Retraction

Retraction: Tren Frenleme Enerjisinin Maksimum Geri Kazanımı İçin Zaman-Planı Optimizasyonu

Year 2020, Ejosat Special Issue 2020 (HORA), 1 - 9, 15.08.2020
This is a retraction to: Tren Frenleme Enerjisinin Maksimum Geri Kazanımı İçin Zaman-Planı Optimizasyonu https://dergipark.org.tr/en/pub/ejosat/issue/56356/778644

Retraction Note

Yayının farklı bir sayıda yayınlanma tercihinden kaynaklı olarak geri çekilmiştir.

Abstract

Bu makalede, Metro İstanbul araçlarında kullanılması için zaman planı oluşturularak maksimum enerji kazanımının optimize edilmesine yönelik yapılan araştırma sonuçları paylaşılmıştır. Bu çalışma ile amaçlanan rejeneratif enerji ile enerji kazanımı elde ederek enerji tasarrufu sağlamaktır. Rejeneratif enerji ile sağlanan enerji kazanımı, elektromanyetik frenleme yapan trenlerin ürettiği enerjiyi hatta bulunan ve hareket etmeye hazır durumunda olan diğer trenlere aktarması ilkesine dayanmaktadır. Trenin frenlemesi esnasında kinetik enerji açığa çıkar ve bu enerji elektrik enerjisine dönüştürülür, elektrik enerjisine dönüşen bu enerji katenere geri iletilir. Katener de hatta alıcı durumunda bulunan diğer bir trene bu enerjiyi iletir ve böylelikle hatta alıcı durumundaki tren bu enerjiyi kullanması ile enerji kazanımı sağlanmış olur. Rejeneratif frenleme enerjisinden en etkin bir şekilde yararlanmanın yollarından biri zaman-planı en iyileştirmesi (optimizasyonu) uygulanmasıdır. Zaman-planı en iyileştirilmesi yapılarak maksimum enerji kazanımı sağlayacak istasyon bekleme süreleri bulunur. Trenlerin istasyondaki en uygun bekleme sürelerini bulmak için bu çalışmada genetik algoritma kullanılmıştır. Genetik algoritma, çok boyutlu ve karmaşık arama uzayında en iyinin hayatta kalması ilkesine dayanan arama ve en iyileme yöntemidir. Genetik algoritmalar, evrimsel süreci bilgisayar ortamında taklit ederek problemlerin çözümünü ararlar. Başlangıç bireyleri tanımlanan kısıtlamalara dikkat edilerek rastgele oluşturulmuştur. Uygunluk fonksiyonu en iyileştirilmiş zaman-planını verecek bekleme sürelerini içeren bireyin değerlendirmesi amacıyla kullanılmaktadır. Her yeni nesilde daha iyi bireyler elde etmek üzere, farklı yöntem ve oranlarda elitizm, çaprazlama ve mutasyon operatörleri uygulanmıştır. Optimizasyonun nihai haline ulaşması iterasyon sayısı ile sınırlandırılmıştır. Gerçekleştirilen çalışma referans olarak alınan çalışmaya kıyasla %30 oranında daha iyi sonuç elde etmiştir. Referans makalede %60 oranında olan enerji kazanımının, bu çalışma ile %78 oranına kadar çıkartılabildiği gözlemlenmiştir.

