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Interactions between electricity-saving measures and carbon emissions from power generation in England and Wales
ARTICLE IN PRESS
Energy Policy 34 (2006) 3434–3446 www.elsevier.com/locate/enpol
Interactions between electricity-saving measures and carbon emissions from power generation in England and Wales R. Bettle, C.H. Pout, E.R. Hitchin Building Research Establishment (BRE), Garston, Watford WD25 9XX, UK Available online 29 August 2005
Abstract The relationship between electricity demand reduction and the consequent change in carbon emissions is central to greenhouse gas emissions policy. This paper examines this relationship for the power system of England and Wales. Previous analysis showed that the commonly used conversion factor based on the system average emission factor significantly underestimates these savings (Hitchin and Pout, 2002. The carbon intensity of electricity: how many kgC per kWhe?. Building Serv. Eng. Res. Technol. 23(4)). Thus any policy analysis based on the system-average emission factor will under-estimate the potential for carbon savings from reductions in electricity demand. The present paper extends the previous analysis by using more detailed modelling to explore differences between demand reductions of differing load shape and magnitude; and the sensitivity of these figures to changes of the fuel mix of the generation system. The new analysis confirms that the carbon savings are consistently greater than those calculated from the annual system average emission factors, and that they vary with end use and scale of demand reduction. However, no systematic differences between enduses could be discerned. It is therefore recommended that a general incremental carbon emission factor should be used for initial assessments irrespective of end-use. Under current expectations of changes to the generation fuel mix, the incremental carbon intensity will fall more rapidly than the system-average mix. A similar reduction in carbon intensity is also present in the other scenarios explored, but is sometimes more and sometimes less marked. r 2005 Elsevier Ltd. All rights reserved. Keywords: Carbon emissions; Electricity generation; Demand reduction
1. Introduction Abating greenhouse gas emissions—especially of carbon dioxide—is a major driver for UK energy policy and the energy saving programmes that stem from it. Leaving aside the possibility of carbon capture from the combustion products, for fossil fuels the amount of carbon abated by energy saving measures can be determined from the change in demand and the carbon content of the fuel. For grid electricity, however, the amount of carbon abated1 depends both on the Corresponding author. Tel.: +44 (0) 1923 664773.
E-mail address: [email protected] (E.R. Hitchin). The model described in this paper could be adapted for other emissions from power stations such as NOx and SOx. 1
0301-4215/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2005.07.014
magnitude of the reduction and on which generation plant, or plants, output changes in response to the fall in demand. The marginal generation plant depends on the total demand on the system at the time that the demand reduction occurs, and on the operation of the system as a whole. The emissions depend on the carbon content of the fuel used and the electrical efficiency of the generation unit. Published carbon emission factors for electricity generally represent the annual system average value— the ratio of total carbon emitted to total electricity generated during a year. These are appropriate for carbon accounting applications where it is necessary to allocate all the carbon emitted from a generating system across all the end-users of the electricity. This type of application is typically a reporting
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process (although sometimes applied to future projections) and normally considers annual emission levels. Examples include: environmental reporting of carbon emissions by businesses; choice of a carbon tax rate for delivered electricity that is fair relative to fossil fuels. In this situation it seems to be impossible to align particular end-users with particular types of generation in a meaningful way2 and pooling emissions via the system-average intensity seems the only practicable measure. We have previously shown (Hitchin and Pout, 2002) that, for England and Wales, this convention can lead to significant underestimation of carbon savings resulting from changes in demand. At the margin the incremental carbon savings were estimated to be typically 20–30% higher than those projected by use of the system average emission factor. For this type of application, we need to know which generating plant or plants will be affected by changes of demand—and how this varies with time on an hourly, daily and seasonal basis. Examples of applications calling for this approach include:
carbon-based choices between electrical and fossil fuel options for the same end-use such as heating economic prioritisation of measures for carbon saving (for example, given a constrained budget, is it more effective to reduce carbon emissions by adding building insulation thus reducing fossil fuel use, or installing more efficient lighting and reducing electricity consumption?) value for money assessments of financial incentives or information programmes aimed at reducing carbon emissions by a mixture of electricity and fossil-fuel saving measures.
In the earlier study we developed a model for grid electricity supply in England and Wales which examined the short-term implication of changing electricity demand patterns for the year 1998/99.3 This earlier work was based on relatively limited information on the operation of the power generation system, and only examined two patterns of electricity saving: a small uniform reduction at all times of the year, and the rapid introduction of 3 GW of electricity generated from embedded CHP plant. 2 Even where there are bilateral contracts between specific generators and end-users, this does not guarantee a rigid link between the enduser’s demand and the operation of a specific plant. Multi-plant power sellers will optimise the operation of their own generation portfolio, and will also buy and sell power into the market. 3 The current paper also focuses on short-term impacts but, for policy decisions, long-term impacts, e.g., decisions to build or retire generating plant, are also important. This has already been discussed in the previous work and the issues are summarised in Section 5 of the current paper.
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The present paper addresses four issues:
re-estimating the ‘‘carbon intensity’’ (kgC abated per kWh of electricity saved) using a more complete model of the electricity generation system examining how carbon intensity varies with end-use (for example, does a kWh of air-conditioning electricity save more or less than a kWh of lighting) examining how carbon intensity varies with the scale of the savings (deeper cuts in demand will affect different generation plants) examining how carbon intensities could change with future changes in the power generation mix.
2. Methodology 2.1. Modelling the generation system For the earlier work we only had access to half hourly marginal plant data, i.e. the plant whose operation changes in response to a marginal change in demand. The other generating plant operating was inferred from the National Grid’s unconstrained merit order (which takes no account of plant availability or bottle-necks in the transmission system). At the time, the merit order itself was based on the prices bid into the pool by each generator, and thus represented the most likely order in which a particular generating plant will be brought in or out of the generation mix depending on demand.4 For the work described here we obtained half hourly data for all generating plant operating over the course of a year. The advantage of this is that we are able to more accurately determine how system emission factors vary over the course of a year and this provides a more robust basis for deriving the merit order for particular seasons and/or times of day. This is because the actual data includes the effects of plant availability and bottlenecks in the transmission system, and also the impact of the bidding strategies of the generators. This revised model was then used to determine the expected reduction in carbon emissions associated with saving electricity for particular end uses of energy at different levels of electricity savings. Scenarios were devised to examine how incremental emissions from grid electricity in England and Wales might change in the future, using both published DTI projections and—in order to illustrate the broader range of possibilities—some more extreme scenarios. 2.1.1. Data sets and assumptions The data used to construct the model consisted of
Data on the electricity generated in England and Wales for each half hour period on a plant by plant
4 See Section 5.4.2 for discussion on the impact of subsequent later changes to the market structure.
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3436 Table 1 Power plant efficiencies
Table 2 Carbon contents of fuels
Efficiency AGR CCGT Coal/gas Coal/oil External Gas Large coal
N/A 45% 34% 34% N/A 36% 35%
Efficiency Medium coal OCGT Oil Pumped hydro PWR Small coal Magnox
33% 31% 35% N/A N/A 31% N/A
basis for the year 2000. This data was obtained from Logica ESIS5. Information on fuel used by different plants (from the National Grid Seven Year Statement (National Grid Company, 19996)). Typical efficiencies for different plant types (from the Digest of United Kingdom Energy Statistics (Department of Trade and Industry, 1999). N-DEEM7 (Pout et al., 2002) emission factors for fuels used to generate electricity (derived from Digest of United Kingdom Energy Statistics and the National Atmospheric Emission Inventory point of use emission factors (Salway, 1999)).
The efficiencies for different types of power generating plant are shown in Table 1, and represent average values for existing power stations.8 Hydro and renewable sources are taken to have no significant carbon emissions associated with their production.9 For simplicity we have also assumed zero emissions for pumped storage hydro and nuclear generated electricity, and for imported electricity which is primarily derived from nuclear and hydro generation sources.10 Table 2 shows the carbon coefficients for fuels used to generate electricity: These are delivered energy emission factors including upstream emissions from fuel extraction processing and distribution, but not transport. They are derived from 5
Logica ESIS were at the time the Energy Settlements and Information Service for the UK Settlement System Administrator for Electricity Pool. 6 National Grid Company—now National Grid Transco—owns the high-voltage electricity system in England and Wales and operates the system across Great Britain. 7 N-DEEM is the Non-Domestic Energy and Emissions Model that has been developed by BRE to provide technical support for climate change policy. 8 Sources Digest of United Kingdom Energy Statistics, ETSU, and the Electricity Association. 9 Hydroelectric generation (other than pumped storage) is, in practice, negligible. 10 There are some emissions associated with the processing of nuclear fuel and it could also be argued that pumped storage hydro should also attract some of the emission burden to reflect grid electricity losses.
tC/MWh
Gas
Oil
Coal
0.052
0.071
0.082
Table 3 Carbon emissions for each type of generating plant Plant type
tC/MWh
Small coal Medium coal Large coal Oil OCGT Gas Coal/gas Coal/oil CCGT Nuclear, pumped hydro & external sources
0.27 0.25 0.24 0.22 0.25 0.15 0.22 0.24 0.12 0
energy consumption data in the Digest of United Kingdom Energy Statistics (Department of Trade and Industry, 1999) and National Atmospheric Emission Inventory point-of use emission factors (Salway, 1999). By combining the operational efficiency of each generating plant with the appropriate emission factor we get the emission factor for different types of generating plant. See Table 3. These carbon emission factors are for electricity generated, which does not include transmission and distribution loses.11 With the mixed sources, coal/gas and coal/oil we have assumed that in each circumstance coal is the dominant partner and the fuel use is approximately 75% and 25%. 2.1.2. Deriving the merit order from operation data The order in which power stations in the UK are called on in response to an increase in demand depends on both economic and operational factors. In earlier work (Hitchin and Pout, 2002) the unconstrained merit order published in the 1999 National Grid Seven Year Statement was used to provide the merit order for the model. This represents a theoretical merit order and did not take account of plant availability, regular maintenance schedules and bottlenecks in the transmission system. This approach also ignored the distinct diurnal and seasonal differences in plant availability and operation. To overcome the problems associated with using a rigid merit order, here we have adopted a different 11 Transmission and distribution losses account for around 10% of electricity generated in the UK. So the emission factors for electricity delivery to the final user would be around 10% higher than these values.
