This document describes the most commonly used plot types for visualising results from an OSeMOSYS model, based on the Indonesia OSeMOSYS model dashboard. For each plot type, the following aspects are covered:
- A text description of what the plot shows
- x-axis
- y-axis
- How are the other dimensions visualised (e.g. colours)
- Data sources - which OSeMOSYS results parameters are needed
- Aggregation/processing - how is the data aggregated or transformed before plotting?
An interactive example of some of this plots can be found in the Indonesia OSeMOSYS model dashboard.
Contents:
- Total final energy consumption (TFEC)
- TFEC CO2 emissions
- Electricity supply
- Electricity demand
- Cumulative power generation installed capacity
- Electricity CO2 emissions
- Electricity annual investment required
- Electricity annual discounted cost
- Renewable energy share
This plot shows the annual value of total final energy consumption including all type of fuels. It can be displayed as a line plot disaggregating the information by scenario, or as a stacked bar plot for a specific scenario, using final energy users (i.e. sectors) or energy source (e.g. electricity, oil, diesel, natural gas) as stack variable.
Years in ascending order (column y).
TFEC values expressed in a standard energy unit as PJ
(column UseByTechnologyAnnual).
Colors are used to disaggregate scenarios, sectors of energy use or energy
sources. Line patterns, point shapes or bar fill patterns may also be used to
provide more information as subscenarios. The columns scenario, t, f and
Sector can be used for this purpose (see table 1 and table 2).
The UseByTechnologyAnnual.csv file is used (see table 1) for these plots in
combination with a custom excel file (see table 2) that provide information
about the technologies that should be considered and the sectors each technology
belongs to (this is important as no all technologies should be displayed).
The .csv files for each scenario are added together and an extra column is
created to indicate the scenario. Table 2 is merged by technology name
t with the UseByTechnologyAnnual.csv results. The resulting dataframe is
grouped by year y and scenario or y and Sector and the
UseByTechnologyAnnual summed up. The final dataframe is used in a long format
to create the plot.
Table 1. UseByTechnologyAnnual.csv file sample.
| r | t | f | y | UseByTechnologyAnnual | scenario | |
|---|---|---|---|---|---|---|
| 0 | Region1 | BIO_RESPRD | BIO | 2018 | 0.0 | BAU |
| 1 | Region1 | BIO_RESPRD | BIO | 2019 | 0.0 | BAU |
| 2 | Region1 | BIO_RESPRD | BIO | 2020 | 0.0 | BAU |
| 3 | Region1 | BIO_RESPRD | BIO | 2021 | 0.0 | BAU |
| 4 | Region1 | BIO_RESPRD | BIO | 2022 | 0.0 | BAU |
Table 2. Total final energy consumption complementary excel file sample.
| t | Sector | Use | Fuel |
|---|---|---|---|
| RES_COOL_001 | Residential | Cooling | Electricity |
| RES_COOL_002 | Residential | Cooling new users | Electricity |
| RES_CWH_EL_001 | Residential | Cooking and water heating | Electricity |
| RES_EL_APP_001 | Residential | Electrical appliances | Electricity |
| RES_EL_APP_002 | Residential | Electrical appliances new users | Electricity |
| RES_CWH_BIO_001 | Residential | Cooking and water heating | Biomass |
| RES_CWH_KER_001 | Residential | Cooking and water heating | Kerosene |
| RES_CWH_SOLAR | Residential | Water heating mainly | Solar |
| RES_CWH_NGS_001 | Residential | Cooking and water heating | Natural Gas |
| RES_CWH_LPG_001 | Residential | Cooking and water heating | LPG |
| COM_EL_APP_001 | Commercial | Electricity appliances | Electricity |
| COM_CWH_BIO_001 | Commercial | Other uses | Biomass |
| COM_CWH_NGS_001 | Commercial | Other uses | Natural Gas |
The excel file could probably be omitted if naming conventions are used and a
mapping function is implemented to translate such names (from columns t and f)
into a more user friendly output and select the relevant technologies to display.
However, to have an user defined excel file will allow for the user to have full
control about the names.
This plot shows the value of CO2 emissions. It can be displayed as an annual emissions line plot disaggregating the information by scenario, as a total emissions bar plot per scenario, or as a stacked bar of emissions per fuel source for a specific scenario.