References

  • González-Gil, A., Palacin, R., Batty, P., & Powell, J. P. (2014). A systems approach to reduce urban rail energy consumption. Energy Conversion and Management, 80, 509-524.
  • Chen, J. F., Lin, R. L., and Liu, Y. C., 2005: Optimization of an MRT train Schedule: Reducing maximum traction power by using genetic algorithms . Transactions on Power Systems. Vol. 20, no. 3, pp. 1366- 1372.
  • Adinolfi, A., Lamedica, R., Modesto, C., Prudenzi, A., and Vimercati, S., 1998: Experimental assesement of energy saving due to trains regenerative braking in an electrified subway line. Transactions on Power Delivery. Vol. 13, no. 4, pp. 1536-1542.
  • Amit, I., & Goldfarb, D. (1971). The timetable problem for railways. Developments in Operations Research, 2(1), 379-387.
  • Ramos, A., Pena, M. T., Fernández, A., & Cucala, P. (2008). Mathematical programming approach to underground timetabling problem for maximizing time synchronization. Dirección y Organización, (35), 88-95.
  • Nasri, A., Moghadam, M. F., & Mokhtari, H. (2010, June). Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems. In SPEEDAM 2010 (pp. 1218-1221). IEEE.
  • Albrecht, T. (2010). Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control. WIT Transactions on State-of-the-art in Science and Engineering, 39.
  • Peña-Alcaraz, M., Fernández, A., Cucala, A. P., Ramos, A., & Pecharromán, R. R. (2012). Optimal underground timetable design based on power flow for maximizing the use of regenerative-braking energy. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 226(4), 397-408.
  • González-Gil, A., Palacin, R., & Batty, P. (2013). Sustainable urban rail systems: Strategies and technologies for optimal management of regenerative braking energy. Energy conversion and management, 75, 374-388.
  • Yang, X., Li, X., Gao, Z., Wang, H., & Tang, T. (2012). A cooperative scheduling model for timetable optimization in subway systems. IEEE Transactions on Intelligent Transportation Systems, 14(1), 438-447.
  • Yang, X., Ning, B., Li, X., & Tang, T. (2014). A two-objective timetable optimization model in subway systems. IEEE Transactions on Intelligent Transportation Systems, 15(5), 1913-1921.
  • Zhao, L., Li, K., & Su, S. (2014). A multi-objective timetable optimization model for subway systems. In Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume I (pp. 557-565). Springer, Berlin, Heidelberg.
  • Gong, C., Zhang, S., Zhang, F., Jiang, J., & Wang, X. (2014). An integrated energy-efficient operation methodology for metro systems based on a real case of Shanghai metro line one. Energies, 7(11), 7305-7329.
  • Li, X., & Lo, H. K. (2014). Energy minimization in dynamic train scheduling and control for metro rail operations. Transportation Research Part B: Methodological, 70, 269-284.
  • Xu, X., Li, K., & Li, X. (2016). A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption. Journal of Advanced Transportation, 50(1), 69-95.
  • Demirci, I. E., & Celikoglu, H. B. (2018, November). Timetable Optimization for Utilization of Regenerative Braking Energy: A Single Line Case over Istanbul Metro Network. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 2309-2314). IEEE.
  • Amalgamated Report, UITP “Reducing Energy Consumption in Underground Systems, International Metropolitan Railways Committe, , www.uitp.org, 1995
  • Cornic, D. (2010, October). Efficient recovery of braking energy through a reversible dc substation. In Electrical systems for aircraft, railway and ship propulsion (pp. 1-9). IEEE.
  • Gunselmann, W., & Godbersen, C. (2001). Double-layer capacitors store surplus braking energy. Railway Gazette International, 1, 581.
  • Açıkbaş, S. ve Alataş A., 2006: Rayli Sistemlerde Enerji Verimli Sürüş. In Türkiye 10. Enerji Kongresi, 29 Kasım
  • Charnes, A., & Miller, M. H. (1956). A model for the optimal programming of railway freight train movements. Management Science, 3(1), 74-92.
  • Lee, C. K., & Chen, C. H. (2003). Scheduling of train driver for Taiwan railway administration. Journal of Eastern Asia Society of Transportation Studies, 5, 292-306.
  • Ghoseiri, K., Szidarovszky, F., & Asgharpour, M. J. (2004). A multi-objective train scheduling model and solution. Transportation research part B: Methodological, 38(10), 927-952.
  • Dündar, S., & Şahin, İ. (2013). Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways. Transportation Research Part C: Emerging Technologies, 27, 1-15.
  • Martin, P., 1999: Train performance and simulation. In Winter Simulation Conference
  • Açıkbaş, S., 2008: Çok Hatlı Çok Araçlı Raylı Sistemlerde Enerji Tasarrufuna Yönelik Sürüş Kontrolü, Doktora Tezi, İTÜ Fen Bilimleri Enstitüsü
  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, 560 University of Michigan Press. Ann Arbor, MI, 561.
  • Gonzalez, E. L., & Fernandez, M. A. R. (2000). Genetic optimisation of a fuzzy distribution model. International Journal of Physical Distribution & Logistics Management.
  • Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Genetic algorithms for flowshop scheduling problems. Computers & Industrial Engineering, 30(4), 1061-1071.
  • Goldberg, D. E. (1989). Genetic algorithms in search. Optimization, and MachineLearning.
  • Syswerda, G. (1991). Scheduling optimization using genetic algorithms. Handbook of genetic algorithms.
  • Yeo, M. F., & Agyei, E. O. (1998). Optimising engineering problems using genetic algorithms. Engineering Computations.

