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Concept of using heuristic methods in the optimization of electric power systems

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EN
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EN
The dynamic development of information technologies has significantly improved the process of planning and controlling the operation of electrical power systems, nevertheless, we are still looking for methods and solutions which will allow to optimize EPS. The article describes heuristic methods which can be used in electrical power engineering and which will be used to solve OPF (Optimal Power Flow) problems.
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Twórcy
  • Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Computer Science
Bibliografia
  • 1. Horn J., Nafpliotis N., 1993. Multiobjective optimization using the niched pare-to genetic algorithm. IlliGALTechnical Report 93005, IllinoisGenetic Algorithms Laboratory. University of Illinois, Urbana, Illinois.
  • 2. Kacejko P., 2010. Electrical engineering and information technology in modern energy technologies. PAN. 45-58.
  • 3. Kacejko P., Machowski J., Pijarski P., 2011. Standing phase angle reduction at switching operation in transmission network. Rynek Energii nr 5(96)-2011r., 24-35.
  • 4. Kacejko P., Pijarski P, Sobierajski M., Robak S., 2011. Assessment of power system ability to connect new energy sources: Part III – nonlinear optimization of wind generation. Energy market. 44-51.
  • 5. Kacejko P., Pijarski P., 2008. Dynamic Fitting of Generation Level to Thermal Capacity of Overhead Lines. Przegląd Elektrotechniczny, R.84 NR 5/2008, s. 80-83.
  • 6. Kacejko P., Pijarski P., 2009. Connecting of wind farms – limitations instead of oversized investment. Rynek Energii nr 1 (80), 10-15.
  • 7. Kacejko P., Pijarski P., 2014. Heuristic optimization methods applied to minimize the electric power system balancing costs. Electrical Review. 165-168.
  • 8. Karpukhin A., Gritsiv D., Tkachenko A. 2014. Mathematical simulation of infobommunication networks applying chaos theory. ECONTECHMOD. An International Quarterly Journal. Vol. 3, No.3, 33-42.
  • 9. Konak A., Coit D., Smith A., 2006. Multi-objective optimization using ge-netic algorithms: A tutorial. Realiability Engineering and System Safety. vol. 91. Elservier. 992-1007.
  • 10. Kremens Z., Sobierajski M., 1996. Analysis of power systems. WNT. Warsaw. 55-58.
  • 11. Montusiewicz J., Gryniewicz-Jaworska M., 2014. Multi-criteria approach in evolutionary optimization. The problems of modern engineering. Information technology. Publisher Technical University of Lublin. 76-80.
  • 12. Oliskevych M., 2015. Optimization of information flows of logistic supply chain. ECONTECHMOD. An International Quarterly Journal. Vol. 4. No. 4, 71-76.
  • 13. Osyczka A., 2002. Evolutionary Algorithms for Single and Multicriteria Design Optimization. Physica- Verlag, Haidelberg, New York.25-35.
  • 14. Pasierbek A. Polomski M, Sokol R., 2013. Comparison of selected algorithms optimize power flow in the power system. Elektryka. 68-72.
  • 15. Pijarski P. Gryniewicz-Jaworska M., 2015. Optimization in the electric power engineering - problems and challenges. TEKA. Commission of Motorization and Energetics in Agriculture Polish Academy of Sciences. 19-22.
  • 16. Plonka S., Lorek R., 2013. Multi-criteria optimization of manufacturing processes of machine parts. WNT, Warsaw. 56-68.
  • 17. Quintana V., Torres G., 1999. Introduction to interior-point methods. IEEE PICA. Santa Clara, CA
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-33744d11-3c52-4570-8b4e-2d93649e1a05
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