=Paper= {{Paper |id=None |storemode=property |title=TIMES_PT: Integrated Energy System Modeling |pdfUrl=https://ceur-ws.org/Vol-923/paper09.pdf |volume=Vol-923 }} ==TIMES_PT: Integrated Energy System Modeling== https://ceur-ws.org/Vol-923/paper09.pdf

TIMES_PT: Integrated Energy System Modeling

João Pedro Gouveia*, Luís Dias*, Patrícia Fortes*, Júlia Seixas*

jplg@fct.unl.pt; luisdias@fct.unl.pt; p.fs@fct.unl.pt;
mjs@fct.unl.pt

* CENSE - Center for Environmental and Sustainability Research, Faculdade de Ciências e
Tecnologia, Universidade Nova de Lisboa 2829-516, Caparica. Portugal

Abstract. The complexity of energy systems operation and the necessity to de-
sign secure and reliable systems, compatible with greenhouse gas (GHG) miti-
gation goals, have justified the development of energy models. They are capa-
ble of representing detailed energy systems (technical and economic character-
istics) and the interconnections between supply and consumer sectors, assessing
energy consumption and production pathways. Energy modeling tools have
been widely used to help energy planners to assess energy systems; from differ-
ent approaches as the impacts of alternative energy and environmental policies,
or the competitiveness of different energy technologies. This paper provides an
overview of the energy-environmental-economic modeling tool TIMES_PT, the
last generation of the IEA/ETSAP integrated technological energy models, with
a focus on its structure, functioning, and calibration for the case of the Portu-
guese energy system. Applications cases of TIMES_PT, namely for the design
of low carbon scenarios for the long-term, are presented. Innovative develop-
ments on linking TIMES_PT with a macro-economic model (GEM-E3_PT) and
the assessment of non-technological variables are also described.

Keywords: TIMES_PT, Energy modeling, Low carbon scenarios, Energy sys-
tem, Portugal.

1 Introduction

Since the 70’s energy models have been widely used to support energy planning. In
that time, models were used to understand the implications of an oil embargo in ener-
gy supply security. More recently, climate change and the need to reduce GHG emis-
sions has become one of the main issues in energy planning [1]. Energy models out-
line how the transition to a more secure and decarbonized energy system can be
achieved, identifying the competiveness of energy technologies and giving insights
about the most cost-effective energy and environmental policies.
One of the major energy optimization tools used are the bottom-up technology
MARKAL (MARKet ALlocation) and TIMES (The Integrated MARKAL-EFOM
System) models. These models are used by more than 100 institutions and countries
and supported under the ETSAP/IEA.
TIMES_PT [2] is a dynamic linear optimization peer-reviewed model corresponding
to the implementation for Portugal (PT) of the technological based model generator
TIMES [3]. TIMES_PT represents, in detail, the entire chain of the Portuguese energy
system from energy supply, including energy imports and production, to transfor-
mation, distribution, as well as end-uses consumption and energy trade, considering
different energy carriers.
The objective of the TIMES model is the satisfaction of an exogenous energy ser-
vice demand at the minimum total system cost over the entire planning horizon (i.e.
the optimal energy-technology pathways). Thus, supported by a database of more than
2000 technologies, the model determines the optimal mix of technologies and fuels at
each period, the associated emissions and trading activities.
The TIMES_PT model has been extensively used in several national and international
studies and its technological database has been continuously updated and validated by
national stakeholders and international literature. TIMES_PT, as other TIMES mod-
els, is written in General Algebraic Modeling System (GAMS) language [4].
This paper aims to provide an overview of the energy-environmental-economic
modeling tool TIMES_PT, focused on its general features and main components (Sec-
tion 2), and applications cases (Section 3). Section 4 of the paper concludes and pre-
sents innovative features under development.