Retraction: Time-Plan Optimization with Genetic Algorithm for Regain of Energy from Train Tracks

Year 2020, Ejosat Special Issue 2020 (HORA), 1 - 9, 15.08.2020
This is a retraction to: Tren Frenleme Enerjisinin Maksimum Geri Kazanımı İçin Zaman-Planı Optimizasyonu https://dergipark.org.tr/en/pub/ejosat/issue/56356/778644

Retraction Note

Abstract

In this article, the results of the research on optimizing the maximum energy gain by creating a time plan have shared for use in Metro Istanbul vehicles. The purpose of this study is to achieve energy savings by obtaining energy gain with regenerative energy. The energy recovery provided by regenerative energy is based on the principle of transferring the energy produced by the trains that make electromagnetic braking to other trains that are found and ready to move. During the braking of the train, kinetic energy is released and this energy is converted into electrical energy, this energy transformed into electrical energy is transmitted back to the catenary. The catenary transmits this energy to another train that is also in the receiver status, and thus, energy gain is achieved, by using this energy in receiver train on the line. One of the ways to utilize regenerative braking energy in the most effective way is to apply time-plan optimization. By optimizing the time-schedule that provide maximum energy gain, the station waiting times is found. Genetic algorithm was used in this study to find the most suitable waiting times of trains at the station. Genetic algorithm is the search and optimization method based on the principle of the best survival in multi-dimensional and complex search space. Genetic algorithms seek the solution of problems by imitating the evolutionary process in a computer environment. Initial individuals were randomly created by taking into consideration the defined restrictions. The fitness function is used to evaluate the individual which includes the waiting times that give the optimized time-schedule. Elitism, crossover and mutation operators in different methods and proportions have been applied to obtain better individuals in each new generation. The finalization of optimization is limited by the number of iterations. The study carried out has achieved 30% better result than the reference study. It was observed that the 60% energy gain in the reference article can be increased up to 78% with this study.

