Generation Capacities

The temporally resolved total power demand of 500 000 PEVs and thereby the overall impact of road transportation electrification with regard to existing generation capacities is illustrated in Figure 4.8. Depending on the scheduling strategy the additional daily power and energy demand are summarized in Table 4.9 and explained in the following:

• Dumb charging (Figure 4.8a)

Whenever a PEV stops at a CS, an additional power demand is induced to fully charge the battery as fast as possible. This power demand is not tied to any peaks or valleys in the regular load curve but only depends on the itineraries of the PEVs. This way, an aggregated demand peak is formed which is 104 MW higher than the regular demand peak while valleys remain largely unused. The maximum additional daily power demand is 282 MW of which 139 MW remain unsatisfied affecting 2 074 or 1.8 % of the used

4000

Figure 4.8: Regular load curve including the additional power demand induced by 500 000 PEVs using the (a) dumb, (b) mean, (c) price-responsive charging, and (d) price-responsive charging/discharging strategy. The power demand which cannot be satisfied due to reasons of limited substation or power line capacities is separately shown in black.

115 663 locations. The additional total daily energy demand which cannot be satisfied is 981 MWh and thereby conforms to about half of the total demand³.

• Mean charging (Figure 4.8b)

The additional power demand induced by each PEV is distributed over its stop’s duration. Due to the large number of PEVs the additional power demand of the entire

3 The additional daily energy demand induced by 500 000 PEVs is 1 978 MWh and is calculated applying the values provided in Table 4.8 as

Total trip length [km]·Energy demand/km + Total trip duration [h]·Energy demand/hour (aircon) Charging efficiency

Table 4.9: Additional daily power and energy demand induced by 500 000 PEVs from the perspective of the power grid. The demand depends on the applied scheduling strategy.

Dumb charging 0 282 139 6 444 981 1.8

Mean charging 68 106 4 6 442 41 2.4

Pr.-resp. charging 0 464 35 6 340 119 4.7

Pr.-resp. ch./dis. 1 216 -688 417 5 652 – –

PEV population is temporally evenly distributed. This way, generation capacities are additionally almost equally stressed throughout the day. The aggregated peak power demand is 6 442 MW which is an increase of 102 MW compared to the regular demand peak. The maximum additional power demand is 106 MW of which only 4 MW remain unsatisfied. Due to substation or power line limitations, a total of 2 781 and thus 2.4 % of all used locations are affected by a forced reduction in power demand which is assumed in the power flow model as described in Section 2.3.2.2. The additional total daily unsatisfied energy demand is as low as 41 MWh.

• Price-responsive charging (Figure 4.8c)

The target of scheduling the PEVs’ batteries with the price-responsive charging strategy differs from the two previous strategies. While using dumb charging the PEV has to be fully charged as fast as possible; mean charging targets a full battery at the end of each stop. Using price-responsive charging the battery has to be fully charged within the lookahead while at the same time guaranteeing a sufficient charge for conducting each trip. This allows choosing the least cost time periods for charging within the lookahead.

By defining prices reflecting the current load, peaks are not further increased but instead charging happens at demand valleys only. The maximum additional power demand is 464 MW of which 35 MW remain unsatisfied. A total of 4.7 % of all used locations⁴ are affected by the applied reduction of their power demand. The additional total daily energy demand which is left unsatisfied is 119 MWh.

• Price-responsive charging/discharging (Figure 4.8d)

Again, the target of this scheduling strategy has shifted compared to other strategies.

While it is still the goal to fully charge the battery within the lookahead by using least cost time periods, discharging the battery to provide energy to the grid is allowed.

This way, demand valleys are not only used for charging the battery but also demand

4 As explained in Section 4.3.2, only 100 000 agents are scheduled for the price-responsive charging strategy.

The power demand of each agent is thereby scaled by a factor of 5 to emulate a scenario with 500 000 PEVs.

This results in each of the 84 383 locations having to satisfy a higher power demand than in the case of directly scheduling 500 000 PEVs. A load reduction is therefore more likely making the ratio of affected locations using this strategy not comparable with the ratio involving 500 000 agents.

peaks are used for discharging. This allows reducing daily peak demands by more than 10 % (indicated by the blue-white dotted area in Figure 4.8d). Using this strategy, the load curve can be totally flattened showing a constant temporal aggregated power demand of 5 652 MW throughout the day. The total and unsatisfied additional daily energy demand are distorted due to additional charging/discharging cycles targeting the flattening of the daily power demand. Each of those cycles additionally incur power losses resulting in a further energy demand matching the batteries’ charging/discharging efficiency. Respective numbers are not separable into the demand resulting from driving and the one from simply shifting power. They are therefore incomparable with those of previous scheduling strategies which is why they are not provided in Table 4.9.

The price-responsive charging/discharging strategy was implemented as a proof-of-concept that applying a matching strategy a total load curve flattening effect can be achieved only by varying electricity prices and remunerations while still considering battery depreciation.

Remunerations as assumed in this study correlate to the electricity prices and are therefore unrealistically high. By implementing current prices for providing energy to the grid, this scheduling strategy would only marginally differ from the price-responsive charging strategy.

It is the costs arising through battery depreciation and the auxiliary demand caused by conversion losses that currently restrict an economically feasible application of this strategy to mainly providing ancillary services [215] and only in exceptional cases for peak shaving as described in [15]. Although it is beneficial to provide a total load curve flattening effect, the price-responsive charging/discharging strategy is therefore not further considered in the remainder of this study. In this work, the focus is put to the price-responsive charging strategy with its positive load curve valley filling effect.

Both power and energy demand need to be considered when investigating the impact on generation capacities. From the perspective of power generation capacities necessary to compensate the higher peak power demand, the increased requirement is with 1.6 % rather moderate when applying the dumb or mean charging strategy, especially when considering current spare capacities of around 47 %. Using any of the price-responsive scheduling strategies, there is no additional power demand which further increases the maximum power supply.

Regardless of the applied strategy, the additional daily energy demand induced by 500 000 PEVs is about 2 GWh which is as little as 1.5 % of the regular daily demand. Some fraction of this demand depending on the strategy can, however, not be satisfied. Although only small parts of the PNM are affected, detailed investigation on the substation and power line utilization, especially in those parts, is required and provided in the following Sections 4.3.2.2 and 4.3.2.3.