
nd
ary
se
cto
r e
lectr
icity
co
nsu
mp
tio
n p
er
ca
pita
[kW
h]
Year
Secondary Sector
Secondary sector
0
500
1000
1500
2000
1993 1998 2003 2008 2013T
ert
iary
se
cto
r e
lecri
city
co
nsu
mp
tio
n p
er
ca
pita
[kW
h]
Year
Tertiary Sector
Tertiary sector
0
5
10
15
20
1993 1998 2003 2008 2013
Tra
ns
po
rta
tio
n s
ec
tor
ele
ctr
icit
y c
on
su
mp
tio
n p
er
ca
pit
a [
kW
h]
Year
Transportation Sector
Transportation sector
55
For the transportation sector, the main intent of the study is to explore great modifications on the total
vehicle fleet characterization. Emphasis will be given to the maximum possibilities range. The scenarios
are defined using the following nomenclature and described in Table 17:
𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡𝑤,𝑥,𝑦, where {
𝑤 − 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑥 − 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑦 − 𝐸𝑉 𝑝𝑒𝑛𝑒𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠
Table 17 – Proposed scenarios for the Transport 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)
From the possible combinations results, only 28 scenarios were modelled, of which 27 result from the
combination of all the options concerning Population and Vehicle density with the first 3 options of EV
penetration (Optimistic, Medium and Pessimistic) and 1 results from considering a Medium population,
Medium vehicle density and the Azorina scenario concerning EV penetration. This last scenario, defined
as 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡1,1,4, is based on the projections of Azorina of having 2000 electric vehicles in 2020. EV
sales were then assumed over the years to obtain 2000 vehicles in 2020 and then were kept constant.
For the residential sector, the objective is to assess technological and efficiency improvement alterations
on the appliances park. A particular focus will be given to the minimum and maximum ranges. The
scenario definition and combinations are presented on Table 18. The development conditions for each
scenario are independent, since appliances have different functions and their importance on the daily-
basis activities is not the same.
𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙𝑤,𝑥,𝑦, where {
𝑤 − 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑥 − 𝑇𝑒𝑐𝑛𝑜𝑙𝑜𝑔𝑦 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠 𝑦 − 𝐸𝑓𝑓𝑖𝑐𝑒𝑛𝑐𝑦 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑠
Table 18 – 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)
56
Regarding the equipment’s technology, the first hypothesis (new technologies) assumes not only an
equipment’s park electrification, by substituting the conventional technologies (gas-based) used on end-
uses activities, but also the preference for multi-task appliances, such as washing and tumbling
machines. The second hypothesis gives preference to actual equipment’s technological park. This
considerations are detailed on Table 19 and Table 20. Considering all the possible combinations
regarding the residential sector, 12 scenarios were assumed.
Table 19 – Detailed technology scenarios.
Appliances Refrigerators Freezer Dishwashing Washing
New Technologies
1. Fridge without Freezer
(3%)
66% 75%
1. Washing Machine
(36%)
2. Fridge with Freezer
(68%) 2. Tumble Dryer (21%)
3. Combined Fridge (29%) 3. Washing and Tumbling
(45%)
Same Technologies
1. Fridge without Freezer
(4%)
66% 75%
1. Washing Machine
(100%)
2. Fridge with Freezer
(84%) 2. Tumble Dryer (75%)
3. Combined Fridge (12%) 3. Washing and Tumbling
(2%)
Table 20 – Detailed technology scenarios (continuation).
Appliances Stoves Hobs Ind.Oven Heater Boiler Electric Heater Solar Thermal
New
Technologies
1. Gas (17%) 1. Gas
(0,1%)
1. Gas
(0,1%) 44% 2% 31% 25%
2. Electric
(33%)
2. Electric
(50%)
2. Electric
(50%)
Same
Technologies
1. Gas (71%) 1. Gas
(1%) 1. Gas (5%)
94% 5% 0,5% 0,3% 2. Electric
(4,3%)
2. Electric
(19%)
2. Electric
(10%)
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 21 – 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%)
57
5. Results
In this chapter, the results obtained for the proposed scenarios are presented and discussed, wherein
each sector is described along the following four subchapters. In each of these, a global and detailed
comparison is performed, including the assessment of the measures introduced and future outcomes,
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, as the Commerce/Services and
Agriculture/industry sector is done in a simple ways, as referred on section 4.4.4.
In section 5.4, the total energy consumption of the four representative scenarios is presented and a
sensitivity analysis is performed concerning the CO2 emissions factor from the electricity production
system. Finally, section 5.5 presents three technological options that can be used for water heating
which are discussed in terms of their economic viability.
5.1 Transportation sector
The vehicle fleet characterization over the years is analysed, as well as the implications of altering the
fleet composition in terms of fuel and energy consumption, as well as the CO2 emissions. In terms of
electricity consumption, values are obtained for the new total electricity consumption and the
correspondent increase when compared with the BAU scenario and the present total electricity
consumed.
First, a general compilation of the results obtained from the scenarios developed for the passenger fleet
is presented, considering some of the most relevant features. Then, four representative scenarios are
chosen and assessed in terms of energy consumption, energy demand and emissions. Both impacts
are evaluated considering not only the passenger vehicle fleet, but also the total transportation sector.
5.1.1 Scenarios analysis
Using the BAU scenario as a reference and compiling all information regarding all scenarios, the
following figures translate the implications of considering the future scenarios developed in this study.
Figure 46 shows the results obtained for each scenario in terms of energy consumption and CO2
emissions savings in 2030, considering light-passenger fleet only. Figure 47 shows the fuel consumption
savings and increase in electricity consumption. For both figures, positive values of savings represent a
reduction in terms of energy/fuel consumption and emissions when compared to the BAU scenario.
58
Figure 46 - Scenario results compilation for the energy consumption and the CO2 emissions.
Figure 47 - Scenario results compilation for the increase in the electricity consumption and fuel consumption.
The analysis of Figure 46 and Figure 47 shows that these scenarios provide very different outcomes in
terms of energy and electricity consumption and CO2 emissions. When considering all scenarios, 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 %, fuel consumption savings range between 58.4 % and -33.1 % and
the increase in electricity consumption ranges between 35.7 % and -16.5 %.
The impact of demography on the results obtained is quite noticeable. When an optimistic demography
scenario is considered, the energy consumption and CO2 emissions are the highest, in the graph shown
as negative results. The same happens to the electricity consumption, but here with positive increases.
For the vehicle density, the implications of changing this parameter are also visible, although at a smaller
scale then demography.
