**4. GHG emission factor methodologies**

296 Sustainable Growth and Applications in Renewable Energy Sources

software (Gordon & Fung, 2009). The use of regionally specific climate-modeled factors, such as those identified, allowed for a more accurate representation of the benefits associated with GHG reducing technologies, such as photovoltaic, wind, etc. This chapter will demonstrate that using Time Dependent Valuation (TDV) emission factors provide an upper limit while using hourly emission factors provide a lower limit. These factors based on hour-by-hour electricity demand data for the Province of Ontario will provide renewable technology researchers with the tools necessary to make informative decisions concerning

**2. Traditional methodologies to estimate GHG emission factors from the** 

There are two main methods to estimate pollutant and GHG emission Factors from the electricity generation sector: 1) direct measurement or 2) estimation. Direct measurement is considered to be the most accurate since it uses real-time data from the generation sector. However, these data are not readily available and historically, GHG emissions have been estimated from fossil fuel and process-related activities. Estimation is the method used by several countries when preparing their national GHG inventories (ICPP, 1997). In the past, GHG emissions from the electricity generation sector were calculated using the Average GHG Intensity Factor (GHGIFA) (Guler et al., 2008). The GHGIFA is the amount of GHG emissions per kWh electricity produced. This method assumes that the reduction in electricity demand is uniformly distributed amongst all types of electricity generation. For example, the GHGIFA estimated in 1993 was 136 g/kWh for the Province of Ontario. Table 1 shows the GHGIFA values for the years 2004, 2005, and 2006 for the Province of Ontario

> **Annual GHGIFA (g of CO2/kWh) 2004 2005 2006**  200 221 189

The combustion of fossil fuels produces several major greenhouse gases. The amount of emissions from CO2, CH4, SO2, NO, and N2O varies from one fuel to another, and they are calculated using emission factors. These emission factors are commonly expressed in tons of

It is necessary to develop methodology to accurately estimate GHG emissions from the electricity generation sector in order to facilitate the implementation of awareness programmes and renewable technologies which are supported with information on current energy usage. It should be noted that the time of use of electricity is related to GHG emissions generated throughout the day (MacCracken, 2006). Therefore, prior to implementing these programmes and renewable technologies, it is necessary to have an

The Province of Ontario has a very unique mix of electricity production technologies. Hydro and nuclear technologies are generally considered to be base load power (IESO, 2006), since

CO2 per MWh or grams per kWh of electricity produced (Gordon & Fung, 2009).

the selection of renewable technologies.

from the electricity generation sector (EnvCan, 2006).

**electricity generation sector** 

Table 1. Annual Emission Factors

**3. Accuracy of GHG emission factors** 

accurate model for emission and electricity estimation.

Renewable technologies (solar and wind) have become an accepted form of generating electricity and heat in the Province of Ontario. There are many advantages in using solar and wind energy such as taking advantage of an abundant source of free energy (sun and wind), as well as being an effective method in reducing GHG emissions. However, the electricity produced by a renewable technology, such as a photovoltaic (PV), or micro-wind turbine and the availability of solar and wind energy, changes throughout the day. Therefore, an hourly GHG emission factor is needed to truly understand the impact that renewable technologies have on emissions since there is a divergence between when electricity can be generated and when it is required.

Some of these renewable technologies that are being used in the residential and commercial sectors include photovoltaic, micro-wind turbines, ground source heat pumps, and advance solar thermal technologies. Continuous improvement of these technolgies have promoted the development of hybrid homes. The combination of several of these technologies together will result in end-use energy savings and GHG emission reductions. However, prior to implementing any of these technologies, it is necessary to have an accurate estimation of the true reduction potential of GHG emission factors in order to have a clear understanding of the saving potentials associated with renewable technologies.

Currently, Environment Canada uses fuel consumption data from the electricity sector in order to estimate emissions. However, this method can be simplistic and time consuming as well as difficult to use due to the unavailability of certain types of data. Moreover, this method only provides an annual average emission factor which does not reflect the cyclic behaviour of emission factors throughout the day. In 2005, Time Dependent Valuation (TDV) was introduced as a viable method to provide the aformentioned data (MacCracken, 2006). This method was adopted by California as an energy efficient standard for residential and non-residential buildings. Time dependent valuation views energy demand differently depending on the time of use (MacCracken, 2006). California has been able to determine the societal impacts of time of use energy consumption. As a result, this method of analysis would allow for a more accurate representation of the potential reduction of GHGs by using renewable technologies.

This following sections will discuss existing emission factor methodolgy and introduce monthly TDV emission factor methodology.

