A system dynamics model for assessing the UK carbon market
Laura M. Cardenas, Carlos J. Franco and Isaac Dyner
CeiBA, Universidad Nacional de Colombia — Sede Medellin,
Imcardenasa@ unal.edu.co
cjfranco@ unal.edu.co
idyner@ unal.edu.co
Prepared for the
29th International Conference of the System Dynamics Society
Washington, DC, 24 — 28 July, 2011
ABSTRACT
The emissions of greenhouse gases have become a great worldwide concern due to their effects on
climate change. This led to determine, through the Kyoto Protocol, goals to decrease at least 5% of
the GHGs by 2012, with respect to the 1990 levels. This agreement established three explicit
mechanisms: joint implementation, clean development mechanism and emissions trading. In this
context, the European Union created an Emissions Trading Scheme — EU ETS (EU Emissions
Trading Scheme) — that committed 73% of the global carbon market in 2009.
Given that the electricity sector is the main contributor of GHGs, it is important to assess the impact
of the EU ETS on this sector. This article undertakes this task with the support of a system
dynamics model, using the United Kingdom as a case study. Preliminary results indicate that even
under a scenario of low prices for emission allowances this would induce significant changes in the
installed capacity of the electricity sector, replacing fossil-based technologies by cleaner ones, such
as wind and nuclear energy; and also significant reductions of CO2 emissions.
KEYWORDS
System dynamics, emissions trading, energy sector, clean technologies, simulation model, emission
allowances.
1, INTRODUCTION
Climate Change has become a major global concer in recent years. The public is becoming
increasingly aware of this given the magnitude of droughts, glaciers melting, hurricanes and
floods, as well as the extent of their effects, as these are just some of the manifestations in
all continents worldwide (Comision Europea 2006; UNFCCC 2005).
Since the 1990s, initial global agreements (the Kyoto Protocol) have been made to face the
problems, based on studies by the Intergovernmental Panel on Climate Change (IPCC), and
others (Vitousek 1994; Feenstra, et al. 1998). Perhaps the main clear commitment of the
industrialized world has been to reduce by 2012 their GHGs emissions, by at least a 5%
with respect to those registered in 1990 - “first commitment period” (PNUMA and
UNFCCC 2002).
This protocol proposes three mechanisms — joint implementation, clean development
mechanism and finally, the emissions trading — that seek to reduce emissions in the home
country or putting in place projects that increase carbon capture elsewhere (UNFCCC
2005). To implement these three mechanisms, some regions created “Carbon Markets”.
Despite that the Kyoto Protocol is in progress, progress is still limited. It is worth noting the
effort of the European Union through the emissions trading scheme — EU ETS — that
committed 73% of the global carbon Market in 2009, which represented 86% of the
allowances markets (World Bank Institute 2010). The emission transactions by this scheme
are made via EUAs (European Union Allowances), which are equivalent to one ton of
carbon dioxide (World Bank Institute 2008) — the electricity sector contributes to 60% of
these (Zachmann and V on Hirschhausen 2008).
During the Kyoto Protocol first commitment period, which would correspond to the second
phase of the EU ETS, a free EUAs assignation system was used. This system is known as
National Allocation Plan (NAP) and it is equivalent to the 90% of the total number of
permissions (Comision Europea 2003). This allocation of EUAs establishes a maximum
limit to installations that generate carbon dioxide emissions.
The problem appears when these installations produce more emissions, exceeding the
established limit. In the case of the electricity sector, for instance, the cost of fines to
companies is being passed on to consumers, increasing the cost of the electricity (Chen, et
al. 2008) and generates a new dynamics in the European economy (Kara, et al. 2008; Sijm,
et al. 2005; Gulli 2008).
Several researchers have analyzed such dynamics through different methodologies, mostly
statistical and econometrical (Alberola, Chevallier and Chéze 2009; Oberndorfer 2009;
Laurikka and K oljonen 2006). Topics of research have included aspects such as: assessment
of investments in the electricity sector, the effect of EUAs on the electricity price, and this
effect of EUAs on other sectors. With SD there are some studies with Carbon Markets in
USA (Ford 2008; Ford, Vogstadb and Flynn 2007; Ford 2010).