Analysing the results from the different scenarios, a particular feature stands out. As the introduction of
electric vehicles on the future vehicle fleet may seem beneficial in terms of energy consumption, the
1.1.1
1.1.2
1.1.3
1.2.1
1.2.2
1.2.3
1.3.1
1.3.2
1.3.3
2.1.1
2.1.2
2.1.3
2.2.1
2.2.2
2.2.3
2.3.1
2.3.2
2.3.3
3.1.1
3.1.2
3.1.3
3.2.1
3.2.2
3.2.3
3.3.1
3.3.2
3.3.3
Azorina
BAU
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
-70% -60% -50% -40% -30% -20% -10% 0% 10% 20% 30%
Ener
gy C
on
sum
pti
on
Sav
ings
[%
]
CO2 emissions Savings [%]
1.1.1
1.1.2
1.1.3
1.2.1
1.2.2
1.2.3
1.3.1
1.3.2
1.3.3
2.1.1
2.1.2
2.1.3
2.2.1
2.2.2
2.2.3
2.3.1
2.3.2
2.3.3
3.1.1
3.1.2
3.1.3
3.2.1
3.2.2
3.2.3
3.3.1
3.3.2
3.3.3Azorina
BAU
-40%
-20%
0%
20%
40%
60%
80%
-20% -10% 0% 10% 20% 30% 40%Fuel
Co
nsu
mp
tio
n S
avin
gs [
%]
Increase in the Electricity Consumption [%]
- Optimistic Population
- Medium Population
- Pessimistic Population
59
same cannot be said about the emissions. Keeping all the other parameters constant, higher EV
penetration corresponds to higher CO2 emissions. There are two main reasons for this outcome. First,
Terceira still has a high fossil fuel dependency on electricity production (Figure 14), 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).
In terms of fuel consumption, only six scenarios don’t have positive savings, which mostly refer to
optimistic demography scenarios. Examining all scenarios, only a few are capable of producing a
combination of positive outcomes, where the increase of energy consumption is not seen as a negative
effect. The majority of these are based on pessimistic assumptions for vehicle density and population.
Since the calculations performed for the emissions consider the present electricity generation mix, this
set of results could be expanded if there is an increase of renewable resources in the production of
electricity, reducing the amount of CO2 emissions produced when charging the electric vehicles.
5.1.2 Demography and vehicle density
Without a doubt, demography definitely influences the global evolution of the demand for transportation,
as well as the results in terms of emissions. For that reason, the demography and car stock limits have
been studied since they largely influence the dimension of the fleet. Comparing extreme scenarios,
which are optimistic population and vehicle density with pessimistic population and vehicle density, with
a reference one (reference population and VD) the total number of vehicle varies from -20 % (pessimistic
population and VD) to +43 % (high demography and high VD). This has large effects on the energy
consumption and CO2 emissions, as can be seen in Figure 48 and Figure 49, respectively.
Figure 48 - Total energy Consumption for an EV penetration
of 25% by changing the demography and VD(t).
Figure 49 – CO2 Emissions for an EV penetration of 25% by
changing the demography and VD(t).
Comparing to the reference scenario, energy consumption and CO2 emissions can increase by ≈44 %
and be reduced by ≈20 % for the high and low demography and Vehicle density scenarios.
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5.1.3 Detailed Scenarios
After a general presentation and discussion of the scenarios developed for the passenger vehicle fleet
on section 5.1.1 , a more detail assessment was performed, focusing on yearly energy consumption per
energy source, energy demand and respective CO2 emissions evolution.
In this analysis, the yearly energy consumption and emissions are related with the LPV fleet only, while
energy demand takes all transportation sector into consideration.
For this evaluation, four representative scenarios were considered, taking into account the maximum
ranges of values obtained from the model. The first is the business-as-usual scenario (BAU), assumed
as the reference, where the conventional vehicle technologies assume the major share of the passenger
vehicle fleet market. Then, the range limit scenarios were considered, which are Transport2.2.3 and
Transport3.3.1. The first considers an optimistic population and vehicle density evolution, with the lowest
EV penetration, and the latter assumes opposite evolutions, with the highest EV penetration on the
vehicle fleet. Last of all, the scenario which corresponds to Azorina projections (Transport1.1.4). The
results are presented from Figure 50 to Figure 54.
a) b)
Figure 50 – Energy consumption per energy source assuming BAU scenario (a) passenger fleet (b) Transportation sector.
The Transport2.2.3 (Figure 51) scenario shows the optimistic case in terms of vehicle density and
population, which represents a total number of vehicles in 2030 of 36 861 and 72 682 inhabitants. With
a low EV penetration (10 %), the energy consumption of fossil fuel sources is at its highest, with an
increase of 39 % compared with the BAU scenario, and more than the double of the estimated
consumption in scenario transportation3.3.1. As for electricity consumption, an increase of 9 GWh is
verified, but far from the values obtained for transportation3.3.1.
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a) b)
Figure 51 - Energy consumption per energy source assuming Transport2.2.3 (a) passenger fleet (b) Transportation sector.
The Transport3.3.1 (Figure 52) scenario considers the pessimistic case in terms of vehicle density and
population, which represents a total number of vehicles and inhabitants in 2030 of 20 400 and 49 218,
respectively. Comparing with the BAU scenario, the number of passenger vehicles is reduced by 20 %
and the number of inhabitants by 16.4 %. This has direct implications on the number of yearly vehicles
sold. With the EV penetration considered (50%), the percentage of conventional vehicles sold through
the years decreases at a high pace, with an inverse trend in electric vehicles. This scenario reflects the
consequences of introducing changes on the vehicle fleet, by shifting to electric vehicles, with the
conventional technologies gradually reducing their share, as a result of their life-time expectancy.
Considering just the passenger vehicle fleet, the total energy consumption is reduced by almost 36%,
pushed by the consumption decrease of gasoline and diesel by 63 % and 52 %, respectively. On the
contrary, due to high EV penetration, an increase on electricity consumption of 28.7 GWh is verified.
a) b)
Figure 52 - Energy consumption per energy source assuming Transport3.3.1 (a) passenger fleet (b) Transportation sector.
For Transport1.1.4 (Figure 53), the results show a reduction in energy consumption of 11.7 % (15.9 GWh
less), comparing with the BAU scenario, complemented with a decrease in fossil fuel consumption of 29
%. Regarding electricity demand, an increase of 9.6% in total electricity consumption was observed.
