آمار

تعداد نشریات26
تعداد شماره‌ها404
تعداد مقالات3,540
تعداد مشاهده مقاله5,480,515
تعداد دریافت فایل اصل مقاله3,748,095
Kavitha, M., Mahendra, S.J., Chupradit, S., Nurrohkayati, A.S., Kadhim, S.B., Mustafa, Y.F., Jalil, A.T., Ali, M.H., Sunarsi, D., Akhmetov, L.. (1401). A Novel Improved HBMO Algorithm Regarding Generation Expansion Planning in Deregulated Energy Networks. سامانه مدیریت نشریات علمی, 11(Special Issue), 1-7. doi: 10.22098/joape.2023.10221.1725
M. Kavitha; S.J. Mahendra; S. Chupradit; A.S. Nurrohkayati; S.B. Kadhim; Y.F. Mustafa; A.T. Jalil; M.H. Ali; D. Sunarsi; L. Akhmetov. "A Novel Improved HBMO Algorithm Regarding Generation Expansion Planning in Deregulated Energy Networks". سامانه مدیریت نشریات علمی, 11, Special Issue, 1401, 1-7. doi: 10.22098/joape.2023.10221.1725
Kavitha, M., Mahendra, S.J., Chupradit, S., Nurrohkayati, A.S., Kadhim, S.B., Mustafa, Y.F., Jalil, A.T., Ali, M.H., Sunarsi, D., Akhmetov, L.. (1401). 'A Novel Improved HBMO Algorithm Regarding Generation Expansion Planning in Deregulated Energy Networks', سامانه مدیریت نشریات علمی, 11(Special Issue), pp. 1-7. doi: 10.22098/joape.2023.10221.1725
Kavitha, M., Mahendra, S.J., Chupradit, S., Nurrohkayati, A.S., Kadhim, S.B., Mustafa, Y.F., Jalil, A.T., Ali, M.H., Sunarsi, D., Akhmetov, L.. A Novel Improved HBMO Algorithm Regarding Generation Expansion Planning in Deregulated Energy Networks. سامانه مدیریت نشریات علمی, 1401; 11(Special Issue): 1-7. doi: 10.22098/joape.2023.10221.1725

A Novel Improved HBMO Algorithm Regarding Generation Expansion Planning in Deregulated Energy Networks

Journal of Operation and Automation in Power Engineering
دوره 11، Special Issue، اسفند 2023، صفحه 1-7    اصل مقاله (438.01 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10221.1725
نویسندگان
M. Kavitha* 1؛ S.J. Mahendra2؛ S. Chupradit3؛ A.S. Nurrohkayati4؛ S.B. Kadhim5؛ Y.F. Mustafa6؛ A.T. Jalil7، 8، 9؛ M.H. Ali10؛ D. Sunarsi11؛ L. Akhmetov12
1Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences
22 Department of Medical Electronics, Ramaiah Institute of Technology, Bengaluru, E-mail: mahendra.j@msrit.edu
3Department of Occupational Therapy, Faculty of Associated Medical Sciences
4Department of Mechanical Engineering, Universitas Muhammadiyah Kalimantan Timur, Samarinda, Indonesia
5College of Dentistry, Al-Ayen University, Thi-Qar, Iraq
6Department of Pharmaceutical Chemistry, College of Pharmacy, University of Mosul, Mosul, Iraq
7Faculty of Biology and Ecology, Yanka Kupala State University of Grodno, 230023 Grodno, Belarus
8College of technical engineering, The Islamic University, Najaf, Iraq
9Department of Dentistry, Kut University College, Kut, Wasit, 52001, Iraq
10Al-Nisour University College, Baghdad, Iraq
11Universitas Pamulang, Tangerang Selatan, Banten, Indonesia
12Kazan Federal University, Russia
چکیده
Electric energy demand is increasing rapidly in developing countries, making the installation of additional generating units necessary. Private generating stations are encouraged to add new generations in deregulated energy networks. Planning for transmission expansion must ensure increased market competition while maintaining high levels of dependability and system operation safety. New objectives and demands have been made for the transmission expansion issue as a result of the deregulation of the energy network. This study has attempted to provide a new population-base algorithm; called Modified Honey Bee Mating Optimization (MHBMO) for expansion development in deregulated energy systems that are applied in multi-objective processes. In addition, to diminish the elaborateness of the issue the benders decomposition is used in this study which categorize the original issue into two subproblems. First maximizing the profits of each PBGEP (GENCO) and second, satisfying security network constraints (SCGEP). Therefore, using the suggested MHBMO algorithm, value of each GENCO's profit and overall profit could be obtained. To demonstrate the viability and capabilities of the suggested algorithm, the planning methodology has been evaluated using the IEEE 30-bus test system. The results of the current study served as an example of the effectiveness of the suggested methodology.