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Modelled Winter Weekday
Modelled Winter Weekday 12 Thousand Tonnes Carbon
0.16 0.14 kgC/k Wh
0.12 0.10 0.08 0.06 0.04 0.02 0.00
10 8 6 4 2 0
0
10
20
30 40 GW Demand
50
60
0
10
20
30
40
50
60
50
60
GW Demand
Actual Winter Weekday
Actual Winter Weekday 12 Thousand Tonnes Carbon
0.16 0.14 0.12 kgC/k Wh
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0.10 0.08 0.06 0.04 0.02 0.00
10 8 6 4 2 0
0
10
20
30
40
50
60
GW Demand
0
10
20
30
40
GW Demand
Fig. 1. (a) Modelled carbon intensity—winter weekday. (b) Observed carbon intensity—winter weekday. (c) Modelled carbon emissions—winter weekday. (d) Observed carbon emissions—winter weekday.
approach based on analysis of the actual plant operation over a year. Half-hourly operational data for generation plant in England and Wales was analysed to determine the percentage of full load operation. The generating plant was then ranked according its percentage full load operation to give an implied merit order. (This method assumes that the generators that generated near to their capacity were on most of the time and therefore must be higher up the real merit order to be able to achieve this.) To take account of daily and seasonal differences in plant availability and operation, eight separate merit orders representing weekends and weekdays in each of four periods of the year were derived.12 The analyses showed that, in the winter, when there is most demand, the general order is nuclear, CCGT, oil and coal, but this is not in simple blocks of plants of similar type. To confirm the validity of this approach, the implied merit orders13 were used to calculate the average system carbon emission factors at different demand levels and these were then compared to the actual systems emissions at the same demand level. This indicated a good correlation between the actual and calculated 12 Earlier work showed that analysis carried out at this level of disaggregation provided a robust results for marginal plant 13 One for each of the eight time periods.
relationship between emission factor and demand. A sample of comparisons is shown in Figs. 1a–d and 2a–d,. It can be seen that, for a given system output, there is more variability of carbon intensity in practice than is captured by the model,14 but that the mean values and trends are well represented. 2.2. Electricity saving profiles In order to map electricity savings onto power generation, it is necessary to describe how the savings are spread across time. This will obviously vary with end-use. There are many different end-uses of electricity. For this study, we have selected some of the more important end-uses in buildings. They were chosen as they represent a large proportion of the total for carbon savings in the commercial and public sectors and a wide range of profiles. They should therefore give a reasonable indication of the extent to which different end-uses are associated with different carbon intensities. Clearly it would be possible to develop profiles for any other energy saving measures and to develop more sophisticated 14 Part of the reason for this is probably technical constraints on the rate of change of output of some types of plant.
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Modelled Autumn Weekend
Modelled Autumn Weekend
0.16 Thousand Tonnes Carbon
12
0.14 kgC/k Wh
0.12 0.10 0.08 0.06 0.04 0.02 0.00 0
10
20
30 40 GW Demand
50
10 8 6 4 2 0
60
0
10
Actual Autumn Weekend
30 40 GW Demand
50
60
50
60
Actual Autumn Weekend
0.16 Thousand Tonnes Carbon
12
0.14 0.12 kgC/k Wh
20
0.10 0.08 0.06 0.04 0.02 0.00 0
10
20
30 40 GW Demand
50
10 8 6 4 2 0
60
0
10
20
30 40 GW Demand
Fig. 2. (a) Modelled carbon intensity—autumn weekend. (b) Observed carbon intensity—autumn weekend. (c) Modelled carbon emissions—autumn weekend. (d) Observed carbon emissions—autumn weekend.
profiles (which could be sector specific). However, these simple profiles are sufficient to represent the typical energy saving profile for the relevant end use. The assumptions and rationale behind the development of these energy savings profiles used in this work are noted below. Example profiles, demonstrating the range of demand shapes, are shown in Figs. 3a–c. In order to examine difference between end-uses, we have only considered savings scenarios that comprise single end-uses—for example energy saving only for lighting. The modelling could equally be applied to scenarios that comprise combinations of measures: the results will not be simply additive. The specific end-uses we have considered are described in more detail below: in summary they are:
night storage heaters air conditioning in commercial and public buildings always on appliances (e.g. stand-by consumption, refrigerators) lighting for day-lit spaces in commercial and public buildings lighting for deep-plan spaces in commercial and public buildings (also applies to office equipment, provided that this is switched off during unoccupied hours)
lighting for a mixture of day-lit and deep-plan spaces in commercial and public buildings.
2.2.1. Storage heaters The demand profile for storage heaters was built up based on a knowledge of the annual electricity consumption per dwelling (Hayton, 1994) and the maximum charge acceptance of a night storage heater. This gives the total hours of charge needed to meet the heating demand. The total charge hours were then apportioned across the year based on monthly degree days (Hitchin, 1990) with t2 (cut-off temperature for off-peak) ¼ 15.5 1C (i.e. standard degree-days) and t1 (full charge temperature) ¼ 5 1C (roughly in line with electricity industry ‘‘Medallion’’ sizing guidelines).15 To give a half-hourly profile it was assumed that charging period starts at midnight. For the purpose of this modelling exercise it was assumed that storage heating was required 7 days a week. Although relatively crude, this profile should be sufficient to provide a first estimate of the (short-term) carbon emissions avoided when storage heaters are removed. 15 Ideally the figures would vary from day to day according to external temperature—so that sometimes the full 7 h was being used.
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(A)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
(B)
0: 30 2: 00 3: 30 5: 00 6: 30 8: 00 9: 30 11 :0 0 12 :3 0 14 :0 0 15 :3 0 17 :0 0 18 :3 0 20 :0 0 21 :3 0 23 :0 0
Autumn Weekday Commercial Lighting (daylight)
00 :3 0 02 :0 0 03 :3 0 05 :0 0 06 :3 0 08 :0 0 09 :3 0 11 :0 0 12 :3 0 14 :0 0 15 :3 0 17 :0 0 18 :3 0 20 :0 0 21 :3 0 23 :0 0
Spring Weekday Storage Heaters
(C)
Fig. 3. (a) Example of demand profile—summer weekday cooling. (b) Example of demand profile—autumn weekday lighting (daylight space). (c) Example of demand profile—spring weekday storage heaters.
This storage heating profile could be extended to take better account of:
day-to-day temperature variations that not everyone gets the same 7 h of off-peak electricity regional variations imperfect charge control controls that defer charge to the end of the charge period day-to-day temperature variations.
2.2.2. Day-lit commercial premises The end use profiles for lighting in premises with reasonable access to daylight were constructed by assuming the following pattern will occur for all weekdays. When it is dark in the morning, lights will be switched on at 7 a.m. (when cleaning staff arrive) and switched off when it gets light half an hour after sunrise. Then the lights will be switched on again when it gets dark in the evening (half an hour before sunset) and off at 6:30 p.m.
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Average weekday end use profiles were constructed for each of the four seasons under consideration based on sunrise and sunset times. These profiles could be improved by recourse to observed light switching patterns in commercial buildings16 and by taking into account the effect of cloudy days and commercial shut down patterns. 2.2.3. Artificial lighting and/or office equipment in commercial premises This profile is based on the assumption that when equipments such as a computer or lights are turned on they remain on until the building closes down at night. It assumes that the equipment is on at 7 a.m. and remains on until 6.30 p.m. Monday to Friday. The average weekday profile for this end use will be the same for each of the four seasons. This profile could be improved by taking into account late working and bank holidays. 2.2.4. Air conditioning This profile assumes that the equipment is only being used during the week. The profile is based on the simulated demand for cooling for a typical office building. The profile covers energy consumption by air-conditioning unit including pumps, fans and controls and assumes a start up time of 5.30 a.m., remaining on until 10 p.m. 2.2.5. Always on This end use profile represents demand for items that are always on and consuming electricity or have an intermittent use such as items on standby or refrigerators. Profiles were produced for all eight time periods. 2.2.6. Lighting of partially day-lit space These end use profile based on the same assumptions as for day-lit spaces, but with 50% of lights being left on all day. Average weekday end use profiles were constructed for each of the four seasons under consideration based on sunrise and sunset times. These profiles could approximate to either partially day-lit space or day-light space where lights are being left on.
3. Generation mix scenarios Sensitivity to changes in the mix of power stations used in the generation system was explored using two of the energy scenarios from Energy Paper 68 (Department 16 In commercial buildings lights are frequently left on where there is sufficient daylight and some are also left on overnight for security purposes.
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3440 Table 4 Generation mix scenarios Scenario name
Year
Generation mix
Demand
Reference CL Reference CH Reference CL Reference CH Low carbon
2010 2010 2020 2020 2020
As As As As As
No nuclear 1
2020
No nuclear 2
2020
As energy paper 68 CL As energy paper 68 CH As energy paper 68 CL As energy paper 68 CH As energy paper 68 CL but with 20% electricity from renewables at the expense of coal generation As energy paper 68 CL but with no nuclear plant operational, shortfall replaced by new coal generation capacity As energy paper 68 CL but with no nuclear plant operational, shortfall replaced by new CCGT generation capacity
of Trade and Industry, 2000) in both 2010 and 2020,17 and a number of more extreme scenarios. The extreme scenarios are not, strictly speaking, for a specific year, but have been notionally described as 2020. All the enduses listed above were modelled for each of the scenarios. These extreme scenarios are:
Low carbon, which assumes 20% of electricity will be generated from renewables by 2020 at the expense of coal generation, No nuclear 1, which assumes no nuclear generation plant is operating and the shortfall met by new coal generation, and No nuclear 2, which assumes no nuclear generation plant is operating and the shortfall met by CCGT plant.