Analysis of Time Dependent Valuation of Emission Factors from the Electricity Sector 299

Currently, there are several TDV profiles (annual and seasonal) for greenhouse gases for the Province of Ontario in the public domain (Gordon & Fung, 2009). As discussed in Gordon & Fung (2009), the hourly GHG emissions data has been compiled to developed different types (annual and seasonal) of emission factors. The latter has shown that emission factors vary with electricity demand (MacCracken, 2006). It has also been observed that shape and magnitude of GHGIF profiles varies with time of day, year, climate, and geographical location (Gordon & Fung, 2009). Hourly emission data does exist from the power generating sector, but is not publicly available. Therefore, rather than using a single annual GHGIF value for the entire year, seasonal GHGIF profiles based on the electricity demand for the Province of Ontario were developed by Gordon &

The approach detailed below was used in order to provide a better method to properly estimate greenhouse gases within the Province of Ontario. Hourly electricity consumption data from the IESO and hourly GHG emission factors estimated in the previous section were used to determine Seasonal TDV emission factor profiles for the years 2004, 2005, and 2006.

Seasonal TDV NGHGIFA = Seasonal Time Dependent Valuation New Greenhouse Gas

The hourly and averaged values obtained for the seasonal TDV NGHGIFA were compared

Currently, there are several TDV profiles (annual and seasonal) for greenhouse gases for the Province of Ontario in the public domain (Gordon & Fung, 2009). However, monthly GHG emission factors are not available. Therefore, this section will provide renewable technology

The approach detailed below was used in order to provide a better method to properly estimate greenhouse gases within the Province of Ontario. Hourly electricity consumption data from the IESO and hourly GHG emission factors estimated in Section 4.1 were used to determine monthly TDV NGHGIF profiles for the years 2004, 2005, and 2006. These profiles

Monthly TDV NGHGIF <sup>N</sup>

1

i A

 

A j

NGHGIF (h )

N

1

i A

 

A j

(4)

(5)

NGHGIF (h )

N

N

These profiles were calculated using Equation 4 (Gordon & Fung, 2009).

Seasonal TDV NGHGIF

**4.3 Monthly time dependent valuation emission factors** 

were calculated using Equation 5 for each hour in a day.

professionals with monthly TDV profiles for estimating emissions.

**4.2 Seasonal time dependent valuation emission factors** 

Fung (2009).

Where,

i = day number j = hour number

Intensity Factor (g CO<sup>2</sup> /kWh) N = number of days in the season

for the years 2004, 2005, and 2006.

### **4.1 Hourly GHG emission factors**

Different emission factors have been developed in the past: hourly, seasonal, and seasonal time dependent emission factors (Gordon & Fung, 2009). This chapter will introduce monthly TDV emission factors and compare them to existing emission factors. GHG emissions from the electricity generation industry have been calculated using the Average GHG Intensity Factor (GHGIFA) (Guler et al., 2008). This value represents the amount of GHG emissions produced as a result of generating one kWh of electricity. The GHGIFA for 2004, 2005, and 2006 were estimated using the methodology mentioned above in conjunction with the electricity output information from Gordon & Fung (2009). It should be noted that the emission factor for CO2 does not take into consideration CH4 and N2O since these are considered to represent negligible amounts in comparison to CO2, SO2, and NO (Gordon & Fung, 2009). This section will only focus on CO2 emissions since the majority of pollutants are in this form and the purpose of this chapter is to demonstrate emission factor methodology.

The GHG emissions due to coal fired and natural gas plants were determined using Equation 1 (Gordon & Fung, 2009).

$$\text{HCO}\_2\text{=(HCOAL)(i)+(HCOITHER)(j)}\tag{1}$$

Where,

HCO2 = Hourly CO2 production (kg)

HECOAL = Hourly Electricity generated by Coal plants

HEOTHER = Hourly Electricity generated by Other (natural gas, etc.)

i = CO2 emission factor (OPG, 2006)

j = Environment Canada natural gas emission factor (Environment Canada, 2006)

Currently, there is a hourly greenhouse gas emission factor (NHGHGIFA) model which is based on the hour-by-hour demand of electricity in Ontario from nuclear, fossil, hydro, natural gas and wind (Gordon & Fung, 2009). The NHGHGIFA was calculated by dividing the hour-byhour emissions from CO2 by the hour-by-hour total electricity generated from the different sources (Gordon & Fung, 2009). It should be noted that the new greenhouse gas intensity factor (NGHGIFA) was estimated by taking the average of the hourly emission factors for each season. The NGHGIFA was determined using Equations 2 and 3 (Gordon & Fung, 2009).

