**8. Results of load leveling test**

building. In addition, to diminish the effect of ambient temperature on charging and discharg‐ ing behaviors of used EV batteries, the storage room of used EV batteries is controlled to have

The load of office building is estimated as the sum of the base load and fluctuating load, especially the air conditioning demand. The air conditioning demand is calculated using historical data for several previous years and forecasted ambient temperature received from

, can be simply

(1)

) min *EV* (2)

meteorological agency. The demand of office building in certain typical time, *L*<sup>t</sup>

*t BS* ([ *OA ST* ] ( )) *<sup>t</sup> L L fT T* =+ - D

where *L*BS, *f*, *T*OA, *T*ST, and Δ*τ* are the base load for 30 min interval (kWh) of the office building, functional relationship, outside ambient temperature (°C), room temperature inside the office

The available electricity from EVs, *P*EV, which are in motion and not plugged in to the charging poles can be estimated as the correlation of SOC and the remaining distance (both are received

h

remaining travel distance to the designated charging station (km), power consumption of EV

To calculate the peak-cut threshold, a day load duration curve is initially created using the historical data of the averaged office building load for the same month in the last year. A peakcut threshold, *P*thr, can be approximated using Equation (3). **Figure 10** shows the illustration

, ,

where *P*bat, *n*, and *m* are available power from used EV battery (kWh), number of load higher

In general, a load duration curve lines up all the loads in a descending order. Therefore, in this demonstration test, a day load duration curve is created by sorting all 30 min duration of office building loads from the largest to the smallest loads. Therefore, the plotted area represents the total electricity consumed by the office building for a day (starting from 00:00 to 24:00). Furthermore, the generated electricity from PV panels is directly delivered to the building

*n thr ev m bat m pv*

*L nP P P P*

than peak-cut threshold, and number of EVs and used EV batteries, respectively.

*P SOC C d SOC C EV t*, = ´ -´ - ´ ( *EV t EV t EV* ,

(kWh km−1), and minimum SOC threshold for discharging (%), respectively.

*n thr*

>

*for L P*

t

, *η*EV, and SOCmin are SOC of each EV (%), EV battery capacity (kWh),

S = +S +S + (3)

a temperature of 25 °C throughout the year at.

140 Modeling and Simulation for Electric Vehicle Applications

building (°C), and time shift (h), respectively.

from VIS), and can be represented as follows:

, *C*EV, *dt*

of a day load duration curve.

approximated as follows:

where SOCEV,*<sup>t</sup>*

A result of load leveling demonstration test of one representative weekday is shown in **Figure 11**. It consists of the total grid load (net electricity purchased from the grid), building load (the total load consumed by the office building), electricity generated by PV panels, and total charging and discharging from and to EVs and used EV batteries. Charging and dis‐ charging of EV and used EV batteries are represented as dotted blocks in positive and negative sides, respectively. The total grid load during after-noon peak-load time (load leveling test from 13:00 to 16:00) is significantly lower than the building load. This is because of the power generated by PV panels and peak-cut threshold which order the EVs and used EV batteries to discharge their electricity.

Charging and discharging of EVs and used EV batteries in negative sides mainly occur due to charging of used EV batteries during the night time and EVs charging during morning time (before the peak-load time) and during lunch break time. As used EV batteries are charged during night time, the grid load during this time is higher than the building load. EVs generally arrive at the office building at around 08:00 and they are connected to the designated charging stations of EMS. From this moment, charging and discharging behaviors are fully controlled by EMS. Because the building load is still lower than the calculated peak-cut threshold, EVs charging can be conducted until the building load reaches nearly the peak-cut threshold to increase the discharging capacity of the EVs during peak-cut. Moreover, additional charging starts again during noon break (12:00–13:00) because the building load drops drastically creating any marginal grid load.

**Figure 11.** Results of load leveling test during weekdays.

From **Figure 10**, the peak-load occurs twice, i.e., before noon and afternoon, respectively. However, before noon peak-load is lower than the one in afternoon time. The generated electricity by PV is always consumed directly without being charged, hence peak-cut for the before-noon peak-load is conducted only by PV. In addition, the afternoon peak-load starts usually from 13:00 after the end of lunch break time. During this time, the building increases significantly and when it reaches approximately the calculated peak-cut threshold, EMS sends immediately the control command to EVs and used EV batteries to discharge their electricity according to the required amount for load leveling. Hence, the purchased grid load can be kept to lower than the contracted power capacity.