This paper assesses the consequence of EUAs on the electricity market structure in the UK,
based on a systems dynamic model that runs under different scenarios, facilitating an
assessment of the emission commerce. We chose the United Kingdom case, because this
may help us understand what might be the dynamics of power capacity and the CO2
emissions in this country, as well as the dynamics of capacity investment and divestment.
This article is organized as follows: first an analysis of the European Union emissions
trading scheme is presented, then we present the model that was created for the evaluation
of the consequences of this scheme over the electricity sector in the United Kingdom.
Section 3 shows simulation results and finally, in section 4, conclusions are shown.
. THE EU SCHEME AND THE PROBLEM OF REDUCING C02 EMISSIONS
In its Directive 2003/87/ce the European Parliament and Council created a GHG emission
rights Market. This was implemented in 2005 aiming at meeting the goals established in the
Kyoto Protocol (KP), consisting on reducing 8% emissions by 2012 (PNUMA and
UNFCCC 2002; Bayon 2004; Zhang, Y ue-Jun and Wei. 2010). This reaches the 27 member
states of the European Union and some neighboring countries including Iceland,
Liechtenstein and Norway.
EUA auction price ~&
EUA obtained in
+ “vy
Eua Country 42)
EUA demand in vo at
i require
auction 4 EUA price
X
CL.
+
a costs
Ny
7
Demand
Figure 1. European Union Emissions Trading Scheme
The EU ETS aims to require allowances to companies that emit GHG emissions from the
following sectors (combustion plants, oil refineries, coke ovens, iron and steel plants and
factories making cement, glass, lime, bricks, ceramics, pulp and paper). Depending on the
production that each company will determine the emissions during the year and based on
these emissions, each of the companies need a certain number of allowances (EUA: 1 EUA
equals 1 ton of CO2) if the company increased production, higher emissions and greater
number of EUA’s required. The acquisition of these allowances is a cost to the company so
that affect its production. The allocation of these EUA’s is given initially by auction, the
bids are the number of allowances that companies will require and the number of
allowances each government provides will be the supply in the auction, determining a price
for auctioned EUA’s. In a second opportunity is given a secondary market, represented by
companies that for some reason have allowances to sell and the companies that couldn’t
acquire allowances in the auction and need them for their emissions. In the event that the
EUAs are not submitted in the corresponding amounts, the company will be penalized as
follows: first, they will have to get the missing EUAs; second, the name of the installation
will be published in a list of offenders and, third, they will have to pay a 100 Euros fine per
extra ton produced (Comision Europea 2003).
This scheme has the following phases:
II Phase III Phase
(2008-2012) (2013-2020)
Free Free allocation Free allocation _‘for
Allocation (90%) energy sector 0%
(95%)
Auctions Auctions Auctions (100% energy
(5%) (10%) sector)
Table 1. Allocation in the EU ETS
According to what was observed between the first and second phases of the National
Allocation Plans, the percentage of allocations decreased, in line to what was expected, as
the aim is to decrease the emissions, inducing all installations to reduce their emissions in
the long term.
This paper focuses on assessing the possible consequence of the CO2 market adopted by
the EU on the electricity market structure and its reduction of CO2 emissions. For this we
built an sd model which is presented next.
3. MODEL DESCRIPTION
To study the effect of the European Union emission trading scheme on the electricity
sector, a model was build. The corresponding dynamic hypothesis for the system’s problem
is shown in Figure 2.
ice
EUA Supply + price
i Xmen
incentives
EUA price gy
Electricity in
. demand +
Emissions : fe
; Installed
x é capacity
Electricity Ans)
supply _+
Figure 2. Dynamic hypothesis
Figure 2 represents the EU ETS and the dynamics of the electricity sector. This figure has 4
loops: B1, B2, B3 and R1. Depending on the difference between electricity demand and
capacity we obtain the margin of the system, which establishes the price of electricity, this
affects the demand because of the elasticity between these variables and thus gets the
balance cycle B1. Also, the price shows signals to the model to invest in capacity,
determining the balance cycle B3. Besides, installed capacity determines the electricity
supply, which sets emissions at fossil fuel-based technologies, and it affects the price of
electricity with the addition of EUA price or Carbon price and finally obtains cycles R1 and
B2. sector”.