Years in ascending order (column y) or scenarios (column scenario).
Total CO2 emissions values expressed in Mton (column AnnualTechnologyEmission).
Colors are used to disaggregate scenarios or stack emissions values of
different fuel sources. The columns scenario or t can be used for this
purpose (see table 3).
The AnnualTechnologyEmission.csv file is used (see table 3) for these plots.
These .csv files for each scenario are added together and an extra column is
created to indicate the scenario. The resulting dataframe is
grouped by year y and scenario or y and technology t and the
AnnualTechnologyEmission summed up. The final dataframe is used in a long format
to create the plot.
Table 3. AnnualTechnologyEmission.csv file sample.
| r | t | e | y | AnnualTechnologyEmission | scenario | |
|---|---|---|---|---|---|---|
| 0 | Region1 | BIO_RESPRD | CH4 | 2018 | 0.0 | BAU |
| 1 | Region1 | BIO_RESPRD | CH4 | 2019 | 0.0 | BAU |
| 2 | Region1 | BIO_RESPRD | CH4 | 2020 | 0.0 | BAU |
| 3 | Region1 | BIO_RESPRD | CH4 | 2021 | 0.0 | BAU |
| 4 | Region1 | BIO_RESPRD | CH4 | 2022 | 0.0 | BAU |
The bar graph version of this plot, would require a function to translate the
standard names used in t, to an user friendly format. Moreover, if different
emission gasses are reported, then the column e could also be used for the
color attribute.
This plot shows the annual value of total electricity generation. It can be displayed as a line plot disaggregating the information by scenario, or as a stacked bar plot for a specific scenario, using energy sources as stack variable.
Years in ascending order (column y).
Total electricity generation values expressed in PJ (column ProductionByTechnologyAnnual).
Colors are used to disaggregate scenarios or stack generation values of
different energy sources. The columns scenario and Source can be used for this
purpose (see table 4).
The ProductionByTechnologyAnnual.csv file is used (see table 4) for these
plots in combination with a custom excel file (see table 5) that provides
information about the technologies that should be considered and the energy sources
each technology belongs to. The .csv files for each scenario are added together
and an extra column is created to indicate the scenario. Table 5 is merged
by technology name t with the combined ProductionByTechnologyAnnual.csv
results for all scenarios. The resulting dataframe is
grouped by year y and scenario or y and Source and the
ProductionByTechnologyAnnual summed up. The final dataframe is used in a long format
to create the plot.
Table 4. ProductionByTechnologyAnnual.csv file sample.
| r | t | f | y | ProductionByTechnologyAnnual | scenario | |
|---|---|---|---|---|---|---|
| 0 | Region1 | BIO_RESPRD | BIO | 2018 | 0.0 | BAU |
| 1 | Region1 | BIO_RESPRD | BIO | 2019 | 0.0 | BAU |
| 2 | Region1 | BIO_RESPRD | BIO | 2020 | 0.0 | BAU |
| 3 | Region1 | BIO_RESPRD | BIO | 2021 | 0.0 | BAU |
| 4 | Region1 | BIO_RESPRD | BIO | 2022 | 0.0 | BAU |
Table 5. Energy generation complementary excel file sample.
| t | Use | Source | Type | EmissionActivityRatio |
|---|---|---|---|---|
| PWR_COA_001 | Centralised electricity supply | Coal | Fossil fuel | 0.0961 |
| PWR_NGS_001 | Centralised electricity supply | Natural Gas | Fossil fuel | 0.0561 |
| PWR_OILPRD_001 | Centralised electricity supply | Fuel oil | Fossil fuel | 0.0741 |
| PWR_BIO_001 | Centralised electricity supply | Biomass | Renewable | 0.1225 |
The excel file could probably be omitted if naming conventions are used and a
mapping function is implemented to translate such names (from columns t and f)
into a more user friendly output and select the relevant technologies to display.
This plot shows the annual value of total electricity demand. It can be displayed as a line plot disaggregating the information by scenario, or as a stacked bar plot for a specific scenario, using final energy users (i.e. sectors) as stack variable.
Years in ascending order (column y).
Total electricity demand values expressed in PJ (column UseByTechnologyAnnual).
