Integrating combined heat and power in index decomposition analysis of the power sector

Abstract

Different technological and policy solutions have been developed to decarbonise energy systems and improve energy efficiency. Combined heat and power (CHP) is one solution that can bring about emission reductions by improving fuel use efficiency through the simultaneous generation of electricity and heat. Index decomposition analysis (IDA) is a tool that has been widely used to study emission trends and quantify the contribution of different measures to emission reductions. Techniques to estimate the emission reductions from CHP using IDA, however, have yet to be developed. This study develops new decomposition frameworks for two methods that have been used to estimate the carbon intensities of CHP and compares their performance theoretically and empirically using the data of Canada, Denmark and Finland. Based on the analysis, the substitution method is recommended as it can be more universally applied to different energy systems. Through the substitution method, the impact of different climate mitigation measures such as power generation efficiency and CHP can be compared within a single IDA framework.

Appendices

Appendix A

With reference to Eq. (3) and from Ang (Ang, 2015), the additive LMDI-I decomposition formulae for a temporal decomposition analysis of the electricity sector are as follows:

$$\Delta {C}_{{G}_{e}}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{G}_{e}^{T}}{{G}_{e}^{0}})$$

(10)

$$\Delta {C}_{p}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{p}^{T}}{{p}^{0}})$$

(11)

$$\Delta {C}_{m}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{m}_{i}^{T}}{{m}_{i}^{0}})$$

(12)

$$\Delta {C}_{u}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{u}_{i}^{T}}{{u}_{i}^{0}})$$

(13)

$$\Delta {C}_{e}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{e}_{i}^{T}}{{e}_{i}^{0}})$$

(14)

where $L\left(x,y\right)=\frac{x-y}{1\mathrm{n}x-1\mathrm{n}\mathrm{y}}$ for $x\ne y$, and $L\left(x,y\right)=x$ for $x=y$. Similarly, for the heat sector with reference to Eq. (5),

$$\Delta {C}_{{G}_{h}}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{G}_{h}^{T}}{{G}_{h}^{0}})$$

(15)

$$\Delta {C}_{q}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{q}^{T}}{{q}^{0}})$$

(16)

$$\Delta {C}_{w}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{w}_{i}^{T}}{{w}_{i}^{0}})$$

(17)

$$\Delta {C}_{v}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{v}_{i}^{T}}{{v}_{i}^{0}})$$

(18)

$$\Delta {C}_{e}={\sum }_{i}L\left({C}_{i}^{T},{C}_{i}^{0}\right)\mathrm{l}\mathrm{n}(\frac{{e}_{i}^{T}}{{e}_{i}^{0}})$$

(19)

Table

Table 5 Data used for decomposition of CO₂ emissions from power sectors of Canada, Denmark and Finland

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Appendix B

Analysts may encounter other challenges in decomposition studies that incorporate CHP. Some of these challenges are the inclusion of CHP in economy-wide energy efficiency analysis, treatment of transmission and distribution losses, dealing with biomass CHP systems and the presence of many zero values in the data set. These issues and possible solutions or points to note, are described.

Inclusion of CHP in economy-wide energy efficiency analysis

Besides CO₂ emissions, there is also an interest in quantifying the impact of CHP on a country’s economy-wide energy efficiency. In particular, a number of national agencies (European Commission Joint Research Centre, 2017; Office of Energy Efficiency, 2016) and researchers (Bashmakov & Myshak, 2014; Torrie et al., 2018) have developed economy-wide energy efficiency accounting systems (EEAS) which study the savings from energy efficiency via IDA. In an IDA study of energy use, the energy consumed by the energy sector is usually taken as the losses from the energy transformation process (Goh & Ang, 2019a).

To apply the power loss factor method to an economy-wide EEAS, the same principle of allocation (i.e. all energy consumed by CHP are allocated to electricity generation) used for CO₂ emissions is applied. This means that all energy losses from CHP are allocated to the electricity sector. The resulting IDA identity is:

$${E}_{e}={G}_{e}\frac{O}{{G}_{e}}{\sum }_{ij}\frac{{Q}_{i}}{O}\frac{{Q}_{ij}}{{Q}_{i}}\frac{{E}_{ij}}{{Q}_{ij}+{sH}_{ij}}{K}_{ij}$$

(20)

where $O$ is the total electricity output from fossil fuels, biomass, geothermal and nuclear energy, and $E$ and ${E}_{ij}$ are the total energy losses from electricity and CHP plants, and energy losses from plant $j$. Note that in this case, biomass, geothermal and nuclear energy are classified as energy source $i$ together with fossil fuels as they experience losses in the energy transformation process. This treatment of the energy sector is slightly different from that for CO₂ emissions; for more details on the differences in the treatment of energy losses and CO₂ emissions from the energy sector in IDA studies, see Goh and Ang (2019a). For energy losses from the production of heat, the identity is very similar to that for CO₂ emissions. The CO₂ emissions are replaced by the energy losses due to the generation of heat in the identity in Eq. (7). CHP is assumed to have no energy losses.