کلیدواژه‌ها
Benders decomposition؛ generation expansion planning؛ MHBMO؛ optimization
مراجع
  1. Alizadeh and S. Jadid, “Accelerating the Composite Power System Planning by Benders Decomposition,” J. Oper. Autom. Power Eng., vol. 2, no. 1, pp. 1–9, 2014.
  2. Arasteh, M. Sepasian, and V. Vahidinasab, “Toward a smart distribution system expansion planning by considering demand response resources,” J. Oper. Autom. Power Eng., vol. 3, no. 2, pp. 116–130, 2015.
  3. Li, D. W. Coit, and F. Felder, “Stochastic optimization for electric power generation expansion planning with discrete climate change scenarios,” Electr. Power Syst. Res., vol. 140, pp. 401–412, 2016.
  4. Alayi, R. Kumar, S. R. Seydnouri, M. H. Ahmadi, and A. Issakhov, “Energy, environment and economic analyses of a parabolic trough concentrating photovoltaic/thermal system,” Int. J. Low-Carbon Technol., vol. 16. no. 2, pp. 570–576, 2021.
  5. Alayi, A. Kasaeian, and F. Atabi, “Optical modeling and optimization of parabolic trough concentration photovoltaic/thermal system,” Environ. Prog. Sustainable Energy, vol. 39, no. 2, pp. e13303, 2020.
  6. J. Guerra, D. A. Tejada, and G. V. Reklaitis, “An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems,” Appl. Energy, vol. 170, pp. 1–21, 2016.
  7. Alayi, F. Zishan, M. Mohkam, S. Hoseinzadeh, S. Memon, and D. A. Garcia, “A sustainable energy distribution configuration for microgrids integrated to the national grid using back-to-back converters in a renewable power system,” Electronics, vol. 10, no. 15, pp. 1826, 2021.
  8. Alayi, M. B. Khan, and M. S. G. Mohmammadi, “Feasibility study of grid-connected PV system for peak demand reduction of a residential building in Tehran, Iran,” Math. Modelling Eng. Prob., vol. 7, no. 4, pp. 563–567, 2020.
  9. Pourderogar, H. Harasii, R. Alayi, S. H. Delbari, M. Sadeghzadeh, and A. R. Javaherbakhsh, “Modeling and technical analysis of solar tracking system to find optimal angle for maximum power generation using MOPSO algorithm,” Renewable Energy Res. Appl. vol. 1, no. 2, pp. 211–222, 2020.
  10. Rajesh, K. Karthikeyan, S. Kannan, and C. Thangaraj, “Generation expansion planning based on solar plants with storage,” Renewable Sustainable Energy Rev. vol. 57, pp. 953–964, 2016.
  11. H. Roh, M. Shahidehpour, and Y. Fu, “Marketbased coordination of transmission and generation capacity planning,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1406–1419, 2007.
  12. Alayi, M. H. Ahmadi, A. R. Visei, S. Sharma, and A. Najafi, “Technical and environmental analysis of photovoltaic and solar water heater cogeneration system: a case study of Saveh City,” Int. J. Low-Carbon Technol., vol. 16, no. 2, pp. 447–453, 2021.
  13. Sirikum, A. Techanitisawad, and V. Kachitvichyanukul, “A new efficient GA-benders’ decomposition method: For power generation expansion planning with emission controls,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1092–1100, 2007.
  14. -B. Park, Y.-M. Park, J.-R. Won, and K. Y. Lee, “An improved genetic algorithm for generation expansion planning,” IEEE Trans. Power Syst., vol. 15, no. 3, pp. 916–922, 2000.
  15. Alayi and H. Rouhi, “Techno-economic analysis of electrical energy generation from urban waste in Hamadan, Iran,” Int. J. Des. Nat., vol. 15, no. 3, pp. 337–341, 2020.
