Reliability Assessment under High Penetration of EVs including V2G Strategy
Abstract
:1. Introduction
- We propose a new mechanism for reliability assessment for the distribution networks under high penetration of BEVs, including V2G mode, using a dynamic stochastic BEV consumption model.
- We study the effects of different charging strategies, such as the uncontrolled charging strategy, controlled unidirectional strategy, and controlled bidirectional strategy as well as the impact of penetration of BEVs on the reliability of the power system.
- Various reliability indices, such as the Loss of Load Expectation (LOLE), Loss of Energy Expectation (LOEE), Loss of Load Frequency (LOLF), Energy Not Served per Interruption (ENSPI), System Average Interruption Frequency Index (SAIFI), and System Average Interruption Duration Index (SAIDI), are computed under different charging strategies to assess the impact of these strategies on the reliability of the power system.
2. Dynamic Stochastic BEV Consumption Model
2.1. Travel Behavior Submodel
2.2. Battery Depletion Submodel
3. Reliability Assessment under Different Strategies
- Uncontrolled G2V charging strategy [32], where the power flow is only unidirectional from the grid to the BEV. The BEVs start charging once plugged into the charger without supervision.
- Controlled G2V charging strategy [33], where the power flow is still in one direction from the grid to the BEV. However, the EV chargers accept external control signals to enable/disable the charging process and control the level of charging. This external control signal can be sent by the BEV owner, the utility, or a third party subject to appropriate contracts or agreements.
- Controlled Bidirectional Strategy [34], where the power flow may be in two directions, G2V or V2G. The BEV may discharge to the grid during load peak periods, which can help enhance the power system reliability and stability as well as relief congestions.
- Indirect Controlled Bidirectional Strategy [35]; in this strategy, the power flow can be in both directions. The advantage of this approach is that smart coordination can be used to determine the optimal periods of charging to decrease the energy cost in addition to the advantages of the controlled bidirectional strategy, where the charging is indirectly affected by energy prices.
4. Simulation Results
4.1. Uncontrolled Charging
4.2. Controlled Charging
4.3. Controlled Charging/Discharging
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Purpose | Purpose | ||
---|---|---|---|
1 | Commuting | 6 | Business |
2 | Education | 7 | Escort education |
3 | Shopping | 8 | Other escort and personal business |
4 | Visit friends | 9 | Holiday trip |
5 | Day trip | 10 | Others (entertainment, public activity, etc.) |
Fitted pdf | Parameters | Fitted pdf | Parameters | ||||
---|---|---|---|---|---|---|---|
1 | Lognormal | 3.27 | = 1.02 | 6 | Lognormal | 3.02 | = 1.32 |
2 | Weibull | = 111.75 | = 1.27 | 7 | Weibull | = 83.81 | = 0.93 |
3 | Lognormal | 2.48 | = 1.16 | 8 | Weibull | = 176.47 | = 2.67 |
