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Practical Radial Distribution Feeder for Techno Economic in DERs based on ANN and Chameleon Optimization Algorithms

Year 2023, , 1285 - 1297, 01.10.2023
https://doi.org/10.2339/politeknik.1348672

Abstract

Distributed energy resources (DERs) are a better choice to meet load demand close to load centers. Optimal DER placement and DER ratings lead to power loss reduction, voltage profile improvement, environmental friendliness, dependability, and postponement of system changes. This study uses artificial neural networks and the Chameleon Optimization Algorithm to analyze the best integration of renewable energy sources and electric vehicles in distribution feeders to reduce power loss, regulate voltage levels, and decrease the cost and emissions under unpredictable load demand. In this study, the generated output power of the models is compared to solar photovoltaic generation systems and wind turbine generation systems. As a result, a fitness function with several objectives has been developed to reduce total active power loss while also reducing total cost and emissions generation. The study took into account the influence of EV charging/discharging behavior on the distribution system. The 28-bus rural distribution network in feeders is used to test the suggested methodology. Final analysis of the numerical results showed that the Artificial Neural Network and Chameleon Optimization Algorithms outperformed in terms of power loss (440.94 kw) and average purchase of real power (2224 kw), but these parameters do not favor the other optimization algorithms. This showed that the proposed strategy is both viable and effective.

