Energy Link Optimization in a Wireless Power Transfer Grid under Energy Autonomy Based on the Improved Genetic Algorithm
Abstract
:1. Introduction
2. Wireless Power Transfer Grid under Energy Autonomy
2.1. Grid Introduction
2.2. Node Parameters
- 1
- Each node is assumed to operate with the functional load; therefore, the energy storage in each node is declining with time. Each node will reach three energy situations with the variation of its energy storage:
- –
- Normal situation
- –
- Energy-poor situation
- –
- Energy-disabled situation
Nodes will be removed from WPTG for maintaining its own functional load if they reach the energy-disabled situation. Therefore, the node in the energy-poor situation will call for energy supplies to avoid this worst situation. - 2
- Each node is assumed to be able to detect the surrounding (neighbouring) nodes’ information.
- 3
- Relevant nodes in the energy link act as:
- –
- Power supply node S
- –
- Repeater node R
- –
- Load node T
Without the external power injection, a certain node will be chosen as the power supply node S. Additionally, the power transfer process could be achieved from the power supply node S to the load node T through the repeater node R. As shown in Figure 2, power supply node A transfers power to load node C through repeater node B. Nevertheless, these roles are not fixed, and they will vary with the change of the load node. Thus, when node A calls for energy supplies and transforms into the load node, power supply node B transfers the power through repeater node C to meet its demand. Meanwhile, the bi-directional wireless power transfer [22,23] technology is introduced, which means the power transfer flow is reversible. - 4
- During the power transfer process, the involved nodes will suffer from the extra energy load, which is related to the detailed power conditioning performance. Due to the fact that it is beyond the scope of this paper, it will not be explained in detail.
2.3. Grid Operating Mechanism
3. Energy Link Optimization Model
3.1. Energy Link Analysis
- Power transfer efficiency PTE: During the energy autonomy situation, the power demand of the load node is satisfied by the energy stored in other nodes. The PTE should be improved to reduce the power dissipation during the power transfer process.
- Time delay : The communications are undertaken to inform the repeater nodes to join the energy link; therefore, a time delay is introduced in the power transfer process. In order to improve its real-time performance, the time delay should be reduced.
- Energy load balance : If the nodes with small energy storage are selected to join the energy link, due to the extra energy load during the power transfer process, these will quickly be driven to reach the energy-disabled situation, which should be avoided. Hence, this extra energy load should be distributed among the nodes with higher energy storage to achieve the energy load balance in the WPTG. The minimum energy storage in the selected energy link is utilized to represent this index.
3.2. Optimization Graph
4. Energy Link Optimization Algorithm
4.1. Algorithm Initialization
4.2. Fitness Function
4.3. Algorithm Operators
4.4. Algorithm Adaptive Parameter Mechanism
5. Simulation and Verification
6. Conclusions and Future Directions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
WPT | Wireless power transfer |
WPTG | Wireless power transfer grid |
PTE | Power transfer efficiency |
CAGA | Concentration adaptive genetic algorithm |
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Parameter | Value |
---|---|
Solution group size | 40 |
Maximum evolution generations T | 50 |
Initial crossover rate | |
Initial mutation rate | |
Trend recording number n | 3 |
Constrain value for PTE | |
Constrain value for time delay | |
Constrain value for energy storage | 27 |
Optimization Method | N = 10 | N = 15 | N = 20 |
---|---|---|---|
Traditional GA method | {1,2,4,5,10}/1.1 s | {1,7,6,13,8,15}/1.97 s | {1,5,3,13,8,15,20}/3.1 s |
CAGA | {1,7,5,10}/0.88 s | {1,6,13,8,15}/1.43 s | {1,6,13,8,15,20}/1.91 s |
Optimization Method | Index Weight | Optimized Energy Link | J | |||
---|---|---|---|---|---|---|
Traditional GA method | 0.3291 | 0.5494 | 2.76 | 38 | ||
CAGA | 0.3096 | 0.5426 | 2.27 | 38 | ||
Traditional GA method | 0.4011 | 0.5469 | 3.29 | 36 | ||
CAGA | 0.3763 | 0.5869 | 2.69 | 38 | ||
Traditional GA method | 0.5244 | 0.5469 | 3.29 | 36 | ||
CAGA | 0.493 | 0.5087 | 2.3 | 40 | ||
Traditional GA method | 0.0954 | 0.4362 | 2.12 | 40 | ||
CAGA | 0.0922 | 0.3825 | 1.82 | 40 |
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Zhao, Z.; Sun, Y.; Hu, A.P.; Dai, X.; Tang, C. Energy Link Optimization in a Wireless Power Transfer Grid under Energy Autonomy Based on the Improved Genetic Algorithm. Energies 2016, 9, 682. https://doi.org/10.3390/en9090682
Zhao Z, Sun Y, Hu AP, Dai X, Tang C. Energy Link Optimization in a Wireless Power Transfer Grid under Energy Autonomy Based on the Improved Genetic Algorithm. Energies. 2016; 9(9):682. https://doi.org/10.3390/en9090682
Chicago/Turabian StyleZhao, Zhihao, Yue Sun, Aiguo Patrick Hu, Xin Dai, and Chunsen Tang. 2016. "Energy Link Optimization in a Wireless Power Transfer Grid under Energy Autonomy Based on the Improved Genetic Algorithm" Energies 9, no. 9: 682. https://doi.org/10.3390/en9090682