In this section a brief description of the economical considerations related to this type of investment is described. Prices for chargers are retrieved from [36]. Prices range from 3000, DKK to 23 000, DKK for a dumb charger. Price of a smart charger range from 14200, DKK to 29200, DKK. A price of 5000, DKK is assumed for a dumb charger, and 15000, DKK for a smart charger. The price for upgrading a tariff fuse is assumed a one time 1000, DKK per Amp. It could very well be that if you were to upgrade from a 63 Amps fuse, to 288 Amps, there would be other cost, such as cable upgrade, potential increased effect tariffs, etc. But for sake of simplicity these are not considered. These are prices for household fuse upgrades, and not for a workplace where prices probably would be different. However this is just to give an indication of the price range we are discussing. Figure 4.14 gives an overview of prices for different investments.
Study cases and economic incentives
4 study cases, 1 of which is dumb charging
4000,- DKK per dumb charger 11000,- DKK per smart charger 1000,- DKK/Amp increase
18 3.68(1-p) 288 18 90000 225000 315000
18 3.68(1-p) 208 13 65000 145000 210000
18 3.68(1-p) 63 9 135000 0 135000
Figure 4.14: Investment cost of to charging equipment, depending on type and required current
The first investment with 18 dumb chargers, would cost approximately 315 000, DKK.
If we only consider 13 chargers, the price goes down for both number of chargers, and required fuse size to 210 000, DKK. With the smart charging investment one could stay on the fuse level of 63 Amps and get away with only investing in smart chargers. This would cost approximately 135 000, DKK. As shown above, there are significant saving to be made if smart chargers are selected. A smart charger is more likely to be capable of receiving future software updates, and new smart functions related to the internet of things, and so on. Not to mention the potential for participating in future flexibility markets, as a part of an aggregation of EVs controlled in a distributed manner. User remuneration is not considered in this thesis, but with the distributed control architecture, it is not long before this is attainable.
5 Conclusion
The thesis work focused on designing and implementing coordinated and autonomous charging control in a workplace parking lot, by use of a distributed control architecture. The results presented are based on a model created in Simulink. The first step was to design a charger controller which could coordinate and provide charging to a total number of 18 EVs autonomously. The overall goal was to develop a charger control that could provide behind the meter services such as power limitation, load balancing, by use of a virtual aggregator responding to demandresponse signals. The model utilised historical driving patterns from 18 Nissan Leaf. The work focused on two main research questions, as listed in section 1.2. This chapter aims to conclude them, relying on results an knowledge gained.
• How is it possible to design a coordinated and autonomous charging con
troller? Before starting the design process, information about the distributed con
trol architecture had to be acquired from different sources. This lead to a better understanding of the project, and the design process could start. The design of the coordinated and autonomous charging controller was build step by step in Simulink.
The model should reflect a realistic charging model, and therefore input values, de
lays, and information flow needed to me properly designed. The main inputs are the historical EV data, containing arrival time, departure time, and initial SOC when arriving. Other inputs such as the POCC limit or fuse size, historical PV data, and Office loads was also considered. Measurement cycle from the smart meter, delay related to the onboard EV charging system, and transportation delay from the VA to the other chargers was included. Outputs of the model is the SOC for every EV and the charging power from every charger. The connection logic process is also of great importance, which is the logic that makes sure power limitation is kept, and that the power is distributed fairly between the EVs. A priority logic is also designed to improve charging fairness, and emulate a last call function.
• How is it possible to assess the grid service flexibility of an aggregation of EVs? To assess the grid services flexibility, flexibility have to be defined. In this case, flexibility is defined as the ability to respond to a demand response signal, which is a dynamic power reference distributed to all chargers from the VA. The VA could receive a signal from the DSO or TSO, if the VA has placed a bid in the mFRR market, which the DSO or TSO can then activate if needed. The aggregation of EVs requires a certain size, to be allowed to participate in this market. Usually in the MW size, but depends on legislation in the area. If activated the aggregation of EVs will reduce its demand, and thus provide flexibility to the grid operator. Another type of grid service flexibility could be behind the meter services, such as power balancing with local PV production and consumption. Another mode EVs could provide flexibility is by adjusting the dynamic power reference signal after a price signal from Nordpool. Other operational modes could be to operate below the power limit, for example 75% or 50% of the power limit at the POCC, defined by the fuse and cable connected.
5.1 Future work
Under this section it will be described some future topics and proposals that are not cov
ered in this thesis:
• Improve the connection logic functionality.
• Implement VA redundancy.
• Design and implement a disconnect logic that will allow the VA to disconnect EVs as seen fit, if a sudden flexibility is required.
• Investigate user remuneration from provided flexibility.
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A A
A.1 Spike explanation
The spike investigated is the one highlighted with blue in figure A.1 from case 1a.
06:00 09:00 12:00 15:00 18:00 21:00 Time [hours]
0 2 4 6 8 10 12 14 16 18
Power[kW]
0 10 20 30 40 50 60 70
Ampere[A]
Case 1
Figure A.1: Overview ofPmeasured,largest spike from case 1a marked in blue
The enlarged blue area is shown in figure A.2. Four points are marked illustrating the mea
surements of the 10 second measurement cycle. These points show the actual values, Pmeasuredthat are being sent from the smartmeter to the VA.