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References

  • [1] E. A. Sharew, H. A. Kefale, and Y. G. Werkie, “Power Quality and Performance Analysis of Grid-Connected Solar PV System Based on Recent Grid Integration Requirements,” Int. J. Photoenergy, (2021).
  • [2] B. Xiao, H. Zhu, S. Zhang, Z. OuYang, T. Wang, and S. Sarvazizi, “Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power,” Int. J. Photoenergy, (2022).
  • [3] A. Ab-BelKhair, J. Rahebi, and A. Abdulhamed Mohamed Nureddin, “A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter,” Int. J. Photoenergy, (2020).
  • [4] M. Nemati, M. Braun, and S. Tenbohlen, “Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming,” Appl. Energy, 210, 944–963, (2018).
  • [5] K. S. Swarup, “Ant colony optimization for economic generator scheduling and load dispatch,” in Proceedings of the 6th WSEAS International Conference on Evolutionary Computing, Portugal, 167–175,(2005).
  • [6] N. Karmakar and B. Bhattacharyya, “Hybrid intelligence approach for multi-load level reactive power planning using VAR compensator in power transmission network,” Prot. Control Mod. Power Syst., vol. 6, no. 1, pp. 1–17, 2021.
  • [7] A. A. M. Nureddin, J. Rahebi, and A. Ab-BelKhair, “Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network,” Int. J. Photoenergy, (2020).
  • [8] M. Al-jumaili, J. Rahebi, A. Akbas, and A. Farzamnia, “Economic dispatch optimization for thermal power plants in Iraq,”2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), 140–143,(2017).
  • [9] K. LaCommare and C. Marnay, “Microgrids and heterogeneous power quality and reliability,” Int. J. Distrib. Energy Resour., 4, no. LBNL-777E, (2007).
  • [10] E. Serban and H. Serban, “A control strategy for a distributed power generation microgrid application with voltage-and current-controlled source converter,” IEEE Trans. Power Electron., 25(12):2981–2992, (2010).
  • [11] R. Majumder, A. Ghosh, G. Ledwich, and F. Zare, “Power management and power flow control with back-to-back converters in a utility connected microgrid,” IEEE Trans. Power Syst., 25(2):821–834, (2009).
  • [12] N. Amjady, F. Keynia, and H. Zareipour, “Short-term load forecast of microgrids by a new bilevel prediction strategy,” IEEE Trans. Smart Grid, vol. 1(3):286–294, (2010).
  • [13] H. Sajir, J. Rahebi, A. Abed, and A. Farzamnia, “Reduce power losses and improve voltage level by using distributed generation in radial distributed grid,” 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), 128–133,(2017).
  • [14] A. H. Abed, J. Rahebi, and A. Farzamnia, “Improvement for power quality by using dynamic voltage restorer in electrical distribution networks,” in 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), 122–127,(2017).
  • [15] D. K. Khatod, V. Pant, and J. Sharma, “Evolutionary programming based optimal placement of renewable distributed generators,” IEEE Trans. Power Syst., 28(2): 683–695, (2012).
  • [16] Y. M. Atwa and E. F. El-Saadany, “Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems,” IET Renew. Power Gener., 5(1):79–88, (2011).
  • [17] Y. M. Atwa and E. F. El-Saadany, “Optimal allocation of ESS in distribution systems with a high penetration of wind energy,” IEEE Trans. Power Syst.,25(4): 1815–1822, (2010).
  • [18] Y. M. Atwa, E. F. El-Saadany, M. M. A. Salama, and R. Seethapathy, “Optimal renewable resources mix for distribution system energy loss minimization,” IEEE Trans. Power Syst., 25(1): 360–370, (2009).
  • [19] N. K. Meena, S. Parashar, A. Swarnkar, N. Gupta, and K. R. Niazi, “Improved elephant herding optimization for multiobjective DER accommodation in distribution systems,” IEEE Trans. Ind. informatics, 14(3):1029–1039, (2017).
  • [20] C. Mateo, P. Frías, and K. Tapia-Ahumada, “A comprehensive techno-economic assessment of the impact of natural gas-fueled distributed generation in European electricity distribution networks,” Energy, 192, 116523, (2020).
  • [21] Z. Ullah, M. R. Elkadeem, S. Wang, S. W. Sharshir, and M. Azam, “Planning optimization and stochastic analysis of RE-DGs for techno-economic benefit maximization in distribution networks,” Internet of Things, 11,100210, (2020).
  • [22] M. Dixit, P. Kundu, and H. R. Jariwala, “Techno-economic analysis-based optimal incorporation of distributed energy resources in distribution network under load uncertainty,” Int. J. Ambient Energy, 42(6):605–611, (2021).
  • [23] M. Dixit, P. Kundu, and H. R. Jariwala, “Incorporation of distributed generation and shunt capacitor in radial distribution system for techno-economic benefits,” Eng. Sci. Technol. an Int. J., 20(2): 482–493, (2017).
  • [24] K. Hesaroor and D. Das, “Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources,” Int. Trans. Electr. energy Syst., 29(11):e12099, (2019).
  • [25] A. Maleki, M. G. Khajeh, and M. Ameri, “Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty,” Int. J. Electr. Power Energy Syst., 83, 514–524, (2016).
  • [26] W. Hu, C. Su, Z. Chen, and B. Bak-Jensen, “Optimal operation of plug-in electric vehicles in power systems with high wind power penetrations,” IEEE Trans. Sustain. Energy, 4(3): 577–585, (2013).
  • [27] S. Suthar, S. H. C. Cherukuri, and N. M. Pindoriya, “Peer-to-peer energy trading in smart grid: Frameworks, implementation methodologies, and demonstration projects,” Electr. Power Syst. Res., 214, 108907, (2023).
  • [28] R. Lazdins, A. Mutule, and D. Zalostiba, “PV Energy Communities—Challenges and Barriers from a Consumer Perspective: A Literature Review,” Energies, 14(16): 4873, (2021).
  • [29] M. A. Butturi, F. Lolli, M. A. Sellitto, E. Balugani, R. Gamberini, and B. Rimini, “Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis,” Appl. Energy, 255, 113825, (2019).
  • [30] M. A. Hannan, M. Faisal, P. J. Ker, R. A. Begum, Z. Y. Dong, and C. Zhang, “Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications,” Renew. Sustain. Energy Rev., 131, 110022, (2020).
  • [31] S. M. Zahraee, N. Shiwakoti, and P. Stasinopoulos, “Biomass supply chain environmental and socio-economic analysis: 40-Years comprehensive review of methods, decision issues, sustainability challenges, and the way forward,” Biomass and Bioenergy, 142, 105777, (2020).
  • [32] G. Sree Lakshmi, R. Olena, G. Divya, and I. Hunko, “Electric vehicles integration with renewable energy sources and smart grids,” Advances in Smart Grid Technology, Springer, 397–411(2020).
  • [33] N. I. Nimalsiri, E. L. Ratnam, D. B. Smith, C. P. Mediwaththe, and S. K. Halgamuge, “Coordinated charge and discharge scheduling of electric vehicles for load curve shaping,” IEEE Trans. Intell. Transp. Syst., (2021).
  • [34] M. Kumar, S. Vyas, and A. Datta, “A review on integration of electric vehicles into a smart power grid and vehicle-to-grid impacts,” 2019 8th International Conference on Power Systems (ICPS), 1–5,(2019).
  • [35] S. Shafiq, U. Bin Irshad, M. Al-Muhaini, S. Z. Djokic, and U. Akram, “Reliability evaluation of composite power systems: Evaluating the impact of full and plug-in hybrid electric vehicles,” IEEE Access, 8, 114305–114314, (2020).
  • [36] M. Dixit, “Impact of optimal integration of renewable energy sources and electric vehicles in practical distribution feeder with uncertain load demand,” Int. Trans. Electr. Energy Syst., 30(12):e12668, (2020).
  • [37] A. T. Lemeski, R. Ebrahimi, and A. Zakariazadeh, “Optimal decentralized coordinated operation of electric vehicle aggregators enabling vehicle to grid option using distributed algorithm,” J. Energy Storage, 54, 105213, (2022).
  • [38] M. A. Hannan et al., “Vehicle to grid connected technologies and charging strategies: Operation, control, issues and recommendations,” J. Clean. Prod., 130587, (2022).
  • [39] T. U. Solanke, V. K. Ramachandaramurthy, J. Y. Yong, J. Pasupuleti, P. Kasinathan, and A. Rajagopalan, “A review of strategic charging–discharging control of grid-connected electric vehicles,” J. Energy Storage, 28, 101193,(2020).
  • [40] M. I. Azim, W. Tushar, T. K. Saha, C. Yuen, and D. Smith, “Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks,” Renew. Sustain. Energy Rev., 169, 112908, (2022).
  • [41] M. A. Rogalski and M. A. Duffy, “Local adaptation of a parasite to solar radiation impacts disease transmission potential, spore yield, and host fecundity,” Evolution (N. Y)., 74(8):1856–1864, (2020).
  • [42] M. Sumair, T. Aized, M. M. A. Bhutta, F. A. Siddiqui, L. Tehreem, and A. Chaudhry, “Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications,” Renew. Energy, 191, 291–304, (2022).
  • [43] M. S. Braik, “Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems,” Expert Syst. Appl., 174,114685, (2021).
  • [44] B. Shboul et al., “A new ANN model for hourly solar radiation and wind speed prediction: A case study over the north & south of the Arabian Peninsula,” Sustain. Energy Technol. Assessments, 46, 101248, (2021).
  • [45] V. Black, “Cost and performance data for power generation technologies,” Prep. Natl. Renew. Energy Lab., (2012).
  • [46] J.-H. Teng, S.-W. Luan, D.-J. Lee, and Y.-Q. Huang, “Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems,” IEEE Trans. Power Syst., 28(2): 1425–1433,(2012).
  • [47] A. Ellis et al., “Reactive power interconnection requirements for PV and wind plants–recommendations to NERC,” Sandia Natl. Lab. Albuquerque, New Mex., 87185, (2012).
  • [48] A. Naderipour et al., “Carrier wave optimization for multi-level photovoltaic system to improvement of power quality in industrial environments based on Salp swarm algorithm,” Environ. Technol. Innov., 21, 101197, (2021).

