Point Estimate for Optimizing Single-Phase PV Placement to Mitigate Voltage Unbalance
Main Article Content
Abstract
Voltage unbalance in distribution networks is a critical issue that can result in equipment malfunctions, reduced efficiency, and increased operational costs. This research explores the use of single-phase photovoltaic (PV) systems to mitigate voltage unbalance, with particular attention to the impact of demand variations. The study utilizes the Point Estimate (PE) method to model demand variation, comparing its performance to that of Monte Carlo Simulation (MCS). The findings reveal that while both methods effectively identify optimal PV system locations, MCS offers a more comprehensive analysis by accounting for a broader range of demand scenarios, leading to recommendations for larger PV system sizes. However, PE proves to be a more computationally efficient alternative. Two case studies, involving the IEEE 34-bus and IEEE 123-bus distribution networks, demonstrate that the proposed method can significantly reduce voltage unbalance, with the optimal solutions involving PV installations at key buses identified through heuristic optimization techniques.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
B. Uzum, A. Onen, H. M. Hasanien, and S. M. Muyeen, “Rooftop solar pv penetration impacts on distribution network and further growth factors—a comprehensive review,” Electronics, vol. 10, no. 1, p. 55, 2020.
K. Girigoudar and L. A. Roald, “On the impact of different voltage unbalance metrics in distribution system optimization,” Electric Power Systems Research, vol. 189, p. 106656, 2020.
C. Lee, S. Im, J. Jung, and B. Lee, “BESS deployment strategy in Jeju carbon-free islands for reducing renewable energy curtailment,” Energies, vol. 13, no. 22, p. 6082, 2020.
T. Weckesser, D. F. Dominković, E. M. Blomgren, A. Schledorn, and H. Madsen, “Renewable Energy Communities: Optimal sizing and distribution grid impact of photo-voltaics and battery storage,” Applied Energy, vol. 301, p. 117408, 2021.
M. Vanin, T. Van Acker, H. Ergun, R. D’hulst, K. Vanthournout, and D. Van Hertem, “Congestion mitigation in unbalanced residential networks with OPF-based demand management,” Sustainable Energy, Grids and Networks, vol. 32, p. 100936, 2022.
Y.-D. Lee, W.-C. Lin, J.-L. Jiang, J.-H. Cai, W.-T. Huang, and K.-C. Yao, “Optimal individual phase
voltage regulation strategies in active distribution networks with high PV penetration using the
sparrow search algorithm,” Energies, vol. 14, no. 24,p. 8370, 2021.
A. M. Nour, A. A. Helal, M. M. El-Saadawi, and A. Y. Hatata, “Voltage imbalance mitigation in
an active distribution network using decentralized current control,” Protection and Control of Modern Power Systems, vol. 8, no. 2, pp. 1–17, 2023.
I. Afandi, A. P. Agalgaonkar, and S. Perera, “Integrated volt/var control method for voltage
regulation and voltage unbalance reduction in active distribution networks,” Energies, vol. 15, no. 6, p. 2225, 2022.
S. Kim and J. Hur, “A probabilistic modeling based on Monte Carlo simulation of wind powered EV charging stations for steady-states security analysis,” Energies, vol. 13, no. 20, p.5260, 2020.
Y. S. Duarte, J. Szpytko, and A. M. del Castillo Serpa, “Monte Carlo simulation model to coordinate the preventive maintenance scheduling of generating units in isolated distributed Power Systems,” Electric Power Systems Research, vol. 182, p. 106237, 2020.
X. Wang, R.-P. Liu, X. Wang, Y. Hou, and F. Bouffard, “A data-driven uncertainty quantification method for stochastic economic dispatch,” IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 812–815, 2021.
A. R. Abbasi, “Probabilistic load flow based on holomorphic embedding, kernel density estimator and saddle point approximation including correlated uncertainty variables,” Electric Power Systems Research, vol. 183, p. 106178, 2020.
L. A. Gallego, J. F. Franco, and L. G. Cordero, “A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation,” International Journal of Electrical Power & Energy Systems, vol. 131, p. 107049, 2021.
Z. Xie and Z. Wu, “Maximum power point tracking algorithm of PV system based on irradiance estimation and multi-Kernel extreme learning machine,” Sustainable Energy Technologies and Assessments, vol. 44, p. 101090, 2021.
A. M. Malkawi, A. Odat, and A. Bashaireh, “A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation,” Sustainability, vol. 14, no. 13, p. 7699, 2022.
M. U. Hashmi, A. Koirala, R. Lundholm, H. Ergun, and D. Van Hertem, “Evaluation of voltage magnitude based unbalance metric for low voltage distribution networks,” in 2022 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 2022, pp. 1–5. Accessed: Oct. 30, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9916772/
W. Pinthurat, B. Hredzak, G. Konstantinou, and J. Fletcher, “Techniques for compensation of unbalanced conditions in LV distribution networks with integrated renewable generation: An overview,” Electric Power Systems Research, vol. 214, p. 108932, 2023.
S. Sundarajoo and D. Soomro, “A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding,” ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 21, no. 2, pp. 249825– 249825, 2023.
N. Nimpitiwan and S. Kaitwanidvilai, “Robust Control Design for Three-Phase Power Inverters using Genetic Algorithm,” ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 10, no. 1, pp. 80–88, 2012.
S. Mirjalili, “Genetic Algorithm,” in Evolutionary Algorithms and Neural Networks, vol. 780, in Studies in Computational Intelligence, vol. 780. , Cham: Springer International Publishing, 2019, pp. 43–55. doi: 10.1007/978-3-319-93025-1_4.
R. M. Ciric, A. P. Feltrin, and L. F. Ochoa, “Power flow in four-wire distribution networks-general approach,” IEEE Transactions on Power Systems, vol. 18, no. 4, pp. 1283–1290, 2003.
A. T. Arnholt, “Statistics: The Art and Science of Learning From Data,” The American Statistician, vol. 61, no. 2, pp. 181–182, May 2007, doi: 10.1198/000313007X190808.
J. M. Morales and J. Perez-Ruiz, “Point estimate schemes to solve the probabilistic power flow,” IEEE Transactions on power systems, vol. 22, no. 4, pp. 1594–1601, 2007.
R. Y. Rubinstein and D. P. Kroese, Simulation and the Monte Carlo method. John Wiley & Sons, 2016.
R. Strahan, “Electricity Engineers’ Association of NZ submission to Standards Australia
for AS/NZS 4777.1 (2016),” 2016, Accessed: Nov. 11, 2024. [Online]. Available: https://ir.canterbury.ac.nz/bitstream/10092/100845/
“Load Profile :: Load Research of PEA.” Accessed: Aug. 08, 2024. [Online]. Available:
