Intelligent Regulation Method for a Controllable Load Used for Improving Wind Power Integration
Abstract
:1. Introduction
2. Wind Power Integration Assessment Model
2.1. Grid Power Balance Model
2.2. Wind Power Integration Assessment
2.2.1. Grid Backup Margin
2.2.2. Wind Power Acceptability
3. Analysis of the Key Factors Affecting Wind Power Integration
3.1. Essential Factor of Integration
3.2. The Influence of the Source Grid Load Coordination on the Integration
4. Interactive Load Observer and Controller Design
4.1. Interactive Load Observer Design
4.1.1. Feature Decomposition Function
4.1.2. Feature Extraction Function
4.2. Interactive Load Controller Design
4.2.1. Constructing a Sub-Control Matrix
4.2.2. Establishing a Peak-Valley Interactive Control Model
4.3. Interactive Load Intelligent Control Architecture
5. Instance Grid Verification
5.1. Evaluating the Wind Power Integration Capability of the Instance Grid
5.2. Source Grid Load Factor Contribution Ratio
5.3. Intelligent Control Method for the Interactive Load
6. Conclusions
- (1)
- This paper has established a wind power acceptance capability evaluation index and quantitatively solves the target power system’s ability to accept wind power. The key factors of the source network load coordination process and the source network load acceptance ability index have been combined to quantitatively calculate the relative extent of the source network load different variables contributing to the grid wind power acceptance.
- (2)
- In order to improve the acceptance of the wind power, an intelligent regulation method for the controllable load has been constructed. The internal and external control loops have been designed through the theoretical model of the interactive load observer and controller, so that an interactive load intelligent control structure has been constructed.
- (3)
- This paper has applied the proposed theory and method to the Liaoning regional power grid. Firstly, for the typical day of winter reception, the contribution of wind power acceptance capacity and source network load to the admission process has been quantitatively calculated. Then, the method that is proposed in this paper has been used, and the load curve has been corrected through feature observation and interactive control, which effectively improves the acceptance ability of the regional power grid.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Operational Variable | PLmin | βw | a | Pw | ||
---|---|---|---|---|---|---|
Calculated (MW) | 19,020 | 14,830 | 12,550 | 0.5 | 0.2 | 2450 |
Source-Grid-Load Variable | δf | δh | δn | δc | δL |
---|---|---|---|---|---|
Contribution Degree | 0.36 | 0.29 | 0 | −0.07 | 0.42 |
Operational Variable (MW) | PLmin | βw | a | Pw | ||
---|---|---|---|---|---|---|
Before Interaction | 19,020 | 14,830 | 12,550 | 0.5 | 0.2 | 2450 |
Original Method | 18,620 | 14,830 | 12,300 | 0.5 | 0.2 | 271 |
Method of this Paper | 18,420 | 15,330 | 12,200 | 0.5 | 0.2 | 3350 |
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Li, J.-J.; Shao, B.-Z.; Li, J.-H.; Ge, W.-C.; Zhang, J.-H.; Zhou, H.-Y. Intelligent Regulation Method for a Controllable Load Used for Improving Wind Power Integration. Energies 2018, 11, 3085. https://doi.org/10.3390/en11113085
Li J-J, Shao B-Z, Li J-H, Ge W-C, Zhang J-H, Zhou H-Y. Intelligent Regulation Method for a Controllable Load Used for Improving Wind Power Integration. Energies. 2018; 11(11):3085. https://doi.org/10.3390/en11113085
Chicago/Turabian StyleLi, Jia-Jue, Bao-Zhu Shao, Jun-Hui Li, Wei-Chun Ge, Jia-Hui Zhang, and Heng-Yu Zhou. 2018. "Intelligent Regulation Method for a Controllable Load Used for Improving Wind Power Integration" Energies 11, no. 11: 3085. https://doi.org/10.3390/en11113085