YSA ve Bukalemun Optimizasyon Algoritmalarına Dayalı DER'lerde Tekno Ekonomik için Pratik Radyal Dağıtım Besleyicisi

Year 2023, , 1285 - 1297, 01.10.2023
https://doi.org/10.2339/politeknik.1348672

Abstract

Dağıtılmış enerji kaynakları (DER'ler), yük merkezlerine yakın yük talebini karşılamak için daha iyi bir seçimdir. Optimum DER yerleşimi ve DER değerleri, güç kaybının azaltılmasına, voltaj profilinin iyileştirilmesine, çevre dostu olmasına, güvenilirliğe ve sistem değişikliklerinin ertelenmesine yol açar. Bu çalışma, güç kaybını azaltmak, voltaj seviyelerini düzenlemek ve öngörülemeyen yük talebi altında maliyet ve emisyonları azaltmak amacıyla yenilenebilir enerji kaynaklarının ve elektrikli araçların dağıtım besleyicilerindeki en iyi entegrasyonunu analiz etmek için yapay sinir ağlarını ve Bukalemun Optimizasyon Algoritmasını kullanmaktadır. Bu çalışmada modellerin üretilen çıkış güçleri güneş fotovoltaik üretim sistemleri ve rüzgar türbini üretim sistemleri ile karşılaştırılmıştır. Sonuç olarak, toplam aktif güç kaybını azaltırken aynı zamanda toplam maliyeti ve emisyon üretimini de azaltmak için çeşitli hedefleri olan bir uygunluk fonksiyonu geliştirilmiştir. Çalışma, EV şarj/deşarj davranışının dağıtım sistemi üzerindeki etkisini dikkate aldı. Önerilen metodolojiyi test etmek için fiderlerdeki 28 otobüslü kırsal dağıtım ağı kullanılmıştır. Sayısal sonuçların son analizi, Yapay Sinir Ağı ve Bukalemun Optimizasyon Algoritmalarının güç kaybı (440,94 kw) ve ortalama gerçek güç alımı (2224 kw) açısından daha iyi performans gösterdiğini ancak bu parametrelerin diğer optimizasyon algoritmalarını desteklemediğini gösterdi. Bu, önerilen stratejinin hem uygulanabilir hem de etkili olduğunu gösterdi.

