A Network Partition-Based Optimal Reactive Power Allocation and Sizing Method in Active Distribution Network
Abstract
1. Introduction
- (1)
- A network partition and critical node identification method based on the improved partition and critical nodes identification index, and an adaptive spectral clustering and singular value entropy-based network partition method of ADN are proposed. This method enables more accurate and efficient identification of partitions and critical nodes at the same time, providing a more effective foundation for reactive power compensation.
- (2)
- An optimal allocation and sizing of reactive power compensation approach that employs an immune genetic algorithm is presented to determine the location and sizing of SVG in order to minimize the total system loss and voltage fluctuation. It enhances optimization efficiency and system voltage stability by minimizing total power loss and voltage deviations under multiple PV scenarios.
2. Network Partition and Critical Node Identification
2.1. Active Distribution Network
2.2. Index of Network Partitioning and Critical Node Identification
2.2.1. Index of Critical Node Identification
2.2.2. Index of Regional Coupling
2.2.3. The Synthesis Model of Partition
- (1)
- Restriction on the number of nodes in a partition: A single node cannot be an area by itself, and the number of nodes in a single area cannot exceed 2/3 of the total number of nodes.
- (2)
- Connectivity constraint: Nodes in one partition must be connected. Nodes without connections cannot be divided into the same partition.
2.3. Improved Immune Genetic Algorithm
Initial Scheme Acquisition
3. Optimal Configuration of Reactive Power Compensation
3.1. Clustering and Typical Scenario Extraction
3.2. Optimal Sizing of the Reactive Power Compensation
- (1)
- Power flow constraintPower flow equations represent the relationship between network voltage and power.
- (2)
- Voltage constraintVoltage limits guarantee the voltage of all the system nodes within the acceptable limits.
- (3)
- Transformer constraintsThe following two inequality constraints represent the limits of transformer tap adjustment in order to prevent excessive adjustment of the transformer tap.
- (4)
- Reactive power compensator constraintsEach reactive power compensator has a limit on its output, which is related to its capacity.
- (5)
- Constraint of the zonal reactive power balanceThe reactive power in each zone is provided by the reactive power compensator deployed at the critical nodes in the same zone, so the reactive power balance index in the area is defined as follows:
- (6)
- Reactive power balance chance constraintConsidering that node load fluctuations in ADN are random and frequent, a chance constraint is introduced to ensure that the capacity of reactive power compensation equipment can meet the demands in the majority of scenarios. The chance constraint is defined as follows:
4. Case Study
4.1. Network Partition and Critical Node Identification
4.2. Reactive Power Compensation Configuration
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
PV | Photovoltaic |
ADN | Active distribution network |
GMM | Gaussian mixed model |
PSO | Particle Swarm Optimization |
BIC | Bayesian information criterion |
SVG | Static Var Generator |
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PV Location (Nodes) | PV Capacity (MW) |
---|---|
16, 18, 28 | 1.0 |
19, 33 | 1.2 |
14, 25 | 1.5 |
22 | 1.8 |
Scheme | Fitness Value | Partition Scheme |
---|---|---|
A | 603.787 | (23, 24, 25), (10, 11, 12, 13, 14, 15, 16, 17, 18), (26, 27, 28, 29, 30, 31, 32, 33), (1, 2, 3, 4, 5, 6, 7, 8, 9), (19, 20, 21, 22) |
B | \ | (1, 2, 19, 20, 21, 22), (3, 4, 5, 23, 24, 25), (6, 7, 8, 9, 10, 11, 26, 27), (12, 13, 14, 15, 16, 17, 18), (28, 29, 30, 31, 32, 33) |
C | \ | (1, 2, 3, 4, 5, 6, 7, 8, 19), (9, 10, 11, 12, 13, 14, 15, 16, 17, 18), (20, 21, 22), (23, 24, 25), (26, 27, 28, 29, 30), (31, 32, 33) |
Scheme A | Scheme B | Scheme C | |||
---|---|---|---|---|---|
Critical Node | Singular Value Entropy | Critical Node | Singular Value Entropy | Critical Node | Singular Value Entropy |
6 | 0.424 | 2 | 0.424 | 6 | 0.424 |
15 | 0.418 | 3 | 0.419 | 12 | 0.419 |
20 | 0.414 | 9 | 0.417 | 22 | 0.419 |
24 | 0.419 | 13 | 0.419 | 23 | 0.412 |
30 | 0.410 | 29 | 0.411 | 30 | 0.410 |
\ | \ | \ | \ | 32 | 0.411 |
Partition | Critical Node | Buses in the Partition | Loads (kVA) |
---|---|---|---|
1 | 6 | 1,2,3,4,5,6,7,8,9 | 890 + j450 |
2 | 15 | 10,11,12,13,14,15,16,17,18 | 615 + j290 |
3 | 20 | 19,20,21,22 | 360 + j160 |
4 | 24 | 23,24,25 | 930 + j450 |
5 | 30 | 26,27,28,29,30,31,32,33 | 920 + j950 |
Node | SVG Capacity (MVar) |
---|---|
6 | 1.70 |
15 | 1.70 |
20 | 0.50 |
24 | 1.00 |
30 | 1.60 |
Node | 1 a.m. (MVar) | 13 p.m. (MVar) |
---|---|---|
6 | −0.85505 | 1.69072 |
15 | −0.80164 | 1.69971 |
20 | −0.11940 | 0.31579 |
24 | −0.41520 | −0.71154 |
30 | −1.56592 | 0.97247 |
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Gan, D.; Ling, H.; Mao, Z.; Gu, R.; Zhou, K.; Lin, K. A Network Partition-Based Optimal Reactive Power Allocation and Sizing Method in Active Distribution Network. Processes 2025, 13, 2524. https://doi.org/10.3390/pr13082524
Gan D, Ling H, Mao Z, Gu R, Zhou K, Lin K. A Network Partition-Based Optimal Reactive Power Allocation and Sizing Method in Active Distribution Network. Processes. 2025; 13(8):2524. https://doi.org/10.3390/pr13082524
Chicago/Turabian StyleGan, Deshu, Huabao Ling, Zhijian Mao, Ran Gu, Kangxin Zhou, and Keman Lin. 2025. "A Network Partition-Based Optimal Reactive Power Allocation and Sizing Method in Active Distribution Network" Processes 13, no. 8: 2524. https://doi.org/10.3390/pr13082524
APA StyleGan, D., Ling, H., Mao, Z., Gu, R., Zhou, K., & Lin, K. (2025). A Network Partition-Based Optimal Reactive Power Allocation and Sizing Method in Active Distribution Network. Processes, 13(8), 2524. https://doi.org/10.3390/pr13082524