Although this scenario shows promising results, being one of the best scenarios that consider medium
population and VD, these results will be difficult to achieve, since it would require an early and
aggressive introduction of EV on the fleet stock, with a pace so high that could introduce almost 2000
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62
electric vehicles in 5 years. For this to happen, policies and substantial incentives must be given to the
inhabitants to promote the shift from conventional technologies to EV.
a) b)
Figure 53 - Energy consumption per energy source assuming Transport1.1.4 (a) passenger fleet b) Transportation sector.
For the global transportation sector, the Transport3.3.1 scenario indicates a reduction on the total energy
consumption of 16 %, with an electricity penetration as a source of final energy demand in 2030 of 16
%. This scenario is followed by Transport1.1.4, with 5 % reduction on energy consumption and 11%
penetration of electricity at final demand. These reductions are obtained due to the high efficiency of
electric vehicles. As for Transport2.2.3., the total energy consumption corresponds to 348 GWh, 15%
more than the reference and 28.7 % than Transport3.3.1
Figure 54 shows the results obtained for the scenarios considered in terms of CO2 emissions, the best
results also come from the scenario Transport3.3.1, with 32 618 tonnes produced, 11.5 %, 17.7 % and
41 % less than the BAU, Transport1.1.4 and Transport2.2.3 scenarios, respectively. The interesting fact is
that, even with a high EV penetration, the amount of emissions produced with the Azorina projections
are higher than those in the BAU scenario, proving the implications of the electricity production mix
being composed essentially by fossil fuels, as previously discussed.
Figure 54 – CO2 emissions of the detailed scenarios.
In this case, a way to reduce the carbon footprint and reduce energy consumption is to combine high
efficiency of the electric motor vehicles with the introduction of more renewable and endogenous
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63
sources on the electricity production mix. Nonetheless, it is important to notice that, even with the
different scenarios considered for the passenger fleet, this sector is still overly fossil fuel dependent.
This is illustrated by the fact that even in the scenario that provided the best results, the fossil fuel share
is still 84 % of the sector energy demand. As such, any plan to reduce the impacts of the transportation
sector should include other types of vehicles, such as heavy-duty vehicles and light commercial vehicles.
5.2 Residential sector
In this section, the implications of promoting energy efficiency measures/incentives on the appliances
stock and water-heating equipment’s are evaluated. The main results in each scenario concern future
energy consumption, total and per energy vector and CO2 emissions. All these outcomes are compared
with the reference scenario and the data presented on the ICESD report.
First, an overall compilation of the results obtained for all scenarios for the residential sector is presented
and then 4 scenarios are detailed, evaluated in terms of energy consumption and emissions.
5.2.1 Scenario compilation analysis
The results of each scenario demonstrate the impacts of changes in appliances. Figure 55 presents the
reduction of energy consumption and CO2 emissions, when compared to the BAU scenario, while Figure
56 shows the reduction of electricity and butane consumption, also when compared with the BAU
scenario.
Figure 55 - Scenario compilation for the residential sector (Energy vs Emissions).
Figure 56 - Scenario compilation for the residential sector (Electricity vs Butane).
The analysis of Figure 55 and Figure 56 illustrate the range of results obtained from considering different
scenarios. Considering all scenarios, it can be seen that energy consumption savings range between
36.7 % and -16.6 %, CO2 emissions savings between 41.1 % and -11.0 %, Butane consumption
reduction between -10.7 % and 62.8 % and electricity consumption reduction range between -33.7 %
and 19.4 %.
64
The influence of demography is very perceptible, since it directly influences the future number of
appliances and water equipment in the island, but without the relevance seen for the transportation
sector, as high reductions can still be achieved even in pessimistic demography scenario.
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. The introduction of solar thermal was also
found to have a significant impact, since approximately 84 % of the energy demand is covered by a
renewable source (sun), absent of the emission of pollutants. Only the other 16 % contribute to electricity
consumption and emissions.
As for the efficiency class parameter, its influence is more noticeable as the number of equipment on
the island increases, which is directly related with the number of inhabitants. Since this analysis only
considers white goods, it does not lead to changes in energy vectors, only on the electricity consumption
used to satisfy energy demand.
Almost all scenarios produce positive outcomes, when compared with the BAU scenario, for the
parameters studied because all consider an increase in efficiency of appliances. This happens since the
model assumes only appliances with an efficiency classification equal or higher than A is available in
the market, replacing older appliances with lower efficiencies. The only cases in which no positive results
are obtained are when an optimistic demography scenario, which corresponds to the maximum number
of equipment’s, is considered, associated with no technological changes.
5.2.2 Detailed Scenarios
In this section, four representative scenarios are detailed and examined in terms of energy consumption
and emissions. The energy consumption per energy vector and emissions analysis are related to
appliances and water heating equipment, while energy demand takes all residential sector into
consideration.
The first scenario considered was the business-as-usual (BAU) scenario, assumed as reference, where
conventional technologies and appliances efficiency class distribution are assumed constant over the
years. Next, the 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙2,2,2 and 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙3,1,1 scenarios were considered. The first considers the
optimistic demography scenario, without technological changes and assuming the lowest efficiency
class available (A) on appliance stock sales. The second considers a pessimistic demography scenario,
technological changes and preference for the most efficient appliances class (A+++). Finally, the
𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1,1,1 scenario was considered, which represents the equipment and appliances park
transformation, through the promotion of new technologies and the most efficiency class choose to be
part of the future appliances stock. The results are presented from Figure 57 to Figure 61.
65
a) b)
Figure 57 – Energy consumption per energy vector assuming BAU scenario (a) Appliances + DWH (b) Residential sector.
The 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙2,2,2 scenario (Figure 58) may be seen as the “worst case scenario” where there is an
increase of the number of inhabitants and no interest in promoting, encouraging or grow awareness to
shift to new and more efficient energy solutions. In this case, the total number of appliances considered
was 211 659 units, which represents more 61 444 than in the BAU and 90 068 than in the
𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙3,1,1 scenario. This situation results in an increase of 14% and 45% in terms of energy and
10 % and 47 % in terms of CO2 emissions (Figure 61), when compared with the reference and the
𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙3,1,1 scenario, respectively. Without technological changes and if lower efficient class
equipment are maintained, the butane and electricity demand for the residential sector reaches its
highest values in 2030, corresponding to 281 969.6 GJ and 273 799.3 GJ of butane and electricity
(Figure 58), which represent an increase of 10.7% and 15.2% when compared with BAU scenario.
a) b)
Figure 58 – Energy consumption per energy vector assuming 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙2,2,2 (a) Appliances + DWH (b) Residential sector.
