The E-Mobility Component
The E-Mobility component is an electrical appliance that can be charged variably and can also serve as a temporary power storage device. The component is primarily designed to map individual electric vehicles or entire vehicle fleets. E-Mobility can also map so-called intelligent consumers (with flexible demands).
Component Template
The component template E-Mobility.e-ctpl is located in the Component template library folder Electricity Supply.
Integration Into a Scheme
The following illustration shows an example of the integration of the component into a scheme using variant 2 from Tutorial 110.
How the Component Works
The E-Mobility component serves to examine the interface of the electric vehicle with the power grid (charging or discharging station) shown in the scheme. The component concentrates on the interactions of the battery with this power grid. The charging and, if necessary, discharging processes are optimized within a specified grid connection period. Besides the performance limits, it takes into account which times how much energy is needed and when energy is available at a low cost.
In order to reduce the performance price, peak loads can be reduced by flexible charging of the vehicle and using car batteries as storage for the local electric grid.
The total energy demand and total energy consumption of the vehicle are considered for the period during which the vehicle is disconnected from the mapped power grid. Details of the driving behavior do not play a role.
The E-Mobility component maps complex processes in relation to time. The conventions for entering load and capacity profiles must be strictly observed.
The component is only used in time-dependent simulations. The component is not suitable for the consideration of a stationary point in time of the energy system.
Tutorials 28 und 110 show how the componet E-Mobility works.
Vehicle Characteristics
Information on Vehicle characteristics and demands can be provided either as (possibly superimposed) Time series or as Schedule. The input method is selected in the drop-down list under the heading Derive data from schedule or time series (see following figure).
Schedule
The Schedule is suitable for individual vehicles, especially if the demand cannot be accurately forecasted.
The schedule defines the typical daily routine of an electric vehicle with the following information.
The Storage capacity is the capacity of the battery. A capacity reduced, e.g., by degradation, compared to the nominal capacity can be commented and converted in the expression field (see following figure).
The Energy demand can be given per Operating period or per km. In the second case, the Driving distance per operating period must also be specified.
Under Schedule, the Times of Disconnecting from the grid and Connecting to the grid are specified. A distinction can be made between working days and Weekend days. The First and the Last day of weekend can be selected from all weekdays. In this way, for example, the time from Friday to Sunday or only a Sunday can be defined as weekend.
If the input options Schedule and Discharging to grid are activated simultaneously, a Look-ahead (time frame for calculation) must be selected.
If structural optimization is carried out with time series compression in a project, errors occur in the E-Mobility component when the data are derived from schedule. A corresponding warning is displayed.
You can carry out structural optimization without compression, but please note that this will increase the computing time. To counteract the increase in calculation time, you can set the simulation filter to every 2nd or 3rd time step (Grid).
Time Series
If more precise data on the use of a vehicle or a vehicle fleet are available, specify the loading and unloading behavior by Time series. Time series are also appropriate if the usage behavior of the vehicle varies greatly in different weeks.
The following Notes on time series generation must be observed.
Available storage capacity: Specify the capacity of the battery only in the time steps during the grid connection. The capacity is 0 in the remaining time.
Energy demand of the vehicle(s): Specify the energy consumption during an operating period in the last time step of the grid connection phase.
Residual energy when plugging in: Enter the remaining energy of the battery, if available, in the first time step of the grid connection phase.
The Available storage capacity implies the battery capacity connected to the grid. At times when no battery is connected to the grid, the available storage capacity is 0.
If a vehicle with a usable capacity of 40 kWh is connected to the grid between 4:00 p.m. of one day and 7:00 a.m. of the following day, the storage capacity is indicated as 40 kWh in these time steps. In the time steps between 7:00 a.m. and 4:00 p.m., when the vehicle is not connected to the network, the Available storage capacity is 0.
The Energy demand of the vehicle(s) (red in the following figures) is always indicated in the last time step of the phase in which the vehicle is connected to the grid. In the example in the following figure, all energy is consumed in the usage period; and the capacity (yellow) is exhausted. In order to have sufficient energy available, the battery must be fully charged beforehand. If, on the other hand, the energy demand is only half the capacity, the battery can only be half charged (as an economically sensible result of the simulation).
The value of the Residual energy when plugging in (blue in the following figures) is indicated in the first time step in which the vehicle is connected to the grid.
The Residual energy when plugging in and the Energy demand do not have to correspond mathematically, because the simulation does not consider the time in which the vehicle is disconnected from the grid. The vehicle could theoretically be charged by another grid connection during this time and therefore still have a high residual energy content at the start of the connection to the system grid under consideration.