The generation mix and electricity demand for each of the scenarios is summarised in Table 4. To model the effect of power plant entering or leaving the generation mix in future years, the net change in the proportion of electricity generated by each plant type (compared to 2000) was used to modify the size of the individual plant in the merit order model for each of the eight time periods. Thus, we are effectively assuming that the underlying structure of the merit order, in terms of plant type, will be unchanged in the future. An exception is renewable generation which is an additional plant type and has been assumed to operate as base load when output is available.18 It has been assumed that renewables produce 50% of nominal capacity over a year, to take into account their intermittent nature. To allow comparison with the previous work, results are shown for the average of the CL and CH scenarios for each of 2010 and 2020. 17 The code ‘‘C’’ denotes the central economic growth assumption, ‘‘H’’, high fuel prices and ‘‘L’’, low fuel prices. 18 It will have a high capital cost and a low marginal operational cost.
energy energy energy energy energy
paper paper paper paper paper
68 68 68 68 68
CL CH CL CH CL
As energy paper 68 CL As energy paper 68 CL
4. Results 4.1. General The system average and the average incremental carbon intensities19 were estimated for four levels of electricity saving: 0.5%, 1%, 2% and 5% of total annual consumption (where 5% represents around 3,000,000 GWh pa) for each combination of end-use and generation mix. The results for all scenarios and end uses are summarised in Table 5. The results show that significant differences occur in the emission factors depending on the scale of the savings, the end use and the generation mix, with incremental emission factors ranging between 0.07 kgC/ kWh, for several end uses in the scenario where renewables replace coal generation, and 0.24 kgC/kWh for artificial lighting in 2010 CH. The incremental emission factor is almost always higher than the system average emission factor.20 This is due to generators with lower emission factors generally being base load plant. The incremental emission factor is up to 50% higher than the system average emission factor, confirming that the use of the system average factor is likely to seriously underestimate short-term carbon savings. The weekday incremental emission factor is higher than the weekend incremental emission factor (at least for constant demand end use pattern). This is due to total demand being higher on weekdays and more coal plant therefore having to be brought on line to meet demand. Comparing incremental emissions factors for the same end use between the seasons often shows 19 That is, the average of the marginal carbon intensity over all times when the end-use was consuming electricity. 20 Although occasionally the two are similar, and in a few instances, the marginal emission factor is very slightly lower than the system average (particularly in the increased renewables scenarios and daytime loads).
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Table 5 Summary of results Incremental emission factors
System average emission factors
Scale of reduction
0.5%
1%
2%
5%
0.5%
1%
2%
5%
Present Cooling and FPC Storage heaters Artificial lighting/small power Lighting with day lighting Lighting with partial daylighting Always on
0.183 0.191 0.178 0.185 0.172 0.177
0.178 0.191 0.164 0.183 0.171 0.173
0.168 0.183 0.163 0.196 0.178 0.174
0.185 0.170 0.188 0.174 0.187 0.169
0.119 0.119 0.119 0.119 0.119 0.119
0.118 0.118 0.118 0.118 0.118 0.118
0.118 0.118 0.118 0.117 0.118 0.118
0.115 0.116 0.115 0.116 0.115 0.116
EP 68 2010 Cooling and FPC Storage heaters Artificial lighting/small power Lighting with day lighting Lighting with partial daylighting Always on
0.143 0.163 0.137 0.148 0.109 0.146
0.134 0.153 0.127 0.140 0.116 0.138
0.134 0.153 0.131 0.144 0.114 0.133
0.131 0.141 0.132 0.140 0.137 0.135
0.096 0.096 0.096 0.096 0.097 0.096
0.096 0.096 0.096 0.096 0.096 0.096
0.096 0.096 0.096 0.096 0.096 0.096
0.095 0.095 0.095 0.095 0.095 0.095
Renewables replace coal based on EP 68 2020CL Cooling and FPC 0.083 Storage heaters 0.131 Artificial lighting/small power 0.076 Lighting with day lighting 0.089 Lighting with partial daylighting 0.094 Always on 0.084
0.085 0.133 0.077 0.098 0.103 0.095
0.089 0.134 0.084 0.110 0.099 0.099
0.098 0.123 0.091 0.113 0.103 0.101
0.089 0.089 0.089 0.089 0.089 0.089
0.089 0.089 0.089 0.089 0.089 0.089
0.089 0.088 0.089 0.089 0.089 0.089
0.089 0.088 0.089 0.088 0.089 0.089
Coal replaces nuclear based on EP 68 2020CL Cooling and FPC 0.134 Storage heaters 0.140 Artificial lighting/small power 0.128 Lighting with day lighting 0.119 Lighting with partial daylighting 0.135 Always on 0.136
0.137 0.129 0.131 0.126 0.134 0.139
0.134 0.149 0.130 0.135 0.129 0.137
0.138 0.149 0.133 0.143 0.129 0.137
0.120 0.120 0.120 0.120 0.120 0.120
0.120 0.120 0.120 0.120 0.120 0.120
0.120 0.120 0.120 0.120 0.120 0.120
0.119 0.119 0.119 0.119 0.120 0.119
CCGT replaces nuclear based on EP 68 2020CL Cooling and FPC 0.129 Storage heaters 0.120 Artificial lighting/small power 0.117 Lighting with day lighting 0.121 Lighting with partial daylighting 0.131 Always on 0.126
0.133 0.136 0.133 0.123 0.127 0.121
0.126 0.146 0.122 0.128 0.120 0.130
0.129 0.142 0.122 0.135 0.122 0.128
0.115 0.115 0.115 0.115 0.115 0.115
0.115 0.115 0.115 0.115 0.115 0.115
0.115 0.115 0.115 0.115 0.115 0.115
0.115 0.114 0.115 0.114 0.115 0.115
EP 68 2020 Cooling and FPC Storage heaters Artificial lighting/small power Lighting with day lighting Lighting with partial daylighting Always on
0.121 0.145 0.115 0.116 0.105 0.116
0.125 0.146 0.120 0.128 0.110 0.120
0.127 0.139 0.123 0.131 0.122 0.126
0.107 0.106 0.107 0.107 0.107 0.107
0.107 0.106 0.107 0.107 0.107 0.107
0.106 0.106 0.106 0.106 0.107 0.106
0.106 0.105 0.106 0.106 0.106 0.106
0.117 0.141 0.111 0.100 0.105 0.124
a wider range than comparisons between different end uses. 4.2. Impact of different end-use and scale of savings There were differences between the incremental emission factors for the different end uses and reduction scales. While we would like to be able to generalise these into general rules, this does not appear to be possible.
Surprisingly, the most consistent trend is that storage heaters have higher incremental intensities—implying that coal-fired plant is more often the marginal plant at the times when the heaters are charging (winter nights). Hence we conclude that it is not possible (at least at present, and for this system) to extract useful general rules relating incremental intensity to specific end-use or scale of saving. For policy assessment, scenarios consisting of savings resulting from mixtures of
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end-uses will need to be assessed—and savings will not be simply additive—so, for this context, the result is perhaps not particularly onerous.
Table 6 Incremental and average emission factors (always-on end use, 0.5% reduction in demand) Generation scenario
Incremental emission factor
System average emission factor
Present Energy paper 68–2010 (CL) Energy paper 68–2010 (CH) No coal No nuclear 1 No nuclear 2 Energy paper 68-2020 (CL) Energy paper 68-2020 (CH)
0.18 0.14 0.16 0.08 0.14 0.13 0.12 0.13
0.12 0.09 0.10 0.09 0.12 0.12 0.11 0.11
4.3. Impact of changes in the generation mix In both the Energy Paper scenarios (CL and CH), the incremental carbon intensity of electricity falls by about 30% by 2020 as the contribution of coal declines. (By contrast the system-average intensity falls only by about 10%.) In the CH scenario, coal plant is retired more slowly than in the CL scenario, resulting in a slower rate of decline of carbon intensity and a significant difference between the scenarios in 2010. If all coal plants were replaced by renewables, the incremental intensity would fall to less than half its current level—to around 0.08 kgC/kWh. This is not because renewables are marginal generators—they are modelled as base load. Removing coal generation and inserting base load renewables causes nuclear power to become the marginal generation more frequently (especially in the form of imported electricity from France), thus substituting low-carbon nuclear for high carbon coal. The low incremental carbon intensity would weaken the carbon-reducing justification for policies to reduce electricity demand. Replacing current nuclear plant by coal would leave the incremental intensity at a similar value to that expected in 201021 but above the expectation for 2020 (by which time the expected coal burn is relatively low). Replacing nuclear plant by gas would reduce incremental intensity below the expected value for 2010, but leaves it slightly higher than expected in 2020 (nuclear plant is occasionally predicted to be the marginal generator (Table 6)
5. Discussion 5.1. Application of the results: general principles As indicated in the introduction, this analysis represents only one component of the impact of electricity demand changes on carbon emissions: the direct impact of a demand change on plant operation with a fixed generation parc and merit order. There can also be an indirect impact on the composition of the generation parc, if the expected change in demand influences the timing and fuel or technology preference for new generating plant, or the retirement timing of existing plant. While the direct impact should depend upon shortterm operating costs, the longer-tem indirect effect 21 The more extreme generation scenarios have been modelled only with the CL demand scenario, so cannot be compared with CH cases).
affects investment decisions and therefore should depend on expected total costs and operating patterns. In order for there to be a significant indirect impact, the demand changes need to be both significant in magnitude (relative to other causes of demand variations), and caused by measures that are likely to persist. In the context of buildings, some electricity saving measures are inherently reversible while others are less so. For example, the use of compact fluorescent lamps that simply replace conventional GLS bulbs is inherently reversible, and the typical lamp life is a few years. Similarly, most behavioural measures are inherently reversible. Unless there is confidence that like-for-like replacement will take place, its influence on investment decisions will be weak and the indirect impact low. On the other hand, measures that require the installation of long-lived (and often capital intensive) equipment that has a life measured in tens of years are much less likely to be reversed—CHP schemes, or the use of lighting systems that can only operate with energy-efficient lamps, for example. In these cases it seems reasonable to expect that the demand reductions will be long-lived and may have an indirect impact. The nature of any indirect impact may also depend on the specific end-use demand pattern. Changes to summer-peaking air-conditioning, winter-peaking lighting or always-on refrigerators will each have impacts on different parts of the load/duration curve—and therefore on plant operating in different parts of the merit order. This issue has been addressed elsewhere (for example by Voorspools et al., 2000). 5.2. Application of the results: specific suggestions and practical rules Ideally, we would determine carbon savings by modelling the interaction of demand and supply— including both the direct and indirect effects—for each scenario of interest and compare the resulting carbon emissions. This is possible in principle, but the time and
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End of life of measure
Measure implemented
Electricity saving measure
Carbon impact
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Measure proposed
Measure in use
Indirect (may vary from year to year as plant mix changes)
Direct (varies with year)
Assesment of impact of measure
New plant construction
System Planning
Revised Plant Construction or Retiral Schedule
Impact of measure becomes apparent
Operation
Changed power plant mix comes into use
Time
Fig. 4. Timing of impacts.