The 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙3,1,1 scenario (Figure 59) gives the best results, since in this case it is assumed that
population decrease and that the remaining population is stimulated to be more sustainable, through
government strategy and policies development to mitigate the fossil fuel dependency. The total energy
consumption of the appliances considered and DWH reduces by 36.7 % and 45.7 % (Figure 59), and
the emissions by 41.2 % and 47 % (Figure 61), when compared to the reference and 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙2,2,2
scenario. These results are obtained mainly due to the high efficiency class assumed for the appliances
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66
and DHW equipment’s park. Although the solar energy exploitation to heat water does not produce
pollutants, its contribution is small when compared to the total energy consumption (10.2 %). A shift in
energy vectors to satisfy the residential energy needs and end-uses is observed, as electricity gains
preponderance, when compared with butane. At the end of 2030, 71.2 % of the total energy demand is
consumed in the form of electricity and 28.8 % in the form of butane (Figure 59), while the reference it’s
48.5 % to 51.5 %, respectively (Figure 57).
a) b)
Figure 59 – Energy consumption per energy vector assuming 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙3,1,1 (a) Appliances+ DHW (b) Residential sector.
Finally, the 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1,1,1 scenario (Figure 60) is presented as a 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 scenario shows
that, as the appliances become more efficient, the influence of the population parameter becomes less
relevant to the final energy consumption and emissions results.
a) b) Figure 60 – Energy consumption per energy vector assuming 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙1,1,1 a) Appliances + DWH b) Residential sector.
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67
Figure 61 – CO2 emissions for the detailed scenarios considered.
Given the importance of electricity in this sector, the emission factor of the electricity production system
plays an important role in the impacts due to the energy consumption of the sector. This impact could
be mitigated by integrating more renewable energy sources on the electricity production mix, but also
by promoting the adoption of new and more renewable energy systems, like solar thermal or solar PV,
which, combined with high efficiency equipment, could reduce in a significant way not only the total
energy consumption of the sector, but also CO2 emissions.
5.3 Agriculture/Industry and Commerce/Services (Other Sectors)
The total energy consumption and CO2 emissions for the Agriculture/Industry and Commerce/Services
sectors are evaluated on this section. As referred on section 3.3, this analysis is implemented in a
simpler way, focusing only on electricity consumption.
For each sector, three scenarios were considered according to the demographic scenarios presented
on section 4.4.4. In this case, the results are presented considering the scenario effects on the
parameters studied, when compared with respective BAU scenario of each sector and compiled on the
same graph. Assuming 𝐴. 𝐼 - Agriculture and Industry sector and 𝐶. 𝑆 – Commerce and Services sector,
Figure 62 displays the scenario obtained regarding total energy consumption, while Figure 63 shows
the results for CO2 emissions.
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68
Figure 62 – Energy Consumption analysis for Other Sectors.
Figure 63 – CO2 emissions analysis for Other Sectors.
For the Commerce/Services sector, Figure 62 shows a maximum energy consumption increase of 52.9
% and a minimum of 3.7 %, while the Agriculture/Industry sector can increase a maximum of 16.8 %
and a minimum of 2.3 %. As for emissions, Figure 63 demonstrates a maximum and minimum increase
of 57.8 % and 4 % for Commerce/Services and 27.6 % and 3.8 % for Agriculture/Industry, respectively.
The wide range of results for Commerce/Services is interesting, because this is a highly electricity driven
sector. Figure 15, in Section 4.2.2, shows that 35 % of the island electricity consumption comes from
this sector, resulting in the biggest electricity consumption per capita value of all sectors considered. As
so, demographic changes would have a high relevance on the electricity system, with notorious
consequences on both parameters assessed.
On the contrary, the Agriculture/ Industry sector doesn’t rely on electricity as much as the previous one.
With much lower electricity consumption per capita factor, the same demographic changes would have
a much lower impact.
5.4 Total energy and CO2 emissions evolution
In this section, the future total energy consumption and emissions of the island are analysed for four
scenarios, followed by a sensitivity analysis to the evolution of the CO2 emissions factor of the electricity
production system. All economic sectors are included on the analysis but, due to the lack of reliable
data, the fossil fuel consumption over the years was kept constant to the Agriculture/Industry and
Commerce/Services.
The four scenarios were chosen considering the influence of the electricity production system on the
island future energy demand: i) BAU scenario, assumed as the reference, considering current
demographic situation, with conventional technologies already implemented on the island; ii)
𝑃𝑒𝑠𝑠𝑖𝑚𝑖𝑠𝑡𝑖𝑐 scenario, which considers a migration increase and no interest in promoting or encouraging
new and more 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
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A.I - 3
69
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.
5.4.1 Total Energy Consumption and CO2 emissions
The evolutions of total energy consumption and CO2 emissions for each scenario considered are
presented in Figure 64 and Figure 65, respectively.
Figure 64 – Energy consumption for each scenario analysed.
Figure 65 – CO2 emissions for each scenario analysed.
The influence of the measures considered in each scenario in terms of energy consumption are quite
evident. The 𝑂𝑝𝑡𝑖𝑚𝑖𝑠𝑡𝑖𝑐 scenario has the highest energy consumption, 4 % and 13.8 % greater than the
BAU and 𝑀𝑒𝑑𝑃𝑜𝑝𝑐ℎ𝑎𝑛𝑔𝑒𝑠 scenario, respectively. One curious feature in this comparison is the fact that,
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
(+ 4227 vehicles) and equipment park (+ 1598 devices), 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. In this case, the combination of the technological and efficiency
changes (on vehicles and appliances) overcomes the higher population.
Regarding CO2 emissions, the situation is quite different, since demographic changes have
preponderance when compared with the other scenario definition parameters. The result compilation on
Figure 65 highlights the high CO2 emissions factor related with electricity production. Considering the
current situation, electric vehicles produce higher emissions than conventional ones (113 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ,
comparing with 95 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ from diesel). The same situation is verified on the residential sector,
with conventional technologies (heaters) having lower emissions factor (0.22 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ) than those
who rely on electricity (0.57 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ). 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.
2 100
2 200
2 300
2 400
2 500
2 600
2014 2016 2018 2020 2022 2024 2026 2028 2030
Ener
gy C
on
sum
pti
on
[TJ
]
Year
Future Energy Consumption [Terceira]
BAU Optimistic
Pessimistic MedPop_Changes
100 000
150 000
200 000
250 000
300 000
2014 2018 2022 2026 2030
CO
2 em
issi
on
s [T
on
CO
2]
Year
CO2 Emissions of Terceira
BAU Optimistic
Pessimistic MedPop_Changes
70
5.4.2 Sensitive analysis to CO2 emissions evolution
A sensitivity analysis was performed to understand how the evolution of the electricity system may
impact the total amount of CO2 emissions. In this analysis, the scenario 𝐵𝐴𝑈2 refers to the BAU scenario
with changed CO2 emissions factors.