2 General characteristics of TIMES_PT

As abovementioned, TIMES_PT is a peer-reviewed linear programming optimization
bottom-up technology model. This section describes the main features of the model as
well as its specific characteristics that have been improved or updated in the last
years. The TIMES_PT model formulates a single, overall mathematical programming
(optimization) problem that covers the energy supply system, according to equation 1
[3]:
! !"#$!!!
𝑁𝑃𝑉 = !!! !!!"#$% 1 + 𝑑!,! ∗ 𝐴𝑁𝑁𝐶𝑂𝑆𝑇(𝑟, 𝑦) (1)

Where the NPV is the net present value of the total costs, ANNCOST is the total annu-
al cost, d is the general discount rate, r is the region, y is the years, REFYR is the ref-
erence year for discounting and YEARS is the set of years for which there are costs.
TIMES_PT model uses the partial equilibrium version of TIMES, where the demand
for energy services depends endogenously on own price elasticity. The model is usu-
ally run to deliver information on 5-year periods.
TIMES_PT represents the energy system of PT and its possible long-term devel-
opments. The actual system encompasses all the steps from primary resources in place
to the supply of the energy services demanded by energy consumers, through the
chain of processes which transform, transport, distribute and convert energy into ser-
vices [5]. Figure 1 presents an overall view of the structure of the energy system
modeled in TIMES_PT.
Fig. 1. High-level Reference Energy System of a single region model [5].

Each element in the network is characterized by several input parameters. The
TIMES_PT technological database has more than two thousands of existing and fu-
ture energy related technologies. Technologies are described by means of technical
data (e.g. capacity, efficiency), environmental emission coefficients (e.g. CO2, SOx,
NOx), and economic values (e.g. capital cost, date of commercialization). Possible
future developments of the system are driven by reference demands for energy ser-
vices (e.g. commercial lighting, residential space heating, air conditioning, mobility
and many others), and the supply curves of the resources (e.g. amount available at
each price level) [6].
Several assets distinguish TIMES_PT from European aggregated models like PET
(Pan European TIMES [5]): 1) the information on the majority of technological data-
base is validated by national energy and industry related stakeholders reflecting spe-
cific national characteristics; 2) the Portuguese energy system and current policies and
expectations are fully detailed; making TIMES_PT a well-established tool for Portu-
gal. There are also a few general differences for other European national models (e.g.
[7]) like 1) the inclusion of air pollutants like nitrogen oxides, sulfur dioxides and
particulate matter and 2) the disaggregation of the national emissions as included/not
included in the EU-ETS.

2.1 Time horizon and Time slices
TIMES_PT is a long-term model designed to explore the development of the PT en-
ergy system till 2050 through the computation of projections for the period 2005-
2050. While in its original version, developed within NEEDS project [8], the model
was calibrated to 2000 data, the current version is fully recalibrated to 2005 data. For
the year 2010 the model results are partly validated to national statistics [9-10] and
taking into account national short-term expectations (e.g. installed capacity).
Annual flows of energy consumption and production are split by season - spring,
summer, fall, winter; and daily load profiles - night, day and peak, considering the
Portuguese electricity demand profile.

2.2 Representation of the primary supply sectors
The supply side of the TIMES_PT model represents the primary energy sector: re-
source extraction or imports, processing and transport to transformation – plants and
refineries, coke ovens and bio-conversion, etc. – followed by transport and distribu-
tion of the final energy products.
Each primary resource is modeled independently, and represented by a linearized
stepwise supply function. The number of steps approximating each curve depends on
the resource and on the country reserves. The energy commodities are disaggregated
to the level of detail of the extended national energy balances reported by [9].

