Personalisation of Education by AI and Big Data - Lourdes Guàrdia
FYP Review 3 Presentation about EV c.pdf
1. Coordination of Electric Vehicles in
Multi-vector energy system.
Chhangani Saurav Kishor - BT20EEE025
Dounde Suyash Deepak - BT20EEE033
Kamble Mayank Taresh - BT20EEE067
Pushpendra Saini - BT20EEE088
Project Guide - Dr. Soumyabrata Das
2. Index
1. General Introduction
2. Work done till now
3. System description
4. Objective
5. Results
6. Analysis
7. Future Work
8. References
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3. 1. General Introduction
The project focuses on optimized placement of electric vehicles charging stations as
well as renewable energy distributed generators. A literature survey on the placement
of Electric Vehicle charging stations (EVCS) involves a comprehensive review of
various research papers and publications on the topic.
For the optimized placement of electric vehicle charging stations as well as renewable
energy distributed generators, some important factors have to be optimized.
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4. 1. General Introduction
● Location of EVCS Unit
● Size of EVCS
● Total Cost for placement
● Power Loss
● Voltage Deviation
● The Captured Traffic Flow
● Load Variance
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5. 2. Work Done till Now
1] A literature survey on the placement of Electric Vehicle charging stations (EVCS)
involves a comprehensive review of various research papers and publications on the
topic.
2] In the literature Survey, a number of research papers regarding optimized
placement of electric vehicles charging stations have been studied. Based on those
research papers we have observed some research gaps like User Behavior and Demand
Prediction, Impact on Grid Infrastructure, Grid Capacity and Stability, Public vs.
Private Charging Infrastructure, Considering the financial aspect, Long-term Effects of
Pricing etc.
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6. 2. Work Done till Now
3] We have obtained some combinations of two nodes and their corresponding load
size in the view of minimum power loss
4] One parking lot
In this we added all of the vehicle load on 1 node, and we did that for every node and
observed power loss trends.
Minimum power loss = 206.037 kW (for node 2)
Maximum power loss = 352.9917 kW (for node 18)
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8. 2. Work Done till Now
Out of the favorable pairs, 22 and 7 makes the best pair because they are on different
radial partitions of 33 bus system and 7 is almost in the middle of the main radial
partition.
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9. 3. System Description
The IEEE 33-bus system is a commonly used benchmark power system in the field of power
system analysis and research. It is part of the IEEE test cases for power system studies and has
33 buses representing different nodes in a power distribution or transmission network. These
buses are connected by transmission lines and transformers, forming a network that can be
used to study various aspects of power system behavior.
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10. We have selected these set of vehicles to perform our optimisation analysis
Electric car Battery Capacity (kWh) Range (km) Quantity Total Battery Capacity (kWh)
Tata Tigor EV 26 306 13 338
Audi e-tron SUV and
Sportback 91 484 2 182
Mahindra e2o 16 120 4 64
BMW iX 76.6 425 1 76.6
Total number of vehicles = 20
Total vehicle load = 660.6 kWh
Total vehicle load (for charging 60 % of SOC) = 396 kWh -[2]
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3. System Description
13. 4. Objective
Maximizing Captured Traffic Flow
Variable 𝐼𝑐𝑎𝑝(𝑡, 𝐴), which indicates whether the distance between trajectory 𝑡 and set 𝐴
is greater than a given threshold threshold.
𝐼𝑐𝑎𝑝(𝑡,𝐴)= 1, 𝑖𝑓 ∃𝑝 ∈ 𝐴, 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑡, 𝑝) ≤ 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑
0 ,otherwise
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14. 4. Objective
By counting the total number of trajectories captured by set 𝐴, we can get the captured
traffic flow of set 𝐴 as shown.[3]
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15. 4. Objective
Maximum Captured Traffic Flow (MCT)
● In the last review we calculated ideal position of evcs with respect to power loss minimization
upon placement of load, like that we are now trying to find out ideal position of evcs with
respect to another parameter and that is Maximization of Captured Traffic flow.
● By this I mean that how number of trips or distance travelled by an ev is affected on basis of
evcs placement.
● We have developed a code for simulation of 20 vehicles which calculates their distance traveled
and updates their SOC after each iteration until its soc reaches 0.1
● It starts with an soc of 0.9 and distance between each node is 5 kms, SOC calculation formula is
SOC(i) = SOC(i-1) - D(i)/D(max)
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16. 4. Objective
● Now this program works in isolation from the actual IEEE33 bus system upon which we are
projecting these trips on, so to generate 2 random nodes (shortest distance between whose
serves as our range) and calculates shortest distance between them. This helps in random
distances that are generated in the earlier file with assigning them nodes on the grid.
● Now how this trip actually works is that one of the nodes is where evcs is placed and the
vehicles start from their and travel a randomly generated distance and comes back to the
original node and if it's SOC > 0.1 then goes for next iteration/trip.
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17. For example, random nodes generated are 12 and 28. Shortest distance = 45km
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18. 5. Results
Trial 1 : [Node 3, 17] [Range : 70 kms]
No of iterations = 6
Total distance = 4350 km
No of trips = 50
Trial 2 : [Node 12, 26] [Range : 35 kms]
No of iterations = 10
Total distance = 4720 km
No of trips = 104
Trial 3 : [Node 4, 30] [Range : 70 kms]
No of iterations = 10
Total distance = 4860 km
No of trips = 105
These results are not optimised for placements,
random node generation and analysing the
results. For optimisation we will have to use
optimisation algorithms such as PSO.
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19. 6. Analysis
● For optimum location to maximise captured traffic flow using PSO, we found out
that it is generating only two consecutive nodes.
● It is helping in increasing the number of iterations, but the trip distance is very
short.
● We will have to introduce some constraints for average trip distance and a
percentage of generated distance say 30% to be in the higher end of distances
generated
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20. 7. Future Work
● Using PSO algorithm get optimum location of evcs with respect to Maximising
Captured Traffic flow, with the constraints of a percentage of trips say 30% being
in the higher end of generated distances.
● Minimization Of Total Cost for EVCS Placement: Minimizing the total cost of an
electric vehicle charging station can be a crucial aspect for future work. This
involves optimizing various components, such as infrastructure, energy sources,
and operational efficiency. Future research could focus on Optimal site selection,
Renewable energy integration & Predictive maintenance, etc.
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21. 7. Future Work
● Minimization Of Load Variance: Minimizing load variance is a critical
consideration for the optimized placement of electric vehicle (EV) charging
stations. Future work in this area could involve Demand prediction models, Grid
integration strategies, incentivizing off-peak charging & User behavior analyze.
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22. 8. References
[1] R. Rajaram, K. Sathish Kumar, N. Rajasekar,”Power system reconfiguration in a radial distribution
network for reducing losses and to improve voltage profile using modified plant growth simulation
algorithm with Distributed Generation (DG) ”, Energy Reports,Volume 1, November 2015.
[2] Comparison of Electric Cars in India - Battery Capacity and Range (yocharge.com) (as seen on 6/12/23)
[3] Ying Zhang, Yunpeng Hua, Ao Kang, Jiyuan He, Meng Jia, Yao-Yi Chiang, Optimal and efficient planning of
charging stations for electric vehicles in urban areas: formulation, complexity and solutions, Expert Systems with
Applications, Volume 230, 2023, 120442, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120442
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