4 | Lognormal | 2.16 | = 1.38 | 9 | Weibull | = 79.63 | = 1.19 |
5 | Lognormal | 2.76 | = 1.18 | 10 | Lognormal | 3.42 | = 1.29 |
BEV Penetration | Reliability Indices | Charger Rating = 7.2 kW | Charger Rating = 9.6 kW | ||||
---|---|---|---|---|---|---|---|
Uncontrolled | Unidirectional | Bidirectional | Uncontrolled | Unidirectional | Bidirectional | ||
0 % | LOLE (h/yr) | 1.0562 | |||||
LOEE (MWh/yr) | 9.7788 | ||||||
LOLF (int./yr) | 0.2078 | ||||||
ENSPI (MWh/int.) | 47.058 | ||||||
SAIFI (int./cu.) | 0.1154 | ||||||
SAIDI (h/cu.) | 0.5283 | ||||||
20% | LOLE (h/yr) | 1.7882 | 1.88 | 0.8818 | 1.8728 | 1.96 | 0.884 |
LOEE (MWh/yr) | 17.3736 | 10.8958 | 6.5947 | 18.046 | 11.1448 | 6.1485 | |
LOLF (int./yr) | 0.4352 | 0.4228 | 0.1974 | 0.4538 | 0.4482 | 0.2138 | |
ENSPI (MWh/int.) | 39.9209 | 25.7706 | 33.4076 | 39.7663 | 24.8657 | 28.75 | |
SAIFI (int./cu.) | 0.2489 | 0.136 | 0.0706 | 0.2473 | 0.1392 | 0.0668 | |
SAIDI (h/cu.) | 0.9391 | 0.589 | 0.3565 | 0.9755 | 0.6024 | 0.3323 | |
40% | LOLE (h/yr) | 2.9652 | 3.209 | 1.11 | 3.1912 | 3.4398 | 1.1558 |
LOEE (MWh/yr) | 32.265 | 13.1856 | 5.3179 | 34.6965 | 14.2084 | 4.929 | |
LOLF (int./yr) | 0.8222 | 0.8170 | 0.3706 | 0.8122 | 0.8204 | 0.4134 | |
ENSPI (MWh/int.) | 39.243 | 16.139 | 14.3495 | 42.7191 | 17.3189 | 11.923 | |
SAIFI (int./cu.) | 0.5249 | 0.1823 | 0.0627 | 0.5105 | 0.1973 | 0.0624 | |
SAIDI (h/cu.) | 1.7441 | 0.7127 | 0.2875 | 1.8775 | 0.768 | 0.2664 | |
60% | LOLE (h/yr) | 5.9982 | 6.26 | 1.581 | 6.7096 | 6.9426 | 1.6984 |
LOEE (MWh/yr) | 63.3742 | 18.468 | 4.8154 | 70.3812 | 21.535 | 4.6879 | |
LOLF (int./yr) | 2.016 | 2.014 | 0.7138 | 2.0284 | 2.0622 | 0.779 | |
ENSPI (MWh/int.) | 31.4356 | 9.1698 | 6.7461 | 34.6979 | 10.4427 | 6.0178 | |
SAIFI (int./cu.) | 1.2168 | 0.3149 | 0.0655 | 1.1635 | 0.3664 | 0.0722 | |
SAIDI (h/cu.) | 3.4256 | 0.9983 | 0.2603 | 3.8044 | 1.1641 | 0.2534 |
Base Case | 20% Increase in MTTR | 20% Decrease in MTTR | 20% Increase in MTTF | 20% Decrease in MTTF | 20% Increase in MTTR & MTTF | 20% Decrease in MTTR & MTTF | |
---|---|---|---|---|---|---|---|
LOLE | 1.0562 | 1.6780 | 0.6166 | 0.5854 | 1.7692 | 1.0178 | 0.9846 |
LOEE | 9.7788 | 15.4054 | 5.0921 | 4.5251 | 16.6511 | 9.037 | 9.6237 |
LOLF | 0.2078 | 0.3240 | 0.1328 | 0.1282 | 0.3500 | 0.202 | 0.2006 |
ENSPI | 47.058 | 47.5475 | 38.3433 | 35.2971 | 47.5745 | 44.7376 | 47.9745 |
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Mokhtar, M.; Shaaban, M.F.; Ismail, M.H.; Sindi, H.F.; Rawa, M. Reliability Assessment under High Penetration of EVs including V2G Strategy. Energies 2022, 15, 1585. https://doi.org/10.3390/en15041585
Mokhtar M, Shaaban MF, Ismail MH, Sindi HF, Rawa M. Reliability Assessment under High Penetration of EVs including V2G Strategy. Energies. 2022; 15(4):1585. https://doi.org/10.3390/en15041585
Chicago/Turabian StyleMokhtar, Mohamed, Mostafa F. Shaaban, Mahmoud H. Ismail, Hatem F. Sindi, and Muhyaddin Rawa. 2022. "Reliability Assessment under High Penetration of EVs including V2G Strategy" Energies 15, no. 4: 1585. https://doi.org/10.3390/en15041585