Optimal Allocation of Photovoltaic-Green Distributed Generation for Maximizing the Performance of Electrical Distribution Networks
Abstract
:1. Introduction
- (1)
- This study suggests a strategy that uses PSO techniques to maximize PV-GDG system allocation. Finding the optimal size, number, and location of the embedded PV-GDG in the same algorithm process is determined using the PSO by considering particular limitations and requirements.
- (2)
- The method used in this study can be used for any DN, depending on the data and technical specifications of the electrical equipment operating in the network. The IEEE 33 bus test system was an important standard used to make sure the algorithm and analysis were accurate and useful for different Electrical Distribution Networks (EDNs). Choosing Baghdad’s networks as a case study aims to improve the reliability of the methodology. This helps it work better for networks facing operational issues, aged equipment, and shortages in power generation, along with rising annual demands exceeding power production capacity.
- (3)
- An evaluation of this approach involves comparing PSO results with those obtained from the analysis by the CYMEDist program, which represents both the IEEE 33 bus test system and a real case of studies. Additionally, this study assesses PV-GDG parameters using PVsyst 7.2.20 designing software tailored to this network and conducts power flow analysis using the Newton–Raphson (NR) numerical technique.
2. Power Flow Calculation [15,16,17,18,19]
- (1)
- For load buses, where and are specific voltage magnitudes and phase angle are set as the values of slack bus, or 1.0 and 0.0, ( and ) for voltage regulated buses, where and are specified, phase angles are set equal to the slack bus angle ().
- (2)
- The active power and reactive power for the load and buses.
- (3)
- and for the voltage-controlled buses.
- (4)
- The components of the Jacobian matrix (, , , and ) are computed.
- (5)
- The linearly synchronized Equation (2) is solved through a computational method.
- (6)
- In Equations (5) and (6), we computed the new voltage values with phase angles.
- (7)
- The procedure is persistent until the specified precision is more than the residuals and .
3. PSO Technique for Optimal GDG Integration in DNs
4. CYMDist Background
4.1. Databases Employed in CYMDist
4.1.1. Equipment Database
4.1.2. Network Database
5. Practical Implementation and Comparative Study
5.1. IEEE 33 Bus Distribution Test System Analysis
- (1)
- Using CYMEDist software, a simulation of the IEEE 33 bus test system was conducted to perform load flow analysis in the absence of any PV-GDG units. Subsequently, based on the results obtained from the PSO algorithm, the generation units were connected at identified locations with suitable sizes. PV-GDG units are allowed to be installed between bus 2 and bus 33, with a rated power range of 1.0 MW 4.0 MW. Subsequent load flow analyses were repeated for configurations involving one, two, and three PV-GDG units, enabling the evaluation of active power losses and the identification of the bus with the lowest voltage level. Finally, a comparison was made between the outcomes obtained from CYMEDist and those obtained using MATLAB. Table 1 presents a comprehensive analysis of the results obtained when incorporating PV-GDG units into the test system of the IEEE 33 bus system using PSO methodology and CYMEDist software. The observations reported in [27,28,29,30,31] are consistent with the outcomes of this study. Importantly, it is worth highlighting that a comparison of these findings demonstrates a conspicuous level of similarity, as illustrated in Appendix C. The compelling results that demonstrate the effectiveness of PV-GDG units are shown in Figure 6.
- (2)
- This study evaluated the impact of integrating PV-GDG on the voltage drop in a power system, specifically focusing on bus 18. Initially, bus 18 experienced a voltage reduction of approximately 0.904 per unit before incorporating PV-GDG units. The voltage characteristics (profiles) of the IEEE 33 bus distribution system are shown in Figure 7. The figure shows the voltage scenarios when PV-GDG units are removed and added.
Discussion of the IEEE 33 Test System Results
5.2. North of Al-Rusafa 11 kV Distribution Network
5.2.1. Feeder Description (Afaq-11)
5.2.2. Low-Voltage Model
5.2.3. Grid-Connected PV-GDG Design
5.2.4. Consideration of LF and PSO Results and Discussion
- (1)
- Concentrated PV station at the 11 kV level feeder
- (2)
- Dispersed residential PV panels.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
- Voltage drops (requires CYMDist);
- Gauss–Seidel (requires CYMFLOW).
- Newton–Raphson (requires CYMFLOW).
- Fast decoupled (requires CYMFLOW).