effort required make it impractical for many applications. For practical purposes, we need simplified rules. As summarised in Fig. 4, the life-time carbon savings from a measure are a mixture of the direct effect (over some initial period and, in principle, calculated to include exogenous changes to the generation mix during this period), followed by a possible indirect effect lasting until the end of the measure’s life. The duration of the initial period and the importance of the indirect impact vary from case to case and are difficult to estimate reliably. Following discussion with potential UK users of this analysis, we suggest that, for initial assessments, a fixed combination of carbon intensities relating to direct and indirect impacts be used. For simplicity we (arbitrarily) propose the use of an incremental intensity value that is a 50:50 combination of the direct and indirect effect. (For measures that are clearly reversible, the indirect effect should be ignored and only the direct effect considered—the life of such measures will be relatively short.) For England and Wales we take CCGT plant to be the displaced (or delayed) plant for indirect impact. This
reflects the EP68 projections (Department of Trade and Industry. 2000) in which the majority of the variation in generating capacity relates to the timing of the introduction of new CCGT plant. Implicitly, this assumes that changes in demand do not alter retirement times of coal plant. 5.3. Application of the results: other markets We think it likely that the methods we have used and the issues that we raise will apply to other markets. However, the structure of electricity demand and the generation mix can vary greatly and it is highly unlikely that our numerical conclusions apply elsewhere.22 22 It is easy, for example, to find electricity systems with high-carbon coal or lignite base generation and lower-carbon shoulder plant. There are also systems with low-carbon nuclear or hydro-electric base-load and fossil-fuel peak or shoulder plant. Systems with large components of hydro-electric capacity also have additional operating constraints: run-of-river plant availability is usually seasonal, and seasonally stored water can only be used once.
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5.4. Unanswered questions These results are a series of snapshots based on either the operation of the generation system in a particular year or on hypothetical alternative generation mixes. We are conscious that this leaves a number of questions unanswered:
How much variation is there from year to year? J What causes it? J How representative are the data that we have used? J What would be reasonable short-term representative values? What would the impact of emissions trading or carbon taxes be?
These are all issues we would like to explore. In the absence of having funding for this, we comment below on them in general terms. 5.4.1. Causes of variations There are several reasons why the figures can be expected to change, both from year to year and over longer periods. In the short-term, the operation of the generation system may change because of:
fuel price volatility changes in generation plant availability end-use market growth, shrinkage or structural change weather (primarily influencing demand, occasionally generation). In the longer-term:
generation plant will be retired and new plant constructed the overall demand shape will alter (in the extreme, the system might become summer-peaking, for example) the generation market structure may change fuel prices and availability may change new technologies may appear the introduction of carbon taxes or emission trading.
We have reported in Section 4 on sensitivity to changes in generation mix, and to some extent to fuel prices (the CL and CH scenarios differ in their fuel price assumptions). The remaining issues are discussed below. 5.4.2. Changes since the data were collected Since this analysis was carried out, there have been two significant changes: the relative prices of gas and
coal has altered, and the there have been structural changes to the generation market.
At the time that the data used in this analysis were collected, the electricity generation market in England and Wales operated as a pool with a system marginal price. Bidding by generators produced a market-clearing price for each half-hour period, which was paid to each generator called to operate. Additional availability payments were also made.23 This has now been replaced first by NETA (New Electricity Trading Arrangements) and BETTA (British Electricity Trading and Transmission Arrangements) which integrates Scotland into the same market. Both of these systems are based on bilateral contracts with separate dispatch priority and system balancing mechanisms.
The important question for this analysis is the extent to which these changes have altered the dispatching of power generation plant. In an economically perfect market, there would be a well-defined merit order based on short-term marginal costs. In principle, each of the actual market structures is likely to have departed from this ideal to some extent. In this study, we have only examined system operation for the pool market—and then only for 1 year. Under NETA, the operation of generating plants is far less transparent. Superficially the new arrangements do not seem to have caused major changes in dispatch but we do not have analysis to support this impression. Gas prices in the UK have risen significantly since the period covered by the analysis, which has led to an increase in the use of coal. Expectations are that this price rise will decline somewhat as transmission bottlenecks in gas supply are cleared. Some idea of the likely impact of price changes can be gauged by comparing the results for the EP68 CL and CH scenarios (the CL and CH scenarios reflect different fuel price assumptions), though it would be preferable to examine the actual effects empirically. 5.4.3. Emissions trading and carbon taxes Carbon taxes would alter relative fuel prices so as to discourage the use of high-carbon fuels. In a pool-based market (as England and Wales used to have) the inevitable increase in the system marginal price would give windfall profits to lower-carbon generators. In an efficient bilateral-contract market, competition between low-carbon producers should remove this. The impact of emissions trading is more complicated and depends critically on the allocation method for permits. The UK National Allocation Plan for the EU 23 Therefore there is inherent producer surplus for the non-pricesetting generators that are dispatched.
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Emissions Trading System (Department for the Environment and Rural Affairs, 2004) uses a ‘‘grandfathering’’ approach (at least for the first trading period) under which allocations are based on previous emissions. This means that high-carbon plant will receive more permits and the immediate impact on merit order should be minimal.24 If allocation were to be based on nominal generating capacity irrespective of fuel or technology, this would have a similar impact to a carbon tax. IEA analysis (Reinaud, 2003)—based on an apparent assumption that permits will be allocated on the basis of capacity—suggests that, for Europe as a whole, the short-run marginal cost of gas CCGT plant becomes less than coal-fired steam plant at a carbon price of around $19 per tonne of CO2. However, this value is very sensitive to efficiency and fuel price assumptions.
6. Conclusions and recommendations
3445
reduction, we were not able to extract any general rules to describe these. We conclude that an average incremental emission factor, not dependent on end-use, would be appropriate for initial estimates of carbon savings arising from reduced electricity demand. From our analysis, we suggest the value 0.180 kgC/kWh currently, and falling to 0.137 kgC/kWh in 2010 and 0.122 in kgC/kWh—in all cases assuming EP 68 generation mixes. The incremental emission factor only represents savings resulting from short-term impact: the generation mix is assumed to be unchanged. In practice, there are also longer-term impacts because aggregate change in demand influences the technology choice and timing of new (or retired) generation plant. We repeat our previous recommendation that emission savings should take account of both long- and short-term factors, for example using the procedure that we have proposed elsewhere (Market Transformation Programme, 2002). There is scope for further work in several respects. This analysis was based on data from the period before the New Electricity Trading Arrangements (NETA) was introduced into this market. Since the purpose of the change was to improve the operation of the generation market, it is possible that changes to the merit orders may have affected the incremental carbon emissions. It has been suggested—and the suggestion has merit— that the results should be summarised as table of incremental emission factor against demand for each half-hour of each of the eight types of period considered. This would enable the impact of any savings profile to be assessed relatively simply. There is clearly also scope for considering additional and more detailed end use patterns and alternative generation scenarios.25 We are currently seeking funding to pursue these extensions.
A revised model for determining the carbon emissions associated with reductions in electricity demand has been developed. This is based on more complete data than previously, and thus gives a better indication of current emission factors. The revised model also captures the differences in generation between the seasons and between weekends and weekdays by using an implied merit order based on the plant average plant load in each period. Utilising data for all plant operating for each half hourly period (rather than just the marginal plant as was the case with the earlier model) also enables the effectiveness of this modelling approach to be confirmed by demonstrating its ability to estimate system emission factors for various levels of demand. Although used here to explore a range of single enduse saving scenarios, the model could also be used to assess carbon emission implications of packages of policy actions that impact on a range of end-uses. The modelling showed that, for a range of end use profiles and reduction levels (0.5–5% of annual electricity demand) the incremental emission factors were generally around 50% higher than the commonly used corresponding system average emission factor. (However, this difference is likely to reduce by 2020.) This is a significant difference and—notwithstanding the uncertainty about the exact values—we recommend that the carbon impact of policy options be assessed using incremental rather than system average carbon emission factors. Although there were variations of incremental emission factors between different end uses and scales of
Department for the Environment and Rural Affairs, 2004. http:// www.defra.gov.uk/environment/climatechange/trading/eu/nap/ allocation.htm.
24 There may be occasions when the expected price of future permit sales or purchases influences an operating decision, but these seem likely to be rare.
25 In particular the assumption that the merit order remains the same in future scenarios should be explored and, if it is found to be flawed, alternative assumptions be developed.
Acknowledgements This work was supported by the Department of the Environment, Food and Rural Affairs and by The Carbon Trust. The opinions and any errors are the authors’. References
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Department of Trade and Industry, 1999. Digest of United Kingdom Energy Statistics. The Stationary Office, London. Department of Trade and Industry, 2000. Energy Projections for the UK, Energy Paper 68, Energy Use and Energy-Related Emissions of Carbon Dioxide in the UK. The Stationary Office, London. Hayton, J., 1994. Heating consumption measurements in electricallyheated dwellings. Building Services and Engineering Research Technology 15 (1). Hitchin, E.R., 1990. Developments in degree-day methods of estimating energy use. Building and Environment 25 (1). Hitchin, E.R., Pout, C.H., 2002. The carbon intensity of electricity: how many kgC per kWhe? Building Services and Engineering Research Technology 23 (4).
