2. Home
  3. ARCHIVE
  4. 2025
  5. VOL 47, NO 4 (2025)
  6. pp. 73-89

A Parallel Method for Optimizing the Structure of Generating Capacities Using Metaheuristic Algorithms and the Scip Solver

S.Ye. Saukh, T.V. Puchko

Èlektron. model. 2025, 47(4):73-89

https://doi.org/10.15407/emodel.47.04.073

ABSTRACT

A parallel method for solving high-dimensional mixed integer linear programming (MILP) problems, which arise from the optimization of the structure of generating capacities of electric power systems, has been developed. Each such MILP problem is divided into the main task of finding the structure of generating capacities and into a set of independent MILP subtasks of much smaller dimensionality that determine the optimal load modes of power equipment under different operating conditions of the power system. The main problem is solved using metaheuristic algorithms, and the set of independent subproblems is solved in parallel by the SCIP solver, each of which radically speeds up the solution of the problem as a whole. Numerical experiments with the evolutionary center algorithm demonstrated 99 % of global optima and speedups of up to 13.38 on 64 threads. At the same time, both computational speed and me­mory consumption increase almost linearly with the number of threads. The proposed method scales well on HPC platforms and is an effective tool for strategic planning of power systems.

KEYWORDS

generating capacity structure planning, parallel optimization, metaheuristic algorithm, MILP, SCIP.

