A Multi-Period Framework for Coordinated Dispatch of Plug-in Electric Vehicles
Abstract
:1. Introduction
- An integrated framework for multi-period PEV dispatch which pursues a win-win result for both the power system and PEV owners is proposed.
- Interval optimization is adopted in the day-ahead dispatch.
- A PEV-clustered model and a priority-ordering method are proposed to support the multi-period PEV dispatch.
2. The Proposed Multi-Period Dispatch Framework
3. Problem Formulations
3.1. The Day-Ahead Plug-In Electric Vehicles Model
3.2. The PEV-Clustered Model
3.3. The Day-Ahead Dispatch Model
- (1)
- Power flow equality constraints:
- (2)
- Apparent power constrains for distribution lines:
- (3)
- Bus voltage constraints:
- (4)
- Charging/discharging power constraints for PEVs:
- (5)
- Complementary constrains of charging/discharging states:
- (6)
- Charging/discharging quantity equality constraints:
- (7)
- Capacity constraints for PEV’s batteries:
- (8)
- Travel demand constraints:
3.4. The Real-Time Dispatch Model
3.5. The Priority-Ordering Method
- The total charging power demand P’ch,j,t or discharging power demand P’dch,j,t of the PEV charging station at the current time is obtained from the day-ahead dispatch;
- scan PEVs connected to the certain charging station at the current time, and cluster them into PEVAs according to their UST;
- if charging power P’ch,j,t is required to achieve the day-ahead plan, set PEVAs to charge at their rated charging power from the one with the lowest level of UST; then, gradually upgrade the UST level, until meeting the requirement of the total charging power P’ch,j,t;
- else, set PEVAs to discharge from the one with the highest level of UST; then, gradually degrade the UST level, until meeting the requirement of the total discharging power P’dch,j,t.
4. Approach for Solving the Proposed Models
5. Case Studies and Discussion
5.1. Case Description
5.2. The Performance of the Multi-Period Dispatch
5.3. Interval Optimization and the Uncertainty Level
5.4. Sensitivity Analysis
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Names of PEVs | Node Location | Number of PEVs | Types of PEVs |
---|---|---|---|
1 | 9 | 200 | A |
2 | 9 | 200 | B |
3 | 13 | 400 | A |
4 | 13 | 300 | B |
5 | 16 | 400 | A |
6 | 16 | 300 | B |
7 | 29 | 300 | A |
8 | 29 | 400 | B |
Parameters | ω | ω′ | γ | γ′ | α | α′ | ηch | ηdch | SOCevmin | SOCevmax | ESOC |
Value | 0.2 | 0.2 | 3 | 3 | 2% | 2% | 90% | 90% | 5% | 95% | 90% |
Number | Day-Ahead Cluster Number | Real-Time Cluster Number (UST) | Real-Time Dispatch Response Time (s) | Objective Value (MW) | Difference Between Load Peak and Valley (MW) |
---|---|---|---|---|---|
1 | 5 × 5 × 3 × 2 1 | 250 | 0.4502 | 0.2286 | 0.3505 |
500 | 0.9978 | 0.2147 | 0.2808 | ||
2500 | 2.7850 | 0.2135 | 0.2796 | ||
2 | 8 × 8 × 3 × 2 1 | 250 | 0.4516 | 0.2085 | 0.2701 |
500 | 0.9990 | 0.1903 | 0.2357 | ||
2500 | 2.7924 | 0.1876 | 0.2300 | ||
3 | 10 × 10 × 4 × 2 1 | 250 | 0.4507 | 0.1963 | 0.2486 |
500 | 0.9852 | 0.1890 | 0.2259 | ||
2500 | 2.7952 | 0.1871 | 0.2215 |
Scenarios | System Wind Power Level (%) 1 | System PEV Level (%) 1 | Objective Value (MW) |
---|---|---|---|
1 | 80 | 80 | 0.1947 |
2 | 80 | 100 | 0.1953 |
3 | 80 | 120 | 0.2077 |
4 | 100 | 80 | 0.2031 |
5 | 100 | 100 | 0.1876 |
6 | 100 | 120 | 0.1902 |
7 | 120 | 80 | 0.2181 |
8 | 120 | 100 | 0.1926 |
9 | 120 | 120 | 0.1869 |
γ | γ’ | Day-Ahead Objective Value (MW) | Real Objective Value (MW) | Security Calibration |
---|---|---|---|---|
2 | 2 | 0.1535 | 0.1558 | 99.88% |
2 | 3 | 0.1535 | 0.1642 | 99.23% |
3 | 3 | 0.1892 | 0.1876 | 99.97% |
3 | 4 | 0.1892 | 0.2214 | 93.59% |
4 | 4 | 0.2201 | 0.2212 | 99.37% |
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Huang, Y.; Guo, C.; Ding, Y.; Wang, L.; Zhu, B.; Xu, L. A Multi-Period Framework for Coordinated Dispatch of Plug-in Electric Vehicles. Energies 2016, 9, 370. https://doi.org/10.3390/en9050370
Huang Y, Guo C, Ding Y, Wang L, Zhu B, Xu L. A Multi-Period Framework for Coordinated Dispatch of Plug-in Electric Vehicles. Energies. 2016; 9(5):370. https://doi.org/10.3390/en9050370
Chicago/Turabian StyleHuang, Yinuo, Chuangxin Guo, Yi Ding, Licheng Wang, Bingquan Zhu, and Lizhong Xu. 2016. "A Multi-Period Framework for Coordinated Dispatch of Plug-in Electric Vehicles" Energies 9, no. 5: 370. https://doi.org/10.3390/en9050370