Market Transformation Programme, 2002. Briefing Note BNXS01: Carbon Emission Factors for UK Energy Use www.mtprog.com. National Grid Company plc, 1999. 1999 Seven Year Statement. Pout, C.H., MacKenzie, F., Bettle, R., 2002. Carbon Dioxide Emissions from Non-Domestic Buildings: 2000 and Beyond. CRC Ltd. Reinaud, J., 2003. Emissions Trading and its Possible Impacts on Investment Decisions in the Power Sector. IEA. Salway, A.G., 1999. UK Greenhouse Gas Inventory 1990–1997, Department of the Environment. Transport and the Regions (NETCEN), November 1999. Voorspools, et al., 2000. An evaluation method for calculating the emission responsibility of specific electric applications. Energy Policy 28, 967–980.
Energy Policy 34 (2006) 3434–3446 www.elsevier.com/locate/enpol
Interactions between electricity-saving measures and carbon emissions from power generation in England and Wales R. Bettle, C.H. Pout, E.R. Hitchin Building Research Establishment (BRE), Garston, Watford WD25 9XX, UK Available online 29 August 2005
Abstract The relationship between electricity demand reduction and the consequent change in carbon emissions is central to greenhouse gas emissions policy. This paper examines this relationship for the power system of England and Wales. Previous analysis showed that the commonly used conversion factor based on the system average emission factor significantly underestimates these savings (Hitchin and Pout, 2002. The carbon intensity of electricity: how many kgC per kWhe?. Building Serv. Eng. Res. Technol. 23(4)). Thus any policy analysis based on the system-average emission factor will under-estimate the potential for carbon savings from reductions in electricity demand. The present paper extends the previous analysis by using more detailed modelling to explore differences between demand reductions of differing load shape and magnitude; and the sensitivity of these figures to changes of the fuel mix of the generation system. The new analysis confirms that the carbon savings are consistently greater than those calculated from the annual system average emission factors, and that they vary with end use and scale of demand reduction. However, no systematic differences between enduses could be discerned. It is therefore recommended that a general incremental carbon emission factor should be used for initial assessments irrespective of end-use. Under current expectations of changes to the generation fuel mix, the incremental carbon intensity will fall more rapidly than the system-average mix. A similar reduction in carbon intensity is also present in the other scenarios explored, but is sometimes more and sometimes less marked. r 2005 Elsevier Ltd. All rights reserved. Keywords: Carbon emissions; Electricity generation; Demand reduction
1. Introduction Abating greenhouse gas emissions—especially of carbon dioxide—is a major driver for UK energy policy and the energy saving programmes that stem from it. Leaving aside the possibility of carbon capture from the combustion products, for fossil fuels the amount of carbon abated by energy saving measures can be determined from the change in demand and the carbon content of the fuel. For grid electricity, however, the amount of carbon abated1 depends both on the Corresponding author. Tel.: +44 (0) 1923 664773.
E-mail address: [email protected] (E.R. Hitchin). The model described in this paper could be adapted for other emissions from power stations such as NOx and SOx. 1
0301-4215/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2005.07.014
magnitude of the reduction and on which generation plant, or plants, output changes in response to the fall in demand. The marginal generation plant depends on the total demand on the system at the time that the demand reduction occurs, and on the operation of the system as a whole. The emissions depend on the carbon content of the fuel used and the electrical efficiency of the generation unit. Published carbon emission factors for electricity generally represent the annual system average value— the ratio of total carbon emitted to total electricity generated during a year. These are appropriate for carbon accounting applications where it is necessary to allocate all the carbon emitted from a generating system across all the end-users of the electricity. This type of application is typically a reporting
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process (although sometimes applied to future projections) and normally considers annual emission levels. Examples include: environmental reporting of carbon emissions by businesses; choice of a carbon tax rate for delivered electricity that is fair relative to fossil fuels. In this situation it seems to be impossible to align particular end-users with particular types of generation in a meaningful way2 and pooling emissions via the system-average intensity seems the only practicable measure. We have previously shown (Hitchin and Pout, 2002) that, for England and Wales, this convention can lead to significant underestimation of carbon savings resulting from changes in demand. At the margin the incremental carbon savings were estimated to be typically 20–30% higher than those projected by use of the system average emission factor. For this type of application, we need to know which generating plant or plants will be affected by changes of demand—and how this varies with time on an hourly, daily and seasonal basis. Examples of applications calling for this approach include:
carbon-based choices between electrical and fossil fuel options for the same end-use such as heating economic prioritisation of measures for carbon saving (for example, given a constrained budget, is it more effective to reduce carbon emissions by adding building insulation thus reducing fossil fuel use, or installing more efficient lighting and reducing electricity consumption?) value for money assessments of financial incentives or information programmes aimed at reducing carbon emissions by a mixture of electricity and fossil-fuel saving measures.
In the earlier study we developed a model for grid electricity supply in England and Wales which examined the short-term implication of changing electricity demand patterns for the year 1998/99.3 This earlier work was based on relatively limited information on the operation of the power generation system, and only examined two patterns of electricity saving: a small uniform reduction at all times of the year, and the rapid introduction of 3 GW of electricity generated from embedded CHP plant. 2 Even where there are bilateral contracts between specific generators and end-users, this does not guarantee a rigid link between the enduser’s demand and the operation of a specific plant. Multi-plant power sellers will optimise the operation of their own generation portfolio, and will also buy and sell power into the market. 3 The current paper also focuses on short-term impacts but, for policy decisions, long-term impacts, e.g., decisions to build or retire generating plant, are also important. This has already been discussed in the previous work and the issues are summarised in Section 5 of the current paper.
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The present paper addresses four issues:
re-estimating the ‘‘carbon intensity’’ (kgC abated per kWh of electricity saved) using a more complete model of the electricity generation system examining how carbon intensity varies with end-use (for example, does a kWh of air-conditioning electricity save more or less than a kWh of lighting) examining how carbon intensity varies with the scale of the savings (deeper cuts in demand will affect different generation plants) examining how carbon intensities could change with future changes in the power generation mix.
2. Methodology 2.1. Modelling the generation system For the earlier work we only had access to half hourly marginal plant data, i.e. the plant whose operation changes in response to a marginal change in demand. The other generating plant operating was inferred from the National Grid’s unconstrained merit order (which takes no account of plant availability or bottle-necks in the transmission system). At the time, the merit order itself was based on the prices bid into the pool by each generator, and thus represented the most likely order in which a particular generating plant will be brought in or out of the generation mix depending on demand.4 For the work described here we obtained half hourly data for all generating plant operating over the course of a year. The advantage of this is that we are able to more accurately determine how system emission factors vary over the course of a year and this provides a more robust basis for deriving the merit order for particular seasons and/or times of day. This is because the actual data includes the effects of plant availability and bottlenecks in the transmission system, and also the impact of the bidding strategies of the generators. This revised model was then used to determine the expected reduction in carbon emissions associated with saving electricity for particular end uses of energy at different levels of electricity savings. Scenarios were devised to examine how incremental emissions from grid electricity in England and Wales might change in the future, using both published DTI projections and—in order to illustrate the broader range of possibilities—some more extreme scenarios. 2.1.1. Data sets and assumptions The data used to construct the model consisted of
Data on the electricity generated in England and Wales for each half hour period on a plant by plant
4 See Section 5.4.2 for discussion on the impact of subsequent later changes to the market structure.
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3436 Table 1 Power plant efficiencies
Table 2 Carbon contents of fuels
Efficiency AGR CCGT Coal/gas Coal/oil External Gas Large coal
N/A 45% 34% 34% N/A 36% 35%
Efficiency Medium coal OCGT Oil Pumped hydro PWR Small coal Magnox
33% 31% 35% N/A N/A 31% N/A
basis for the year 2000. This data was obtained from Logica ESIS5. Information on fuel used by different plants (from the National Grid Seven Year Statement (National Grid Company, 19996)). Typical efficiencies for different plant types (from the Digest of United Kingdom Energy Statistics (Department of Trade and Industry, 1999). N-DEEM7 (Pout et al., 2002) emission factors for fuels used to generate electricity (derived from Digest of United Kingdom Energy Statistics and the National Atmospheric Emission Inventory point of use emission factors (Salway, 1999)).
The efficiencies for different types of power generating plant are shown in Table 1, and represent average values for existing power stations.8 Hydro and renewable sources are taken to have no significant carbon emissions associated with their production.9 For simplicity we have also assumed zero emissions for pumped storage hydro and nuclear generated electricity, and for imported electricity which is primarily derived from nuclear and hydro generation sources.10 Table 2 shows the carbon coefficients for fuels used to generate electricity: These are delivered energy emission factors including upstream emissions from fuel extraction processing and distribution, but not transport. They are derived from 5
Logica ESIS were at the time the Energy Settlements and Information Service for the UK Settlement System Administrator for Electricity Pool. 6 National Grid Company—now National Grid Transco—owns the high-voltage electricity system in England and Wales and operates the system across Great Britain. 7 N-DEEM is the Non-Domestic Energy and Emissions Model that has been developed by BRE to provide technical support for climate change policy. 8 Sources Digest of United Kingdom Energy Statistics, ETSU, and the Electricity Association. 9 Hydroelectric generation (other than pumped storage) is, in practice, negligible. 10 There are some emissions associated with the processing of nuclear fuel and it could also be argued that pumped storage hydro should also attract some of the emission burden to reflect grid electricity losses.
tC/MWh
Gas
Oil
Coal
0.052
0.071
0.082
Table 3 Carbon emissions for each type of generating plant Plant type
tC/MWh
Small coal Medium coal Large coal Oil OCGT Gas Coal/gas Coal/oil CCGT Nuclear, pumped hydro & external sources
0.27 0.25 0.24 0.22 0.25 0.15 0.22 0.24 0.12 0
energy consumption data in the Digest of United Kingdom Energy Statistics (Department of Trade and Industry, 1999) and National Atmospheric Emission Inventory point-of use emission factors (Salway, 1999). By combining the operational efficiency of each generating plant with the appropriate emission factor we get the emission factor for different types of generating plant. See Table 3. These carbon emission factors are for electricity generated, which does not include transmission and distribution loses.11 With the mixed sources, coal/gas and coal/oil we have assumed that in each circumstance coal is the dominant partner and the fuel use is approximately 75% and 25%. 2.1.2. Deriving the merit order from operation data The order in which power stations in the UK are called on in response to an increase in demand depends on both economic and operational factors. In earlier work (Hitchin and Pout, 2002) the unconstrained merit order published in the 1999 National Grid Seven Year Statement was used to provide the merit order for the model. This represents a theoretical merit order and did not take account of plant availability, regular maintenance schedules and bottlenecks in the transmission system. This approach also ignored the distinct diurnal and seasonal differences in plant availability and operation. To overcome the problems associated with using a rigid merit order, here we have adopted a different 11 Transmission and distribution losses account for around 10% of electricity generated in the UK. So the emission factors for electricity delivery to the final user would be around 10% higher than these values.