  16. Taherkhani, S. H. Hosseini, M. S. Javadi, and J. P. Catalão, “Scenario-based probabilistic multi-stage optimization for transmission expansion planning incorporating wind generation integration,” Electr. Power Syst. Res., vol. 189, pp. 106601, 2020.
  17. S. Dagoumas and N. E. Koltsaklis, “Review of models for integrating renewable energy in the generation expansion planning,” Appl. Energy, vol. 242, pp. 1573–1587, 2019.
  18. -K. Cao, K. Von Krbek, M. Wetzel, F. Cebulla, and S. Schreck, “Classification and evaluation of concepts for improving the performance of Appl. Energy system optimization models,” Energies, vol. 12, no. 24, pp. 4656, 2019.
  19. Rezaie-Balf, S. Fani Nowbandegani, S. Z. Samadi, H. Fallah, and S. Alaghmand, “An ensemble decompositionbased artificial intelligence approach for daily streamflow prediction,” Water, vol. 11, no. 4, pp. 709, 2019.
  20. Khandelwal, A. Bhargava, A. Sharma, and H. Sharma, “Transmission network expansion planning using state-of-art nature inspired algorithms: a survey,” Int. J. Swarm Intell., vol. 4, no. 1, pp. 73–92, 2019.
  21. Baqeri and H. Falaghi, “A practical approach for reliability assessment in multi-objective transmission expansion planning,” Iran. Electric Ind. J. Qual. Productivity, vol. 9, no. 2, pp. 1–12, 2020.
  22. K. Singh and S. K. Parida, “A review on distributed generation allocation and planning in deregulated electricity market,” Renewable Sustainable Energy Rev. vol. 82, pp. 4132–4141, 2018.
  23. Akbari and M. S. Ayubirad, “Seismic optimum design of steel structures using gradient-based and genetic algorithm methods,” Int. J. Civ. Eng., vol. 15, no. 2, pp. 135–148, 2017.
  24. Ayubi Rad and M. S. Ayubirad, “Comparison of artificial neural network and coupled simulated annealing based least square support vector regression models for prediction of compressive strength of high-performance concrete,” Sci. Iran., vol. 24, no. 2, pp. 487–496, 2017.
  25. G. Olanlari, S. S. Gougheri, and A. Nikoofard, “A MultiObjective Generation Expansion Planning with Modeling Load Demand Uncertainty by a Deep Learning-Based Approach,” in 10th Smart Grid Conference (SGC) (IEEE, 2020), pp. 1–6, 2020.
  26. Kasule and K. Ayan, “Forecasting Uganda’s net electricity consumption using a hybrid PSO-ABC Algorithm,” Arabian J. Sci. Eng., vol. 44, no. 4, pp. 3021–3031, 2019.
  27. L. Gbadamosi and N. I. Nwulu, “Reliability assessment of composite generation and transmission expansion planning incorporating renewable energy sources,” J. Renewable and Sustainable Energy vol. 12, no. 2, pp. 026301, 2020.
  28. Haripriya, B. Devulal, and M. Nagajyothi, “Optimal Design of Transmission System Expansion Planning in Deregulated Environment,” in 3rd Int. Conf. Intell. Sustainable Syst. (ICISS), (IEEE, 2020), pp. 1404–1407.
  29. Rahmani-Andebili and H. Shen, “Price-controlled energy management of smart homes for maximizing profit of a GENCO,” IEEE Trans. Syst. Man Cybern.: Syst., vol. 49, no. 4, pp. 697–709, 2017.
  30. Soares, B. Canizes, M. A. F. Ghazvini, Z. Vale, and G. K. Venayagamoorthy, “Two-stage stochastic model using benders’ decomposition for large-scale energy resource management in smart grids,” IEEE Trans. Ind. Appl., vol. 53, no. 6, pp. 5905—5914, 2017.
  31. Ghasemi, “A fuzzified multi objective interactive honey bee mating optimization for environmental/economic power dispatch with valve point effect,” Int. J. Electr. Power Energy Syst. , vol. 49, pp. 308—321, 2013.
  32. T. Firmo and L. L. Legey, “Generation expansion planning: an iterative genetic algorithm approach,” IEEE Trans. Power Syst. , vol. 17, no.3, pp. 901—906, 2002.
آمار
تعداد مشاهده مقاله: 768
تعداد دریافت فایل اصل مقاله: 598


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب