This document provides an overview of the methodology for PPS6 EDEN activity 1 on the impact analysis of integrating hydrogen technologies into the Portuguese energy system. The analysis will cover 2020-2050, examining transportation and stationary applications of hydrogen in Portugal. Four scenarios will be examined that combine two learning curves (high and moderate) with two penetration rates (high and moderate) for hydrogen technologies. Key inputs to the analysis, like transportation demand projections and heating demands, are obtained from other modeling exercises and policy documents. The analysis will be performed using the TIMES modeling framework.

1. Endogenizar o Desenvolvimento de Energias Novas
PPS 6 – Scoping Report Version 1
Francisco Borges, Luke Murray and Hugo Seymour
Instituto Superior Técnico, June 2007

2. Introduction
This document aims to collate and reference the key relevant information to be used in the
development of PPS6 EDEN, activity 1 (F1) “Impact Analysis”. It aims to provide an initial report
as to the methodology to be followed, the sources of information to be used, and the main
assumptions made. This document is designed to start an on-going and dynamic process and
will be added to regularly over the following months following further research and feedback
from the project partners.
Scope of the Roadmap:
The scope of the EDEN roadmap is shaped by its need to be a useful resource for advising
policy makers about the effects of integrating hydrogen technologies (for both transport and
stationary applications) into the Portuguese energy system. The following detailed parameters
are used to define the scope:
Time Frame:
The time frame of the EDEN roadmap will cover the period between the present and the year
2050. The main intervals to be considered are 2020, 2030 and 2050, using 2000 as the base
year. It is expected that the transition period (until 2030) will be more accurate as it is based on
existing information and therefore will be very detailed. In comparison, the period after 2030
until 2050 will be dealt with in a more visionary way (since information is limited in some areas).
Geographical Coverage:
The geographical coverage of the EDEN PPS 6 deals is for the whole of Portugal. The
methodology chosen has been deliberately developed to dovetail in with the HyWays European
Hydrogen Energy Roadmap so as to enable comparison with Europe as a whole and with the
10 MS represented in that project. Analysis will also be developed at a regional level within
Portugal, with a specific infrastructure analysis aiming to identify the location of the early user
centers and how key hydrogen infrastructure is likely to develop.
Technological Coverage:
Two categories of hydrogen end use technologies are considered in this road mapping
exercise: transport and stationary applications, they are defined below:

3. Transport applications: Hydrogen applications related to the road transport in general and
passenger cars and buses (mentioned in the rest of the document as transport). A list of the
types of vehicles considered will be released to the working partners in a further draft of this
scoping report.
Stationary applications: Hydrogen applications considered are CHP for housing, commercial
and services sectors. A full detailed list of CHP fuel cells to be considered will be released in the
next draft of this scoping report.
Baseline Scenario:
In order to quantify the effects and impacts of a hydrogen introduction in the Portuguese energy
system a business-as-usual scenario is set up to enable comparisons with “case-study”
scenarios (see list bellow for more information). The Baseline Scenario (BS) used on EDEN’s
PPS 6 was developed under the TIMES_PT model (http//air.dcea.fct.unl.pt/projects/e2pol).
Assumptions were made in the creation of this BS these assumptions are described below:
Assumptions in the Baseline Scenario:
1. A ban on nuclear power due to the political unacceptability of this option in the modeled
time horizon;
2. A minimum of electricity generated from gas combined cycle from 2010-2030, following
the energy sources diversification policy and support to use of natural gas. The
generated electricity corresponds to at least 1100 MW installed capacity;
3. New coal power plants will only be available from 2015 onwards following energy
sources diversification policy and support to use of natural gas;
4. It is assumed that coal technologies without sequestration will not be implemented from
2015 onwards, following expected GHG control policies;
5. Electricity generation from municipal waste will continue until 2030 following present
waste management policy;
6. Electricity generation from wood residues will continue throughout the lifetime of base-
year plants following forest fire control policies;
7. A minimum of 1.1 MW installed capacity of wind onshore is set up in 2005, following the
existing feed-in-tariffs for renewable electricity, although this is not included in the costs
of renewable electricity generation technologies in TIMES_PT;
8. In 2010 biofuels consumption will be at least 5.75% of the consumed diesel and
gasoline in transport, following the Directive 2003/30/EC;
9. From 2015 onwards at least 39% of generated electricity will be from renewable
sources following the Directive 2001/77/EC;

4. 10. From 2010 onwards the total CO2 emissions can not exceed 63 344 GgCO2, following
the reasoning of the national commitment within the EU burden sharing agreement;
(Cleto, Simões, Fortes and Seixas, 2007)
Learning Curves:
A learning curve or experimental curve is a macro - model of human activity for accumulating
knowledge or experiences, and is usually adapted to industrial production process. (Tsuchiya
and Kobayashi, 2002) In EDEN the concept of the learning curves is used to forecast the
evolution of technology prices over time, taking in account the number of units of that same
technology produced and the learning rate of that same technology. In EDEN’s context the
learning curves considered are for hydrogen technologies, Transport and CHP applications for
the “residential” and “commerce and services” sectors. Two different learning curves will be
used in the TIMES modeling exercise and combined with two different penetration rates (see
below), making up 4 different scenarios. These two learning curves will be called High and
Moderate learning curves respectively.
Hydrogen Scenarios:
Four hydrogen scenarios are created using different combinations of projected rates of
deployment (Penetration rates – see next section) of hydrogen based end use applications and
learning curves, (table 1). These scenarios are compared with the BS enabling an assessment
of the impacts of integrating hydrogen into the energy system in different quantities and prices.
Penetration rate
high moderate
Learning high scenario 1 (HH) scenario 2 (MH)
curve moderate scenario 3 (HM) scenario 4 (MM)
Table 1 – Four Hydrogen Scenarios
Penetration Rates:
A penetration rate is the comparative proportion of a certain type of technology within a certain
category that includes different technologies implemented (with the same function or output). In
the scope of EDEN’s PPS 6 two penetration rates are used and they are defined as:
Transport applications: % of the fleet within the types vehicles considered in this
•
study

5. Stationary applications: % of demand for heat in the “Residential” “Commercial and
•
Services” sector satisfied through the use of hydrogen CHP.
The penetration rates selected were extracted from the European Project “HyWays”
(www.hyways.de). The reasons for using “HyWays” penetration rates are the proven robustness
of its methodology and the possible comparability of the Portuguese results with the 10
countries mapped under this European project.
Penetration Rates for Transport:
Two different penetration rates were considered for transport: high and moderate. In both cases
the transport applications are driven into the market in between 2010 and 2020. The more
optimistic approximates to nearly 75% of hydrogen technologies penetration in 2050 whereas
the more cautious one to 40% (table 2).
Total share of car fleet 2010 2020 2030 2040 2050
[%] *
High - 3.3% 23.7% 54.4% 74.5%
Moderate - 0.7% 7.6% 22.6% 40.0%
Table 2 – Penetration rates for a potential development of hydrogen vehicles
Penetration Rates for Stationary Applications:
The penetration rates for stationary applications appear in a slightly different way than those
used in the transport sector. This happens due to the different sectors to be considered
“Residential” and “Commerce and Services”. Taking this into consideration, four different
penetration rates (two for “Residential” and two for “Commerce and Services”) were selected
(tables 3 and 4). It is important to mention that both “high” penetration rates (for “Residential”
and “Commerce and Services”) are combined under the heading “high” penetration rate for
stationary applications, the same happens with the “moderate” penetration rates.
Total share of CHP installed 2010 2020 2030 2040 2050
capacity in Households [%]
High - 1% 4% 8% 10%
Moderate - 0.1% 0.5% 2% 5%
Table 3 – Penetration rates for a potential development of hydrogen stationary applications in
households