Two different evolutions for the CO2 emissions factor were considered: a 2% yearly increase and 2%
decrease of the emission factor parameter [𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ] until 2030. Figure 66 and Figure 67 show the
evolution of the total of CO2 emissions for the increase and decrease, respectively.
Figure 66 – CO2 emissions sensitivity to emission factor increase.
Figure 67 – CO2 emissions sensitivity to emission factor decrease.
The Figure 66 shows that a yearly increase of 2 % in the electricity emission factor, therefore from
0.58 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ in 2015 to 0.77 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ in 2030, results in a CO2 emissions increase of 35.2 %
and 5.9 % for the worst and best case scenario, respectively, compared to BAU scenario.
For the yearly 2 % decrease in the electricity emission factor, from 0.58 𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ in 2015 to 0.43
𝑘𝑔𝐶𝑂2/𝑘𝑊ℎ in 2030, Figure 67 shows a 25.7 % and 1.3 % emissions decrease for 𝑃𝑒𝑠𝑠𝑖𝑚𝑖𝑠𝑡𝑖𝑐 and
𝑂𝑝𝑡𝑖𝑚𝑖𝑡𝑖𝑠𝑡𝑖𝑐, respectively, when compared with the BAU scenario.
From this analysis, it is clear that the emission factor parameter has bigger influence on the scenarios
that consider technological and efficiency changes, since most technological changers considered result
in the electrification of demand. This can easily be seen from a comparison between the 𝐵𝐴𝑈2 and
𝑀𝑒𝑑𝑃𝑜𝑝𝑐ℎ𝑎𝑛𝑔𝑒𝑠 scenarios. As the emission factor increases/decreases, the discrepancy in terms of total
CO2 emissions between both scenarios increases. One important implication of changes in the electricity
CO2 emissions factor is the electric vehicles CO2 emissions factor. With the 2 % yearly decrease, it is
possible to reach 83 gCO2/km, which is much lower than the values presented for the diesel and gasoline
vehicles (95 and 159 gCO2/km, respectively).
These results highlight the need to decrease the electricity emissions factor, which can be achieved
through the integration of renewables and endogenous energy sources on the electricity production mix.
150 000
200 000
250 000
300 000
350 000
2015 2018 2021 2024 2027 2030
CO
2 e
mis
sio
ns
[To
nC
O2
]
Year
Increase of CO2 Emissions Scenario
BAU BAU_2 Optimistic
Pessimistic MedPop_Changes
100 000
120 000
140 000
160 000
180 000
200 000
220 000
2015 2018 2021 2024 2027 2030
CO
2 e
mis
sio
ns
[To
nC
O2
]
Year
Decrease of CO2 Emissions Scenario
BAU BAU_2 Optimistic
Pessimistic MedPop_Changes
71
5.5 DWH equipment - economic analysis
In this subsection, three technological options to substitute the conventional heaters in Terceira Island
are analysed. This analysis focus on the economic feasibility of each technology, considering the initial
investment. For the analysis, 100 % of investment was done by household consumers and it was
considered with a discount rate of 3 %.These values are obtained from “Boletim de Verão 2016” [101]
, provided by “Banco de Portugal”. The main parameters used to assess the results are the Net Present
Value (NPV), Internal Rate of Return (IRR) and Payback Period.
The Net Present Value (NPV) [102] is one of the most used financial investment indicators. It represents
all updated cash flows and it’s obtained using equation (34).
𝑁𝑃𝑉 = ∑
𝐶𝐹𝑘
(1 + 𝑖)𝑘+
𝑉𝑅𝑘
(1 + 𝑖)𝑘
𝑛
𝑘=0
− 𝐼𝑜 (34)
Where 𝐶𝐹𝑘 are the cash flows corresponding to year 𝑘, 𝑉𝑅𝑘 is the residual value on year 𝑘, 𝐼𝑜 the initial
investment and 𝑖 is the discount rate. The project acceptance criterion is based on the NPV values. For
values greater than zero, the return on capital is greater than desired, equal to zero the same and lower
is worse than desired. This criterion can only be used as comparison for identical projects (CF structure,
lifespan and discount rate). If this is verified, the project chosen should be the one with greater NPV.
The Internal Rate of Return (IRR) is the discount rate for which the NPV of the project is equal to zero.
It can also be defined as the discount rate at which the present value of all future cash flow is equal to
the initial investment or, in other words, the rate at which an investment breaks even. This can be
obtained solving equation (35), using iterative process.
∑
𝐶𝐹𝑘
(1 + 𝑖)𝑘+
𝑉𝑅𝑘
(1 + 𝑖)𝑘
𝑛
𝑘=0
= 0 (35)
The higher a project's IRR is, the more desirable it is to undertake the project. This parameter can be
misleading if used alone. Depending on the initial investment costs, a project may have a low IRR but a
high NPV, meaning that, while the pace of the returns provided by the project may be slow, this may
also be adding a great deal of overall value.
The “Payback Period” (PP) is the period of time necessary to return the cost of investment, in other
words, to recoup the money expended on an investment, reaching the break-even point. If used alone,
it doesn’t indicate the project profitability and that’s why is often called as a liquidity parameter.
5.5.1 Equipment and scenario definition
Family preference for solar thermal systems to heat water in the residential sector is currently growing.
Not only do they grant significant ecologic benefits, by using a renewable source, but also contribute to
obtain monthly bill savings.
The equipment considered, associated with technological, functional and demand covered
characteristics, were presented on section 4.4.2. Table 22 shows the assumed economical
characteristics of the solar systems considered on this analysis.
72
Table 22 – Assumed economic characteristics of solar thermal systems.
System Investment [€] Life-Time [Years] O&M [€/year] RV [€]
Flat-Plate 1 840 € 20 64,40 € 0 €
CPC 1 990 € 20 69,65 € 0 €
Evacuated tubes 2 100 € 20 73,50 € 0 €
Thermosiphon solar water heater
1 980 € 20 69,30 € 0 €
In Table 22, the investment and life-time values are based on the simulation using Solterm 5.1 program
[98] and the operation and maintenance costs [O&M] were adapted from Santos [103], which considers
3.5 % of the initial investment per year. It should be highlighted that, in this case, there is no end-user
financial support by the government that supports the initial investment.
