An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties
Abstract
1. Introduction
- The research addresses the computational inefficiency of running optimal power flow (OPF) multiple times by proposing an ML-based approach to predict optimal setpoints of voltage-regulating devices under different loading conditions. The approach reduces the need for repeatedly solving OPF problems, enhancing real-time operational efficiency.
- The proposed coordinated control scheme involving PV smart inverters, DSTATCOM, and OLTC enables effective voltage regulation and power flow management by leveraging the combined capabilities of multiple devices in the power system.
- The centralized AC optimal power flow (CACCOPF) adopted in this research ensures that the control strategy accounts for nonlinear power system dynamics, improving the accuracy and robustness of the coordination.
- The use of ML models as local controllers to determine the best operational points of PV smart inverters, DSTATCOM, and OLTC allows a scalable and communication-efficient control mechanism, enabling faster decision making.
2. Proposed Framework
3. Mathematical Modeling
3.1. Nonlinear Centralized AC OPF (CACOPF)
3.2. OLTC Model
3.3. Smart Inverter Model
3.4. Distribution Static Synchronous Compensator (DSTATCOM) Model
4. Objective Function and Constraints
4.1. Objective Function
4.2. Constraints
4.2.1. Voltage Constraint
4.2.2. Thermal Capacity Constraint
4.2.3. Tap Position of OLTC Constraints
4.2.4. Smart Inverter (SI) Constraints
4.2.5. Power Flow Constraints
5. Machine Learning Approaches in Predicting the Optimal Setpoints of PVSIs and Voltage Regulating Devices
6. Simulation Setup
7. Results and Analysis
7.1. Daily Voltage Profile and Optimal Setpoint of OLTC, PVSI, and DSTATCOM Network
7.2. Machine Learning Prediction Results
7.2.1. Smart Inverter Reactive Power Prediction
7.2.2. OLTC Taps Position Classification Performance
7.2.3. DSTATCOM Reactive Power Prediction Performance
7.3. Testing of the DNN-Based Controller
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Scenarios | Maximum Active Power (MW) | Average Voltage Deviation (PU) |
---|---|---|
1 | 130.239 | 0.0425 |
2 | 131.4998 | 0.0340 |
PVSI | ELM | DNN | DT | |||
---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | |
Q1 | 0.106 | 0.0870 | 0.0543 | 0.0455 | 0.0933 | 0.0914 |
Q2 | 0.0985 | 0.0830 | 0.0683 | 0.0457 | 0.0898 | 0.0832 |
Q3 | 0.0789 | 0.0970 | 0.0675 | 0.0551 | 0.0668 | 0.0543 |
Q4 | 0.1024 | 0.2305 | 0.0435 | 0.0572 | 0.0932 | 0.0891 |
ELM | DNN | DT | |||
---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE |
0.786 | 0.907 | 0.0267 | 0.0229 | 0.034 | 0.0498 |
Techniques | Average Voltage Deviation (PU) | Simulation Time (Seconds) |
---|---|---|
Optimizer | 0.0392 | 108.45 |
DNN Controller | 0.0413 | 2.32 |
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Makanju, T.D.; Hasan, A.N.; Famoriji, O.J.; Shongwe, T. An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties. Energies 2025, 18, 3481. https://doi.org/10.3390/en18133481
Makanju TD, Hasan AN, Famoriji OJ, Shongwe T. An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties. Energies. 2025; 18(13):3481. https://doi.org/10.3390/en18133481
Chicago/Turabian StyleMakanju, Tolulope David, Ali N. Hasan, Oluwole John Famoriji, and Thokozani Shongwe. 2025. "An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties" Energies 18, no. 13: 3481. https://doi.org/10.3390/en18133481
APA StyleMakanju, T. D., Hasan, A. N., Famoriji, O. J., & Shongwe, T. (2025). An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties. Energies, 18(13), 3481. https://doi.org/10.3390/en18133481