6. Total share of CHP installed 2010 2020 2030 2040 2050
capacity in Commercial and
Services sector [%]
High - 0.3% 1.3% 2.7% 3.3%
Moderate - >0% 0.2% 0.7% 1.7%
Table 4 – Penetration rates for a potential development of hydrogen stationary applications in
the commerce and services sector
Transport Demand:
The demand for transport is an important input in this road mapping exercise since it gives the
values on which the transport sector analysis is based. The categories considered are “Road
Bus Intercity”, “Road Bus Urban”, “Road Car Long Distance” and “Road Car Short Distance”.
The demand for transport values used in EDEN’s PPS6 was extracted from the Portuguese
1
Plan for Climatic Changes (PNAC) . In PNAC the estimation of the demand for transport doesn’t
go beyond 2030 (chart 1). The options for extending these demand projections for transport are
still under consideration and will be outlined in the next draft
Demand for Road Transport Passengers
(From PNAC)
Road.Bus.Intercity.
80 000
70 000
60 000 Road.Bus.Urban.
50 000
PKM
40 000
Road.Car.Long
30 000 Distance.
20 000
Road.Car.Short
10 000
Distance.
0
2000 2005 2010 2015 2020 2025 2030
Year
Chart 1 – Demand for Transport 2000 - 2030
Heating Demand for Residential and Commercial and Services sectors:
The demand values for heating demand for the residential and commercial services sectors
were obtained trough socio-economic parameters fed into TIMES model. These socio-economic
parameters were obtained from the GEM-E3 model. GEM-E3 is an applied general equilibrium
1
Plano Nacional para as Alterações Climáticas

7. model, simultaneously representing World regions or European countries, linked through
endogenous bilateral trade and environmental flows. (http://www.gem-e3.net/)
Demand for Heating in the Residencial Sector
9
8
7
6
Multi
5
(PJ)
Rural
4
Urban
3
2
1
0
2000 2005 2010 2015 2020 2025 2030
Chart 2 – Demand for Heating in the Residential Sector 2000 – 2030
The graphical representation of the heating demands for both sectors is shown in charts 2 and
3. In the Residential Sector (chart 2) it’s shown that the demand for heating decreases over
time. This change can be explained due to the expected increase of thermal efficiency in the
sector.
Demand for Heating in the Commerce and Services
sectors
35
30
25
20
(PJ)
15
10
Large Heating
5
Small Heating
0
2000 2005 2010 2015 2020 2025 2030
Chart 3 – Demand for Heating in the Commerce and Services sector 2000-2030
Hydrogen Chains:

8. Even though TIMES model is quite a powerful tool in terms of optimization, a set of chains was
chosen to be fed into the model. This happens in order to enable the model to become more
sensitive to a set of factors:
- The characteristics of the current and foreseeable energy supply system and energy
infrastructure up to 2050
- The available natural resources
- The application and penetration rate to be expected for hydrogen
- The current hydrogen production sites and infrastructure (if present)
- The available and anticipated hydrogen and energy technologies
The chains to be analyzed and their likely introduction date were obtained after consultation of
the EDEN stakeholders, for full list of chains selected see the vision and chain selection
document.
References:
Haruki Tsuchiya and Osamu Kobayashi (2002) – “Fuel Cell Cost Study by Learning Curve” –
presented at Stanford University, USA
J.Cleto, S. Simões, P. Fortes and J. Seixas (2007) - “Marginal CO2 abatement costs for the
Portuguese energy system – Scenarios analysis for 2030” – Faculdade de Ciências e
Tecnologia da Universidade de Lisboa, DCEA.
Plano Nacional para as Alterações Climáticas (Abril de 2006) – Anexo Técnico “Transportes” –
Coordinated by J.Seixas
TIMES_PT at: http//air.dcea.fct.unl.pt/projects/e2pol
Hyways at: www.hyways.de