A Survey on Equivalence Modeling for Large-Scale Photovoltaic Power Plants
Abstract
:1. Introduction
2. Composition of PV Power Plants
3. Steady-State Equivalent Model of PV Power Plants
3.1. The Type of Nodes
3.2. The Overall Equivalent Model
4. Transient Equivalent Model of PV Power Plants
4.1. The Single-Machine Equivalent Model of PV Power Plants
4.1.1. Capacity Equal Weighted Method
4.1.2. Parameter Identification Method
4.2. The Multi-Machine Equivalent Models of PV Power Plants
4.2.1. Grouping Index
4.2.2. Grouping Method
5. Calculation of Equivalent Parameters
5.1. Capacity Equal Weighted Method in Multi-Machine Equivalence
5.2. Parameter Identification Method in Multi-Machine Equivalence
6. Equivalence of Power Collection System
7. Conclusions
- (1)
- How to extract the most critical grouping index in operational variables to make the grouping result more accurate and reasonable. It is difficult to characterize the transient characteristics of the PV power generation units comprehensively through the existing grouping indexes. Whether it can be combined with statistical principles and principal component analysis to extract the main features for equivalence modeling is worth studying.
- (2)
- In practical projects, due to the fact that some modules of the model are encapsulated, the corresponding grouping indexes cannot be measured and acquired. The question of how to extract easily observed variables based on engineering practice as grouping indexes needs to be solved.
- (3)
- How to establish a dynamic equivalent model of PV power plant considering environmental changes in longer time scales.
- (4)
- When the location or the type of fault is changed, it is debatable whether the original equivalent model is applicable. Finding the equivalent method suitable for various fault conditions and simulating the transient response characteristics accurately needs to be further explored.
- (5)
- Validation of models, model standardization, and evaluation indicators of models still require further improvement.
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Inverter Control Method | Node Type | |
---|---|---|
Current control | PI node to PQ node | |
Constant power factor control | PQ node | |
Voltage control | Overvoltage | PV node |
Reactive power overflows | PQ node |
Equivalent Basis | Practical Application | Results |
---|---|---|
Capacity equal weighting | Basic parameter aggregation [32,33] | Simple and easy to operate; Low precision; Ideal situation |
The equivalence of wind, PV and energy storage system [34] | ||
“Double multiplication” model that runs at full capacity [35] | ||
Parameter aggregation of Thevenin equivalent model [36] | ||
Parameter aggregation Norton equivalent model [37] | ||
Parameter identification | Crow search algorithm [38] | Higher precision; Difficult to operate; Actual situation |
Least Squares [39] | ||
Non-linear system identification technology [40] |
Equivalent Basis | Essential Feature | Advantages | Disadvantages | Application Situation |
---|---|---|---|---|
Control parameters | Group with algorithms | Reflect the essential characteristics; High precision | The control parameters are difficult to quantify | Control parameters are known |
External characteristics | Group with algorithms | Can easily be measured; The physical meaning is clear | cannot accurately reproduce the transient response characteristics | Control parameter are unknown |
Iconic demarcation point | Group directly | Eliminate the dependence on the clustering algorithm | The information of boundary points is difficult to obtain | There is a definite group boundary condition |
Grouping Index | Practical Application | Results |
---|---|---|
Feature distance based on control parameters | Multiply control parameters by the sensitivity coefficient of parameters [41] | Improved index can fully reflect the impact of control parameters on the system |
Establish an offline database of parameter sensitivity and reference response curves [44] | Increasing adaptability to actual conditions | |
The indexes of feature distance are calculated respectively for PI control (proportional integral) and low voltage control strategy [45] | The disturbance conditions of different control strategies are separated to improve the equivalent precision | |
The filter inductance coefficient of PV inverter is included in the indexes of feature distance [46] | The dynamic response characteristics of the inverter are fully reflected |
The Basis of the Grouping Method | Features | Practical Example | Results |
---|---|---|---|
Clustering algorithm | Grouping data indexes by algorithm | K-means clustering [42] | Fast speed; The accuracy is easily affected by the K value |
Fuzzy clustering [41,55] | Accurate; Complex calculations, not suitable for large samples | ||
Spectral clustering [56,57] | Suitable for clustering on any shape of sample space; Sensitive to parameters | ||
SVM (support vector machine) clustering [58] | Small sample; Avoids dimension disasters; Not applicable to large samples and multiple categories | ||
Operating state | Grouping by iconic demarcation point | The action of protection circuit [50]; | Eliminates the dependence on the clustering algorithm; The operating state is difficult to evaluate; Some operational characteristics are not considered |
The switching state of the unloading resistance [51,52] |
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Han, P.; Lin, Z.; Wang, L.; Fan, G.; Zhang, X. A Survey on Equivalence Modeling for Large-Scale Photovoltaic Power Plants. Energies 2018, 11, 1463. https://doi.org/10.3390/en11061463
Han P, Lin Z, Wang L, Fan G, Zhang X. A Survey on Equivalence Modeling for Large-Scale Photovoltaic Power Plants. Energies. 2018; 11(6):1463. https://doi.org/10.3390/en11061463
Chicago/Turabian StyleHan, Pingping, Zihao Lin, Lei Wang, Guijun Fan, and Xiaoan Zhang. 2018. "A Survey on Equivalence Modeling for Large-Scale Photovoltaic Power Plants" Energies 11, no. 6: 1463. https://doi.org/10.3390/en11061463