Likewise, for the substitution method, the principle of assigning heat output from CHP a reference value of ${r}_{i}$ for energy source $i$ is applied. This means that the energy losses from the generation of electricity via CHP is the difference between the energy losses from CHP and the energy losses from heat generation from CHP based on this reference value. The corresponding identities for energy consumption which are based on energy losses are as follows:

$${E}_{e}={G}_{e}\frac{O}{{G}_{e}}{\sum }_{ij}\frac{{Q}_{i}}{O}\frac{{Q}_{ij}}{{Q}_{i}}\frac{{E}_{ij}{-H}_{ij}(1-{r}_{i})/{r}_{i}}{{Q}_{ij}}$$

(21)

$${E}_{h}={G}_{h}\frac{H}{{G}_{h}}{\sum }_{ik}\frac{{H}_{i}}{H}\frac{{H}_{ik}}{{H}_{i}}\frac{{E}_{ik,h}}{{H}_{ik}}$$

(22)

where ${E}_{e}$ and ${E}_{h}$ are the total energy losses from electricity and heat generation, ${H}_{ik}$ and ${E}_{ik,h}$ are the heat output and energy losses from plant $k$ and $\frac{{E}_{ik,h}}{{H}_{ik}}=(1-{r}_{i})/{r}_{i}$ for CHP.$\frac{{H}_{ik}}{{H}_{i}}$ is the plant share effect. Note that if ${r}_{i}$ is 1 (i.e. 100% efficiency or no conversion losses), Eq. (21) can be considered to be the same as Eq. (20) where the intensity and CHP adjustment factor are multiplied to obtain the intensity $\frac{{E}_{ij}}{{Q}_{ij}}$.

Transmission and distribution losses

CHP systems in industry and in distributed systems are usually located nearer to consumption centres in comparison to centralised power generation plants. This means that CHP can also reduce energy losses and emissions associated with transmission and distribution losses. It is desirable to quantify this impact of CHP. However, it can only be quantified in the study of energy consumption, but not in a study of CO₂ emissions as the impact of transmission and distribution losses is aggregated by end-use energy consumption. This is a drawback of the proposed approach.

Biomass CHP systems

Biomass CHP systems pose some unique problems for decomposition analysis. They have been used in countries such as Denmark and Finland (IEA, 2018c), and in decarbonisation scenarios (Agnolucci et al., 2009; Mishra et al., 2014). Biomass is considered to be a zero-emissions source in CO₂ emission decomposition but in energy analysis, it is not considered a source with 100% efficiency in electricity generation. This means that the combustion of biomass for electricity production results in energy losses. For CO₂ emissions, the adoption of biomass CHP is captured by the fossil share effect. There is no differentiation between the adoption of biomass CHP and regular biomass generation plants. For energy consumption, biomass is classified as a type of fuel $i$ and therefore the impact of CHP is captured in the same way as other fossil fuel CHP plants.

Different CHP systems

In countries with extensive usage of CHP systems, it may be useful to make comparisons between different CHP systems. The decomposition framework can be extended to compare the efficiencies of different CHP systems through the inclusion of an additional level of disaggregation $l$, where $l$ is the type of CHP system for a particular fossil fuel. More granular data by CHP system will be required. This form of analysis is suitable for in-depth studies of changes in emissions of a single country over time where it is important to attribute emission reductions to specific CHP systems or technologies. Such an extension can be performed when the emission reductions attributed to CHP are very large and more analysis is required to reveal more refined decomposition results.

Zero values in the data set

An issue that analysts may encounter in the decomposition of electricity and heat output from CHP is the presence of many zero values in the data set. This problem arises because there are more levels of disaggregation required in a decomposition study involving CHP in comparison to a conventional decomposition of the power sector and not all countries have CHP systems for each fuel type studied.

Zero values are an issue only when they change to or from a non-zero value over time or space. For instance, countries may rely entirely on CHP or electricity or heat plants for a particular fuel type but may adopt or decommission these plants in a later year, especially if the decomposition is conducted over a very long time period. Such zero value changes are also likely to be more pronounced in cross-country comparisons as countries are unlikely to have similar generation systems.

Zero values may generate results that are very different between the two methods as LMDI assigns the entire change to the structure effect that corresponds to the highest level of analysis that the zero value to non-zero value occurs. This means that for the substitution method, zero values can result in the allocation of the entire change in emissions to the plant share effect, the mix effect or the activity effect based on both the small value and analytical limit approaches (Ang & Liu, 2007). As the substitution and power loss factor methods have different levels of disaggregation, when a data set contains too many zero values, the aggregate results may exhibit significant differences. A way to reduce the number of zero values that change to a non-zero value is to compute changes annually in the decomposition study of a single country over time such that gradual changes will be captured in the decomposition, resulting in fewer zero values.

Appendix C

Table

Table 6 CHP intensities of countries with available data from IEA world energy balances between 1990 and 2014

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