Appendix B
Origin Bus | Receiving Bus | R p.u. | X p.u. | PL kW | QL kVAR | Origin Bus | Receiving Bus | R p.u. | X p.u. | PL kW | QL kVAR |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 0.0575 | 0.029 | 100 | 60 | 14 | 15 | 0.37 | 0.329 | 60 | 10 |
2 | 3 | 0.3076 | 0.157 | 90 | 40 | 15 | 16 | 0.466 | 0.341 | 60 | 20 |
2 | 19 | 0.1023 | 0.098 | 90 | 40 | 16 | 17 | 0.806 | 1.076 | 60 | 20 |
3 | 4 | 0.228 | 0.116 | 120 | 80 | 17 | 18 | 0.456 | 0.359 | 90 | 40 |
3 | 23 | 0.116 | 0.192 | 90 | 50 | 19 | 20 | 0.94 | 0.847 | 90 | 40 |
4 | 5 | 0.238 | 0.121 | 60 | 30 | 20 | 21 | 0.256 | 0.299 | 90 | 40 |
5 | 6 | 0.511 | 0.441 | 60 | 20 | 21 | 22 | 0.443 | 0.586 | 90 | 40 |
6 | 7 | 0.117 | 0.386 | 200 | 100 | 23 | 24 | 0.561 | 0.443 | 420 | 200 |
6 | 26 | 0.127 | 0.065 | 60 | 25 | 24 | 25 | 0.56 | 0.438 | 420 | 200 |
7 | 8 | 1.068 | 0.771 | 200 | 100 | 26 | 27 | 0.178 | 0.09 | 60 | 25 |
8 | 9 | 0.642 | 0.462 | 60 | 20 | 27 | 28 | 0.662 | 0.584 | 60 | 20 |
9 | 10 | 0.633 | 0.462 | 60 | 20 | 28 | 29 | 0.503 | 0.438 | 120 | 70 |
10 | 11 | 0.123 | 0.041 | 45 | 30 | 29 | 30 | 0.317 | 0.162 | 200 | 600 |
11 | 12 | 0.234 | 0.077 | 60 | 35 | 30 | 31 | 0.609 | 0.602 | 150 | 70 |
12 | 13 | 0.918 | 0.722 | 60 | 35 | 31 | 32 | 0.194 | 0.226 | 210 | 100 |
13 | 14 | 0.339 | 0.446 | 120 | 80 | 32 | 33 | 0.213 | 0.331 | 60 | 40 |
Appendix C
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Number of PV-GDG | Locations (Bus No.) | Size (kW) | Loss (kW) MATLAB R2022b | Loss (kW) CYME 9.04 | Min. Voltage MATLAB R2022b at Bus 18 (p.u.) | Min. Voltage CYME 9.04 at Bus 18 (p.u.) |
---|---|---|---|---|---|---|
Without PV-GDG | -------- | ------ | 211 | 211 | 0.9037 | 0.904 |
1 | 6 | 2600 | 111.03 | 111 | 0.943 | 0.943 |
2590 | 111 | 111 | 0.942 | 0.942 | ||
2492 | 111.2 | 111.16 | 0.941 | 0.941 | ||
2 | 13 | 849 | 87.2 | 87.17 | 0.968 | 0.965 |
30 | 1152 | |||||
3 | 9 | 1062 | 75.8 | 75.87 | 0.960 | 0.959 |
24 | 1045 | |||||
30 | 952 |
Origin Bus | Receiving Bus | R p.u. | X p.u. | PL kW | QL kVAR |
---|---|---|---|---|---|
1 | 2 | 0.1 | 0.2 | 0 | 0 |
2 | 3 | 0.01 | 0.09 | 0 | 40 |
3 | 4 | 0.42 | 0.31 | 0 | 0 |
4 | 5 | 0.01 | 0.02 | 240 | 149 |
5 | 6 | 0.01 | 0.02 | 240 | 149 |
6 | 7 | 0.07 | 0.09 | 240 | 149 |
6 | 10 | 0.07 | 0.09 | 240 | 149 |
7 | 8 | 0.01 | 0.02 | 240 | 149 |
8 | 9 | 0.06 | 0.07 | 240 | 149 |
10 | 11 | 0.07 | 0.09 | 240 | 149 |
11 | 12 | 0.03 | 0.04 | 240 | 149 |
11 | 13 | 0.07 | 0.09 | 240 | 149 |
13 | 14 | 0.02 | 0.03 | 240 | 149 |
13 | 17 | 0.02 | 0.03 | 240 | 149 |
13 | 19 | 0.05 | 0.06 | 240 | 149 |
13 | 20 | 0.11 | 0.15 | 240 | 149 |
14 | 15 | 0.01 | 0.02 | 240 | 149 |
15 | 16 | 0.01 | 0.02 | 240 | 149 |
17 | 18 | 0.02 | 0.03 | 240 | 149 |
20 | 21 | 0.01 | 0.02 | 240 | 149 |
20 | 24 | 0.01 | 0.02 | 240 | 149 |
21 | 22 | 0.01 | 0.02 | 240 | 149 |
22 | 23 | 0.02 | 0.02 | 240 | 149 |
PV-GDG Unit Capacity kW | No. Series Module | No. Parallel Strings | E Annual Energy Yield MWh/Year | PR % | Cinv. % | Capacity Factor % | Max. String Voltage @ 50 °C V | Isc max A |