5.846 5.847 5.848 5.849 5.85 5.851
Figure A.2: Detailed overview ofPmeasured, largest spike from Case 1a
The same points from figure A.2 in terms of time, are marked in figure A.3. The yaxis shows individual charging power, the points describe the totalPmeasuredat the same time.
The first point at t = 58461, also represent the Pref erence at 14.49 kW. The system is in perfect balance before the event starts, in other words the measured error, ∆P = 0.
Shortly after this, between 12 seconds, charger 8 disconnects. The smartmeter is not distributing a new signal until 8 more seconds has passed, as seen in figure A.2. On the contrary, the VA is immediately notified that a charger has disconnected, and allows the next charger, which is first in the queue, to connect, but not to start charging. The VA starts to distribute the∆Ptemporarily, for 10 seconds, as described in equation 3.5. Normally this is a function that is activated when a charger connects, to reduce overshoot that occur the moment the charging begins. At the point of the second measurement cycle, the output power has been reduced for a period of 8 out of the 10 seconds, as is should do. At this time, the smartmeter also distributes a new value,Pmeasured. To summarise, the∆Ptemporarily function is still active, output power have been reduced, and charger 8 recently disconnected. These three factors add up, and changes the error term to a bigger then desired. ∆P is now a large negative value, that goes into the charging controller.
The PID will try to eliminate the error by increasing the output signal, and thus increase charging power. The short delay before charging power increases is due to the transport delay from the VA, to the different chargers. Since the error term is very negative, the increased charging power response is equivalent positive. Around the same time charger 6 starts charging, the∆Ptemporarily function deactivates, which causes the slope of the charging to slightly reduce it self. However the response form the controller is so strong that within the third measurement cycle, thePmeasured have exceeded the 63 Amp limit, and the result is a spike in total power consumption. This spike is regarded as a worst case, where the smartmeter have just measured before a charger disconnects. Also, case 1 is without priority, which has a side effect that tend to reduce spikes, although that is not the primary function of the priority.
5.846 5.847 5.848 5.849 5.85 5.851
Time [seconds] #104
1.5 2 2.5 3 3.5
Power[kW]
charger1 charger2 charger3 charger4 charger5 charger6 charger8 charger9 charger10
First measurement cycle at time 58461
Last measurement before charger 8 disconnects Pmeasured 14.49
Second measurement cycle at 58471 Pmeasured 9.31596
Thrid measurement cycle at time 58481 Pmeasured 16.4261
Charger 6 starts charging
Figure A.3: Detailed overview of chargers, largest spike from Case 1a
A.2 Connection queue logic explanation
A.2.1 Queuing logic
The reason for case 1c is not charging to its fully extent is related to mainly two reasons.
The first reason requires an explanation of the queuing logic. The charging queue logic consist of a integrator that can be thought of as a counting device. When a charger is de
nied charging, the integrator starts counting, and its value increases. If a second charger is denied charging, another integrator start counting. Every charger has an counter. To be allowed to charge, it has to be the the counter with the highest counted value, as well as other criteria such a as available capacity at POCC. Ideally the integrator would been reset, when the charger was allowed to charge. But due to time constrain of the thesis, this was not possible to achieve without violating other aspects, more important to the model, such as power limitation. The downside to this is that the counter for the charger second in queue has to surpass the counted value of the charger first in queue. This cause a delay for charging to start, and can effect the individual SOC of the charger that has to wait. However, in the meantime the other chargers will increase their power output.
Therefore the total energy charged will only have a slightly reduced value. But when it is getting close to the end of the day, and this queue delay could cause an EV to not fulfil its goal, because of the reduced available charging time due to the counting delay.
A.2.2 Connection logic
The second reason is related to the connection logic. To be able to connect, there has to be available capacity at the POCC. This is checked by reducing the∆P temporarily, with an value given by equation 3.5. The function that checks available capacity is triggered by an EV connecting, or disconnecting. This works fine most of the time. But some times not, and that is related to the described queue logic above. In the event, that two chargers disconnects relatively close in time, charger 1 and charger 2. And there are two chargers in queue, charger 3, and charger 4. The first charger in queue, charger 3, will connect just after the charger 1 disconnected, since this charger have waited for the longest time.
Now, because the integrator counter is not reset, charger 4, which is in queue will now have to count until it surpass the waiting time of the charger 3. In the mean time charger 2 disconnect, triggers the capacity check function, but there is no charger which is first in the queue, because charger 4 is waiting for the counter to surpass charger 3. After the
∆P temporarily reduction, other chargers will increase their charging power again to use all of the available capacity at the POCC. Charger 4 will have to wait until next time the POCC is reduced.
Technical University of Denmark
348, Ørsteds Pl.
2800 Kongens Lyngby Tlf. 4525 1700
NTNU Norwegian University of Science and Technology Faculty of Natural Sciences Department of Electric Power Engineering
Teis Kloster Skogland
Charging flexibility from electric vehicles via autonomous chargers in a workplace
Juli 2021
Master’s thesis in Innovative Sustainable Energy Engineering -System Integration of Wind Power
Supervisor: Mattia Marinelli
Co-supervisor: Kristian Sevdari, Lisa Calearo, Simone Striani, Venkatachalam Lakshmanan, Kenta Suzuki
July 2021