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Modelled Winter Weekday
Modelled Winter Weekday 12 Thousand Tonnes Carbon
0.16 0.14 kgC/k Wh
0.12 0.10 0.08 0.06 0.04 0.02 0.00
10 8 6 4 2 0
0
10
20
30 40 GW Demand
50
60
0
10
20
30
40
50
60
50
60
GW Demand
Actual Winter Weekday
Actual Winter Weekday 12 Thousand Tonnes Carbon
0.16 0.14 0.12 kgC/k Wh
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0.10 0.08 0.06 0.04 0.02 0.00
10 8 6 4 2 0
0
10
20
30
40
50
60
GW Demand
0
10
20
30
40
GW Demand
Fig. 1. (a) Modelled carbon intensity—winter weekday. (b) Observed carbon intensity—winter weekday. (c) Modelled carbon emissions—winter weekday. (d) Observed carbon emissions—winter weekday.
approach based on analysis of the actual plant operation over a year. Half-hourly operational data for generation plant in England and Wales was analysed to determine the percentage of full load operation. The generating plant was then ranked according its percentage full load operation to give an implied merit order. (This method assumes that the generators that generated near to their capacity were on most of the time and therefore must be higher up the real merit order to be able to achieve this.) To take account of daily and seasonal differences in plant availability and operation, eight separate merit orders representing weekends and weekdays in each of four periods of the year were derived.12 The analyses showed that, in the winter, when there is most demand, the general order is nuclear, CCGT, oil and coal, but this is not in simple blocks of plants of similar type. To confirm the validity of this approach, the implied merit orders13 were used to calculate the average system carbon emission factors at different demand levels and these were then compared to the actual systems emissions at the same demand level. This indicated a good correlation between the actual and calculated 12 Earlier work showed that analysis carried out at this level of disaggregation provided a robust results for marginal plant 13 One for each of the eight time periods.
relationship between emission factor and demand. A sample of comparisons is shown in Figs. 1a–d and 2a–d,. It can be seen that, for a given system output, there is more variability of carbon intensity in practice than is captured by the model,14 but that the mean values and trends are well represented. 2.2. Electricity saving profiles In order to map electricity savings onto power generation, it is necessary to describe how the savings are spread across time. This will obviously vary with end-use. There are many different end-uses of electricity. For this study, we have selected some of the more important end-uses in buildings. They were chosen as they represent a large proportion of the total for carbon savings in the commercial and public sectors and a wide range of profiles. They should therefore give a reasonable indication of the extent to which different end-uses are associated with different carbon intensities. Clearly it would be possible to develop profiles for any other energy saving measures and to develop more sophisticated 14 Part of the reason for this is probably technical constraints on the rate of change of output of some types of plant.
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3438
Modelled Autumn Weekend
Modelled Autumn Weekend
0.16 Thousand Tonnes Carbon
12
0.14 kgC/k Wh
0.12 0.10 0.08 0.06 0.04 0.02 0.00 0
10
20
30 40 GW Demand
50
10 8 6 4 2 0
60
0
10
Actual Autumn Weekend
30 40 GW Demand
50
60
50
60
Actual Autumn Weekend
0.16 Thousand Tonnes Carbon
12
0.14 0.12 kgC/k Wh
20
0.10 0.08 0.06 0.04 0.02 0.00 0
10
20
30 40 GW Demand
50
10 8 6 4 2 0
60
0
10
20
30 40 GW Demand
Fig. 2. (a) Modelled carbon intensity—autumn weekend. (b) Observed carbon intensity—autumn weekend. (c) Modelled carbon emissions—autumn weekend. (d) Observed carbon emissions—autumn weekend.
profiles (which could be sector specific). However, these simple profiles are sufficient to represent the typical energy saving profile for the relevant end use. The assumptions and rationale behind the development of these energy savings profiles used in this work are noted below. Example profiles, demonstrating the range of demand shapes, are shown in Figs. 3a–c. In order to examine difference between end-uses, we have only considered savings scenarios that comprise single end-uses—for example energy saving only for lighting. The modelling could equally be applied to scenarios that comprise combinations of measures: the results will not be simply additive. The specific end-uses we have considered are described in more detail below: in summary they are:
night storage heaters air conditioning in commercial and public buildings always on appliances (e.g. stand-by consumption, refrigerators) lighting for day-lit spaces in commercial and public buildings lighting for deep-plan spaces in commercial and public buildings (also applies to office equipment, provided that this is switched off during unoccupied hours)
lighting for a mixture of day-lit and deep-plan spaces in commercial and public buildings.
2.2.1. Storage heaters The demand profile for storage heaters was built up based on a knowledge of the annual electricity consumption per dwelling (Hayton, 1994) and the maximum charge acceptance of a night storage heater. This gives the total hours of charge needed to meet the heating demand. The total charge hours were then apportioned across the year based on monthly degree days (Hitchin, 1990) with t2 (cut-off temperature for off-peak) ¼ 15.5 1C (i.e. standard degree-days) and t1 (full charge temperature) ¼ 5 1C (roughly in line with electricity industry ‘‘Medallion’’ sizing guidelines).15 To give a half-hourly profile it was assumed that charging period starts at midnight. For the purpose of this modelling exercise it was assumed that storage heating was required 7 days a week. Although relatively crude, this profile should be sufficient to provide a first estimate of the (short-term) carbon emissions avoided when storage heaters are removed. 15 Ideally the figures would vary from day to day according to external temperature—so that sometimes the full 7 h was being used.
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(A)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
(B)
0: 30 2: 00 3: 30 5: 00 6: 30 8: 00 9: 30 11 :0 0 12 :3 0 14 :0 0 15 :3 0 17 :0 0 18 :3 0 20 :0 0 21 :3 0 23 :0 0
Autumn Weekday Commercial Lighting (daylight)
00 :3 0 02 :0 0 03 :3 0 05 :0 0 06 :3 0 08 :0 0 09 :3 0 11 :0 0 12 :3 0 14 :0 0 15 :3 0 17 :0 0 18 :3 0 20 :0 0 21 :3 0 23 :0 0
Spring Weekday Storage Heaters
(C)
Fig. 3. (a) Example of demand profile—summer weekday cooling. (b) Example of demand profile—autumn weekday lighting (daylight space). (c) Example of demand profile—spring weekday storage heaters.
This storage heating profile could be extended to take better account of:
day-to-day temperature variations that not everyone gets the same 7 h of off-peak electricity regional variations imperfect charge control controls that defer charge to the end of the charge period day-to-day temperature variations.
2.2.2. Day-lit commercial premises The end use profiles for lighting in premises with reasonable access to daylight were constructed by assuming the following pattern will occur for all weekdays. When it is dark in the morning, lights will be switched on at 7 a.m. (when cleaning staff arrive) and switched off when it gets light half an hour after sunrise. Then the lights will be switched on again when it gets dark in the evening (half an hour before sunset) and off at 6:30 p.m.
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Average weekday end use profiles were constructed for each of the four seasons under consideration based on sunrise and sunset times. These profiles could be improved by recourse to observed light switching patterns in commercial buildings16 and by taking into account the effect of cloudy days and commercial shut down patterns. 2.2.3. Artificial lighting and/or office equipment in commercial premises This profile is based on the assumption that when equipments such as a computer or lights are turned on they remain on until the building closes down at night. It assumes that the equipment is on at 7 a.m. and remains on until 6.30 p.m. Monday to Friday. The average weekday profile for this end use will be the same for each of the four seasons. This profile could be improved by taking into account late working and bank holidays. 2.2.4. Air conditioning This profile assumes that the equipment is only being used during the week. The profile is based on the simulated demand for cooling for a typical office building. The profile covers energy consumption by air-conditioning unit including pumps, fans and controls and assumes a start up time of 5.30 a.m., remaining on until 10 p.m. 2.2.5. Always on This end use profile represents demand for items that are always on and consuming electricity or have an intermittent use such as items on standby or refrigerators. Profiles were produced for all eight time periods. 2.2.6. Lighting of partially day-lit space These end use profile based on the same assumptions as for day-lit spaces, but with 50% of lights being left on all day. Average weekday end use profiles were constructed for each of the four seasons under consideration based on sunrise and sunset times. These profiles could approximate to either partially day-lit space or day-light space where lights are being left on.
3. Generation mix scenarios Sensitivity to changes in the mix of power stations used in the generation system was explored using two of the energy scenarios from Energy Paper 68 (Department 16 In commercial buildings lights are frequently left on where there is sufficient daylight and some are also left on overnight for security purposes.
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3440 Table 4 Generation mix scenarios Scenario name
Year
Generation mix
Demand
Reference CL Reference CH Reference CL Reference CH Low carbon
2010 2010 2020 2020 2020
As As As As As
No nuclear 1
2020
No nuclear 2
2020
As energy paper 68 CL As energy paper 68 CH As energy paper 68 CL As energy paper 68 CH As energy paper 68 CL but with 20% electricity from renewables at the expense of coal generation As energy paper 68 CL but with no nuclear plant operational, shortfall replaced by new coal generation capacity As energy paper 68 CL but with no nuclear plant operational, shortfall replaced by new CCGT generation capacity
of Trade and Industry, 2000) in both 2010 and 2020,17 and a number of more extreme scenarios. The extreme scenarios are not, strictly speaking, for a specific year, but have been notionally described as 2020. All the enduses listed above were modelled for each of the scenarios. These extreme scenarios are:
Low carbon, which assumes 20% of electricity will be generated from renewables by 2020 at the expense of coal generation, No nuclear 1, which assumes no nuclear generation plant is operating and the shortfall met by new coal generation, and No nuclear 2, which assumes no nuclear generation plant is operating and the shortfall met by CCGT plant.