References

  • González-Gil, A., Palacin, R., Batty, P., & Powell, J. P. (2014). A systems approach to reduce urban rail energy consumption. Energy Conversion and Management, 80, 509-524.
  • Chen, J. F., Lin, R. L., and Liu, Y. C., 2005: Optimization of an MRT train Schedule: Reducing maximum traction power by using genetic algorithms . Transactions on Power Systems. Vol. 20, no. 3, pp. 1366- 1372.
  • Adinolfi, A., Lamedica, R., Modesto, C., Prudenzi, A., and Vimercati, S., 1998: Experimental assesement of energy saving due to trains regenerative braking in an electrified subway line. Transactions on Power Delivery. Vol. 13, no. 4, pp. 1536-1542.
  • Amit, I., & Goldfarb, D. (1971). The timetable problem for railways. Developments in Operations Research, 2(1), 379-387.
  • Ramos, A., Pena, M. T., Fernández, A., & Cucala, P. (2008). Mathematical programming approach to underground timetabling problem for maximizing time synchronization. Dirección y Organización, (35), 88-95.
  • Nasri, A., Moghadam, M. F., & Mokhtari, H. (2010, June). Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems. In SPEEDAM 2010 (pp. 1218-1221). IEEE.
  • Albrecht, T. (2010). Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control. WIT Transactions on State-of-the-art in Science and Engineering, 39.
  • Peña-Alcaraz, M., Fernández, A., Cucala, A. P., Ramos, A., & Pecharromán, R. R. (2012). Optimal underground timetable design based on power flow for maximizing the use of regenerative-braking energy. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 226(4), 397-408.
  • González-Gil, A., Palacin, R., & Batty, P. (2013). Sustainable urban rail systems: Strategies and technologies for optimal management of regenerative braking energy. Energy conversion and management, 75, 374-388.
  • Yang, X., Li, X., Gao, Z., Wang, H., & Tang, T. (2012). A cooperative scheduling model for timetable optimization in subway systems. IEEE Transactions on Intelligent Transportation Systems, 14(1), 438-447.
  • Yang, X., Ning, B., Li, X., & Tang, T. (2014). A two-objective timetable optimization model in subway systems. IEEE Transactions on Intelligent Transportation Systems, 15(5), 1913-1921.
  • Zhao, L., Li, K., & Su, S. (2014). A multi-objective timetable optimization model for subway systems. In Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume I (pp. 557-565). Springer, Berlin, Heidelberg.
  • Gong, C., Zhang, S., Zhang, F., Jiang, J., & Wang, X. (2014). An integrated energy-efficient operation methodology for metro systems based on a real case of Shanghai metro line one. Energies, 7(11), 7305-7329.
  • Li, X., & Lo, H. K. (2014). Energy minimization in dynamic train scheduling and control for metro rail operations. Transportation Research Part B: Methodological, 70, 269-284.
  • Xu, X., Li, K., & Li, X. (2016). A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption. Journal of Advanced Transportation, 50(1), 69-95.
  • Demirci, I. E., & Celikoglu, H. B. (2018, November). Timetable Optimization for Utilization of Regenerative Braking Energy: A Single Line Case over Istanbul Metro Network. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 2309-2314). IEEE.
  • Amalgamated Report, UITP “Reducing Energy Consumption in Underground Systems, International Metropolitan Railways Committe, , www.uitp.org, 1995
  • Cornic, D. (2010, October). Efficient recovery of braking energy through a reversible dc substation. In Electrical systems for aircraft, railway and ship propulsion (pp. 1-9). IEEE.
  • Gunselmann, W., & Godbersen, C. (2001). Double-layer capacitors store surplus braking energy. Railway Gazette International, 1, 581.
  • Açıkbaş, S. ve Alataş A., 2006: Rayli Sistemlerde Enerji Verimli Sürüş. In Türkiye 10. Enerji Kongresi, 29 Kasım
  • Charnes, A., & Miller, M. H. (1956). A model for the optimal programming of railway freight train movements. Management Science, 3(1), 74-92.
  • Lee, C. K., & Chen, C. H. (2003). Scheduling of train driver for Taiwan railway administration. Journal of Eastern Asia Society of Transportation Studies, 5, 292-306.
  • Ghoseiri, K., Szidarovszky, F., & Asgharpour, M. J. (2004). A multi-objective train scheduling model and solution. Transportation research part B: Methodological, 38(10), 927-952.
  • Dündar, S., & Şahin, İ. (2013). Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways. Transportation Research Part C: Emerging Technologies, 27, 1-15.
  • Martin, P., 1999: Train performance and simulation. In Winter Simulation Conference
  • Açıkbaş, S., 2008: Çok Hatlı Çok Araçlı Raylı Sistemlerde Enerji Tasarrufuna Yönelik Sürüş Kontrolü, Doktora Tezi, İTÜ Fen Bilimleri Enstitüsü
  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, 560 University of Michigan Press. Ann Arbor, MI, 561.
  • Gonzalez, E. L., & Fernandez, M. A. R. (2000). Genetic optimisation of a fuzzy distribution model. International Journal of Physical Distribution & Logistics Management.
  • Murata, T., Ishibuchi, H., & Tanaka, H. (1996). Genetic algorithms for flowshop scheduling problems. Computers & Industrial Engineering, 30(4), 1061-1071.
  • Goldberg, D. E. (1989). Genetic algorithms in search. Optimization, and MachineLearning.
  • Syswerda, G. (1991). Scheduling optimization using genetic algorithms. Handbook of genetic algorithms.
  • Yeo, M. F., & Agyei, E. O. (1998). Optimising engineering problems using genetic algorithms. Engineering Computations.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Büşra Tural 0000-0003-3645-8761

Metin Turan This is me 0000-0002-1941-6693

Publication Date August 15, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (HORA)