Distribution systems are becoming more and more complex with the growth of distributed generation, behind the meter storage and electric vehicles (EVs). The classical model of a distribution network with top down unidirectional flow of electricity and predictable daily consumption patterns is no longer valid, and new approaches are required to cater for the changes in consumption pattern brought about by EVs.
The biggest immediate effect will be that caused by private EV charging, which typically will happen in the evening hours when the contribution from rooftop solar will be zero, and energy will be drawn directly from the grid. There are two main places where the batteries of EVs can be recharged: either at a car park, corporate or public, or at home. This includes pluggable hybrid EVs (PHEVs) which can be charged at home or otherwise.
From the distribution system operator point of view, the power losses during charging are an economic concern and have to be minimised and transformer and feeder overloads have to be avoided. Not only power losses, but also power quality (e.g. voltage profile, unbalance, harmonics, etc.,) are essential to the distribution grid operator as well as to grid customers. Voltage deviations are a power quality concern. Too large voltage deviations cause reliability problems which must be avoided to assure good operation of electric appliances.Overnight recharging can also increase the loading of base-load power plants and smoothen their daily cycle or avoid additional generator start-ups which would otherwise decrease the overall efficiency.
From the EV owner’s point of view, the batteries of the EV have to be charged overnight so the driver can drive off in the morning with a fully-charged battery. This gives opportunities for intelligent or smart charging. The coordination of the charging could be done remotely in order to shift the demand to periods of lower load consumption and thus avoiding higher peaks in electricity consumption. Uncoordinated charging of EVs decreases the efficiency of the distribution grid .
The greatest immediate impact is expected to come from the increase in the number of privately owned electric vehicles (EV). Studies show that in most cases of urban commuter vehicles, charging will take place at both the residence and the workplace. Charging at the workplace, e.g. a commercial or industrial site, is not expected to create problems, because of the total demand on the site, but charging at residences poses a problem. Most residences have single phase feeds, with a maximum load of the order of 12 kW, and are fed from a distribution transformer on a block or street basis. Most EV charging is expected to take place at the residence, and studies are based on this assumption.
The overall effect of EV charging the grid and generation capacity is expected to be minimal, but there is expected to be a significant impact on distribution networks, which are not designed to handle additional loads of this size. Clustered charging, when multiple electric vehicles charge in the same area, is a risk to local distribution infrastructure. Ways of managing local load such static price signals will prove to be less effective than before. The dynamic nature of shifting load as a result of vehicle charging is more challenging to predict and manage. The charging of EVs has an impact on the distribution grid because these vehicles consume a large amount of electrical energy, and uncontrolled, unregulated or unscheduled charging can result in large and undesirable peaks in electrical consumption.
EV charging methods
The most common method of charging is via a fixed charge rate charger, which supplies energy to the battery at a fixed rate. The power consumption of chargers depends on the level of charger used:
The EV battery capacity will determine the charge time of the vehicle. Several regimes are possible:
In this regime the battery is charged to its full capacity, irrespective of the state of charge. two further options are possible
The battery is only charged with the amount of energy required for the next trip, irrespective of the state of charge. This may include a strategic reserve amount.
The actual charge may depend on the time available at the charging point, e.g. work etc. The general thinking assumes that the EV owner will want to leave home with a fully charged battery . This has something to do with the phenomenon of “range anxiety” among EV owners. Partial charging may apply when the vehicle owner needs to leave the charging point before full charge state has been achieved. Many of the models assume that a vehicle will remain at the charging point once it is there and ignores possible multiple trips during the day and use of the vehicle during the evening at home.
Impact of EV charging on the distribution grid
EV charging introduces a regular daily additional load to the distribution grid. This results in:
The primary impact will be increased peaks, which can result in voltage stability problems and increased losses. A further impact will however be on the lifetime of grid equipment, such as transformers and switches, which will now carry a higher average load and have a higher load factor , independent of the way in which charging is controlled, and this is expected to result in decreased life expectancy and higher failure rates. Several studies have attempted to approach this problem with controlled charging, but this does not reduce the increase in average power and load factor.
The impact will depend on the configuration of the distribution branch affected, including such factors as transformer rating, transformer load factor, the number of consumers in the branch, the average load per consumer, the individual peak loads, and the combined peak load on the branch. Branches feeding a small number customers with low loads can be affected more than large feeds with higher individual loads. the main problem foreseen is that that actual loading of distribution circuits is unknown on most cases, and could vary from lightly loaded to close to full loading before the addition of EV chargers. The current tendency is to design distribution circuits with higher load factors and less overload capacity to cut costs, and this could place the circuit at risk when EV chargers are added.
Several studies have been done on small distribution networks consisting of a 25 kVA distribution transformer feeding between six and eight customers [2, 3]. The load limits for the transformers in these networks are based on loading and utilization curves that pre-date the adoption of EVs. When these loading limits are exceeded, the transformer insulation will deteriorate at a faster rate and the normal life of the transformer is reduced . Fig.2 shows the effect of adding a single EV with a 6 kW charger to such a network.