$$\text{NHGHIGIF}\_A = \frac{\text{HCO}\_2}{\text{HEGTOTAL}} \tag{2}$$

$$\text{NGHGHIF}\_{\text{A}} = \sum\_{i=1}^{8760} \frac{\text{NHGHGHIF}\_{\text{Ai}}}{8760} \tag{3}$$

Where,

NHGHGIFA= New Hourly Greenhouse Gas Intensity Factor (g CO<sup>2</sup> /kWh)

NGHGIFA = New Greenhouse Gas Intensity Factor (g CO<sup>2</sup> /kWh)

HCO2 = Hourly CO2 production (g)

HEGTOTAL= Hourly Electricity Generated Total (kWh)

i = hour

The values obtained for the NGHGIFA were compared for the years 2004, 2005, and 2006 (Gordon & Fung, 2009).

### **4.2 Seasonal time dependent valuation emission factors**

Currently, there are several TDV profiles (annual and seasonal) for greenhouse gases for the Province of Ontario in the public domain (Gordon & Fung, 2009). As discussed in Gordon & Fung (2009), the hourly GHG emissions data has been compiled to developed different types (annual and seasonal) of emission factors. The latter has shown that emission factors vary with electricity demand (MacCracken, 2006). It has also been observed that shape and magnitude of GHGIF profiles varies with time of day, year, climate, and geographical location (Gordon & Fung, 2009). Hourly emission data does exist from the power generating sector, but is not publicly available. Therefore, rather than using a single annual GHGIF value for the entire year, seasonal GHGIF profiles based on the electricity demand for the Province of Ontario were developed by Gordon & Fung (2009).

The approach detailed below was used in order to provide a better method to properly estimate greenhouse gases within the Province of Ontario. Hourly electricity consumption data from the IESO and hourly GHG emission factors estimated in the previous section were used to determine Seasonal TDV emission factor profiles for the years 2004, 2005, and 2006. These profiles were calculated using Equation 4 (Gordon & Fung, 2009).

$$\text{Sesasonal TDV NGHGIF}\_{\text{A}} = \frac{\sum\_{\text{i=l}}^{\text{N}} \text{NGHGIF}\_{\text{A}}(\text{h}\_{\text{j}})}{\text{N}} \tag{4}$$

Where,

298 Sustainable Growth and Applications in Renewable Energy Sources

Different emission factors have been developed in the past: hourly, seasonal, and seasonal time dependent emission factors (Gordon & Fung, 2009). This chapter will introduce monthly TDV emission factors and compare them to existing emission factors. GHG emissions from the electricity generation industry have been calculated using the Average GHG Intensity Factor (GHGIFA) (Guler et al., 2008). This value represents the amount of GHG emissions produced as a result of generating one kWh of electricity. The GHGIFA for 2004, 2005, and 2006 were estimated using the methodology mentioned above in conjunction with the electricity output information from Gordon & Fung (2009). It should be noted that the emission factor for CO2 does not take into consideration CH4 and N2O since these are considered to represent negligible amounts in comparison to CO2, SO2, and NO (Gordon & Fung, 2009). This section will only focus on CO2 emissions since the majority of pollutants are in this form and the

The GHG emissions due to coal fired and natural gas plants were determined using

HCO =(HECOAL)(i)+(HEOTHER)( <sup>2</sup> j) (1)

purpose of this chapter is to demonstrate emission factor methodology.

HEOTHER = Hourly Electricity generated by Other (natural gas, etc.)

j = Environment Canada natural gas emission factor (Environment Canada, 2006)

The NGHGIFA was determined using Equations 2 and 3 (Gordon & Fung, 2009).

A i NHGHGIF NGHGIF 

NHGHGIFA= New Hourly Greenhouse Gas Intensity Factor (g CO<sup>2</sup> /kWh)

NGHGIFA = New Greenhouse Gas Intensity Factor (g CO<sup>2</sup> /kWh)

HEGTOTAL= Hourly Electricity Generated Total (kWh)

A HCO NHGHGIF

8760

The values obtained for the NGHGIFA were compared for the years 2004, 2005, and 2006

<sup>1</sup> 8760

Currently, there is a hourly greenhouse gas emission factor (NHGHGIFA) model which is based on the hour-by-hour demand of electricity in Ontario from nuclear, fossil, hydro, natural gas and wind (Gordon & Fung, 2009). The NHGHGIFA was calculated by dividing the hour-byhour emissions from CO2 by the hour-by-hour total electricity generated from the different sources (Gordon & Fung, 2009). It should be noted that the new greenhouse gas intensity factor (NGHGIFA) was estimated by taking the average of the hourly emission factors for each season.

