Profiles
ETMoses relies on the use of time profiles to describe both the load that technologies exert on the network as well as the financial properties of the network over time. Profiles are currently provided with a time resolution of 15 minutes, but the calculation is versatile enough to deal with different time resolutions. The profiles heavily determine the meaningfulness the results from ETMoses.
The load profiles determine the electricity load of baseloads and other technologies on a 15 minute basis. Various profiles are available by default.
The financial profiles determine the price of electricity on a 15 minute basis. Various profiles are available by default. These financial profiles can be used in a market model when the Price curve option under Tariff is selected.
The behavior profiles are binary profiles that can be applied to the hybrid heat pump and specifies when the hybrid heat pumps needs to switch to using gas (i.e. when the profile equals 1). This is related to the outside temperature; when this temperature drops below 4 degrees, the COP of the electric part of the hybrid heat pump becomes to low. When the profile equals 0, the the hybrid heat pump can run both on electricity or on gas, depending on the strategies applied and potential congestion of the network.
Examples of the profiles can be found on the website under Profiles -> Show all
Users can upload their own profiles for use in ETMoses; this works for load, financial and behavior profiles. Each type of profile has its own option in the ETMoses profiles menu. Depending on the type various attributes of the new profile have to be entered.
The following attributes have to be supplied when creating a new load profile:
- Key: a recognizable name for the profile
- Name: optional display name for the profile
-
Default capacity: (use with caution) the default capacity (in
kW) for the technology to which this profile will be assigned; this capacity will overwrite the default capacity of the technology itself -
Default volume: (use with caution) the default volume (in
kWh) for the technology to which this profile will be assigned; this capacity will overwrite the default volume of the technology itself -
Default demand: (use with caution) the default demand (in
kWh) for the technology to which this profile will be assigned; this capacity will overwrite the default demand of the technology itself - Permissions: select if profile should only be available to you or can be used by everyone
- Included in concurrency: select if the profile can be included in the concurrency feature (see concurrency for more details)
- Load profile category: select in which category the newly created profile should be listed
- Curve: the actual load profile itself; the profile should have a 15 minute resolution and be specified for an entire year (i.e. contain 35040 data points)
- Permitted technologies: select to which technologies the new profile can be assigned by the user
Note that the unit of the profile should be kW.
A new financial profile requires three attributes:
- Key: a recognizable name for the profile
- Name: optional display name for the profile
- Curve: the actual financial profile itself; the profile should have a 15 minute resolution and be specified for an entire year (i.e. contain 35040 data points)
Note that the unit of the profile should be EUR.
A new behavior profile requires three attributes:
- Key: a recognizable name for the profile
- Name: optional display name for the profile
- Curve: the actual behavior profile itself; the profile should have a 15 minute resolution and be specified for an entire year (i.e. contain 35040 data points)
Note that these profile should be binary and can therefore only contain 0 and 1.
Quintel has researched and modeled various profiles that are available to all users. In the sections below, the sources and modelling approach behind these profiles are described. The section below follow the same structure as the list of all available profiles that can be found on the ETMoses website.
Within ETMoses, base load profiles are used to describe the load exerted to the network by all technologies within a household or building other than heat pumps or electric vehicles for which we use separate profiles. We distinguish two types of base load: inflexible and flexible. As the name suggests, the inflexible part cannot be moved in time or changed at all, because the technologies or appliances that cause this load (e.g. electric heaters and kitchen tools) need to be switched on at that time. The flexible profile contains the loads caused by washing machines, dish washers and dryers. The flexible load can be postponed by at most 3 hours (in the case of individual load profiles) or decreased to 0 (in the case of aggregated load profiles).
The individual load profiles for households have been based on measured smart meter data that is downloaded from the Liander website (under `Slimme meter -> Levering).
The smart meter load profiles are processed in order to obtain an inflexible and flexible profile. The script that is used for this processing is available on GitHub.
No profiles have been researched or generated for individual buildings yet.
We use Energie Data Services Nederland (EDSN) profiles of type E1x and E2x as aggregated households load profiles. The profiles have been downloaded from the NEDU website. We use 2013 profiles.
We use Energie Data Services Nederland (EDSN) profiles of type E3x and E4x as aggregated buildings load profiles. The profiles have been downloaded from the NEDU website. We use 2013 profiles.
As we could not find any reliable, measured load profiles for electric vehicles (EV), we generated several load profiles ourselves. We drew inspiration from the report Laadstrategie elektrisch wegvervoer (Movares, 2013).
Instead of load profiles which describe the load exerted on the network by EV, we generate so called 'availability profiles'. These profiles describe the minimum charge that needs to be present in the battery of the EV as well as when the EV is connected to a power outlet for (dis)charging. This information is used to determine when the EV needs to start charging as well as if the battery in the EV can be used to store excess electricity produced by solar PV or to discharge when electricity is required locally, e.g. in the house that the EV is connected to.
