Solar Power Integration Into the Transmission Network for Reducing Power Loss and Optimizing Generation Costs – A Comparative Analysis
Online First: 13/05/2026
Corressponding author's email:
kienlc@hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.2026.2026Keywords:
Renewables, Transmission network, Power loss, Generation cost, Solar power plantsAbstract
This paper investigates the integration of Solar Power Plants (SPP) into transmission grids through comparative assessment of two single-objective approaches: minimization of power loss and minimization of generation costs, while maintaining system operational safety conditions. Using the Particle Swarm Optimization algorithm on a modified IEEE 30-bus test system, the study sequentially determined the optimal SPP capacity and location for each objectives separately. The findings reveal that strategic placement and sizing are paramount, confirming that system benefits are non-linear with capacity increases and that the optimal bus location is highly site-specific. Crucially, comparative analysis demonstrates the clear superiority of the generation cost minimization objective. This economic-centric strategy not only achieves the largest reduction in total system cost but also concurrently provides superior technical performance by significantly reducing power losses. This dual success is achieved because the optimization algorithm exploits the locational value and zero-fuel-cost characteristic of solar power, inherently guiding the system toward an operating point that is both economically optimal and physically efficient. This research establishes a core principle for modern smart-grids: the most economically efficient design consistently delivers the most technically robust solution.
Downloads: 0
References
REN21, Renewables 2024 Global Status Report – Global Overview, Paris, France, 2024.
International Energy Agency, Renewables 2023: Analysis and Forecast to 2028. Paris, France: IEA Publications, 2024.
J. M. Barrett, “Future of the power grid series: Challenges and requirements for tomorrow’s electrical power grid,” Lexington Institute, Arlington, VA, USA, 2016.
R. A. Julio, T. Erik, N. Damir, and M. Ralph, “Modernizing the grid: Challenges and opportunities for a sustainable future,” IEEE Power Energy Mag., vol. 15, no. 3, pp. 74–83, 2017. DOI: https://doi.org/10.1109/MPE.2017.2660819
A. B. Birchfield, T. Xu, K. M. Gegner, K. S. Shetye, and T. J. Overbye, “Grid structural characteristics as validation criteria for synthetic networks,” IEEE Trans. Power Syst., vol. 32, no. 4, pp. 3258–3265, 2017. DOI: https://doi.org/10.1109/TPWRS.2016.2616385
A. T. Mohamed, N. T. Vladimir, and A. Z. D. Ahmed, “Optimal sitting and sizing of renewable distributed generations in distribution networks using a hybrid PSO-GSA optimization algorithm,” in Proc. 2017 IEEE Int. Conf. Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Milan, Italy, Jun. 2017.
M. A. Muhammad, J. Ghauth, M. Hazlie, and B. A. B. Ab-Halim, “Optimal placement and sizing of a DG based on a new power stability index and line losses,” Int. J. Electr. Power Energy Syst., vol. 43, no. 1, pp. 1296–1304, 2012. DOI: https://doi.org/10.1016/j.ijepes.2012.05.053
M. Ilyes, M. Sidhant, and D. Deepjyoti, “Stochastic AC optimal power flow: A data-driven approach,” Electr. Power Syst. Res., vol. 189, p. 106567, 2020. DOI: https://doi.org/10.1016/j.epsr.2020.106567
C. Xinyue, X. Yinliang, S. Hongbin, and K. Irfan, “A distributed robust optimization approach for the economic dispatch of flexible resources,” Int. J. Electr. Power Energy Syst., vol. 124, p. 106360, 2021. DOI: https://doi.org/10.1016/j.ijepes.2020.106360
X. Peng, J. Panida, and S. Chanan, “A distributionally robust optimization model for unit commitment considering uncertain wind power generation,” IEEE Trans. Power Syst., vol. 32, no. 1, pp. 39–49, 2017. DOI: https://doi.org/10.1109/TPWRS.2016.2544795
R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 12–19, 2011. DOI: https://doi.org/10.1109/TPWRS.2010.2051168