As referred on section 4.4.2, EDA is promoting and subsidising the installation of electric heaters [100],
substituting the current conventional fossil-fuel systems. The high efficiency and expected yearly bill
savings are reasons to consider the replacement. EDA has created an incentive program, which grants
100 euros incentive on the purchase of electric heaters if buyers adopt dual or triple-tariff for their
electricity plan [100]. Three of those equipment were chosen for this analysis. To estimate the yearly
energy costs, the electricity prices of 2015 and 2016 charged by EDA [104], [105], were considered and
kept constant through the years. Table 23 displays the technical and economic properties of electric
heaters assumed in this evaluation.
Table 23 – Assumed properties of the electric heaters considered [106].
Electric Heater Brand Power [kW] Reservoir[l] Heating Time [h] Cost [€] Life –time
[years]
VLS 100 ARISTON 1/1,5 100 1,5/2,18 330 25
ES 100 -5E VULCANO 2 100 2,9 210 25
ES 120 -5E VULCANO 2 120 3,5 235 25
The properties presented in Table 23 were defined based on the commercial solutions available by EDA
[106]. Adding to this, a theoretical electric heater model was included for the analysis, which considers
90 % efficiency, according to values defined on REH [64].
Although it was not assumed as an alternative technological solution on the residential scenarios
previously presented (section 4.4.2), heat pumps are also a possible solution of implementation, since
REH considers this technology as a substitute for new residential houses that do not possess minimum
solar fraction requirements. These high efficiency thermal machines use electricity to transfer heat from
one place (cold source) to another (hot source) instead of producing heat directly, spending a small
amount of energy. To have this effect it’s necessary to produce work. This technology can be seen as
73
a direct alternative to electric heaters, since they share the same energy source to heat water. The high
efficiency, easy operation and long lifespans are some of the best features of this systems.
In this case, three models were chosen from a recent campaign, endorsed by EDP [107], to promote
the installation of heat pumps on residential houses. Table 24 shows the assumed main properties of
the equipment considered in this analysis.
Table 24 – Technical and economic properties of Heat Pumps [108], [109].
Heat Pumps Brand Thermal
Power [W] COP
Reservoir [l]
Heating Time [h]
Cost [€] Lifespan [Years]
NUOS 80 ARISTON 930 3 80 4,08 1 135,00 € 20
NUOS 100 ARISTON 930 3 100 5,67 1 235,00 € 20
NUOS 120 ARISTON 810 2,6 120 6,33 1 335,00 € 20
As assumed for electric heaters, a theoretical heat pump model was considered, with a coefficient of
performance of 2.5, as established in REH [64].
To assess the DHW technological transition, two scenarios are defined. Considering that only electricity-
based equipment are used as alternatives, the first scenario assumes a simple-tariff, while the second
has a dual-tariff with 50 % of the consumption during peak time and the other 50 % in off-peak periods.
For electric heaters, the simple-tariff scenario does not consider EDA incentives, because those are
only available to consumers with dual-tariff. Both tariffs are obtained from EDA [104], [105]. No financial
support by competent authorities was considered.
The revenues of each technology are estimated based on the yearly price difference paid between using
the existing technologies (conventional heaters) and proposed changes.
Although monetary costs for each equipment are presented, it should be taken into account that those
values are merely representative, as technology and energy prices are susceptible to variations with
time, due to future technological and exploitation improvements.
5.5.2 Economic results
In this subchapter, the economic viability of the equipment presented in section 5.5.1 are analysed.
Comparing the simple and dual-tariff scenarios (Table 26 and Table 27 in Appendix C), better results
are obtained if the latter is considered, since these technologies can use off-peak periods to heat water,
taking advantage of lower electricity prices period. As such, Table 25 summarizes the technology
profitability criteria analysis for the second scenario considered.
74
Table 25 - In-depth measures profitability criteria analysis for dual-tariff scenario.
As shown on Table 25, the implementation of heat pumps is the most attractive solution in economic
terms. The small initial investment (Table 24), combined with high energy efficiency of this equipment,
results in high savings and a quick return of investment, allowing a faster payback period. It should be
enhanced that, considering an average value of 2.3 people per household and 40 litres of water per
person per day, the NUOS 80 isn’t ideal to satisfy the daily water heating needs. This solution should
only be taken into consideration in households with a maximum of 2 persons. On the other hand, the
results obtained for electric heaters are quite disappointing. Even assuming the initial investment support
and dual-tariff, at the current electricity prices, the yearly electricity bill from electric heaters (example:
232.4 €) is much higher than the other options proposed and worse than using conventional
technologies (220.8 €), making this an unattractive solution for householders to invest. Without
measures that could potentially reduce electricity prices, this option will never be competitive as the
others.
Due to high investment values and without the old government economic incentive to promote solar
thermal equipment installation [110], only the “domestic kit” can be seen as a cost-effective choice,
although with lower NPV and payback period than heat pumps. Considering the “domestic kit” lifespan,
the user still gets economic surplus (Table 27), higher than other technologies, enough to be considered
as an attractive investment. Even so, with all the technical difficulties (complex piping system, water
reservoir and solar panels), architectural acceptance issues and low economic outcomes, consumers
may consider that opting for a solar thermal device is not suitable to their needs. Since this technology
provides the best net saving revenues (Table 27), if some economic incentive/support is given to
householders to cover some of the initial investment, this solution becomes significantly more appealing.
Technology Equipment Elec. Supplier NPV IRR PP
Flat Plate EDA -155,73 € -0,17% 17.8
CPC EDA -387,36 € -1,26% 20.2
Evacuated Tubes EDA -192,39 € -0,51% 18.5
Thermosiphon solar water heater EDA 90,60 € 0,41% 16.6
VLS 100 EDA -424,84 € - -
ES 100 -5E EDA -1 523,01 € - -
ES 120 -5E EDA -2 611,23 € - -
Conventional Electric Heater EDA -116,89 € -9,75% -
Theoretical EDA 679,33 € 7,53% 11.1
NUOS 80 EDA 1 025,36 € 10,08% 9.0
NUOS 100 EDA 449,86 € 6,10% 12.7
NUOS 120 EDA 129,46 € 3,87% 15.8
Cenario 2 - Duo-Tariff w/o Financial Support ( 50 % Peak/ 50 % Off-Peak)
Solar Thermal
Electric Heater
Heat Pump
75
6. Conclusions and future work
Conclusions
For this thesis, 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. These parameters allow
a detailed analysis of the energy services and influence on future energy transitions to be performed,
taking into consideration technological shifts and efficiency promotion, based on a range of scenarios.
