Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders
<p>Load profile trend of Republic of Korea for a summer day.</p> "> Figure 2
<p>The comprehensive control flow chart of the proposed strategy at the three levels: distribution system operator (DSO), aggregator and electric vehicles (EVs).</p> "> Figure 3
<p>IEEE 13-node feeder configuration.</p> "> Figure 4
<p>KEPCO X S/S—Z D/L feeder configuration.</p> "> Figure 5
<p>Net load profile of IEEE 13-node distribution feeder.</p> "> Figure 6
<p>EV charging and discharging power profile.</p> "> Figure 7
<p>Net load curve of KEPCO feeder.</p> "> Figure 8
<p>Net EV load on the feeder.</p> "> Figure 9
<p>Indices comparison on IEEE 13-node distribution feeder and KEPCO X S/S-Z D/L feeder.</p> ">
Abstract
:1. Introduction
- Development of an on-line multi EV aggregator coordination scheme which is better suited to administer the incoming/outgoing EVs at smaller intervals. The proposed scheme is rigorous in its approach as it integrates the key objectives of aggregator coordination, load leveling and EV mobility to function in a compatible manner.
- Since the scheme is developed for low power level residential chargers, the power allocation to EVs is constant based on the charger’s rated power. Hence, the variable power allocation complexity is averted.
2. EV Architecture
3. Peak Shaving and Valley Filling Scheme for Aggregators
3.1. Calculation of Power Deviation
3.2. Power Allocation to EV Aggregators
3.3. Power Allocation to EVs under Each Aggregator
3.3.1. Objective Function
3.3.2. Constraints
- During the charging/discharging operation, the battery energy of an EV is maintained to be within the minimum battery capacity and . The minimum battery capacity constraint prevents the over-discharging of the EV and it also makes the EV capable for emergency travel. The minimum battery capacity refers to the amount of energy required to travel a specific distance and is the corresponding minimum percentage SoC. This specific distance in this study corresponds to the average daily mileage for private cars in South Korea.
- The whole V2G/G2V operation of an EV is constrained within its arrival time and expected departure time represented by:
- When an EV is connected to the system according to Equation (11), the EV charging/discharging power based on the charger rating is:
- The last constraint ensures that the total power provided or absorbed by all EVs within an aggregator does not exceed the allocated power of the aggregator by DSO.
3.4. Control Process
4. Simulation Cases
4.1. IEEE 13-Node Distribution Feeder
EV Fleet
4.2. KEPCO X S/S-Z D/L Feeder
EV Fleet
4.3. Performance Measuring Indices
- Peak shaving index gives the ratio of the total energy shaved to the total energy expected to be shaved by the EVs in the feeder during peak period:
- Valley filling index indicates the ratio of the total energy absorbed to the overall energy anticipated to be absorbed by the EVs in the feeder during off-peak period:
- The load factor of the feeder is given by:
- The variance of load is calculated as:
5. Results
5.1. Implementation of Proposed Scheme on IEEE 13-Node Distribution Feeder
5.2. Implementation of Proposed Scheme on KEPCO X S/S-Z D/L Feeder
5.3. Comparison of Proposed Scheme on Various Feeders
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Location | Aggregator | Number of EVs |
---|---|---|
632 | AG1 | 13 |
634 | AG2 | 26 |
645 | AG3 | 11 |
646 | AG4 | 15 |
671 | AG5 | 74 |
692 | AG6 | 11 |
675 | AG7 | 54 |
611 | AG8 | 11 |
652 | AG9 | 8 |
Total | 223 |
Location | Aggregator | Number of EVs |
---|---|---|
L1 | AG1 | 77 |
L2 | AG2 | 25 |
L3 | AG3 | 54 |
L4 | AG4 | 116 |
L5 | AG5 | 36 |
L6 | AG6 | 47 |
L7 | AG7 | 77 |
L8 | AG8 | 77 |
L9 | AG9 | 83 |
L10 | AG10 | 20 |
L11 | AG11 | 284 |
Total | 896 |
Entity | Proposed Scheme | Uncontrolled Charging |
---|---|---|
23.7% | - | |
61.32% | 8.91% | |
88.47% | 86.57% | |
0.07 MW | 0.17 MW |
Entity | Proposed Scheme | Uncontrolled Charging |
---|---|---|
24.25% | - | |
59.3% | 7.95% | |
88.28% | 86.67% | |
1.13 MW | 2.69 MW |
Entity | IEEE-13 Feeder | KEPCO Feeder |
---|---|---|
Mean | 99.34% | 99.2% |
SD | 0.73% | 0.93% |
0.47 | 0.47 | |
0.13 | 0.16 | |
Median () | 0.46 | 0.48 |
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Khan, S.U.; Mehmood, K.K.; Haider, Z.M.; Rafique, M.K.; Khan, M.O.; Kim, C.-H. Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders. Energies 2021, 14, 352. https://doi.org/10.3390/en14020352
Khan SU, Mehmood KK, Haider ZM, Rafique MK, Khan MO, Kim C-H. Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders. Energies. 2021; 14(2):352. https://doi.org/10.3390/en14020352
Chicago/Turabian StyleKhan, Saad Ullah, Khawaja Khalid Mehmood, Zunaib Maqsood Haider, Muhammad Kashif Rafique, Muhammad Omer Khan, and Chul-Hwan Kim. 2021. "Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders" Energies 14, no. 2: 352. https://doi.org/10.3390/en14020352
APA StyleKhan, S. U., Mehmood, K. K., Haider, Z. M., Rafique, M. K., Khan, M. O., & Kim, C. -H. (2021). Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders. Energies, 14(2), 352. https://doi.org/10.3390/en14020352