Operation/Look-Ahead
Because the energy demand of the vehicle or intelligent consumer is only due after the charging process, it must be ensured that the corresponding charge state is reached. The battery must be charged in the current time step (which may be economically disadvantageous) in order to have the energy available in the future time step after the vehicle will be disconnected from the power grid.
Therefore, the Look-ahead, a time frame for calculation, and the Result time frame must be specified for the optimization on the Simulation ribbon in the group Optimization.
An exception is the setting where the demand is specified via a Schedule and no Discharging to grid is possible. In this special case, the required minimum energy can be calculated, but “intelligent” charging is not possible.
The default setting for the Look-ahead of one hour is usually not sufficient for the E-Mobility component. A larger Look-ahead causes longer calculation times, but can also achieve lower operating costs.
The Look-ahead must be at least as long as the charging period needed to reach the required battery capacity.
If a battery with a storage capacity of 40 kWh is charged with the maximum charging power of 15 kW, a charging time of approximately three hours is required. The Look-ahead must be at least 3 h.
If no Look-ahead is specified, model message 8505 appears.
Several Vehicles/Vehicle Fleet
One E-Mobility component can also map several vehicles.
With the superimposed curves, the conventions observed are not visible at first glance (see figure below). Nevertheless, the conventions must be observed meticulously in the individual steps. Otherwise, the optimization problem may not be solved and the root cause analysis will become complicated.
No conclusions about the individual vehicles can be drawn from the superimposed time series of a fleet. If an entire fleet is to be exactly replicated, a separate component must be used for each vehicle.
The higher the number of components, the more complex the energy system and the longer the computing time.
For example, a fleet is divided into morning, midday, and evening active vehicles.
Charging Characteristics
Settings for charging and discharging behavior are made in the Charging characteristics section (see following figure). If the battery of the vehicle can also serve as a buffer, select Discharging to grid with a check mark in the checkbox. In this case, the vehicle can supply energy if required to the power grid or to the household. If this selection is not made, the vehicle’s battery can only be charged while it is connected to the grid. The battery can then be discharged only due to losses and while driving.
If Discharging to grid is excluded and the Vehicle characteristics are entered via Time series, the Available storage capacity should not be greater than the Energy demand of the vehicle(s).
An example of this can be found below under Tips: Adjustment of storage capacity and energy demand.
The Maximum charging power and Maximum discharging power and the respective Charging and Discharging efficiencies are entered (analogous to the other TOP-Energy storage models). If only one vehicle is shown, the Maximum charging power corresponds to that of this vehicle. If a fleet of ten vehicles is shown that are loaded simultaneously, the Maximum charging power is ten times the charging power of a single vehicle.
Intelligent Consumer (flexible power consumption)
The E-Mobility component can be used to represent an intelligent consumer (flexible electricity demand).
In the Technical input data under Charging characteristics the option Discharging to grid must not be activated by checking the checkbox.
A Heat pump is to be used to heat water in a hot water storage tank. The Heat pump is to be operated between 9 a.m. and 6 p.m. at an economically favorable time so that hot water is available after 6 p.m.
In order to achieve the desired simulation result, the energy demand for water heating must be entered both as the Available storage capacity and as the Energy demand of the E-Mobility component.
Tips
The E-Mobility component is mathematically complex due to its storage behavior. It is designed to handle different scenarios (e.g., individual vehicles and vehicle fleets).
The following tips can help to improve the convergence behavior of the component project-specifically and to shorten the computing time.
Capacity exceeding Energy demand can only be used if Discharging to grid is permitted (by a checkmark).
In the following figure, the Energy demand of the vehicle(s) does not always correspond to 100 % of the Available storage capacity, because in some operating periods less energy is needed than the Available storage capacity of the battery could provide. If the Discharging to grid option is selected, the Available storage capacity can be used as buffer storage by the grid. This can be useful.
If Discharging to grid is not possible, the Available storage capacity in the input data should be reduced to the estimated Energy demand of the vehicle(s), as shown in the following figure. This minimizes the mathematical problem and shortens the computing time.
The following figure shows the adaptation of the Energy demand to the Storage capacity. The battery is fully charged at the end of each charging cycle. This option is convenient if a shorter Look-ahead is to be expected.
If Discharging to grid is deactivated, a very small value (less than or equal to 1 W) may still be displayed in the Technical output data for the Discharging power because by default the model is given a margin of 1 W for the Discharging power even if discharging is prohibited, in order to effect numerical improvements. This margin can be reduced or increased by adjusting the equation
lP_el_out <= 1 [W]
in line 81 under Energy view in the Model equations. If you want to force the zero, replace the equation with
lP_el_out == 0 [W] .
However, limiting it to zero can cause problems, so that not all time steps are solved. In this case, the Look-ahead can be enlarged to facilitate solvability.
