8.5. Adding a service demand

In this section, we detail how to add a service demand to MUSE.

In the residential sector, a service demand could be cooking. Houses require energy to cook food and a technology to service this demand, such as an electric stove.

This process consists of setting a demand, either through inputs derived from the user or correlations of GDP and population which reflect the socio-economic development of a region or country. In addition, a technology must be added to service this new demand.

8.5.1. Addition of cooking demand

Firstly, we must add the demand section. In this example, we will add a cooking preset demand. To achieve this, we will now edit the Residential2020Consumption.csv and Residential2050Consumption.csv files, found within the technodata/preset/ directory.

The Residential2020Consumption.csv file allows us to specify the demand in 2020 for each region and technology per timeslice. The Residential2050Consumption.csv file does the same but for the year 2050. The datapoints between these are interpolated.

Firstly, we must add the new service demand: cook as a column in these two files. Next, we add the demand. Again, the modified entries are in bold:

	RegionName	ProcessName	Timeslice	electricity	gas	heat	CO2f	wind	cook
0	R1	gasboiler	1	0	0	1	0	0	1
1	R1	gasboiler	2	0	0	1.5	0	0	2
2	R1	gasboiler	3	0	0	1	0	0	1
3	R1	gasboiler	4	0	0	1.5	0	0	1.5
4	R1	gasboiler	5	0	0	3	0	0	2
5	R1	gasboiler	6	0	0	2	0	0	3
6	R1	gasboiler	7	0	0	2	0	0	2
7	R1	gasboiler	8	0	0	2	0	0	3
8	R2	gasboiler	1	0	0	1	0	0	1
9	R2	gasboiler	2	0	0	1.5	0	0	1
10	R2	gasboiler	3	0	0	1	0	0	1
11	R2	gasboiler	4	0	0	1.5	0	0	1.5
12	R2	gasboiler	5	0	0	3	0	0	2
13	R2	gasboiler	6	0	0	2	0	0	2
14	R2	gasboiler	7	0	0	2	0	0	2.5
15	R2	gasboiler	8	0	0	2	0	0	2

As can be seen, we only need to add a cook column in the file, as well as the demand level for each timeslice and each region. This can be seen through the addition of a positive number in the cook column.

The process is very similar for the Residential2050Consumption.csv file, however, for this example, we often placed larger numbers to indicate higher demand in 2050. For the complete file see the link here.

Next, we must edit the files within the input folder. For this, we must add the cook service demand to each of these files.

First, we will amend the BaseYearExport.csv and BaseYearImport.csv files. For this, we say that there is no import or export of the cook service demand. A brief example is outlined below for BaseYearExport.csv:

RegionName	Attribute	Time	electricity	gas	heat	CO2f	wind	solar	cook
Unit		Year	PJ	PJ	PJ	kt	PJ	PJ	PJ
R1	Exports	2010	0	0	0	0	0	0	0
…	…	…	…	…	…	…	…	…	…
R2	Exports	2100	0	0	0	0	0	0	0

The same is true for the BaseYearImport.csv file:

RegionName	Attribute	Time	electricity	gas	heat	CO2f	wind	solar	cook
Unit		Year	PJ	PJ	PJ	kt	PJ	PJ	PJ
R1	Imports	2010	0	0	0	0	0	0	0
…	…	…	…	…	…	…	…	…	…
R2	Imports	2100	0	0	0	0	0	0	0

Next, we must edit the GlobalCommodities.csv file. This is where we define the new commodity cook. It tells MUSE the commodity type, name, emissions factor of CO2 and heat rate, amongst other things.

The example used for this tutorial is below:

Commodity	CommodityType	CommodityName	CommodityEmissionFactor_CO2	HeatRate	Unit
Electricity	Energy	electricity	0	1	PJ
Gas	Energy	gas	56.1	1	PJ
Heat	Energy	heat	0	1	PJ
Wind	Energy	wind	0	1	PJ
CO2fuelcomsbustion	Environmental	CO2f	0	1	kt
Solar	Energy	solar	0	1	PJ
Cook	Energy	cook	0	1	PJ

Finally, the Projections.csv file must be changed. This is a large file which details the expected cost of the technology in the first benchmark year of the simulation. Due to its size, we will only show two rows of the new column cook.

