31 Investigation of the Behaviour of a New Nanogrid Concept …
449
To calculate the charging process the characteristics of the battery charging, the
SOC of all batteries, the solar irradiance and the time of the calendar events are
necessary. If all these information are present, we can simulate the charging of the
devices.
One goal is to calculate if the batteries of the devices can be charged for the
calendar event or if the calendar event is in time too close then the SOC of the device
battery is the output. Also the SOC of the nanogrid battery is to be calculate.
To make the calculations a bit simpler we just used two type of solar irradiance,
one for the shaded day (diffuse light) and one for total irradiance (the nominal power
of the PV cells is reached).
We run the simulations for different cases. In Table 31.1 we see the results of
the simulation. The time resolution were 5 min for the simulation. An event was
scheduled for 60 min after the start of the simulation, so charging of the devices
batteries was forced. We run two simulations with the same starting conditions of
batteries and the same scheduled event but different solar irradiance.
We can see in Table 31.1 in the grey row, that for the scheduled event we get the
phones battery charged to 44% and the laptop battery to 62% level. The remaining
cells shows if the event is pushed further away for further 110 min then we can charge
both the phones and laptops battery, whereas charging is forced in all cases.
The difference in irradiation is not seen in the Table 31.1 because the nanogrid
battery was initially also at 10% SOC and the priority is on the charging of the devices
battery.
If we examine the level of the nanogrids battery at the end of the charging (after
170 min) we can see that in the case one the battery SOC is 15% whereas in case
two the SOC is only 9%. In case one we had overall higher irradiance than in case
two as we can see on the SOC of the nanogrids battery.
31.6 Summary
A new nanogrid concept was proposed where the batteries of ICT devices in an office
were also included in the nanogrids energy storage. This raises new possibilities and
also new problems for the ESMS. The ESMS has more possibilities to store energy
and select batteries to charge but the demand of the users of the devices should be
taken into account. This means that the ESMS system should have knowledge about
the activity of the users to be able to fully charge the laptops and phones batteries
before the user leaves the office for an appointment.
The usual variability in renewable energy sources as solar irradiance and wind
makes it hard to predict the necessary time for the charging. If the nanogrid has its
own battery than the case can be easier as long as the battery’s charging level is
sufficient for charging the ICT devices batteries.
The simulations shows that the ESMS can calculate with the storage capacity of
the ICT devices and it is also possible to calculate the necessary time for charging
the ICT devices battery for the out of office usage.
449
To calculate the charging process the characteristics of the battery charging, the
SOC of all batteries, the solar irradiance and the time of the calendar events are
necessary. If all these information are present, we can simulate the charging of the
devices.
One goal is to calculate if the batteries of the devices can be charged for the
calendar event or if the calendar event is in time too close then the SOC of the device
battery is the output. Also the SOC of the nanogrid battery is to be calculate.
To make the calculations a bit simpler we just used two type of solar irradiance,
one for the shaded day (diffuse light) and one for total irradiance (the nominal power
of the PV cells is reached).
We run the simulations for different cases. In Table 31.1 we see the results of
the simulation. The time resolution were 5 min for the simulation. An event was
scheduled for 60 min after the start of the simulation, so charging of the devices
batteries was forced. We run two simulations with the same starting conditions of
batteries and the same scheduled event but different solar irradiance.
We can see in Table 31.1 in the grey row, that for the scheduled event we get the
phones battery charged to 44% and the laptop battery to 62% level. The remaining
cells shows if the event is pushed further away for further 110 min then we can charge
both the phones and laptops battery, whereas charging is forced in all cases.
The difference in irradiation is not seen in the Table 31.1 because the nanogrid
battery was initially also at 10% SOC and the priority is on the charging of the devices
battery.
If we examine the level of the nanogrids battery at the end of the charging (after
170 min) we can see that in the case one the battery SOC is 15% whereas in case
two the SOC is only 9%. In case one we had overall higher irradiance than in case
two as we can see on the SOC of the nanogrids battery.
31.6 Summary
A new nanogrid concept was proposed where the batteries of ICT devices in an office
were also included in the nanogrids energy storage. This raises new possibilities and
also new problems for the ESMS. The ESMS has more possibilities to store energy
and select batteries to charge but the demand of the users of the devices should be
taken into account. This means that the ESMS system should have knowledge about
the activity of the users to be able to fully charge the laptops and phones batteries
before the user leaves the office for an appointment.
The usual variability in renewable energy sources as solar irradiance and wind
makes it hard to predict the necessary time for the charging. If the nanogrid has its
own battery than the case can be easier as long as the battery’s charging level is
sufficient for charging the ICT devices batteries.
The simulations shows that the ESMS can calculate with the storage capacity of
the ICT devices and it is also possible to calculate the necessary time for charging
the ICT devices battery for the out of office usage.