For a generator, the term emissions factor refers to the ratio of its CO2 emissions over the
electricity it generates. The emissions factors are commonly expressed in kgCO2/kWh.
Additionally, the grid’s emissions factor refers to a group of generators, rather than a single one. These values are typically averaged over a range of generators. This study uses the term Average Emissions Factor (AEF) to refer to the ratio of the CO2 emissions of the
grid’s entire generation fleet over its electrical output. Additionally, this study uses the
term Marginal Emissions Factor (MEF) to refer to the ratio of the CO2 emissions of the
grid’s marginal generators over the their electricity generation.
To indicate the difference between the marginal and average emissions factors, Figure 4.2 shows a representative load duration curve of a grid. It is assumed that a peak shaving intervention is implemented where the grid’s net demand, at a given time, reduce from 𝑃𝑒𝑙𝑛𝑑(𝑞, 1) to 𝑃𝑒𝑙𝑛𝑑(𝑞, 2). In this case, the cumulative installed capacity of base-load and mid-merit generation will be sufficient to meet 𝑃𝑑𝑛𝑑(𝑞, 2). Based on this, the emissions
factor of the displaced electricity is the emissions factor of the mid-merit plants. These plants become the marginal plants after the peak shaving intervention. The average emissions factor, on the contrary, allocates the demand reduction to all of the generating parties.
Figure 4.2 Difference between the average and marginal emissions factors
The difference between AEF and MEF had been highlighted in in the AM12 and CP1 document – the guides designated to CHP and heat network applications in the UK [5][6]. These documents highlight that the use of MEF is a better representative of the actual emissions savings, when the analysis intends to assess the CO2 savings from a given
intervention – such as heat pumps or small-scale cogeneration systems. The AM12 and CP1 documents further note that the difference between the AEF and MEF are likely to
grow due to the increasing installed capacity of the grid-delivered, zero carbon electricity. It is possible to find these statements in the academic literature as well [90–94].
4.5. Literature review
The methods to estimate the MEF differs based on their applications, namely, short-run and long-run. The short-run emissions factor is typically calculated assuming negligible structural changes to the grid [91, 92]. Here, the term structural change refers to significant changes to the grid’s net demand and/or generation portfolio. In terms of net demand, structural change may refer to the large-scale adoption of heat pumps, or electric vehicles. In terms of generation, an example of the structural change is the phase out of coal plants. In other words, the short-run emissions factor assumes small changes over the incumbent electricity grid. On the contrary, the term long-run emissions factor is calculated where the change in the electricity demand and supply of the grid are explicitly taken into the account [92].
Considering the small structural change, a good source of estimating the short-run MEF is the grid’s historic electricity demand and supply. Due to its reliance on actual data, this approach of calculating the short-run MEF is commonly referred to as the empirical approach [93]. In this approach, the dispatch data of the electricity system is used where the generation fleet is commonly classified based on the generation type. The method developed based on the empirical approach in [93] calculates the short-run MEF as the ratio of the change in the grid’s CO2 emissions over the change in the grid’s net demand.
Considering the half-hourly resolution of grid’s demand and dispatch data, the empirical approach is an temporally explicit [95]. Due to this, it is possible to observe various trends in the short-run MEFs of the grid.
There are multiple studies in the literature which used the empirical approach to calculate the short-run MEF. Hawkes estimated the short-run MEF of the GB using the dispatch data from 2002 to 2009 [93]. He estimated the average MEF, for the stated interval, to be equal to 0.69 kgCO2⁄kWh. Additionally, he disaggregated the dispatch data according to the overall net demand, time of the day, time of the year, and the generation year itself. He highlighted that in general CO2 intensive generation types – such as coal – are likely to be on the margin for lower- or mid-merit system demand levels. Additionally,
he stated the importance of a temporally explicit method by stating the high variability of the short-run MEF from one settlement period to another.
Hawkes further analysed the CO2 reduction of various demand-side interventions. These
interventions consisted of four residential heating interventions, namely, which heat pumps (with and without electric back-up) and FTL- and FEL-operated Stirling micro- CHP units. He stated that the variation in the CO2 intensity of electricity displaced by
different interventions were insignificant. This study uses the term CO2 intensity to refer
to the resulting CO2 emissions of the electricity up to the point of its consumption. This
study calculates the CO2 intensity of the electricity delivered by the grid as the sum of its
MEF and the transmission and distribution losses. The term CO2 intensity is frequently
used in Chapter 5, where the CO2 emissions displaced by the cogeneration systems are
evaluated.
The empirical approach has been adopted in the case of other electricity systems as well. McKenna et al. [95] used the dispatch data of large generators from the all-island Ireland from the beginning of 2008 until the end of 2012. They estimated that the short-run MEF of the stated grid was found to be equal to 0.54 kgCO2⁄kWh. Siler-Evans et al. [90] investigated the trends of the short-run MEFs over the electricity generation mix in the United States. In this paper, they did a spatial analysis by dividing the electricity generation mix of the United States to eight independent regions – without considering the imports and exports between regions. They found that the short-run MEF for the regions with higher shares of coal-generated electricity tend to experience higher inter- seasonal variation. Similar to Hawkes’s study, these studies highlighted the importance of temporally explicit approach when estimating the short-run MEFs[90, 91].
An inherent advantage of the empirical approach is its ability to account for various constraints and drivers which are embedded in the grid’s operation. The fact that dispatch data contains all the operational constraints (e.g. ramp-rate, start-up duration) and the logistical limitations (e.g. transmissions line capacity, planned or unplanned maintenance), makes it ideal in terms of capturing the details of plant operations. The disadvantage of empirical approach is that it is not ideal to conduct a long-run analyses [96].
The long-run analysis of the grid involves some sorts of energy system modelling [92]. In broad terms the estimation of long-run MEFs consists of four steps: to estimate the net demand in the future; to constitute the merit order stack from the available generation portfolio; to determine the marginal generation; to calculate the marginal emissions factor. In addition to the short-run analysis, Hawkes estimated the long-run MEFs of the GB’s grid in [92]. In this paper, he used an optimisation tool to minimise the cost of meeting system’s demand, for a given time in the future. Then, he constructed the merit order based on marginal cost. The marginal generation mix was then identified based on the merit order and the estimated net demand. Finally, he calculated the emissions factors of the marginal generation mix of the GB’s grid in the future. He found that the average long-run MEFs vary between 0.26 and 0.53kgCO2/kWh, for the interval between
2014 and 2024. In addition to this, his analysis estimated that by 2035 the long-run MEF approaches zero.