We generated 11 different EV availability profiles for 11 different drivers. We assume that each driver uses his car to commute to work on workdays and to go to other activities on both weekend days. The commute to work follows the same pattern every workday (i.e. the same departure and arrival times and distance travelled). In order to avoid the charging to start on exactly the same time every workday, we add a random time to the departure and arrival times in the interval [-30, 30] minutes. We do the same for weekend days. For weekend days we pick a random trip from a list of 14 'other' activities. Both the work and othe data come from table D.1 in the PhD thesis of Remco Verzijlbergh (2013). Finally we take into account (public) holidays and sick leave by assuming that the EV stays at home for about 18% of the work days, selected randomly.
The script assumes a charging power of 3.7 kW. Depending on the required charge the script works out when charging needs to start in order to fulfill future demand.
The availability profiles are used to determine the absolute minimum charge that needs to be present in the car battery to have enough "fuel" to run the next trip. The remaining battery volume could be used to store electricity (and use it later). From the required charge for the next trip and the charging rate, we calculate when the car has to start charging.
The availability is expressed as a percentage of the battery volume (i.e. 25 kWh).
As we could not find any reliable, measured load profiles for heat pumps, we generated several load profiles ourselves.
In ETMoses, heat pumps for how water are considered to always have a buffer tank. The heat pump is used to store energy in the tank by heating up the water in it. Hot water consumption extracts energy (i.e. heat) from the tank.
Like with EV, the profiles for heat pumps are used to describe more than just the instantaneous load exerted on the network by heat pumps. In fact heat pumps are described by two profiles: a use profile describing the instanteneous hot water demand (expressed as a fraction of the maximum amount of energy that can be stored in the tank) and an availability profile describing how much energy needs to be stored in the tank to fulfill future demand. If the tank does not contain enough energy to meet the demand, we assume that a (non-electric) back-up burner will meet the remaining demand.
We generate typical domestic hot water (DHW) profiles for a period of one year and time steps of 15 minutes. The script is based in great part on Realistic Domestic Hot-Water Profiles in Different Time Scales, Jordan (2001)](http://sel.me.wisc.edu/trnsys/trnlib/iea-shc-task26/iea-shc-task26-load-profiles-description-jordan.pdf). This study assumes a daily average DHW use of 200 liters and distinguishes four types of DHW consumption, each with an associated volume and average daily occurence:
- type A: short load (1 liter per event, 28 occurences per day)
- type B: medium load (6 liter per event, 12 occurences per day)
- type C: bath (140 liter per event, 0.143 occurences per day (once a week))
- type D: shower (40 liter per event, 2 occurences per day)
According to Jordan (2001), the duration of each of these types is shorter than 15 minutes (i.e. the time resolution of our simulation). Hence we decided to only model the probability that an event occurs within each 15 minute time step and assign the entire volume of that event to that 15 minute bin. The probability of each type of event varies throughout the year (i.e. slightly more DHW consumption in winter), throughout the week (more in the weekend) and throughout the day (e.g. no DHW consumption during the night). We calculate the overall probability that events of all four types occur at a specific 15 minutes time window. From these probability functions we generate random DHW patterns.
The script returns two types of profiles:
- use profiles: randomly generated profiles with a time resolution of 15. To match the needs of ETMoses these exported profiles are scaled to the maximal storage volume of the boiler used in ETM for P2H
- availability profiles: the profiles indicate how full the boiler has to be in order to meet future demands. The profiles are derived from the use profile by and are also expressed as a fraction of the maximal storage volume
As we could not find any reliable, measured load profiles for heat pumps, we generated several load profiles ourselves. This script generates load profiles for heat pumps based on the outdoor temperature profile measured in De Bilt and downloaded from the KNMI website, the solar irradiance in Maastricht, also downloaded from the SoDa website and some simple characteristics of the house like thermostat temperature, surface areas and insulation values. The temperature and solar data for 2013 have been used.
The solar PV load profiles have been downloaded from the SoDa website for the year 2013. To match the needs of ETMoses these profiles have been scaled such that their peak load is equal to 1.
This category contains older profiles that are needed for previously created LESs.
Contains profiles that do not fall in any of the other categories.
Power-to-heat is implemented in ETMoses as a small electric boiler (~100 liter) before an existing condensing combi boiler. The water in the electric boiler can be heated with excess (or cheap) electricity, effectively preheating the water that goes into the combi boiler and leaving less heating to be done for the combi boiler. This reduces the gas consumption.
In order to describe how much energy is drawn from the boiler, ETMoses contains use profiles. These profiles have been constructed in the same way as the use profiles for heat pumps for hot water (see detailed description above).
