A Systematic Review of Optimal Power Flow Studies with Renewable Energy Sources Penetration
Abstract
The increasing penetration Renewable Energy Source (RES) in electrical power grid, the power system must minimize costs and comply with operational and safety requirements. Optimal power flow (OPF) contributes to this objective by determining the optimal control settings, satisfying system and safety constraints, and enhancing operational efficiency. A systematic review was conducted to assess how OPF studies have been evaluated in the literature. The review analyzed 64 journal articles to identify specific OPF studies from three perspectives: type of RES penetration, objective function, and method of OPF. The results indicate that (a) wind turbines are the most commonly used RES model in OPF studies, (b) cost function is the primary objective that the majority of research has considered, and (c) novel meta-heuristics have often been used in OPF methods under uncertain RES penetration. This systematic review identifies research gaps and topics for further research to study OPF with RES penetration. The findings are expected to benefit researchers in deciding the best OPF method under uncertain RES.
##Keywords:##
Optimal Power Flow (OPF), Renewable energy, Power grid.
Published
Mar 30, 2024
How to Cite
MAULANA, Ricky; SYAFII, Syafii.
A Systematic Review of Optimal Power Flow Studies with Renewable Energy Sources Penetration.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 68, n. 1, p. 24-32, mar. 2024.
ISSN 2527-6085.
Available at: <https://isomase.org/Journals/index.php/jomase/article/view/357>. Date accessed: 22 may 2026.
doi: http://dx.doi.org/10.36842/jomase.v68i1.357.
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[9] Khaled, U., Eltamaly, A.M. & Beroual, A. (2017). Optimal power flow using particle swarm optimization of renewable hybrid distributed generation. Energies, 10(7), 1013. doi: 10.3390/en10071013.
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[12] Attia, A.F., El Sehiemy, R.A. & Hasanien, H.M. (2018). Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, 331-343. doi: 10.1016/j.ijepes.2018.01.024.
[13] Sulaiman, M.H., Mustaffa, Z., Mohamad, A.J., Saari, M. M. & Mohamed, M.R. (2021). Optimal power flow with stochastic solar power using barnacles mating optimizer. International transactions on electrical energy systems, 31(5), e12858. doi: 10.1002/2050-7038.12858.
[14] Sulaiman, M.H., Mustaffa, Z. & Rashid, M.I.M. (2023). An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators. Results in Control and Optimization, 10, 100187. doi: 10.1016/j.rico.2022.100187.
[15] Agrawal, S., Pandya, S., Jangir, P., Kalita, K. & Chakraborty, S. (2023). A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems. Decision Analytics Journal, 8, 100299. doi: 10.1016/j.dajour.2023.100299.
[16] Bauer, R., Mühlpfordt, T., Ludwig, N. & Hagenmeyer, V. (2023). Analytical uncertainty propagation for multi-period stochastic optimal power flow. Sustainable Energy, Grids and Networks, 33, 100969. doi: 10.1016/j.segan.2022.100969.
[17] Ali, M.A., Kamel, S., Hassan, M.H., Ahmed, E.M. & Alanazi, M. (2022). Optimal power flow solution of power systems with renewable energy sources using white sharks algorithm. Sustainability, 14(10), 6049. doi: 10.3390/su14106049.
[18] Adhikari, A., Jurado, F., Naetiladdanon, S., Sangswang, A., Kamel, S. & Ebeed, M. (2023). Stochastic optimal power flow analysis of power system with renewable energy sources using Adaptive Lightning Attachment Procedure Optimizer. International Journal of Electrical Power & Energy Systems, 153, 109314. doi: 10.1016/j.ijepes.2023.109314.
[19] Biswas, P.P., Suganthan, P.N., & Amaratunga, G.A. (2017). Optimal power flow solutions incorporating stochastic wind and solar power. Energy conversion and management, 148, 1194-1207. doi: 10.1016/j.enconman.2017.06.071.
[20] Alghamdi, A.S. (2022). Optimal power flow in wind–photovoltaic energy regulation systems using a modified turbulent water flow-based optimization. Sustainability, 14(24), 16444. doi: 10.3390/su142416444.
[21] Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B. & Talebi, H.A. (2017). Application of RBF neural networks and unscented transformation in probabilistic power-flow of microgrids including correlated wind/PV units and plug-in hybrid electric vehicles. Simulation Modelling Practice and Theory, 72, 51-68. doi: 10.1016/j.simpat.2016.12.006.
[22] Hmida, J.B., Chambers, T. & Lee, J. (2019). Solving constrained optimal power flow with renewables using hybrid modified imperialist competitive algorithm and sequential quadratic programming. Electric Power Systems Research, 177, 105989. doi: 10.1016/j.epsr.2019.105989.
[23] Daqaq, F., Kamel, S., Ouassaid, M., Ellaia, R. & Agwa, A.M. (2022). Non-dominated sorting manta ray foraging optimization for multi-objective optimal power flow with wind/solar/small-hydro energy sources. Fractal and Fractional, 6(4), 194. doi: 10.3390/fractalfract6040194.
[24] Elattar, E.E. & ElSayed, S.K. (2019). Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy, 178, 598-609. doi: 10.1016/j.energy.2019.04.159.
[25] Gallego, L.A., Franco, J.F. & Cordero, L.G. (2021). 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, 131, 107049. doi: 10.1016/j.ijepes.2021.107049.
[26] Guvenc, U., Duman, S., Kahraman, H.T., Aras, S. & Kat?, M. (2021). Fitness–Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources. Applied Soft Computing, 108, 107421. doi: 10.1016/j.asoc.2021.107421.
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