Consider a residence with a peak load of 12 kW, connected to a distribution transformer serving 40 such residences, which will have a peak load of 480kVA . Taking a diversity factor of 0,6 gives a maximum rating of about 300 kW. If the distribution transformer is allowed to run at 150% of the rating during peaks then the rating would be 200 kW. if the average domestic peak load is 4 kW , the average peak loading would be 160 kW. Adding 10 simultaneous chargers raised the demand by 60 Kw to 220 kW. Adding five simultaneous chargers raises to 190 kW for several hours. If the feeder is reduced to 20 residences and a 100 kW transformer the average load becomes 80 kW and adding the EVs raises the load to 140 kW and 110 kW respectively, for the full charging period. The use of fast chargers exacerbates the problem. Depending on the design of the distribution network and the demand profiles, it can be seen that adding EV charging with a small number of vehicles could have a significant effect on the distribution network loading.
Distribution transformer ageing
Power transformers are one of the most expensive components in a distribution network. With the increasing penetration of electric vehicles, new load peak may be created, which may exceed the transformer capacity. Therefore, in a residential house, owning an electric vehicle may mean a need to upgrade the utility’s local transformer or lead to early replacement .Reduction in transformer life expectancy will result in an increase of costs to utilities and consumers. Hence, the reduced transformer life becomes a very important impact when extra load is taken into consideration. Several studies have attempted to estimate the impact on transformer life of EV charging, with and without charging control. Fig. 3 shows the results one such study, where a charging algorithms (DP) have been developed to reduce the impact of EV charging.
Another concern is phase imbalance. EV chargers are added in a random fashion to the distribution network, and in the worst case, with a low penetration of EVs, all the EV chargers could be placed on the same phase. This creates a peak on one phase only, with resultant problems of current and voltage unbalance.
Control of charging
Distribution planners will have to take into consideration these operating issues while developing both short- and long-term system development plans. Close monitoring of peak load forecasts, line and equipment capabilities is required to avoid overloading facilities.
Based on operating data and the loading of distribution transformers and feeders during peak and off-peak periods, distribution transformer and feeder upgrades may need to be considered. However, before implementing these upgrades, the advantages of time-of-use (TOU) pricing and other forms of demand side management may help offset these operating issues and planners would also have to consider this strategy. Justifications for demand side management include avoiding transformer damage and loss of power to customers which may take a long time to restore .
Ultimately the network will have to be strengthened to accommodate a high percentage of EVs up the max amount. With multi car households predominating, EV charging could be the critical factor in the design of distribution network. In the mean time there are a number of algorithms available which could be used to mitigate the effects of EV charging on a existing network. Several very complex algorithms have been developed which require communication between the charger and a network controller , but there are also failrly simple methods by which charging can be controlled to reduce the load.
Staggered charging attempts to spread the charging period in time blocks over the full day rather than a single charge period. This technique would be more applicable to fleet EVs than privately owned vehicles, as the charging rate for an individual vehicle cannot be changed, and is independent of the state of charge of the battery, see Fig. 4.
Time of use (ToU) metering
EVs are equipped with charge controllers that can set the charging period to specific times of day. A common way of shifting the peaks and reducing the total demand is time of usage charging, which offers lower rates during off peak periods and encourages charging during the late night early morning period, which would still allow a full charge to be achieved before leaving the next day. This has proved to be beneficial, but has the drawback that many users set the start of charge to the same time, resulting in a sudden increase of load on the network. This also has the effect of not allowing transformers to cool down during the off peak period. This type of control can also be implemented with smart meters, where the EV charger is fed from the meter load control circuit. Although ToU charging reduces the peak load it creates a new high load during the off-peak period.
Direct load control (DLU)
This is a method of charge control by the utility that limits or controls the charge rate based on conditions on the network, and the state of charge of the battery. Assume that all EVs begin the charging process at a “start time” according to the real-time pricing and direct load control (DLC) signal, and finish charging at a customer-specified “stop time”. Individual EVs have different initial state of charge (SOC ) levels but the same target SOC values. Fig. 5 shows an envelope of possible EV charging paths confined by both ToU and DLC signals, in which the x-axis represents the time and the y-axis represents EV battery SOC.
The blue curve on the left is the earliest charging path between points A and B. With this path, a PEV is charged with largest allowable charging rate continuously until the target SOC is reached. Any path on the left side of the blue path is not allowed due to the DLC signals. The red path represents the latest possible charging path for a PEV to be charged from initial SOC to target SOC within a specified charging period. With the red path, the charging is not started until the last possible time to finish charging by following the DLC signals.
Variable charging rates
An option which has not been explored is the use of variable charging rates, ie setting the charge rate according to the state of charge of the battery and the time required to reach full charge. Assuming the same charging time is available for all EVs, a reduction in peak charging current could be achieved. ( Fig.6) For four vehicles the combined charging rate amounts to 50% of the full charging rate for all 4 EVs. This however will require the use of “smart” chargers, which have not yet appeared on the market.
 E Schmidt: “The impact of growing electric vehicle adoption on electric utility grids”, Fleetcarma, August 2017.
 C Cao: “Mitigation of the impact of high plug-in electric vehicle penetration on residential distribution grid using smart charging strategies”, Energies 2016, issue 9.
 S Orioefo: “Effects of large-scale penetration of electric vehicles on the distribution network and mitigation by demand side management”, Msc Thesis, Virginia polytech.
 O Beaude: “Reducing the Impact of EV Charging Operations on the Distribution Network”, Transactions on smart grid, September 2015.
 G Putrus, et al: “Impact of electric vehicles on power distribution networks”, Northumbria University.
Send your comments to email@example.com
The post The effect of EV charging on distribution system planning and modelling appeared first on EE Publishers.
Source: EE plublishers