The generation mix and electricity demand for each of the scenarios is summarised in Table 4. To model the effect of power plant entering or leaving the generation mix in future years, the net change in the proportion of electricity generated by each plant type (compared to 2000) was used to modify the size of the individual plant in the merit order model for each of the eight time periods. Thus, we are effectively assuming that the underlying structure of the merit order, in terms of plant type, will be unchanged in the future. An exception is renewable generation which is an additional plant type and has been assumed to operate as base load when output is available.18 It has been assumed that renewables produce 50% of nominal capacity over a year, to take into account their intermittent nature. To allow comparison with the previous work, results are shown for the average of the CL and CH scenarios for each of 2010 and 2020. 17 The code ‘‘C’’ denotes the central economic growth assumption, ‘‘H’’, high fuel prices and ‘‘L’’, low fuel prices. 18 It will have a high capital cost and a low marginal operational cost.
energy energy energy energy energy
paper paper paper paper paper
68 68 68 68 68
CL CH CL CH CL
As energy paper 68 CL As energy paper 68 CL
4. Results 4.1. General The system average and the average incremental carbon intensities19 were estimated for four levels of electricity saving: 0.5%, 1%, 2% and 5% of total annual consumption (where 5% represents around 3,000,000 GWh pa) for each combination of end-use and generation mix. The results for all scenarios and end uses are summarised in Table 5. The results show that significant differences occur in the emission factors depending on the scale of the savings, the end use and the generation mix, with incremental emission factors ranging between 0.07 kgC/ kWh, for several end uses in the scenario where renewables replace coal generation, and 0.24 kgC/kWh for artificial lighting in 2010 CH. The incremental emission factor is almost always higher than the system average emission factor.20 This is due to generators with lower emission factors generally being base load plant. The incremental emission factor is up to 50% higher than the system average emission factor, confirming that the use of the system average factor is likely to seriously underestimate short-term carbon savings. The weekday incremental emission factor is higher than the weekend incremental emission factor (at least for constant demand end use pattern). This is due to total demand being higher on weekdays and more coal plant therefore having to be brought on line to meet demand. Comparing incremental emissions factors for the same end use between the seasons often shows 19 That is, the average of the marginal carbon intensity over all times when the end-use was consuming electricity. 20 Although occasionally the two are similar, and in a few instances, the marginal emission factor is very slightly lower than the system average (particularly in the increased renewables scenarios and daytime loads).
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Table 5 Summary of results Incremental emission factors
System average emission factors
Scale of reduction
0.5%
1%
2%
5%
0.5%
1%
2%
5%
Present Cooling and FPC Storage heaters Artificial lighting/small power Lighting with day lighting Lighting with partial daylighting Always on
0.183 0.191 0.178 0.185 0.172 0.177
0.178 0.191 0.164 0.183 0.171 0.173
0.168 0.183 0.163 0.196 0.178 0.174
0.185 0.170 0.188 0.174 0.187 0.169
0.119 0.119 0.119 0.119 0.119 0.119
0.118 0.118 0.118 0.118 0.118 0.118
0.118 0.118 0.118 0.117 0.118 0.118
0.115 0.116 0.115 0.116 0.115 0.116
EP 68 2010 Cooling and FPC Storage heaters Artificial lighting/small power Lighting with day lighting Lighting with partial daylighting Always on
0.143 0.163 0.137 0.148 0.109 0.146
0.134 0.153 0.127 0.140 0.116 0.138
0.134 0.153 0.131 0.144 0.114 0.133
0.131 0.141 0.132 0.140 0.137 0.135
0.096 0.096 0.096 0.096 0.097 0.096
0.096 0.096 0.096 0.096 0.096 0.096
0.096 0.096 0.096 0.096 0.096 0.096
0.095 0.095 0.095 0.095 0.095 0.095
Renewables replace coal based on EP 68 2020CL Cooling and FPC 0.083 Storage heaters 0.131 Artificial lighting/small power 0.076 Lighting with day lighting 0.089 Lighting with partial daylighting 0.094 Always on 0.084
0.085 0.133 0.077 0.098 0.103 0.095
0.089 0.134 0.084 0.110 0.099 0.099
0.098 0.123 0.091 0.113 0.103 0.101
0.089 0.089 0.089 0.089 0.089 0.089
0.089 0.089 0.089 0.089 0.089 0.089
0.089 0.088 0.089 0.089 0.089 0.089
0.089 0.088 0.089 0.088 0.089 0.089
Coal replaces nuclear based on EP 68 2020CL Cooling and FPC 0.134 Storage heaters 0.140 Artificial lighting/small power 0.128 Lighting with day lighting 0.119 Lighting with partial daylighting 0.135 Always on 0.136
0.137 0.129 0.131 0.126 0.134 0.139
0.134 0.149 0.130 0.135 0.129 0.137
0.138 0.149 0.133 0.143 0.129 0.137
0.120 0.120 0.120 0.120 0.120 0.120
0.120 0.120 0.120 0.120 0.120 0.120
0.120 0.120 0.120 0.120 0.120 0.120
0.119 0.119 0.119 0.119 0.120 0.119
CCGT replaces nuclear based on EP 68 2020CL Cooling and FPC 0.129 Storage heaters 0.120 Artificial lighting/small power 0.117 Lighting with day lighting 0.121 Lighting with partial daylighting 0.131 Always on 0.126
0.133 0.136 0.133 0.123 0.127 0.121
0.126 0.146 0.122 0.128 0.120 0.130
0.129 0.142 0.122 0.135 0.122 0.128
0.115 0.115 0.115 0.115 0.115 0.115
0.115 0.115 0.115 0.115 0.115 0.115
0.115 0.115 0.115 0.115 0.115 0.115
0.115 0.114 0.115 0.114 0.115 0.115
EP 68 2020 Cooling and FPC Storage heaters Artificial lighting/small power Lighting with day lighting Lighting with partial daylighting Always on
0.121 0.145 0.115 0.116 0.105 0.116
0.125 0.146 0.120 0.128 0.110 0.120
0.127 0.139 0.123 0.131 0.122 0.126
0.107 0.106 0.107 0.107 0.107 0.107
0.107 0.106 0.107 0.107 0.107 0.107
0.106 0.106 0.106 0.106 0.107 0.106
0.106 0.105 0.106 0.106 0.106 0.106
0.117 0.141 0.111 0.100 0.105 0.124
a wider range than comparisons between different end uses. 4.2. Impact of different end-use and scale of savings There were differences between the incremental emission factors for the different end uses and reduction scales. While we would like to be able to generalise these into general rules, this does not appear to be possible.
Surprisingly, the most consistent trend is that storage heaters have higher incremental intensities—implying that coal-fired plant is more often the marginal plant at the times when the heaters are charging (winter nights). Hence we conclude that it is not possible (at least at present, and for this system) to extract useful general rules relating incremental intensity to specific end-use or scale of saving. For policy assessment, scenarios consisting of savings resulting from mixtures of
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end-uses will need to be assessed—and savings will not be simply additive—so, for this context, the result is perhaps not particularly onerous.
Table 6 Incremental and average emission factors (always-on end use, 0.5% reduction in demand) Generation scenario
Incremental emission factor
System average emission factor
Present Energy paper 68–2010 (CL) Energy paper 68–2010 (CH) No coal No nuclear 1 No nuclear 2 Energy paper 68-2020 (CL) Energy paper 68-2020 (CH)
0.18 0.14 0.16 0.08 0.14 0.13 0.12 0.13
0.12 0.09 0.10 0.09 0.12 0.12 0.11 0.11
4.3. Impact of changes in the generation mix In both the Energy Paper scenarios (CL and CH), the incremental carbon intensity of electricity falls by about 30% by 2020 as the contribution of coal declines. (By contrast the system-average intensity falls only by about 10%.) In the CH scenario, coal plant is retired more slowly than in the CL scenario, resulting in a slower rate of decline of carbon intensity and a significant difference between the scenarios in 2010. If all coal plants were replaced by renewables, the incremental intensity would fall to less than half its current level—to around 0.08 kgC/kWh. This is not because renewables are marginal generators—they are modelled as base load. Removing coal generation and inserting base load renewables causes nuclear power to become the marginal generation more frequently (especially in the form of imported electricity from France), thus substituting low-carbon nuclear for high carbon coal. The low incremental carbon intensity would weaken the carbon-reducing justification for policies to reduce electricity demand. Replacing current nuclear plant by coal would leave the incremental intensity at a similar value to that expected in 201021 but above the expectation for 2020 (by which time the expected coal burn is relatively low). Replacing nuclear plant by gas would reduce incremental intensity below the expected value for 2010, but leaves it slightly higher than expected in 2020 (nuclear plant is occasionally predicted to be the marginal generator (Table 6)
5. Discussion 5.1. Application of the results: general principles As indicated in the introduction, this analysis represents only one component of the impact of electricity demand changes on carbon emissions: the direct impact of a demand change on plant operation with a fixed generation parc and merit order. There can also be an indirect impact on the composition of the generation parc, if the expected change in demand influences the timing and fuel or technology preference for new generating plant, or the retirement timing of existing plant. While the direct impact should depend upon shortterm operating costs, the longer-tem indirect effect 21 The more extreme generation scenarios have been modelled only with the CL demand scenario, so cannot be compared with CH cases).