---|---|---|---|---|---|---|---|---|
800 | 19 | 69 | 1517 | 84.85 | 105 | 21.6 | 658 | 1221 |
1000 | 19 | 87 | 1941 | 86.08 | 101 | 22.1 | 658 | 1540 |
1300 | 19 | 112 | 2461 | 84.79 | 106 | 21.6 | 658 | 1983 |
1350 | 19 | 116 | 2547 | 84.74 | 109 | 21.5 | 658 | 2053 |
PVGDG-Optimization | Load Power Factor (LPF = 0.85) | Load Power Factor (LPF = 0.91) | PVGDG Power Factor (PF) % | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Number | Location (Bus No.) | Capacity MW | Power Losses kW | Losses Reduction % | Voltage Profile p.u. | Voltage Drop Improvement % | Power Losses kW | Losses Reduction % | Voltage Profile p.u. | Voltage Drop Improvement % | |
Without | ---- | ---- | 499.7 | ------ | 0.844 | ------ | 415.34 | ------ | 0.866 | ------- | ----- |
1 | 13 | 4 | 388.5 | 22.3 | 0.870 | 3.1 | 310.9 | 25.2 | 0.892 | 3 | 100 |
322.7 | 35.4 | 0.891 | 5.7 | 267.4 | 35.6 | 0.913 | 5.4 | 91 | |||
2 | 11 | 2 | 397 | 20.6 | 0.870 | 3.2 | 318.6 | 23.3 | 0.892 | 3 | 100 |
20 | 2 | 330.6 | 33.9 | 0.892 | 5.8 | 274.4 | 34 | 0.913 | 5.4 | 91 | |
3 | 11 | 1.35 | 400.4 333.4 | 19.9 33.3 | 0.869 0.891 | 3.1 5.7 | 321.7 277.1 | 22.5 33.3 | 0.892 0.913 | 3 5.4 | 100 91 |
15 | 1.35 | ||||||||||
24 | 1.3 | ||||||||||
4 | 11 | 1 | 396.3 329 | 20.7 34.2 | 0.869 0.891 | 3.1 5.7 | 317.8 272.7 | 23.5 34.3 | 0.892 0.913 | 3 5.4 | 100 91 |
13 | 1 | ||||||||||
15 | 1 | ||||||||||
22 | 1 | ||||||||||
5 | 7 | 0.8 | 399.1 334.5 | 20.1 33.1 | 0.870 0.892 | 3.2 5.8 | 321.2 278.9 | 22.7 32.9 | 0.893 0.914 | 3.1 5.5 | 100 91 |
16 | 0.8 | ||||||||||
17 | 0.8 | ||||||||||
21 | 0.8 | ||||||||||
22 | 0.8 |
PVGDG-Optimization | Load Power Factor (LPF = 0.85) | Load Power Factor (LPF = 0.91) | PVGDG (PF) % | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Number | Location (Bus No.) | Capacity MW | Power Losses kW | Losses Reduction % | Voltage Profile p.u. | Voltage Drop Improvement % | Power Losses kW | Losses Reduction % | Voltage Profile p.u. | Voltage Drop Improvement % | |
Without | ---- | ---- | 499.7 | ------ | 0.844 | ------ | 415.34 | ------ | 0.866 | ------- | ----- |
400 | Consumers | 400 × 10 | 128.9 | 74.2 | 0.916 | 8.5 | 70.7 | 83 | 0.936 | 8 | 100 |
24.4 | 95.1 | 0.962 | 14.1 | 9.25 | 97.8 | 0.981 | 13.3 | 91 |
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Majeed, A.A.; Abderrahim, M.; Alkhazraji, A.A. Optimal Allocation of Photovoltaic-Green Distributed Generation for Maximizing the Performance of Electrical Distribution Networks. Energies 2024, 17, 1376. https://doi.org/10.3390/en17061376
Majeed AA, Abderrahim M, Alkhazraji AA. Optimal Allocation of Photovoltaic-Green Distributed Generation for Maximizing the Performance of Electrical Distribution Networks. Energies. 2024; 17(6):1376. https://doi.org/10.3390/en17061376
Chicago/Turabian StyleMajeed, Ammar Abbas, Mohamed Abderrahim, and Afaneen Anwer Alkhazraji. 2024. "Optimal Allocation of Photovoltaic-Green Distributed Generation for Maximizing the Performance of Electrical Distribution Networks" Energies 17, no. 6: 1376. https://doi.org/10.3390/en17061376