REFERENCES

  1. Saukh, S.Y., & Borysenko, A.V. (2025). Modeling the electric power system of Ukraine and assessing its resilience under conditions of systematic terrorist attacks. Tekhnichna Elektrodynamika, 2025(2), 57-70. https://doi.org/10.15407/techned2025.02.057 [In Ukrainian]
  2. Lefstad, L., Allesson, J., Busch, H., & Carton, W. (2024). Burying problems? Imaginaries of carbon capture and storage in Scandinavia. Energy Research \& Social Science, 113, 103564. https://doi.org/10.1016/j.erss.2024.103564
  3. Morales Pedraza, J. (2024). China toward a green economy: Current situation and perspective in the use of different energy sources for electricity generation. Academia Green Energy, 1(1). https://doi.org/10.20935/AcadEnergy6236
  4. Hong, Y.-Y., & Apolinario, G. (2021). Uncertainty in unit commitment in power systems: A review of models, methods, and applications. Energies, 14(20), 6658. https://doi.org/ 10.3390/en14206658
  5. Morales-España, G., Ramírez-Elizondo, L., & Hobbs, B.F. (2017). Hidden power system inflexibilities imposed by traditional unit commitment formulations. Applied Energy, 191, 223-238. https://doi.org/10.1016/j.apenergy.2017.01.089
  6. Knueven, B., Ostrowski, J., & Watson, J.-P. (2020). On mixed-integer programming formulations for the unit commitment problem. INFORMS Journal on Computing, ijoc.2019.0944. https://doi.org/10.1287/ijoc.2019.0944
  7. Gaur, A.S., Das, P., Jain, A., Bhakar, R., & Mathur, J. (2019). Long-term energy system planning considering short-term operational constraints. Energy Strategy Reviews, 26, 100383. https://doi.org/10.1016/j.esr.2019.100383
  8. Saukh, S., & Borysenko, А. (2024). Cluster and representative models for generation units of flexible grids with small modular reactors. Nuclear and Radiation Safety, (1), 49–58. https://doi.org/10.32918/nrs.2024.1(101).05
  9. Vojvodic, G., Novoa, L.J., & Jarrah, A.I. (2023). Experimentation with Benders decomposition for solving the two-timescale stochastic generation capacity expansion problem. EURO Journal on Computational Optimization, 11, 100059. https://doi.org/10.1016/ j.ejco.2023.100059
  10. Yagi, K., & Sioshansi, R. (2024). Nested Benders’s decomposition of capacity-planning problems for electricity systems with hydroelectric and renewable generation. Computational Management Science, 21(1), 16. https://doi.org/10.1007/s10287-023-00469-9
  11. Rahmaniani, R., Crainic, T.G., Gendreau, M., & Rei, W. (2017). The Benders decomposition algorithm: A literature review. European Journal of Operational Research, 259(3), 801-817. https://doi.org/10.1016/j.ejor.2016.12.005
  12. Syama, S., Ramprabhakar, J., Anand, R., & Guerrero, J.M. (2024). An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems. Scientific Reports, 14(1), 23964. https://doi.org/10.1038/s41598-024-75743-0
  13. Ploskas, N., & Sahinidis, N.V. (2022). Review and comparison of algorithms and software for mixed-integer derivative-free optimization. Journal of Global Optimization, 82(3), 433-462. https://doi.org/10.1007/s10898-021-01085-0
  14. Marty, T., Hansen, N., Auger, A., Semet, Y., & Héron, S. (2024). LB+IC-CMA-ES: Two simple modifications of CMA-ES to handle mixed-integer problems. У M. Affenzeller, S.M. Winkler, A.V. Kononova, H. Trautmann, T. Tušar, P. Machado & T. Bäck (Ред.), Parallel Problem Solving from Nature — PPSN XVIII (Т. 15149, с. 284-299). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70068-2_18
  15. Gonzalez, M., López-Espín, J.J., Aparicio, J., & Talbi, E.-G. (2022). A hyper-matheuristic approach for solving mixed integer linear optimization models in the context of data envelopment analysis. PeerJ Computer Science, 8, e828. https://doi.org/10.7717/peerj-cs.828
  16. Schlüter, M., Egea, J.A., & Banga, J.R. (2009). Extended ant colony optimization for non-convex mixed integer nonlinear programming. Computers \& Operations Research, 36(7), 2217–2229. https://doi.org/10.1016/j.cor.2008.08.015
  17. Mejía-de-Dios, J.-A., & Mezura-Montes, E. (2019). A new evolutionary optimization method based on center of mass. У K. Deep, M. Jain & S. Salhi (Ред.), Decision science in action (с. 65–74). Springer Singapore. https://doi.org/10.1007/978-981-13-0860-4_6
  18. Bolusani, S., Besançon, M., Bestuzheva, K., Chmiela, A., Dionísio, J., Donkiewicz, T., van Doornmalen, J., Eifler, L., Ghannam, M., Gleixner, A., Graczyk, C., Halbig, K., Hedtke, I., Hoen, A., Hojny, C., van der Hulst, R., Kamp, D., Koch, T., Kofler, K., ... Xu, L. (2024). The SCIP optimization suite 9.0. https://doi.org/10.48550/ARXIV.2402.17702
  19. Batch System PBSPro (vulcan) — HLRS Platforms. (2025, April 15). https://kb.hlrs.de/ platforms/index.php/Batch_System_PBSPro_(vulcan)
  20. Bezanson, J., Edelman, A., Karpinski, S., & Shah, V.B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59(1), 65-98. https://doi.org/10.1137/141000671
  21. Lubin, M., Dowson, O., Garcia, J.D., Huchette, J., Legat, B., & Vielma, J.P. (2023). JuMP 1.0: Recent improvements to a modeling language for mathematical optimization. Mathematical Programming Computation, 15(3), 581-589. https://doi.org/10.1007/s12532-023-00239-3
  22. Schlueter, M., & Munetomo, M. (2017). MIDACO parallelization scalability on 200 MINLP benchmarks. Journal of Artificial Intelligence and Soft Computing Research, 7(3), 171-181. https://doi.org/10.1515/jaiscr-2017-0012
  23. Nikolaus Hansen, yoshihikoueno, ARF1, Sait Cakmak, Gabi Kadlecová, Guillermo Abad López, Kento Nozawa, Luca Rolshoven, Youhei Akimoto, brieglhostis & Dimo Brockhoff. (2024, 3 вересня). CMA-ES/pycma: r4.0.0. https://doi.org/10.5281/ZENODO. 2559634
  24. Mejía-de-Dios, J.-A., & Mezura-Montes, E. (2022). Metaheuristics: A Julia package for single- and multi-objective optimization. Journal of Open Source Software, 7(78), 4723. https://doi.org/10.21105/joss.04723

Full text: PDF