2.3 Representation of the demand sectors
TIMES_PT model includes five main end-use sectors: Agriculture (AGR), Industry
(IND), Services (SERV), Residential (RSD) and Transport (TRA).
The AGR sector is represented in a simplified way, and future energy demand is driv-
en by the projection of the sectorial economic activity.
Regarding IND, TIMES_PT model breaks-out the national industrial sector in
eleven sub-sectors: hollow and flat glass, high and lower quality paper, chemistry,
cement, iron & steel, lime, other non-ferrous metals, other non-metallic minerals and
other chemical. Each of them includes diverse manufacturing processes and is mod-
eled according to its mass and energy balance.
The SERV sector represents several different economic sub sectors like offices,
banks, hospitals, etc. However, due to the lack of data for PT on specific sub sectors
energy consumption and equipment, this sector is modeled in an aggregated way,
considering two types of SERV - large (>1000m2) and small (<1000m2). The SERV
sector energy demand includes: space heating and cooling, water heating, cooking,
lighting, refrigeration and other electric equipment.
RSD sector includes the same categories as the SERV, but improved disaggregation
on electric equipment including cloth washing and drying machines and cloth wash-
ing, among others. The devices that supply warm water, space heating and cooling are
broken out by building type as its need vary significantly – namely multi apartment
building, single house in urban areas and single house in rural areas.
The TRA sector corresponds to the economic sector “transport services” and pri-
vate mobility. The demand for TRA is first broken out by: road, rail, navigation and
aviation. Road and rail transport are split between passenger and freight. The demand
for road passengers’ transportation is further divided to short and long distance pri-
vate car transport, urban busses, intercity busses and motorcycles. Passenger’s rail
transport is further divided into urban metro transport and intercity train transport.
Freight transport is disaggregated into road transport by heavy and light trucks and
intercity rail transport [11].

2.4 Energy Services Demand
Energy end-use demand is an exogenous model input, commonly generated according
to the methodology presented in [12]. This energy services demand generation is sup-
ported by a top-down method for industry, services and agriculture and bottom-up
calculation for buildings [13] and transport. The top-down method is mainly sustained
by the sector value added growth, while the bottom-up method is more complex and
depends on several drivers, namely the number and characteristics of the dwellings,
occupancy rate and building area, transport typology, population, average travel km,
among other parameters.

2.5 Renewable Energy Potential
For Portugal, the endogenous primary energy potential solely relates to renewable
energy sources (RES) once there are not known endogenous fossil resources. For
most resources the potential is given not only having in mind the technical potential
but also possible deployment of technologies in the near future.
These technical economical potentials restrict the use and future deployment of
each technology, limiting its capacity. Generally speaking, the 2020 figures are in line
with the expectation presented in National Renewable Energy Action Plan [14], after
that the potentials are a result of national stakeholders best guess and analysis (see
[10]).

2.6 Primary Energy Prices
Primary energy prices definition is crucial for setting the boundaries of an energy
system future development. Average primary energy import prices projections are
annually updated based on the scenarios from [15]. The import costs until 2050 for
the different types of liquid biofuels (e.g. bioethanol) and due to no best available
information are linked to the oil energy price. Extraction costs for municipal solid
wastes; biogas and sludge are originated from [16] The import costs for wood bio-
mass are from [17] and endogenous forestry and wood waste biomass production
from [18].

2.7 Technology costs and characteristics
The evolution of the costs of supply and demand technologies between 2010 and
2050, are dependent on the actual expectation in terms of development and implemen-
tation, and are crucial to evaluate the competitiveness of the technologies. The model
combines the technical economic data with energy prices to dynamically calculate
supply cost curves for year and energy demand category. The combination between
supply cost curves defines the competitiveness of the technologies. Fig. 2 presents an
example of a supply curve for cooling services buildings.
70

INVCOST FIXCOST VARCOST FUELCOST

40
M€/PJ

Non,reversible4 Room4air, Roof,top4central4 Air4fans4 Centralized4 Non4reversible4 Centralized4gas4
electricity4heat4 conditioner4 electric4chiller4 electrical4air4 gas4heat4pump4 air4conditioner
pump4 conditioner4

Fig. 2. Example of a supply curve for Services space cooling for 2030 ([11])

TIMES_PT technological database is frequently updated in order to reflect recent
technological developments and national specificities. Table 1 presents, an example
investment costs expectations for different RES and combined cycle natural gas pow-
er plants for Portugal.