For all other sectors, the model considers a top-down formulation. The model was applied to Terceira
Island. Several scenarios were defined taking into account demographic, technologic and efficiency
changes based on actions plans being developed for the Azores archipelago.
For the transportation sector, the analysis shows that introducing EVs on the passenger vehicle fleet
results on considerable energy consumption reductions, up to 21.0 %, mainly due to the high efficiency
of the vehicles, contributing to diminish the fossil fuel dependency of the sector. On the other hand, this
technological shift may aggravate the total CO2 emissions of the sector, due to the high CO2 emission
factor associated to the electricity production system. Although this measure induces promising results
on the passenger vehicle fleet, the transportation sector will continue to be overly fossil fuel dependent,
which requires a more widespread action plan that includes other vehicle segments.
Regarding the residential sector, the substitution of stand-alone conventional technologies for kitchen
and water heating end-uses with multifunction and more efficient electric and renewable based
equipment resulted on promising reductions in terms of energy consumption (32.4 %) and emissions
(37.9 %). Unlike the transportation sector, the measures proposed have a significant impact on the total
energy demand of the residential sector, contributing to reduce the fossil fuel dependency.
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.
The economic viability analysis of DHW technologies showed that heat pumps are the most profitable
and attractive solution for household consumers. However, solar thermal technologies could be more
appealing if some of the initial investment is covered by economic incentives/sustainable policies. At the
current electricity prices, electric heaters cannot compete with the other solutions presented.
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
76
mix when considering the electrification of consumption and large-scale adoption of energy transition
measures, especially if all sectors are included.
In conclusion, the energy demand model developed provides a good support for energy planning
exercises towards introducing new energy transition measures, due to the high modelling detail and
parameters considered. Nevertheless, this detail relies on the data quality and resolution available.
Future Work
Future work should be performed to improve the model developed. Considering the transportation
sector, the detailed analysis performed to the light-passenger vehicles should be extended to other
vehicle segments, such as heavy-duty and light-commercial vehicles. This would provide a detailed
characterization of the total vehicle fleet and allow the analysis of future action plans that could induce
profound changes in the energy demand of the sector.
On the residential sector, although the kitchen and water heating end-uses cover around 79% of the
households total energy consumption (section 4.3.2), the inclusion of lighting, cooling, heating and
electric equipment end-uses would allow a more detailed and reliable assessment, with the possibility
of analysing other measures such as the substitution of light-bulbs for more ecological and efficient
solution (LED) or the introduction of air conditioning, which is a consumer trend that is increasing.
The analysis for the Commerce/Services sector could also be improved, since building services have
stringent requirements for environmental and comfort conditions, which require a detailed analysis to
cooling and heating loads, which have a significant potential to minimize energy consumption in this
sector. As for the Agriculture/Industry sector, an evaluation of equipment substitution would also
contribute to assess fossil fuel mitigation and energy efficiency increases. The implementation of these
ideas will only be possible if more detailed and reliable data is available.
Another possibility of improvement would be to further detail the analysis of CO2 emissions and costs,
by considering the investment and energy costs of introducing new energy sources and technologies
on the electricity production system. This connection with the electricity production system could also
benefit from the model being improved to perform daily and hourly calculations. This would also allow
the model to study other issues such as different EVs time of charging strategies and occupancy
patterns.
77
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A-1
Appendices
Data Sources and Challenges
DGEG
The General Management of Energy and Geology (DGEG) is a Portuguese Public Administrative entity
that promotes, evaluates and contributes to the design of policies related with the energy and geological
resources, from a sustainable perspective and security of supply guarantee. It allows to access the most
detailed and freely dataset regarding statistics on Energy and Geological resources operations, applied
to the National Statistic System. These operations are interpreted as surveys with a pre-determined
statistic methodology, covering the collection, treatment, analysis and diffusion of the respective data
related with a certain population. As mentioned, the information available in DGEG is wide and
addresses different areas, such as fuel prices, petroleum and derivatives, natural gas, electricity,
renewables, energy balances and so on. This surveys usually have one year interval and display the
geographical resolution for NUTS I, NUTS II, district and municipality.
The Figure 68 and Figure 69 clarify the configuration of the data available concerning the fossil fuel
sources and electricity, respectively, hence used to the following case studies:
Figure 68 – Petroleum and derivatives data framework [81].
The Voltage category is desegregated on the following topics: High Voltage, Low Voltage and self-
consumption.
Figure 69 – Electricity data framework [85].
A-2
The configuration of the electrical grid presented on DGEG files corresponds to the Portuguese
mainland electrical system. Since the transportation system of electric energy from Terceira Island is
constituted by a grid of 30 kV – been, by definition, a medium voltage grid – the values associated with
High voltage on the data are, in fact, Medium voltage.
The previous figures show the detail obtained from the analysis of the datasheet’s regarding the
categories announced before. From this detail it is possible to treat the data and obtain which sectors
have a larger contribution to the final demand and consumption, as well as the contribution of the
associated energy sources, doing an association with the economic activities presented on the
datasheets,. To get that, all the activities were selected, characterized with a number and then
associated with a specific sector.
EDA
The Electricidade dos Açores (EDA) is an energy utility based on Azores, owned by several
shareholders, where the majority belongs to the Government of Azores. This company is active in
different operation areas, such as Energy Production, through maintenance of the production
equipment’s from the different islands, Energy Distribution, by managing the transportation and
distribution of electric energy of Azores, and Commercialization, to ensure quality service and the
consumer satisfaction at a lower cost of energy supplied.
EDA, S.A has a vast and detailed library with information about production and consumption of
electricity in the islands that belong to the archipelago. The reports are elaborated every month and
each island is detailed in two major segments. The first is Electricity Production, with the disaggregation
of the energy sources (fossil and renewable) used to produce electricity, peak and off-peak, from which
power plant, with the respective values and evolution through the year (Figure 70). The second one is
Consumption, with the energy expended by the respective sectors, divided by Low and Medium voltage,
and the monthly evolution of the correspondent year. Both segments are associated with the respective
accumulated values and the information contained for the present month is compared with the
homologous from the previous year.
Figure 70 - Electricity production of Terceira in June 2014 [26].
A-3
From the detailed information presented on EDA reports and after some data treatment, is possible to
obtain, for the electricity production, the monthly production diagrams, per energy source, for every year
available, giving thorough information about the penetration of the different energy sources, with
particular interest on the renewables, as well as the total electricity produced. For the energy
consumption, monthly consumption diagrams are obtained, per sector, for low and medium voltage,
from 2006 to 2014.