References

  • [1] E. A. Sharew, H. A. Kefale, and Y. G. Werkie, “Power Quality and Performance Analysis of Grid-Connected Solar PV System Based on Recent Grid Integration Requirements,” Int. J. Photoenergy, (2021).
  • [2] B. Xiao, H. Zhu, S. Zhang, Z. OuYang, T. Wang, and S. Sarvazizi, “Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power,” Int. J. Photoenergy, (2022).
  • [3] A. Ab-BelKhair, J. Rahebi, and A. Abdulhamed Mohamed Nureddin, “A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter,” Int. J. Photoenergy, (2020).
  • [4] M. Nemati, M. Braun, and S. Tenbohlen, “Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming,” Appl. Energy, 210, 944–963, (2018).
  • [5] K. S. Swarup, “Ant colony optimization for economic generator scheduling and load dispatch,” in Proceedings of the 6th WSEAS International Conference on Evolutionary Computing, Portugal, 167–175,(2005).
  • [6] N. Karmakar and B. Bhattacharyya, “Hybrid intelligence approach for multi-load level reactive power planning using VAR compensator in power transmission network,” Prot. Control Mod. Power Syst., vol. 6, no. 1, pp. 1–17, 2021.
  • [7] A. A. M. Nureddin, J. Rahebi, and A. Ab-BelKhair, “Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network,” Int. J. Photoenergy, (2020).
  • [8] M. Al-jumaili, J. Rahebi, A. Akbas, and A. Farzamnia, “Economic dispatch optimization for thermal power plants in Iraq,”2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), 140–143,(2017).
  • [9] K. LaCommare and C. Marnay, “Microgrids and heterogeneous power quality and reliability,” Int. J. Distrib. Energy Resour., 4, no. LBNL-777E, (2007).
  • [10] E. Serban and H. Serban, “A control strategy for a distributed power generation microgrid application with voltage-and current-controlled source converter,” IEEE Trans. Power Electron., 25(12):2981–2992, (2010).
  • [11] R. Majumder, A. Ghosh, G. Ledwich, and F. Zare, “Power management and power flow control with back-to-back converters in a utility connected microgrid,” IEEE Trans. Power Syst., 25(2):821–834, (2009).
  • [12] N. Amjady, F. Keynia, and H. Zareipour, “Short-term load forecast of microgrids by a new bilevel prediction strategy,” IEEE Trans. Smart Grid, vol. 1(3):286–294, (2010).
  • [13] H. Sajir, J. Rahebi, A. Abed, and A. Farzamnia, “Reduce power losses and improve voltage level by using distributed generation in radial distributed grid,” 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), 128–133,(2017).
  • [14] A. H. Abed, J. Rahebi, and A. Farzamnia, “Improvement for power quality by using dynamic voltage restorer in electrical distribution networks,” in 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), 122–127,(2017).
  • [15] D. K. Khatod, V. Pant, and J. Sharma, “Evolutionary programming based optimal placement of renewable distributed generators,” IEEE Trans. Power Syst., 28(2): 683–695, (2012).
  • [16] Y. M. Atwa and E. F. El-Saadany, “Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems,” IET Renew. Power Gener., 5(1):79–88, (2011).
  • [17] Y. M. Atwa and E. F. El-Saadany, “Optimal allocation of ESS in distribution systems with a high penetration of wind energy,” IEEE Trans. Power Syst.,25(4): 1815–1822, (2010).
  • [18] Y. M. Atwa, E. F. El-Saadany, M. M. A. Salama, and R. Seethapathy, “Optimal renewable resources mix for distribution system energy loss minimization,” IEEE Trans. Power Syst., 25(1): 360–370, (2009).
  • [19] N. K. Meena, S. Parashar, A. Swarnkar, N. Gupta, and K. R. Niazi, “Improved elephant herding optimization for multiobjective DER accommodation in distribution systems,” IEEE Trans. Ind. informatics, 14(3):1029–1039, (2017).
  • [20] C. Mateo, P. Frías, and K. Tapia-Ahumada, “A comprehensive techno-economic assessment of the impact of natural gas-fueled distributed generation in European electricity distribution networks,” Energy, 192, 116523, (2020).
  • [21] Z. Ullah, M. R. Elkadeem, S. Wang, S. W. Sharshir, and M. Azam, “Planning optimization and stochastic analysis of RE-DGs for techno-economic benefit maximization in distribution networks,” Internet of Things, 11,100210, (2020).
  • [22] M. Dixit, P. Kundu, and H. R. Jariwala, “Techno-economic analysis-based optimal incorporation of distributed energy resources in distribution network under load uncertainty,” Int. J. Ambient Energy, 42(6):605–611, (2021).
  • [23] M. Dixit, P. Kundu, and H. R. Jariwala, “Incorporation of distributed generation and shunt capacitor in radial distribution system for techno-economic benefits,” Eng. Sci. Technol. an Int. J., 20(2): 482–493, (2017).
  • [24] K. Hesaroor and D. Das, “Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources,” Int. Trans. Electr. energy Syst., 29(11):e12099, (2019).