Model components:
The model has four feedback loops included in the dynamic hypothesis (Figure 2). These
are presented in figures 3, 4, 5 and 6.
Tecnologycats
Electricity
Investment
EUA price incentives
Fs
Installed
Emissions capacity
+ en He
Electricity
supply
Figure 3. Carbon price loop
The carbon price loop R1 (Figure 3), indicates that higher electricity supply induces higher
emissions, so EUAs price raises. This EUAs price increases the cost of electricity
generation, incentivizing new power capacity (although not explicitly indicated here, this is
conducted by generation technology — e.g. coal, gas, nuclear renewables).
The margin loop B1, includes the margin variable (the difference between supply and
demand - peak demand) and how this affects electricity price and consequently decreases
electricity demand (Figure 4). The electricity demand loop B2 represents how emissions
from electricity supply influences EUAs price, and this raises electricity price, so this
decreases electricity demand (figure 5). Also electricity demand is affected by GDP.
Technology costs
Electricity
price
f B1
Margin
GDP Electricity
dema <
Figure 4. Margin loop
Note that as more EUAs are required, the price of EUAs increases, which constitutes a cost
to those installations that generate GHG. This cost will be reflected on the generation
supply by technology which determines emissions.
Technology costs
“*
Electricity
- price
EUA price GDP
422) Electricity
demand
+ +
Electricity
supply
Emissions
Figure 5. Electricity demand loop
The installed capacity loop B3-shows how the electricity sector operates (see Figure 6).
Here the electricity price incentivizes new capacity, which increases the margin, affecting
the electricity price.
Technology costs * Electricity
price _.
‘investment
incentives
ts)
Margin
XO Installed
capacity
Figure 6. Installed capacity loop
Next, we present simulation data and assumptions used in the model.
4, DATABASE AND VALIDATION
The model developed is multidimensional; it is divided by the main technologies of the
country's power sector studied. In the UK these technologies are: gas, coal, fuel oil, nuclear,
wind and hydro. Also, Interconnections are added by the significant contribution they make
to the electricity production.
Renewables Other Fuels
7%
Imports
1% Coal
28%
Gas,
45% Nuclear
18%
Figure 7. Installed capacity in UK in 2009 (Department of Energy & Climate Change, 2010)
The information used to feed and classify the costs into the model was taken by the Mott
MacDonald report (Mott MacDonald, 2010). This report sets out the capital costs and
operating costs. Operating costs are fixed operating costs, fuel cost, emission cost, variable
operating cost and decomm and waste fund. Within the model remained the classification
capital costs and operating costs, but the latter were called cost of generation without
carbon cost. Fuel costs (see table 2) were taken from the Department of Energy & Climate
Change (Department of Energy & Climate Change 2009).
Gas price Oil price Coal price
(p/therm) ($/bbl) (E/tonne)
2007 32,0 717 47,3
2008 60,7 106,3 83,1
2009 31,0 62,6 45,0
2010 59,6 71,6 70,3
2011 61,8 72,6 66,5
2012 62,5 73,6 62,6
2013 63,3 74,6 58,8
2014 64,0 75,7 55,0
2015 64,8 76,7 SET
2016 65,5 71,7 511
2017 66,3 78,7 Sut
2018 67,0 79,8 511
2019 67,8 80,8 S11
2020 68,5 81,8 511
Table 2. Fuel cost for UK (Department of Energy & Climate Change 2009)
The expansion plans were taken from (National Grid Electricity Transmission plc, 2010)
and the data for electricity demand was taken from the Department of Energy and Climate
Change in the UK (Department of Energy & Climate Change, 2009) and U.S Energy
Information Administration (EIA). Finally, the information on the number of allowances
and rules of the EU ETS were taken from the "National Allocation Plan approved for Phase
II of the United Kingdom" (Department for Environment, Food and Rural A ffairs, 2007).
In the model delays are very important because for each of the technologies we have
established a different time of construction that determines when new capacity enters
depending on the profits of each technology, and the output of each capacity is determined
by life cycle of each technology. These delays are in the following Table and were supplied
by the study conducted by The Royal Academy of Engineering for the costing of electricity
generation in the UK (PB Power, 2004).
Technology Time of construction
(years)
Gas-fired 2
Coal-fired 4
Nuclear 5
Wind 2
turbines
Table 3. Time of construction for each technology (PB Power 2004)
. SIMULATION RESULTS
The described model was parameterized using United Kingdom data; the model timeline is
determined by the different phases of the European Union scheme, between 2008 and 2020.
Note that in the second phase (2008-2012) 93% of the EU ETS are free, and in the third
phase (2013-2020) 100% is allocated through auctions.
To analyze results, we constructed three scenarios: a base case that shows investment using
historical investment rates, and two allocation policies, ie. from 2008 until 2012 7%
auctions and from 2013, 100% auctions; a second stage with an accelerated investment and
7% auctions; finally, a third stage with an accelerated investment and 100% auctions since
2008.
5.1 Base Case:
This scenario presents a simulation very close to its real operation, i.e. with 100% EUA
auctions from 2013. For investment and divestment we use historical tendencies together
with planned expansions. Fuels prices are projections of the Energy and Climate Change
Department in the United Kingdom. Note that 2500 MW of nuclear capacity is due to
retired by 2015.
Clean technologies (wind power, nuclear, hydro-thermal) do not have a big variation in
their generation costs due to: first, they do not use fossil fuels and second, these
technologies are not affected by the emission cost. Electricity price is then determined by
coal, gas and fuel oil. Note that carbon price can change the “merit order” between coal and
gas technologies.
Figure 8 shows simulation results under the conditions of the base-case scenario. We
remark important increases in gas and wind power; this contrasts with decreases in coal,
hydro, coal and fuel oil power. Nuclear energy has a tendency less marked because it is
decreasing its capacity until 2016 (because several plants are due to shut-down soon). In
2019, we observe reductions in gas capacity at the expense of coal and wind power.
50000 Mw Installed
capacity(Gas]
45000 Mw Installed
40000 Mw capacity(Oil]
35000 Mw Installed
capacity[Coal]
30000 Mw
— Installed
25000 Mw capacity(Nuclear]
20000 Mw — Installed
capacity[Wind]
Hee My —Installled
10000 Mw capacity[Hydro]
5000 Mw — Installed
SS capacity[Interconnec
0 Mw tions]
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Figure 8. Installed capacity for base case
Figure 9 reports simulations of total emissions of electricity generation in the UK.
According to the results of this scenario, reductions in emissions are achieved during the
first few years but they increase again. By 2020 CO2 reduction reaches 21.5%- however it
fails to meet its goal set at 30% by 2020 (i.e. to reach the 1990 level). Thus, other
mechanisms and policies need to be established to reach this goal.
From 2016 a EUA price can be established in approximately 14 Euros/ton, but such price is
not high enough to boost the technological changes in the electrical sector to cleaner or
lower emission technologies.
46 tCO2
44 tCO2 +
42 tcO2
40 tcO2
Millions
38 tCO2
36 tCO2
34 tCO2
32 tCO2 r r r ¥ ¥ ¥
2008 2009 2010 2011 2013 2014 2015 2016 2018 2019
= emissions of the electrical sector
Figure 9. Emissions of the electrical sector for base case
5.2 Scenario 2: accelerated infrastructure with free allocation:
This scenario is based on the following assumptions:
+ During this scenario the free allocation policy reaches 93% of the total; with 7% auctions
+ The costs will consider the fuel price projections made by the DECC
* Generation and auctions will be conducted by merit order
+ The determination of the secondary market price is established by demand and supply
+ Investment and divestment in capacity is undertaken according to financial criteria
+ 2500 MW of nuclear capacity gradually retires by 2015
In this scenario, wind power takes a big portion of the market (see Figure 10). Unlike the
previous scenario, there is remarkable decrease of fossil fuels technologies: Coal-based and
gas-based technologies loose grounds. Nuclear power remains with the same behavior as in
previous scenario.
