Show source-provided references
3. transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach
10.1016/j.ijepes.2019.105460 ↗5. Han, S., Kim, H.-J., and Lee, D. (2020). A Long-term evaluation on transmission line expansion planning with multistage stochastic programming. Energies, 13.
10.3390/en13081899 ↗6. Scenario-based probabilistic multi-stage optimization for transmission expansion planning incorporating wind generation integration
10.1016/j.epsr.2020.106601 ↗7. Mixed-integer linear model for transmission expansion planning with line losses and energy storage systems
10.1049/iet-gtd.2012.0666 ↗9. Mora, C.A., Montoya, O.D., and Trujillo, E.R. (2020). Mixed-integer programming model for transmission network expansion planning with battery energy storage systems (BESS). Energies, 13.
10.3390/en13174386 ↗10. Li, Y.-H., and Wang, J.-X. (2016). Flexible transmission network expansion planning considering uncertain renewable generation and load demand based on hybrid clustering analysis. Appl. Sci., 6.
10.3390/app6010003 ↗14. Pereira, M.V.F., and Pinto, L.M.V.G. (1985). Application of sensitivity analysis of load supplying capability to interactive transmission expansion planning. IEEE Trans. Power Appar. Syst., 381–389.
10.1109/TPAS.1985.319053 ↗16. Transmission network expansion planning under an improved genetic algorithm
10.1109/59.871750 ↗19. A greedy randomized adaptive search procedure for transmission expansion planning
10.1109/59.918294 ↗23. Multi-group particle swarm optimisation for transmission expansion planning solution based on LU decomposition
10.1049/iet-gtd.2016.0923 ↗25. Shaheen, A.M. (2019, January 2–4). Application of multi-verse optimizer for transmission network expansion planning in power systems. Proceedings of the 2019 International Conference on Innovative Trends in Computer Engineering (ITCE’2019), Aswan, Egypt.
10.1109/ITCE.2019.8646329 ↗26. Mohamed, A.W., Hadi, A.A., Fattouh, A.M., and Jambi, K.M. (2017, January 5–8). LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES for Solving CEC 2017 Benchmark Problems. Proceedings of the 2017 IEEE Congress On Evolutionary Computation (Cec), San Sebastián, Spain.
10.1109/CEC.2017.7969307 ↗30. Tanabe, R., and Fukunaga, A.S. (2014, January 6–11). Improving the search performance of SHADE using linear population size reduction. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China.
10.1109/CEC.2014.6900380 ↗31. Fathy, A., Aleem, S.H.E.A., and Rezk, H. (2020). A novel approach for PEM fuel cell parameter estimation using LSHADEEpSin optimization algorithm. Int. J. Energy Res., 1–21.
33. Transmission expansion planning for realistic Network in Egypt using Heuristic Technique
34. Zobaa, A.F., Aleem, S.H.E.A., and Abdelaziz, A.Y. (2018). Classical and Recent Aspects of Power System Optimization, Elsevier.
36. Rawa, M., Abusorrah, A., Al-Turki, Y., Mekhilef, S., Mostafa, M.H., Ali, Z.M., and Aleem, S.H.E.A. (2020). Optimal allocation and economic analysis of battery energy storage systems: Self-consumption rate and hosting capacity enhancement for microgrids with high renewable penetration. Sustainability, 12.
10.3390/su122310144 ↗