Table 1. Wind, solar and combined cycle gas power plant investment cost perspectives

PV Solar PV Solar (plant Combined
Years Wind Onshore Wind Offshore
(Roof panel) technology) cycle power
Investment costs (M€2000/GW)
2010 1012 3140 2202 1966 385
2015 910 3140 1849 1793 381
2020 860 2747 1636 1587 377
2025 835 2551 1488 1443 377
2030 810 2355 1339 1299 377
2035 772 2159 1255 1217 370
2050 658 1570 1087 1054 363

Availability factors are also an important characteristic of a technology, especially for
RES, influencing its future uses. For wind turbines and solar technologies the availa-
bility was defined based on the data from the production of the existing plants and
parks in Portugal and Spain and national stakeholder’s. The availability factors for
hydro power plants are updated to an average Portuguese hydraulicity year.
3 Application cases

TIMES_PT model can be used for a wide set of policy and technological analysis
associated with GHG and air pollutants emissions and energy related activities (e.g.
Fig. 3). This section presents a sample of international and national projects where
TIMES_PT has been used for different purposes.
• COMET - Integrated infrastructure for CO2 transport and storage in the
west Mediterranean - is a EU research project aiming at identifying and as-
sessing the most cost effective infrastructure of CO2 transport and geologic
storage, that will be able to serve the West Mediterranean area (Spain, Portu-
gal and Morocco), as well as the location, capacity and availability of poten-
tial CO2 storage in geological formations.
• HybCO2 - Hybrid approaches to assess economic, environmental and tech-
nological impacts of long term low carbon scenarios: the Portuguese case -
is a national research project aiming to develop and implement two hybrid
modeling tools to improve the cost-effectiveness assessment of ener-
gy/climate policy instruments.
• Low Carbon RoadMap: Portugal 2050 - outlines how the transition to a low
carbon economy in Portugal can be achieved, focusing on changes in the na-
tional energy system and evaluating its economic impact. The model was
used to outline a -60% and -70% GHG decarbonization pathways (face to
1990) [10].
• RoadMap for New Energy Technologies: Portugal 2010-2050 - Policy sup-
port project for assessing the competitiveness of national energy technolo-
gies, namely RES electricity generation and electric mobility technologies
and its long-term impact in the PT energy system [12].
[PJ] 13%
31%
31%
38%
30%
31%
37%
36%
72%
44%
38%
65%
41%
800 90%
87%

600 80%
77% 77% 77% 77%

72% 72%
400 70% 70% 69% 70%
66%
62%
200 60%
58%

0 50%

-‐200 40%

-‐400 30%

-‐600 20%

-‐800 10%

-‐1000 0%
C -‐50C Cefre F -‐50F Fefre C -‐50C Cefre F -‐50F Fefre

2005 2020 2050

Oil Natural
gas Coal Biomass Biomass
imports
Biofuels Biofuel
imports Hydro Solar Waves
Wind Geothermal Biogas
&
W aste External
dependency

Fig. 3. Policy and technological analysis results from TIMES_PT (Primary energy consumption
in Portugal in different emissions scenarios - % of RES (in the top rectangle)) [12]

4 Conclusion

The complexity of energy systems operation and the necessity to design secure and
reliable systems, compatible with GHG mitigation goals, have justified the develop-
ment of energy models. In this paper we describe the linear optimization model
TIMES_PT that has been improved and updated to reflect the PT energy system and
policies, and selected projects supported by it. Although technological based models
have been useful to design future scenarios of energy systems, they present limitations
that have been identified and researched.
Future work will advance energy modeling in two areas: a) by integrating non-
technological features as the case of consumer behavior in residential sector, based on
the knowledge behind energy consumption drivers; b) by linking with an economic
computable general equilibrium (GEM-E3_PT) constituting a hybrid technology-
economic platform (HybTEP), which overcome the state of the art absence of macro-
economic feedbacks of different energy system pathways, namely the impact on gross
domestic product or industry production, underestimating the costs of mitigation poli-
cies. These advancements will improve greatly the ability to model energy systems
and reduce uncertainty for the medium to long term.
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