INQUIETUDES SOBRE ASPECTOS DE EDUCACIÓN Y DESARROLLO

In order to compare the performance of the different charging strategies in a residential area, a one day simulation was run with ∆T set to 5 minutes and ∆t set to 5 seconds. The simulation was conducted on a residential low-voltage distribution network with S= 3 and N = 160. The houses are distributed evenly across phases with maximum 50% EVs randomly connected in three household areas. The topology of the network is given in Figure 3.1.

This distribution network was modelled and implemented using a custom OpenDSS/ Mat-lab simulation platform. OpenDSS [44], an open source electric power system Distribution System Simulator, was used to simulate the power system and calculate the instantaneous power flows and voltage profiles for the test network. Matlab was used to simulate typical residential EV connection, SOC and disconnection patterns (randomly generated for each EV) and to create a wrapper programme to simulate the operation of the network over a period of time for varying household and EV loads based on various charging strategies. The main steps performed by the wrapper programme are summarised in Algorithm 4.

3.7 Comparisons of ‘Continuous’ EV Charging Strategies 52

Algorithm 4 OpenDSS-Matlab Wrapper Programme for EV Charging Simulation for k= 1,2, . . ., M do

1. Sense the amount of energy available P_av(k )

2. Each household generate its current non-EV load, h_i(k ).

3. Calculate the available power for EV charging P_av^ev(k ) (optional) for t= 1,2, . . .,T do

4. Determine the set of EVs currently connected to the grid.

5. For each EV compute its instantaneous charge rate, ci(t), according to the selected algorithm.

6. Generate an updated OpenDSS simulation parameters file.

7. Call the OpenDSS software to simulate the current state of the distribution network.

8. Record the current values of relevant EV and distribution network states (con-nection status, SOC, line voltages, substation power flows etc.)

end for end for

Comment: The wrapper programme provides an generalised OpenDSS/Matlab simulation platform for implementation of the instantaneous based charging approaches. For instance, the selected algorithms in the fifth step could include AIMD, DPF, ICIC, distributed on-off and their extended versions. Note that temporal based charging approaches (e.g. CCCM and CLVM) are not included here since they require different simulation setups.

Residential power consumption profiles for each scenario were generated based on residential customer smart meter electricity trial data provided by the Commission for Energy Regulation(CER) [26] in Ireland. This dataset consists of time series demand data for 4225 residential customers over 536 days, sampled every 30 minutes starting from 15^thof July 2009. The non-EV household load profiles for each of the 160 houses in the test network were generated by randomly selecting load profiles from the CER dataset and upsampling them using linear interpolation to the desired sampling interval ∆T . To illustrate this dataset, we consider a typical summer scenario from the period 22^ndof July to 24^th of July 2009, and a winter scenario from the period 22^ndof January to 24^th of January 2010. A comparison diagram of the average load consumption for 4225 residential smart meters during both periods is shown in Figure 3.5. As expected this shows that the average power consumption during typical Irish winter days is much higher than during summer days. In particular, the peak power consumption during the three winter days is more than 50% greater than the summer peaks. In our simulation study we consider a typical winter day scenario for better illustration of the performance of the algorithms. The average non-EV household load consumption in each of our charging scenarios is illustrated in Figure 3.6.

3.7 Comparisons of ‘Continuous’ EV Charging Strategies 53

12am 12pm 12am + [1d] 12pm 12am + [2d] 12pm 12am + [3d]

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2

Time (hours)

Power (kW)

Summer Winter

Fig. 3.5 Average summer and winter residential power consumption profiles in Ireland as computed from smart meter trial data of 4225 customers [26]

12pm 4pm 8pm 12am 4am 8am 12pm

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

Time(hours)

Power (kW)

Fig. 3.6 Average residential power consumption profiles for 160 customers on January 23^rd 2010 [26]

3.7 Comparisons of ‘Continuous’ EV Charging Strategies 54

The assumptions on EV travelling patterns, and hence SOC and plug-in/out probability distributions, were taken from [36]. For EV connection patterns, we assume that EVs arrive home and connect to the charge points between 4pm-7pm (21% of all daily journeys in Irish Urban area) with the following time distribution: 4pm-5pm (33%), 5pm-6pm (38%), 6pm-7pm (29%). Similarly, for EV disconnection patterns, we assume that most of them depart from home between 7am and 10am (19% of all daily journeys in Irish Urban areas) with the following time distribution: 7am-8am (21%), 8am-9am (47%), 9am-10am (32%).

The battery capacity for each EV was assumed to be 20kWh and the initial energy required for each EV was normally distributed. The mean of the distribution was set to 10kWh and the standard deviation was chosen as 1.5kWh. This means 99.9% of the EVs require between 5kWh and 15kWh and 81.8% require between 8kWh and 12kWh to fully charge their batteries. For comparison purposes, the same plug-in/out and SOC values were used with each method considered. It was assumed that all EVs charged overnight and that once an EV was plugged in it only physically plugged out at the scheduled plug-out time.

Several assumptions are also made with regard to EVs and residential EV charging infrastructure, which are consistent with previous studies in [164, 189]. The assumptions are as follows.

1. The nominal voltage of each EV charge point is set at 230V.

2. The maximum power output from the EV home charger cannot exceed 3.7kW.

3. The maximum available power can be provided from the grid is 400kVA.

4. Each EV has the ability to adapt its charge rate in real-time and continuously.

5. Power flow for EV charging is unidirectional from grid to vehicle (i.e. vehicle-to-grid is not considered).

We now present a comparison of the listed charging strategies grouped according to their different characteristics as follows:

1. Uncoordinated charging strategy

2. Fairness based (continuous chargers) strategies : AIMD, DPF, EnAIMD, EnDPF and ICIC and their TOU price adjusted extensions

3. Cost minimisation strategies: DSC and CCCM 4. Valley-Filling strategies: ODVF and CLVM

3.7 Comparisons of ‘Continuous’ EV Charging Strategies 55