and electrifying services traditionally reliant on fossil fuels [2]. Many stakeholders
have a role in this transition, from businesses to governments and consumers.
Stakeholders need tools to understand how the energy system is likely to evolve and
how it may react to various decisions made within the system.
In this journey, Life Cycle Management (LCM) can help to prevent burden
shifting, but using it alone ignores the various energy dynamics. The use of energy
system optimisation models (ESOM) together with life cycle thinking has the
potential to underpin comprehensive understanding of the energy supply chain and
its influence on sustainable production. There is a small but increasing number of
studies combining life cycle assessment (LCA) and ESOM [3–12], ESOM best
practices start to include LCM practices, such as goal and scope definition or the
use of data quality indicators as a way to quantify epistemic uncertainty (i.e. the
uncertainty associated with data quality) [13]. However, combining both models is
not a simple task. In this chapter, we cover the challenges identified in the integration of ESOM and LCA, as well as existing approaches to address them and their
limitations. For brevity, we focus on inventory level and do not cover impact
assessment issues. Details of the criteria used to select articles are detailed in
Sect. 1.3.
1.1 Energy System Models: Origin and Strengths
Energy infrastructure requires substantial investments and governments have used
mathematical models for a long time to support policy analysis. The use of
mathematical models can help understand the complex interactions that occur in the
energy system, formalising the scattered knowledge about its dynamics [14].
Models of the energy sector grew in importance in the aftermath of the oil crisis of
the 1970s. During that period the International Energy Agency (IEA) was founded
and started developing its ESOM. Broadly speaking, energy models follow two
different paradigms, they either provide scenarios of how the system could evolve
from a normative standpoint (optimisation models), or they attempt to forecast how
the system is likely to change (simulation models) [14]. Optimisation models are
better suited to analyse long-term scenarios [15] and are therefore consistent with
the common long-term temporal scope of LCA studies. This chapter focuses on
bottom-up technology rich models, in particular the MARKAL/TIMES (The
Integrated Markal Efom System) optimisation model generator developed by the
IEA. TIMES is possibly the most widely used general purpose ESM [7, 14].
Alternative models following a similar structure such as TEMOA [16] or
OSeMOSYS [17] are also considered.
TIMES models are based on cost minimisation and provide consistent possible
evolutions of the energy system under a set of user-defined constraints. These
models provide insights to businesses and policymakers, as they allow exploring
potential interactions which are difficult to foresee without a formal mathematical
framework. TIMES models are “bottom-up” models, with often thousands of
250
M. F. Astudillo et al.
have a role in this transition, from businesses to governments and consumers.
Stakeholders need tools to understand how the energy system is likely to evolve and
how it may react to various decisions made within the system.
In this journey, Life Cycle Management (LCM) can help to prevent burden
shifting, but using it alone ignores the various energy dynamics. The use of energy
system optimisation models (ESOM) together with life cycle thinking has the
potential to underpin comprehensive understanding of the energy supply chain and
its influence on sustainable production. There is a small but increasing number of
studies combining life cycle assessment (LCA) and ESOM [3–12], ESOM best
practices start to include LCM practices, such as goal and scope definition or the
use of data quality indicators as a way to quantify epistemic uncertainty (i.e. the
uncertainty associated with data quality) [13]. However, combining both models is
not a simple task. In this chapter, we cover the challenges identified in the integration of ESOM and LCA, as well as existing approaches to address them and their
limitations. For brevity, we focus on inventory level and do not cover impact
assessment issues. Details of the criteria used to select articles are detailed in
Sect. 1.3.
1.1 Energy System Models: Origin and Strengths
Energy infrastructure requires substantial investments and governments have used
mathematical models for a long time to support policy analysis. The use of
mathematical models can help understand the complex interactions that occur in the
energy system, formalising the scattered knowledge about its dynamics [14].
Models of the energy sector grew in importance in the aftermath of the oil crisis of
the 1970s. During that period the International Energy Agency (IEA) was founded
and started developing its ESOM. Broadly speaking, energy models follow two
different paradigms, they either provide scenarios of how the system could evolve
from a normative standpoint (optimisation models), or they attempt to forecast how
the system is likely to change (simulation models) [14]. Optimisation models are
better suited to analyse long-term scenarios [15] and are therefore consistent with
the common long-term temporal scope of LCA studies. This chapter focuses on
bottom-up technology rich models, in particular the MARKAL/TIMES (The
Integrated Markal Efom System) optimisation model generator developed by the
IEA. TIMES is possibly the most widely used general purpose ESM [7, 14].
Alternative models following a similar structure such as TEMOA [16] or
OSeMOSYS [17] are also considered.
TIMES models are based on cost minimisation and provide consistent possible
evolutions of the energy system under a set of user-defined constraints. These
models provide insights to businesses and policymakers, as they allow exploring
potential interactions which are difficult to foresee without a formal mathematical
framework. TIMES models are “bottom-up” models, with often thousands of
250
M. F. Astudillo et al.