However, due to the limitation of available number of EVs and used EV batteries, load leveling only can be performed in a relatively short duration of time. It is estimated that as the number of EVs and used EV batteries taking part in this ancillary service program increases, more significant effect of load leveling can be achieved. In addition, a longer duration of load leveling and lower value of peak-cut threshold can be obtained accordingly.

**Figure 12** shows the total amount of load leveling in a day by each PV panels, EVs, and used EV battery for 8 months of duration of demonstration test. Furthermore, **Figure 13** shows the averaged total load leveling by each PV panels, EVs, and used EV battery in different months. The used EV batteries have the largest and most stable load leveling share compared to EVs and PV. On the other hand, the generated electricity from PV is quite fluctuating because it is influenced strongly by the weather condition, especially solar intensity. Furthermore, the share of EVs in load leveling is also strongly influenced by their main usage as vehicle because it will affect significantly their SOC (available electricity for load leveling).

**Figure 12.** The amount of load leveling in a day by each component (PV, EVs, and used EV batteries).

stations of EMS. From this moment, charging and discharging behaviors are fully controlled by EMS. Because the building load is still lower than the calculated peak-cut threshold, EVs charging can be conducted until the building load reaches nearly the peak-cut threshold to increase the discharging capacity of the EVs during peak-cut. Moreover, additional charging starts again during noon break (12:00–13:00) because the building load drops drastically

From **Figure 10**, the peak-load occurs twice, i.e., before noon and afternoon, respectively. However, before noon peak-load is lower than the one in afternoon time. The generated electricity by PV is always consumed directly without being charged, hence peak-cut for the before-noon peak-load is conducted only by PV. In addition, the afternoon peak-load starts usually from 13:00 after the end of lunch break time. During this time, the building increases significantly and when it reaches approximately the calculated peak-cut threshold, EMS sends immediately the control command to EVs and used EV batteries to discharge their electricity according to the required amount for load leveling. Hence, the purchased grid load can be kept

However, due to the limitation of available number of EVs and used EV batteries, load leveling only can be performed in a relatively short duration of time. It is estimated that as the number of EVs and used EV batteries taking part in this ancillary service program increases, more significant effect of load leveling can be achieved. In addition, a longer duration of load leveling

**Figure 12** shows the total amount of load leveling in a day by each PV panels, EVs, and used EV battery for 8 months of duration of demonstration test. Furthermore, **Figure 13** shows the averaged total load leveling by each PV panels, EVs, and used EV battery in different months. The used EV batteries have the largest and most stable load leveling share compared to EVs and PV. On the other hand, the generated electricity from PV is quite fluctuating because it is influenced strongly by the weather condition, especially solar intensity. Furthermore, the share of EVs in load leveling is also strongly influenced by their main usage as vehicle because it

creating any marginal grid load.

142 Modeling and Simulation for Electric Vehicle Applications

**Figure 11.** Results of load leveling test during weekdays.

to lower than the contracted power capacity.

and lower value of peak-cut threshold can be obtained accordingly.

will affect significantly their SOC (available electricity for load leveling).

**Figure 13.** Averaged total load leveling by each component (PV, EVs, and used EV batteries).

The uncertainties are brought mainly by three factors: EVs, PV, and building load. These factors result in divergence between the predicted and real values. In this demonstration test, because the capacity of office building load is significantly larger than the total electricity which can be provided by PV panels, EVs, and used EV batteries, it is assumed that the strongest factor influencing this uncertainty is the building load, especially demands for air conditioning. Moreover, PV also gives an additional influence to this uncertainty due to its fluctuation.

As the results of conducted demonstration test, the predicted grid load showed a relatively high similarity with the real grid load. However, the difference between the predicted building load and its real load is relatively large due to the above-mentioned uncertainties.

The divergences in office building load and generated electricity from PV panels can be lowered by controlling the charging and discharging amounts from both EVs and used EV batteries. In addition, in this demonstration test, the uncertainty related to EV availability and its capacity do not show any significant impact. It is because the drivers of EVs are basically the employees who are working in the office building and almost the commuting routes are constant every day. It is considered that although there is an uncertainty on EVs' availability and their capacity, the divergence between the predicted and real grid loads could be reduced as long as the capacity of the used EV batteries which are owned by EMS are able to cover those fluctuating factors.