Colors are used to disaggregate scenarios or stack demand values of
different demand sectors. The columns scenario or Sector can be used for this
purpose (see table 1 and table 6).
The UseByTechnologyAnnual.csv file is used (see table 1) for these
plots in combination with a custom excel file (see table 6) that provides
information about the technologies that should be considered and the demand
sectors. The .csv files for each scenario are added together and an extra
column is created to indicate the scenario. Table 6 is merged by technology
name t with the combined UseByTechnologyAnnual.csv results for all scenarios.
The resulting dataframe is
grouped by year y and scenario or y and Sector and the
UseByTechnologyAnnual summed up. The final dataframe is used in a long format
to create the plot.
Table 6. Energy demand complementary excel file sample.
| t | Sector | Use | Fuel | |
|---|---|---|---|---|
| 0 | RES_COOL_001 | Residential | Cooling | Electricity |
| 1 | RES_COOL_002 | Residential | Cooling new users | Electricity |
| 2 | RES_CWH_EL_001 | Residential | Cooking and water heating | Electricity |
| 3 | RES_EL_APP_001 | Residential | Residential appliances | Electricity |
| 4 | RES_EL_APP_002 | Residential | Residential appliances new users | Electricity |
| 5 | COM_EL_APP_001 | Commercial | Commercial uses | Electricity |
| 6 | TRA_BUS_ELC_001 | Transport | Buses | Electricity |
The excel file could probably be omitted if naming conventions are used and a
mapping function is implemented to translate such names (from columns t)
into a more user friendly output and select the relevant technologies to display.
This plot shows the total installed capacity per power generation technology for one scenario at a time.
Years in ascending order (column y).
Total installed capacity expressed in GW (column TotalCapacityAnnual).
Colors are used to stack total installed capacity of each technology. The
column Source is used for this purpose (see table 5).
The TotalCapacityAnnual.csv file is used (see table 7) for these
plots in combination with a custom excel file (see table 5) that provides
information about the technologies that should be considered and the power
generation sources. The .csv files for each scenario are added together and an extra
column is created to indicate the scenario. Table 5 is merged by technology
name t with the combined TotalCapacityAnnual.csv results for all scenarios.
The resulting dataframe is
grouped by year y and Source and the
TotalCapacityAnnual summed up. The final dataframe is used in a long format
to create the plot.
Table 7. TotalCapacityAnnual.csv file sample.
| r | t | y | TotalCapacityAnnual | scenario | |
|---|---|---|---|---|---|
| 0 | Region1 | BIO_RESPRD | 2018 | 298.866133 | BAU |
| 1 | Region1 | BIO_RESPRD | 2019 | 313.137201 | BAU |
| 2 | Region1 | BIO_RESPRD | 2020 | 328.089721 | BAU |
| 3 | Region1 | BIO_RESPRD | 2021 | 343.756236 | BAU |
| 4 | Region1 | BIO_RESPRD | 2022 | 360.170837 | BAU |
The excel file could probably be omitted if naming conventions are used and a
mapping function is implemented to translate such names (from columns t)
into user friendly technology types and select the relevant technologies to
display.
This plot shows the value of CO2 emissions from electricity generation. It can be displayed as an annual emissions line plot disaggregating the information by scenario, or as a total emissions bar plot per scenario, or as a stacked bar of emissions per fuel source for a specific scenario.
Years in ascending order (column y) or scenarios (column scenario).
Total CO2 emissions values expressed in Mton (column UseByTechnologyAnnual *
EmissionActivityRatio).
Colors are used to disaggregate scenarios or stack emissions values of
different fuel sources. The columns scenario or f can be used for this
purpose (see table 3).
The UseByTechnologyAnnual.csv file is used (see table 1) and the
EmissionActivityRatio. These .csv files for each scenario are added together
and an extra column is created to indicate the scenario. A complementary excel
file is used (see table 5) in which the relevant power generation
technologies to display are indicated. This table is merged by technology name
t with the OSeMoSYS results. The resulting dataframe is
grouped by year y and scenario or y and Source and the
Emisions (from the UseByTechnologyAnnual *
EmissionActivityRatio calculation) summed up. The final dataframe is used in a long format
to create the plot.