F. A. Ovat and A. J. Anyandi, “The particle swarm optimization (PSO) algorithm application – A review,” Global J. Eng. Technol. Adv., vol. 3, no. 3, pp. 001–006, 2020. DOI: https://doi.org/10.30574/gjeta.2020.3.3.0033
S. Mohamed, M. H. Hany, and A. Abdulaziz, “A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution,” Ain Shams Eng. J., vol. 12, no. 2, 2020. DOI: https://doi.org/10.1016/j.asej.2020.07.011
S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016. DOI: https://doi.org/10.1016/j.advengsoft.2016.01.008
M. A. Mohamed, A. M. Yahia, A. M. El-Gaafary, and A. M. Hemeida, “Optimal power flow using moth swarm algorithm,” Electr. Power Syst. Res., vol. 142, pp. 190–206, 2017. DOI: https://doi.org/10.1016/j.epsr.2016.09.025
M. A. Abido and A. K. Al-Ali, “Artificial bee colony algorithm for solving multi-objective optimal power flow problem,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 219–230, 2013. DOI: https://doi.org/10.1016/j.ijepes.2013.04.021
A. Kadir and Y. Volkan, “Differential search algorithm for solving multi-objective optimal power flow problem,” Int. J. Electr. Power Energy Syst., vol. 79, pp. 1–10, 2016. DOI: https://doi.org/10.1016/j.ijepes.2015.12.021
S. Duman, U. Güvenç, Y. Sönmez, and N. Yörükeren, “Optimal power flow using gravitational search algorithm,” Energy Convers. Manage., vol. 59, pp. 86–95, 2012. DOI: https://doi.org/10.1016/j.enconman.2012.02.024
G. Gaurav, K. G. Sunil, and A. Saraswat, “A multi-objective techno-economic operation of distribution network considering reactive power support from renewable energy and battery storage system,” J. Energy Storage, vol. 102, p. 114116, 2024. DOI: https://doi.org/10.1016/j.est.2024.114116
X. Pan, T. Zhao, M. Chen, and S. Zhang, “DeepOPF: A deep neural network approach for security-constrained DC optimal power flow,” IEEE Trans. Power Syst., vol. 36, no. 3, pp. 1725–1735, 2021. DOI: https://doi.org/10.1109/TPWRS.2020.3026379
Y. Du and F. Li, “Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning,” IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1066–1076, 2020. DOI: https://doi.org/10.1109/TSG.2019.2930299
C. Barrera-Singaña, M. P. Comech, and H. Arcos, “A comprehensive review on the integration of renewable energy through advanced planning and optimization techniques,” Energies, vol. 18, no. 11, p. 2961, 2025. DOI: https://doi.org/10.3390/en18112961
R. K. Varma, M. Salama, R. Seethapathy, and C. Champion, “Large-scale photovoltaic solar power integration in transmission and distribution networks,” in Proc. IEEE Power Energy Soc. General Meeting, Calgary, AB, Canada, 2009. DOI: https://doi.org/10.1109/PES.2009.5275321
M. S. Tareq, A. E. S. Ayman, A. Mohammed, A. T. Qasem, A. S. Mhd, and S. Mirjalili, “Particle swarm optimization: A comprehensive survey,” IEEE Access, pp. 10031–10061, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3142859
L. T. Nghia, N. H. M. Vu, H. T. Trang, and N. N. Au, “Optimizing the power system operation problem towards minimizing generation and damage costs due to load shedding,” Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11643–11648, 2023. DOI: https://doi.org/10.48084/etasr.6221
M. A. Abido, “Optimal power flow using particle swarm optimization,” Int. J. Electr. Power Energy Syst., vol. 24, pp. 563–571, 2002. DOI: https://doi.org/10.1016/S0142-0615(01)00067-9
N. Emad, H. Rahmat-Allah, and R. Hemmati, “A new restructuring of centralized congestion management focusing on flow-gate and locational price impacts,” Int. Trans. Electr. Energy Syst., pp. 2482–2497, 2017. DOI: https://doi.org/10.1002/etep.2482
M. Mustafa, Z. Rabie, A. Salem, D. M. Ridha, and B. Seddik, “Optimal power flow for transmission system with photovoltaic based DG using biogeography-based optimization,” in Proc. Int. Conf. Electrical Sciences and Technologies in Maghreb (CISTEM’18), Algiers, Algeria, 2018.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Journal of Technical Education Science
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright © JTE.