RegionName	Attribute	Time	…	cook
Unit		Year	…	MUS$2010/PJ
R1	CommodityPrice	2010	…	100
…	…	…	…	…
R2	CommodityPrice	2100	…	100

We set every price of cook to be 100MUS$2010/PJ

8.5.2. Addition of cooking technology

Next, we must add a technology to service this new demand. This is achieved through a similar process as the section in the “1. adding a new technology” section. However, we must be careful to specify the end-use of the technology as cook.

For this example, we will add two competing technologies to service the cooking demand: electric_stove and gas_stove to the Technodata.csv file in /technodata/residential/Technodata.csv.

Again for the interests of space, we have omitted the existing gasboiler and heatpump technologies. But we copy the gasboiler row for R1 and paste it for the new electric_stove for both R1 and R2. For gas_stove we copy and paste the data for heatpump from region R1 for both R1 and R2.

An important modification, however, is specifying the end-use for these new technologies to be cook and not heat.

ProcessName	RegionName	Time	Level	cap_par	…	Fuel	EndUse	Agent2
Unit		Year		MUS$2010/PJ_a	…			Retrofit
gasboiler	R1	2020	fixed	3.8	…	gas	heat	1
…	…	…	…	…	…	…	…	…
electric_stove	R1	2020	fixed	3.8	…	electricity	cook	1
electric_stove	R2	2020	fixed	3.8	…	electricity	cook	1
gas_stove	R1	2020	fixed	8.8667	…	gas	cook	1
gas_stove	R2	2020	fixed	8.8667	…	gas	cook	1

As can be seen we have added two technologies, in the two regions with different cap_par costs. We specified their respective fuels, and the enduse for both is cook. For the full file please see here.

We must also add the data for these new technologies to the following files:

• CommIn.csv

• CommOut.csv

• ExistingCapacity.csv

This is largely a similar process to the tutorial shown in “adding a new technology”. We must add the input to each of the technologies (gas and electricity for gas_stove and electric_stove respectively), outputs of cook for both and the existing capacity for each technology in each region.

Due to the additional demand for gas and electricity brought on by the new cook demand, it is necessary to relax the growth constraints for gassupply1 in the technodata/gas/technodata.csv file. For this example, we set this file as follows:

ProcessName	RegionName	Time	…	MaxCapacityAddition	MaxCapacityGrowth	TotalCapacityLimit	…	Agent2
Unit		Year	…	PJ	%	PJ	…	Retrofit
gassupply1	R1	2020	…	100	5	500	…	1
gassupply1	R2	2020	…	100	5	120	…	1

To prevent repetition of the “adding a new technology” section, we will leave the full files here.

Again, we run the simulation with our modified input files using the following command, in the relevant directory:

python -m muse settings.toml

Once this has run we are ready to visualise our results.

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

mca_capacity = pd.read_csv(
    "../tutorial-code/5-add-service-demand/Results/MCACapacity.csv"
)
mca_capacity.head()

	Unnamed: 0	agent	capacity	dst_region	index	installed	region	sector	technology	type	year
0	7	A1	5.0	R1	8	2020	R1	residential	gasboiler	retrofit	2020
1	9	A1	5.0	R1	16	2020	R1	residential	gasstove	retrofit	2020
2	21	A1	5.0	R2	8	2020	R2	residential	gasboiler	retrofit	2020
3	23	A1	5.0	R2	16	2020	R2	residential	gasstove	retrofit	2020
4	35	A2	5.0	R1	8	2020	R1	residential	gasboiler	retrofit	2020

for name, sector in mca_capacity.groupby("sector"):
    print("{} sector:".format(name))
    fig, ax = plt.subplots(1, 2)
    sns.lineplot(
        data=sector[sector.region == "R1"],
        x="year",
        y="capacity",
        hue="technology",
        ax=ax[0],
    )
    sns.lineplot(
        data=sector[sector.region == "R2"],
        x="year",
        y="capacity",
        hue="technology",
        ax=ax[1],
    )
    plt.show()
    plt.close()

gas sector:

power sector:

residential sector:

We can see our new technology, the gas_stove is used and the electric_stove is not used at all. Therefore, there is an increase in gassupply1 to accommodate for this growth in demand. However, this is not enough to displace windturbine by gasCCGT in the power sector.

8.5.3. Next steps

In the next section we will use a regression function to estimate electricity demand from GDP and population data.