Eurostat
Eurostat is the statistical office of the European Union and its task is to provide the European Union with
statistical information at European level and promote harmonisation of statistical methods across its
member’s states that enable comparisons between countries and regions. The main areas of statistical
activities provided are EU policy indicators, economy and finance, population and social conditions,
industry, Agriculture, Transport, Energy and Science.
ICESD
ICESD is a survey, which resulted from the collaboration between the Directorate-General for Energy
and Geology (DGEG) and the National Statistics Institute (NSI) being co-financed by the EU
(EUROSTAT), with the aiming of meet user needs, through a collection of data on the energy
consumption of the domestic sector in Portugal and detailed statistical data, that allows an updated
information of the consumption of the various sources of energy within this sector, as well as its
breakdown by final end-use (heating, cooling, kitchen, etc.) and household expenditure related with
energy consumption. The characterization of these consumptions and variables that support the trend
within this sector will allow better decisions as far as the implementation of energy strategy policies are
concerned.
SREA
The “Serviço Regional de Estatística dos Açores” works as a delegation of the National Statistics
Institute in Azores and has the mission of collect, treat and disclose quality statistical information
available to users, promoting reliable decision-making situations, public debate and investigation. This
delegation treats all the statistical information regarding the Azores archipelago considering different
categories, such as demography, activity sectors (agriculture, industry, etc.), energy, economy,
transportation, health and so on. This data is then divided in yearly and monthly time-frames and
disaggregated per island. This statistical information was used to obtain the population characteristics
and household equipment distribution and penetration of Terceira.
A-4
ASF
The “Autoridade de Supervisão de Seguros e Fundos de Pensões” it’s a national authority responsible
to ensure the proper functioning of the insurances sector in Portugal. This entity collects and treats
information to provide freely accessible dataset regarding insurances and pension’s funds. In terms of
vehicle insurances, the data is yearly divided and characterized by regional areas, vehicle categories
and year of construction, where the latter has information for each category and national region.
ACAP
The “Associação Automóvel de Portugal” it’s a national entity that represents the entire automotive
sector, responsible for the development of strategies and actions to promote car business. It acts like
an active voice to defend the sector interests to public entities and national or international organizations.
This association is responsible for the publication on the automotive sector statistics, which provides
information regarding all the vehicles sold and produced in Portugal.
European Vehicle Market Statistics
This yearly report offers a statistical portrait of passenger car, light commercial and heavy-duty vehicle
fleet sin the European Union throughout the years. It gives emphasis to on vehicle technologies, fuel
consumption, emissions of greenhouse gases and other air pollutants and yearly vehicle sales, per
technology. This data is collected from reports elaborated from different agencies/associations who work
on the sector, for each European Union members. From this, the new vehicle sales per technology was
considered, in percentage, to obtain the yearly fleet park characterization of the island.
B-5
Solar thermal panels characteristics
Figure 71 - Characteristics of different solar thermal technologies.
Design ModelEfficiency
[%]
Area
[m^2]
Nominal
Power
[kW]
Colectors/Kits
[nº]
Total Area
[m^2/dwelli
ng]
Support system Reservoir
[l]
Flat-Plate Kaplan 2.0 0,78 1,84 1,3 2 3,7 electric 200
CPC AO Sol 3+(NS) 0,73 1,99 1,4 2 4 electric 200
Evacuated tubes Calpak 12VT 0,5 1,27 1,8 3 3,8 electric 200
Evacuated tubes Calpak 20VT 0,5 2,1 1,5 2 3 electric 200
thermosiphon solar
water heaterVulcano Tss 200 FCB - 1,95 1,4 2 3,9 electric 200
Colector Characteristics Per House
C-6
DHW technologies economic analysis
Table 26 – Economic analysis using simple-tariff scenario.
Technologies Investment
[€]
Cash Flow
[€/year]
Accumulated Cash
Flow [€]
Payback
time[years]
Solar
Thermal
Flat-Plate 1 840 96.72 571.1 19.1
CPC 1 990 91.33 293.9 21.8
Evacuated tubes 2 100 109.18 630.2 19.2
Thermosiphon solar
water heater 1 980 115.13 887.9 17.3
Electric
Heaters
VLS 100 330 -43.5 -1 415.8 -
ES 100 -5E 210 -125.11 -3 335.7 -
ES 120 -5E 235 -196.59 -5 147.3 -
Theoretical 258 -27.5 943.9 -
Heat
Pumps
NUOS 80 1 135 145.9 2 491.9 9.7
NUOS 100 1 235 116.6 1 658.0 14.6
NUOS 120 1 335 104.5 1 253.33 19.0
Theoretical 1 235 100.3 1 272.2 12.3
C-7
Table 27 – Economic analysis assuming dual-tariff scenario.
Technologies Investment
[€]
Cash Flow
[€/year]
Accumulated Cash
Flow [€]
Payback
time[years]
Solar
Thermal
Flat-Plate 1 840 103.5 748.9 17.8
CPC 1 990 98.4 471.2 20.2
Evacuated tubes 2 100 113.7 742.7 18.5
Thermosiphon solar
water heater 1 980 119.1 996.5 16.6
Electric
Heaters
VLS 100 330 -11.97 -528.4 -
ES 100 -5E 210 -83.8 -2 204.7 -
ES 120 -5E 235 -146.8 -3 803.5 -
Theoretical 238 2.3 -104.23 -
Heat
Pumps
NUOS 80 1 135 125.8 2010.6 9.0
NUOS 100 1 235 97.5 1 203.2 12.7
NUOS 120 1 335 84.3 772.9 15.8
Theoretical 1 235 111.1 1 542.5 11.1
C-8
Table 28 - In-depth measures profitability criteria analysis for simple-tariff scenario.
Technology Equipment Elec. Supplier VAL TIR PP
Flat Plate EDA -155,73 € -0,17% 19.1
CPC EDA -387,36 € -1,85% 21.8
Evacuated Tubes EDA -192,39 € -0,84% 19.2
Thermosiphon solar water heater EDA 17,19 € 0,08% 17.3
VLS 100 EDA -1 054,22 € #NUM! -
ES 100 -5E EDA -2 317,00 € #NUM! -
ES 120 -5E EDA -3 549,41 € #NUM! -
Conventional Electric Heater EDA -713,97 € #NUM! -
Theoretical EDA 496,68 € 6,40% 9.7
NUOS 80 EDA 871,18 € 9,13% 14.6
NUOS 100 EDA 235,59 € 4,68% 19.0
NUOS 120 EDA -109,75 € 2,22% 12.3
Solar Thermal
Electric Heater
Heat Pump
Cenario 1 - Simple tariff w/o Financial support
