298
as International Energy Agency (IEA) provide these data, even if the often-rough
categorization of fuel and power plant categories in the statistics calls for assumptions and extrapolations increasing uncertainties. However, predicting and capturing changes in time of the electricity sector – being relevant in consequential LCA
(CLCA) – is a challenging task, for both temporal scopes: short-term and long-term
horizon.
2.1.2.1 Short-Term
In countries with de-regulated energy sectors, an independent system operator coordinates most of the markets by using price based dispatch systems. Price bids from
generators, defi ning the supply curve would be ideal for analyzing the short-term
variation of power plant production following different resolution: from hourly to
annually and then be able to consider intermittency of renewable energy, intertemporal arbitrage, spinning and non-spinning reserves or ramp-rate limitations of
producers.
However, price bids are not always publicly available. In the absence of such
data, a procedure for integrating the short-term time variations of technologies is
missing. Such a procedure could play an important role in increasing the robustness
of LCA studies and refi ning their environmental impact estimates. Additionally, not
all electricity markets have the same extent of de-regulation. As an example, leading
players like China, the world largest consumer of electricity, still relies on a considerably more complex multi-level dispatch hierarchy partially based on generator
output planning (Kahrl and Wang 2014 ).
2.1.2.2 Long-Term
In the long-term, additional capacity would need to be installed to cover increases
in demand. Changes in the electricity sector depend on political, environmental
and economic considerations that are substantially uncertain and country
specifi c.
Different techniques are available to estimate prospective electricity mix. These
techniques are useful from the average ALCA perspective and also from the CLCA
perspective.
Future supply mixes can be estimated from national forecast, such as IEA annual
energy outlook (e.g. Hertwich et al. 2015 ). In the absence of such available data,
specifi c models (e.g. partial equilibrium models) can be used to estimate future
average supply mixes: LEAP, TIMES, (see Pfenninger et al. 2014 for a review).
CLCA often follows the step-wise procedure presented by Ekvall and Weidema
( 2004 ) and updated in Weidema et al. ( 2009 ) to identify marginal technologies but
its application to the electricity sector is not yet satisfactory (Treyer and Bauer
M.F. Astudillo et al.
as International Energy Agency (IEA) provide these data, even if the often-rough
categorization of fuel and power plant categories in the statistics calls for assumptions and extrapolations increasing uncertainties. However, predicting and capturing changes in time of the electricity sector – being relevant in consequential LCA
(CLCA) – is a challenging task, for both temporal scopes: short-term and long-term
horizon.
2.1.2.1 Short-Term
In countries with de-regulated energy sectors, an independent system operator coordinates most of the markets by using price based dispatch systems. Price bids from
generators, defi ning the supply curve would be ideal for analyzing the short-term
variation of power plant production following different resolution: from hourly to
annually and then be able to consider intermittency of renewable energy, intertemporal arbitrage, spinning and non-spinning reserves or ramp-rate limitations of
producers.
However, price bids are not always publicly available. In the absence of such
data, a procedure for integrating the short-term time variations of technologies is
missing. Such a procedure could play an important role in increasing the robustness
of LCA studies and refi ning their environmental impact estimates. Additionally, not
all electricity markets have the same extent of de-regulation. As an example, leading
players like China, the world largest consumer of electricity, still relies on a considerably more complex multi-level dispatch hierarchy partially based on generator
output planning (Kahrl and Wang 2014 ).
2.1.2.2 Long-Term
In the long-term, additional capacity would need to be installed to cover increases
in demand. Changes in the electricity sector depend on political, environmental
and economic considerations that are substantially uncertain and country
specifi c.
Different techniques are available to estimate prospective electricity mix. These
techniques are useful from the average ALCA perspective and also from the CLCA
perspective.
Future supply mixes can be estimated from national forecast, such as IEA annual
energy outlook (e.g. Hertwich et al. 2015 ). In the absence of such available data,
specifi c models (e.g. partial equilibrium models) can be used to estimate future
average supply mixes: LEAP, TIMES, (see Pfenninger et al. 2014 for a review).
CLCA often follows the step-wise procedure presented by Ekvall and Weidema
( 2004 ) and updated in Weidema et al. ( 2009 ) to identify marginal technologies but
its application to the electricity sector is not yet satisfactory (Treyer and Bauer
M.F. Astudillo et al.