  • [25] A. Maleki, M. G. Khajeh, and M. Ameri, “Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty,” Int. J. Electr. Power Energy Syst., 83, 514–524, (2016).
  • [26] W. Hu, C. Su, Z. Chen, and B. Bak-Jensen, “Optimal operation of plug-in electric vehicles in power systems with high wind power penetrations,” IEEE Trans. Sustain. Energy, 4(3): 577–585, (2013).
  • [27] S. Suthar, S. H. C. Cherukuri, and N. M. Pindoriya, “Peer-to-peer energy trading in smart grid: Frameworks, implementation methodologies, and demonstration projects,” Electr. Power Syst. Res., 214, 108907, (2023).
  • [28] R. Lazdins, A. Mutule, and D. Zalostiba, “PV Energy Communities—Challenges and Barriers from a Consumer Perspective: A Literature Review,” Energies, 14(16): 4873, (2021).
  • [29] M. A. Butturi, F. Lolli, M. A. Sellitto, E. Balugani, R. Gamberini, and B. Rimini, “Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis,” Appl. Energy, 255, 113825, (2019).
  • [30] M. A. Hannan, M. Faisal, P. J. Ker, R. A. Begum, Z. Y. Dong, and C. Zhang, “Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications,” Renew. Sustain. Energy Rev., 131, 110022, (2020).
  • [31] S. M. Zahraee, N. Shiwakoti, and P. Stasinopoulos, “Biomass supply chain environmental and socio-economic analysis: 40-Years comprehensive review of methods, decision issues, sustainability challenges, and the way forward,” Biomass and Bioenergy, 142, 105777, (2020).
  • [32] G. Sree Lakshmi, R. Olena, G. Divya, and I. Hunko, “Electric vehicles integration with renewable energy sources and smart grids,” Advances in Smart Grid Technology, Springer, 397–411(2020).
  • [33] N. I. Nimalsiri, E. L. Ratnam, D. B. Smith, C. P. Mediwaththe, and S. K. Halgamuge, “Coordinated charge and discharge scheduling of electric vehicles for load curve shaping,” IEEE Trans. Intell. Transp. Syst., (2021).
  • [34] M. Kumar, S. Vyas, and A. Datta, “A review on integration of electric vehicles into a smart power grid and vehicle-to-grid impacts,” 2019 8th International Conference on Power Systems (ICPS), 1–5,(2019).
  • [35] S. Shafiq, U. Bin Irshad, M. Al-Muhaini, S. Z. Djokic, and U. Akram, “Reliability evaluation of composite power systems: Evaluating the impact of full and plug-in hybrid electric vehicles,” IEEE Access, 8, 114305–114314, (2020).
  • [36] M. Dixit, “Impact of optimal integration of renewable energy sources and electric vehicles in practical distribution feeder with uncertain load demand,” Int. Trans. Electr. Energy Syst., 30(12):e12668, (2020).
  • [37] A. T. Lemeski, R. Ebrahimi, and A. Zakariazadeh, “Optimal decentralized coordinated operation of electric vehicle aggregators enabling vehicle to grid option using distributed algorithm,” J. Energy Storage, 54, 105213, (2022).
  • [38] M. A. Hannan et al., “Vehicle to grid connected technologies and charging strategies: Operation, control, issues and recommendations,” J. Clean. Prod., 130587, (2022).
  • [39] T. U. Solanke, V. K. Ramachandaramurthy, J. Y. Yong, J. Pasupuleti, P. Kasinathan, and A. Rajagopalan, “A review of strategic charging–discharging control of grid-connected electric vehicles,” J. Energy Storage, 28, 101193,(2020).
  • [40] M. I. Azim, W. Tushar, T. K. Saha, C. Yuen, and D. Smith, “Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks,” Renew. Sustain. Energy Rev., 169, 112908, (2022).
  • [41] M. A. Rogalski and M. A. Duffy, “Local adaptation of a parasite to solar radiation impacts disease transmission potential, spore yield, and host fecundity,” Evolution (N. Y)., 74(8):1856–1864, (2020).
  • [42] M. Sumair, T. Aized, M. M. A. Bhutta, F. A. Siddiqui, L. Tehreem, and A. Chaudhry, “Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications,” Renew. Energy, 191, 291–304, (2022).
  • [43] M. S. Braik, “Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems,” Expert Syst. Appl., 174,114685, (2021).
  • [44] B. Shboul et al., “A new ANN model for hourly solar radiation and wind speed prediction: A case study over the north & south of the Arabian Peninsula,” Sustain. Energy Technol. Assessments, 46, 101248, (2021).
  • [45] V. Black, “Cost and performance data for power generation technologies,” Prep. Natl. Renew. Energy Lab., (2012).
  • [46] J.-H. Teng, S.-W. Luan, D.-J. Lee, and Y.-Q. Huang, “Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems,” IEEE Trans. Power Syst., 28(2): 1425–1433,(2012).
  • [47] A. Ellis et al., “Reactive power interconnection requirements for PV and wind plants–recommendations to NERC,” Sandia Natl. Lab. Albuquerque, New Mex., 87185, (2012).
  • [48] A. Naderipour et al., “Carrier wave optimization for multi-level photovoltaic system to improvement of power quality in industrial environments based on Salp swarm algorithm,” Environ. Technol. Innov., 21, 101197, (2021).
There are 48 citations in total.