The excel file could probably be omitted if naming conventions are used and a
mapping function is implemented to translate such names (from columns t and f)
into a more user friendly output and select the relevant technologies to display.
Note: we had an issue with this plot, as in the first versions of the model
we where sourcing the technology emissions directly from the
AnnualTechnologyEmission file. However, at a later stage the model changed and
now the emissions needed to be calculated by UseByTechnologyAnnual *
EmissionActivityRatio.
This plot shows the capital investment for new electricity generation capacity. It can be displayed as an annual investment line plot disaggregating the information by scenario, as a total costs bar plot per scenario, or as a stacked bar of annual investments per technology for a specific scenario.
Years in ascending order (column y) or scenarios (column scenario).
Total investment expressed in M$ (column CapitalInvestment).
Colors are used to disaggregate scenarios or stack emissions values of
different technologies. The columns scenario or Source can be used for this
purpose (see table 8 and table 5).
The CapitalInvestment.csv file is used (see table 8). These .csv files
for each scenario are added together and an extra column is created to indicate
the scenario. A complementary excel file is used (see table 5) in which the
relevant power generation technologies to display are indicated. This table is
merged by technology name t with the OSeMoSYS results. The resulting dataframe is
grouped by year y and scenario or y and Source and the
CapitalInvestment summed up. The final dataframe is used in a long format
to create the plot.
Table 8. CapitalInvestment.csv file sample.
| r | t | y | CapitalInvestment | scenario | |
|---|---|---|---|---|---|
| 0 | Region1 | BIO_RESPRD | 2018 | 0.029887 | BAU |
| 1 | Region1 | BIO_RESPRD | 2019 | 0.031314 | BAU |
| 2 | Region1 | BIO_RESPRD | 2020 | 0.032809 | BAU |
| 3 | Region1 | BIO_RESPRD | 2021 | 0.034376 | BAU |
| 4 | Region1 | BIO_RESPRD | 2022 | 0.036017 | BAU |
The excel file could probably be omitted if naming conventions are used and a
mapping function is implemented to translate such names (from columns t)
into user friendly technology types and select the relevant technologies to
display.
This plot shows the total discounted cost. It can be displayed as an annual discounted costs line plot disaggregating the information by scenario, or as a total costs bar plot per scenario.
Years in ascending order (column y).
Total investment expressed in M$ (column TotalDiscountedCost).
Colors are used to disaggregate scenarios. The column scenario is used.
The TotalDiscountedCost.csv file is used (see table 9). These .csv files
for each scenario are added together and an extra column is created to indicate
the scenario. The resulting dataframe is
grouped by year y and scenario and the
TotalDiscountedCost summed up. The final dataframe is used in a long format
to create the plot.
Table 9. TotalDiscountedCost.csv file sample.
| r | y | TotalDiscountedCost | scenario | |
|---|---|---|---|---|
| 0 | Region1 | 2018 | 83512.552122 | BAU |
| 1 | Region1 | 2019 | 80671.629752 | BAU |
| 2 | Region1 | 2020 | 80369.940058 | BAU |
| 3 | Region1 | 2021 | 80579.205662 | BAU |
| 4 | Region1 | 2022 | 78726.087087 | BAU |
This plot shows the annual RE share in electricity generation. It can be displayed as an area plot for a single scenario, or as afacet plot for all scenarios. The fossil fuel and renewable energy sources are disaggregated by olors.
Years in ascending order (column y).
Percentage of energy generated by each energy type.
Colors are used to disaggregate energy types (i.e. fossil or renewable).
The column scenario is used to facet the results per scenario.
The ProductionByTechnologyAnnual.csv file is used (see table 4) in
combination with the energy generation excel file (see table 5). These
.csv files for each scenario are added together and an extra column is created
to indicate the scenario. Then they are merged with the excel file on technology
t. Moreover, the dataframe is grouped by scenario, year y and energy
Type, and the ProductionByTechnologyAnnual values are summed up. Then, the
generation shares per scenario, per year and per energy type, are calculated by
dividing the ProductionByTechnologyAnnual per scenario, year y and energy
Type by the sum of ProductionByTechnologyAnnual per scenario and year y.
The dataframe is then used in a long format to create the plot.