50000 Mw
45000 Mw
Installed
capacity[Gas]
installed
capacity(Oil]
——Installed
capacity{Coal]
Installed
capacity[Nuclear]
—Installed
capacity[Wind]
—Installed
capacity[Hydro]
—Installed
qr SS_ capacity[Interconn
ections]
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Figure 10. Installed capacity with free allocation policy
In figure 11 we can observe emissions under this scenario. Unlike the previous scenario, the
tendency here is more encouraging, despite some oscillations. This is mainly due to big
divestment of fossil fuels technologies and increases of clean technologies, which reduce
considerably emissions in electricity generation by about 28,4% (just missing the goal).
46
44
42
40
38
36
34
32
tco2
tco2
tco2
tco2
tco2
tco2
tco2
tco2
2008 2009 2010 2011 2013 2014 2015 2016 2018 2019
= emissions of the electrical sector
Figure 11. Emissions of the electrical sector with free allocation policy
5.3 Scenario 3: accelerated infrastructure with 100% auctions:
This scenario is based on the following assumptions:
+ During this stage 100% auctions policy is analyzed
* The costs will take into account the projections of fuel prices made by the DECC
* Generation and auctions by merit order
+ The determination of the secondary market price is established by supply and demand
+ Investment and divestment in capacity is undertaken according to financial criteria
+ 2500 MW of nuclear capacity gradually retires by 2015
Figure 12 presents the results of this scenario. This scenario exacerbates results shown in
the previous case as conditions here are slightly more aggressive.
45000
40000
35000
30000
25000
20000
15000
10000
5000
Mw
Mw
Mw
Mw
Mw
Mw
Mw
Mw
Mw
Mw
= Installed
capacity[Gas]
——'Installed
capacity[Oil]
——'Installed
capacity[Coal]
——— Installed
capacity[Nuclear]
——'Installed
capacity[Wind]
—— Installed
capacity[Hydro]
——Installed
no capacity[Interconne
ctions]
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Figure 12. Installed capacity with a 100% auctions policy
As long as electricity demand is met, it becomes harder for gas and coal technologies to
maintain their market share, as the high costs of emission do not favor profitability on these
technologies, leading to more rapid divestment.
46 tCO2 4
44 tCO2 +
42 tcO2 +
40 tcO2 +
Millions
38 tCO2 +
36 tCO2 +
34 tCO2 +
32 tCO2 r :
2008 2009 2010 2011 2013 2014 2015 2016 2018 2019
= emissions of the electrical sector
Figure 13. Electrical sector emissions with 100% auctions policy
Regarding emissions, the behavior here is similar to the one observed in the previous
scenario, only that at a faster rate. Reductions of emissions have a marked tendency,
maintained once again by decreases of fossil fuel generation. In this scenario the 30%
reduction is attain.
. CONCLUSIONS
Results indicate that the United Kingdom will meet the 12.5% goal established by the
Kyoto Protocol during the first commitment period. CO2 reduction will be of the order of
25% by 2012. This will mainly attain as a consequence of the liberalization of its electricity
sector in the 90’s, which led to replace coal based technologies by gas generation — a “dash
for gas”.
Additionally, the carbon market contributes to the penetration of clean technologies, such as
wind power. The role of gas-based technology is also remarkable as this reduces GHG
emissions.
The methodology used — a combination of scenarios and sd — proved to be helpful for
assessing the impact of the CO2 market on a technology shift in the UK, which contributes
to reduce emissions at a level that will meet the goal established by the Kyoto protocol. The
lessons, in the case of the United Kingdom, is that in relatively short periods it is possible to
achieve transformations in the composition of the generation capacity of the electricity
generation, first by liberalizing its market, and once again by creating an emissions trading
market.
The three scenarios for simulations suggest a revision in the quantity of emission
allowances to be auctioned during the third phase of the scheme. The model assumes
similar quantities as established in the second phase. The corresponding auction may be
unsuccessful as the total volume of bids might fall short of the volume of auctioned
allowances.
Results observed in this article show that the 100% auction policy tends to produce
promising effects regarding emissions in the electricity sector and a shift towards
efficiency. It is observed that such policy may lead heavily divest fossil-based technologies.
However, further research is needed to assess the efficiency and effectiveness of auctions as
the ones proposed by the EU.
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