2

Ai

HEGTOTAL (2)

(3)

**4.1 Hourly GHG emission factors** 

Equation 1 (Gordon & Fung, 2009).

HCO2 = Hourly CO2 production (kg)

i = CO2 emission factor (OPG, 2006)

HCO2 = Hourly CO2 production (g)

HECOAL = Hourly Electricity generated by Coal plants

Where,

Where,

i = hour

(Gordon & Fung, 2009).

Seasonal TDV NGHGIFA = Seasonal Time Dependent Valuation New Greenhouse Gas Intensity Factor (g CO<sup>2</sup> /kWh)


The hourly and averaged values obtained for the seasonal TDV NGHGIFA were compared for the years 2004, 2005, and 2006.

### **4.3 Monthly time dependent valuation emission factors**

Currently, there are several TDV profiles (annual and seasonal) for greenhouse gases for the Province of Ontario in the public domain (Gordon & Fung, 2009). However, monthly GHG emission factors are not available. Therefore, this section will provide renewable technology professionals with monthly TDV profiles for estimating emissions.

The approach detailed below was used in order to provide a better method to properly estimate greenhouse gases within the Province of Ontario. Hourly electricity consumption data from the IESO and hourly GHG emission factors estimated in Section 4.1 were used to determine monthly TDV NGHGIF profiles for the years 2004, 2005, and 2006. These profiles were calculated using Equation 5 for each hour in a day.

$$\text{Monthly TDV NGHGIF}\_{\text{A}} = \frac{\sum\_{\text{i=l}}^{\text{N}} \text{NGHGIF}\_{\text{A}}(\text{h}\_{\text{j}})}{\text{N}} \tag{5}$$

Analysis of Time Dependent Valuation of Emission Factors from the Electricity Sector 301

Table A-1 in Appendix A shows the annual TDV emission factors (Gordon & Fung, 2009). It can be observed that emissions throughout the day vary considerably. It should be noted

Table 3 shows the annual average TDV GHG emission factors. These values were obtained

Annual 224.2 237.3 207.2

Seasonal TDV emission factors were also developed for the years 2004, 2005, and 2006 (Gordon & Fung, 2009) as shown in Table 4. Table A-2 and A-3 in Appendix A show the seasonal TDV emission factor profiles for 2004, 2005, and 2006. The following can be observed from Table 4: For the year 2004 – the highest emission factors were in the fall (afternoons) and winter

For the years 2005 and 2006 the highest emission factor was observed in the summer.

Winter 264.7 246.4 213.4 Spring 182.0 221.5 179.9 Summer 190.1 256.6 229.5 Fall 259.8 224.8 206.1

Monthly TDV emission factors were developed for the years 2004, 2005, and 2006 as shown in Table 5. Table A-4 in Appendix A shows the monthly TDV emission factor profiles for

For the year 2004 – the highest and lowest emission factor was observed in January and

For the year 2005 – the highest and lowest emission factor was observed in August and

For the year 2005 – the highest and lowest emission factor was observed in July and

This section discussed the different types of existing and new GHG emission factors for the years 2004, 2005, and 2006. The hourly emission factor proved to be the most accurate and

However, it is the user's responsability to select the appropiate emission factor depending on the type of analysis conducted. In certain cases it might be more practical to employ seasonal, time dependent valuation (seasonal or monthly), or annual average emission

**Season NGHGIFA (g of CO2/kWh)** 

**NGHGIFA (g of CO2/kWh) 2004 2005 2006** 

**2004 2005 2006** 

that the maximum TDV values for the years 2004, 2005, and 2006 occurred at 1 p.m.

by using the annual TDV GHG emission factors in Table A-1 in Appendix A.

**6.2 Annual time dependent valuation emission factors** 

Table 3. Annual average TDV GHG emission factors

Table 4. seasonal average TDV GHG emission factors

**6.4 Monthly time dependent valuation emission factors** 

2004, 2005, and 2006. The following can be observed from Table 4:

monthly TDV were more accurate than using the seasonal average value.

factors to estimate CO2 emissions without sacrificing much accuracy.

(early mornings).

May, respectively.

May, respectively.

April, respectively.

**6.3 Seasonal time dependent valuation emission factors** 

Where,

Monthly TDV NGHGIFA = Monthly Time Dependent Valuation New Greenhouse Gas Intensity Factor (g CO<sup>2</sup> /kWh)

N = number of days in the month

i = day number

j = hour number

The hourly and average values obtained for the monthly TDV NGHGIFA were compared for the years 2004, 2005, and 2006.