affects investment decisions and therefore should depend on expected total costs and operating patterns. In order for there to be a significant indirect impact, the demand changes need to be both significant in magnitude (relative to other causes of demand variations), and caused by measures that are likely to persist. In the context of buildings, some electricity saving measures are inherently reversible while others are less so. For example, the use of compact fluorescent lamps that simply replace conventional GLS bulbs is inherently reversible, and the typical lamp life is a few years. Similarly, most behavioural measures are inherently reversible. Unless there is confidence that like-for-like replacement will take place, its influence on investment decisions will be weak and the indirect impact low. On the other hand, measures that require the installation of long-lived (and often capital intensive) equipment that has a life measured in tens of years are much less likely to be reversed—CHP schemes, or the use of lighting systems that can only operate with energy-efficient lamps, for example. In these cases it seems reasonable to expect that the demand reductions will be long-lived and may have an indirect impact. The nature of any indirect impact may also depend on the specific end-use demand pattern. Changes to summer-peaking air-conditioning, winter-peaking lighting or always-on refrigerators will each have impacts on different parts of the load/duration curve—and therefore on plant operating in different parts of the merit order. This issue has been addressed elsewhere (for example by Voorspools et al., 2000). 5.2. Application of the results: specific suggestions and practical rules Ideally, we would determine carbon savings by modelling the interaction of demand and supply— including both the direct and indirect effects—for each scenario of interest and compare the resulting carbon emissions. This is possible in principle, but the time and
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End of life of measure
Measure implemented
Electricity saving measure
Carbon impact
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Measure proposed
Measure in use
Indirect (may vary from year to year as plant mix changes)
Direct (varies with year)
Assesment of impact of measure
New plant construction
System Planning
Revised Plant Construction or Retiral Schedule
Impact of measure becomes apparent
Operation
Changed power plant mix comes into use
Time
Fig. 4. Timing of impacts.
effort required make it impractical for many applications. For practical purposes, we need simplified rules. As summarised in Fig. 4, the life-time carbon savings from a measure are a mixture of the direct effect (over some initial period and, in principle, calculated to include exogenous changes to the generation mix during this period), followed by a possible indirect effect lasting until the end of the measure’s life. The duration of the initial period and the importance of the indirect impact vary from case to case and are difficult to estimate reliably. Following discussion with potential UK users of this analysis, we suggest that, for initial assessments, a fixed combination of carbon intensities relating to direct and indirect impacts be used. For simplicity we (arbitrarily) propose the use of an incremental intensity value that is a 50:50 combination of the direct and indirect effect. (For measures that are clearly reversible, the indirect effect should be ignored and only the direct effect considered—the life of such measures will be relatively short.) For England and Wales we take CCGT plant to be the displaced (or delayed) plant for indirect impact. This
reflects the EP68 projections (Department of Trade and Industry. 2000) in which the majority of the variation in generating capacity relates to the timing of the introduction of new CCGT plant. Implicitly, this assumes that changes in demand do not alter retirement times of coal plant. 5.3. Application of the results: other markets We think it likely that the methods we have used and the issues that we raise will apply to other markets. However, the structure of electricity demand and the generation mix can vary greatly and it is highly unlikely that our numerical conclusions apply elsewhere.22 22 It is easy, for example, to find electricity systems with high-carbon coal or lignite base generation and lower-carbon shoulder plant. There are also systems with low-carbon nuclear or hydro-electric base-load and fossil-fuel peak or shoulder plant. Systems with large components of hydro-electric capacity also have additional operating constraints: run-of-river plant availability is usually seasonal, and seasonally stored water can only be used once.
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5.4. Unanswered questions These results are a series of snapshots based on either the operation of the generation system in a particular year or on hypothetical alternative generation mixes. We are conscious that this leaves a number of questions unanswered:
How much variation is there from year to year? J What causes it? J How representative are the data that we have used? J What would be reasonable short-term representative values? What would the impact of emissions trading or carbon taxes be?
These are all issues we would like to explore. In the absence of having funding for this, we comment below on them in general terms. 5.4.1. Causes of variations There are several reasons why the figures can be expected to change, both from year to year and over longer periods. In the short-term, the operation of the generation system may change because of:
fuel price volatility changes in generation plant availability end-use market growth, shrinkage or structural change weather (primarily influencing demand, occasionally generation). In the longer-term:
generation plant will be retired and new plant constructed the overall demand shape will alter (in the extreme, the system might become summer-peaking, for example) the generation market structure may change fuel prices and availability may change new technologies may appear the introduction of carbon taxes or emission trading.
We have reported in Section 4 on sensitivity to changes in generation mix, and to some extent to fuel prices (the CL and CH scenarios differ in their fuel price assumptions). The remaining issues are discussed below. 5.4.2. Changes since the data were collected Since this analysis was carried out, there have been two significant changes: the relative prices of gas and
coal has altered, and the there have been structural changes to the generation market.
At the time that the data used in this analysis were collected, the electricity generation market in England and Wales operated as a pool with a system marginal price. Bidding by generators produced a market-clearing price for each half-hour period, which was paid to each generator called to operate. Additional availability payments were also made.23 This has now been replaced first by NETA (New Electricity Trading Arrangements) and BETTA (British Electricity Trading and Transmission Arrangements) which integrates Scotland into the same market. Both of these systems are based on bilateral contracts with separate dispatch priority and system balancing mechanisms.
The important question for this analysis is the extent to which these changes have altered the dispatching of power generation plant. In an economically perfect market, there would be a well-defined merit order based on short-term marginal costs. In principle, each of the actual market structures is likely to have departed from this ideal to some extent. In this study, we have only examined system operation for the pool market—and then only for 1 year. Under NETA, the operation of generating plants is far less transparent. Superficially the new arrangements do not seem to have caused major changes in dispatch but we do not have analysis to support this impression. Gas prices in the UK have risen significantly since the period covered by the analysis, which has led to an increase in the use of coal. Expectations are that this price rise will decline somewhat as transmission bottlenecks in gas supply are cleared. Some idea of the likely impact of price changes can be gauged by comparing the results for the EP68 CL and CH scenarios (the CL and CH scenarios reflect different fuel price assumptions), though it would be preferable to examine the actual effects empirically. 5.4.3. Emissions trading and carbon taxes Carbon taxes would alter relative fuel prices so as to discourage the use of high-carbon fuels. In a pool-based market (as England and Wales used to have) the inevitable increase in the system marginal price would give windfall profits to lower-carbon generators. In an efficient bilateral-contract market, competition between low-carbon producers should remove this. The impact of emissions trading is more complicated and depends critically on the allocation method for permits. The UK National Allocation Plan for the EU 23 Therefore there is inherent producer surplus for the non-pricesetting generators that are dispatched.
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Emissions Trading System (Department for the Environment and Rural Affairs, 2004) uses a ‘‘grandfathering’’ approach (at least for the first trading period) under which allocations are based on previous emissions. This means that high-carbon plant will receive more permits and the immediate impact on merit order should be minimal.24 If allocation were to be based on nominal generating capacity irrespective of fuel or technology, this would have a similar impact to a carbon tax. IEA analysis (Reinaud, 2003)—based on an apparent assumption that permits will be allocated on the basis of capacity—suggests that, for Europe as a whole, the short-run marginal cost of gas CCGT plant becomes less than coal-fired steam plant at a carbon price of around $19 per tonne of CO2. However, this value is very sensitive to efficiency and fuel price assumptions.
6. Conclusions and recommendations
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reduction, we were not able to extract any general rules to describe these. We conclude that an average incremental emission factor, not dependent on end-use, would be appropriate for initial estimates of carbon savings arising from reduced electricity demand. From our analysis, we suggest the value 0.180 kgC/kWh currently, and falling to 0.137 kgC/kWh in 2010 and 0.122 in kgC/kWh—in all cases assuming EP 68 generation mixes. The incremental emission factor only represents savings resulting from short-term impact: the generation mix is assumed to be unchanged. In practice, there are also longer-term impacts because aggregate change in demand influences the technology choice and timing of new (or retired) generation plant. We repeat our previous recommendation that emission savings should take account of both long- and short-term factors, for example using the procedure that we have proposed elsewhere (Market Transformation Programme, 2002). There is scope for further work in several respects. This analysis was based on data from the period before the New Electricity Trading Arrangements (NETA) was introduced into this market. Since the purpose of the change was to improve the operation of the generation market, it is possible that changes to the merit orders may have affected the incremental carbon emissions. It has been suggested—and the suggestion has merit— that the results should be summarised as table of incremental emission factor against demand for each half-hour of each of the eight types of period considered. This would enable the impact of any savings profile to be assessed relatively simply. There is clearly also scope for considering additional and more detailed end use patterns and alternative generation scenarios.25 We are currently seeking funding to pursue these extensions.
A revised model for determining the carbon emissions associated with reductions in electricity demand has been developed. This is based on more complete data than previously, and thus gives a better indication of current emission factors. The revised model also captures the differences in generation between the seasons and between weekends and weekdays by using an implied merit order based on the plant average plant load in each period. Utilising data for all plant operating for each half hourly period (rather than just the marginal plant as was the case with the earlier model) also enables the effectiveness of this modelling approach to be confirmed by demonstrating its ability to estimate system emission factors for various levels of demand. Although used here to explore a range of single enduse saving scenarios, the model could also be used to assess carbon emission implications of packages of policy actions that impact on a range of end-uses. The modelling showed that, for a range of end use profiles and reduction levels (0.5–5% of annual electricity demand) the incremental emission factors were generally around 50% higher than the commonly used corresponding system average emission factor. (However, this difference is likely to reduce by 2020.) This is a significant difference and—notwithstanding the uncertainty about the exact values—we recommend that the carbon impact of policy options be assessed using incremental rather than system average carbon emission factors. Although there were variations of incremental emission factors between different end uses and scales of
Department for the Environment and Rural Affairs, 2004. http:// www.defra.gov.uk/environment/climatechange/trading/eu/nap/ allocation.htm.
24 There may be occasions when the expected price of future permit sales or purchases influences an operating decision, but these seem likely to be rare.
25 In particular the assumption that the merit order remains the same in future scenarios should be explored and, if it is found to be flawed, alternative assumptions be developed.
Acknowledgements This work was supported by the Department of the Environment, Food and Rural Affairs and by The Carbon Trust. The opinions and any errors are the authors’. References
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