Details

Primary Language English
Subjects Electrical Energy Transmission, Networks and Systems
Journal Section Research Article
Authors

Jemaa Bojod 0009-0002-4003-8145

Bilgehan Erkal 0000-0002-1405-6932

Early Pub Date September 21, 2023
Publication Date October 1, 2023
Submission Date August 23, 2023
Published in Issue Year 2023

Cite

APA Bojod, J., & Erkal, B. (2023). Practical Radial Distribution Feeder for Techno Economic in DERs based on ANN and Chameleon Optimization Algorithms. Politeknik Dergisi, 26(3), 1285-1297. https://doi.org/10.2339/politeknik.1348672
AMA Bojod J, Erkal B. Practical Radial Distribution Feeder for Techno Economic in DERs based on ANN and Chameleon Optimization Algorithms. Politeknik Dergisi. October 2023;26(3):1285-1297. doi:10.2339/politeknik.1348672
Chicago Bojod, Jemaa, and Bilgehan Erkal. “Practical Radial Distribution Feeder for Techno Economic in DERs Based on ANN and Chameleon Optimization Algorithms”. Politeknik Dergisi 26, no. 3 (October 2023): 1285-97. https://doi.org/10.2339/politeknik.1348672.
EndNote Bojod J, Erkal B (October 1, 2023) Practical Radial Distribution Feeder for Techno Economic in DERs based on ANN and Chameleon Optimization Algorithms. Politeknik Dergisi 26 3 1285–1297.
IEEE J. Bojod and B. Erkal, “Practical Radial Distribution Feeder for Techno Economic in DERs based on ANN and Chameleon Optimization Algorithms”, Politeknik Dergisi, vol. 26, no. 3, pp. 1285–1297, 2023, doi: 10.2339/politeknik.1348672.
ISNAD Bojod, Jemaa - Erkal, Bilgehan. “Practical Radial Distribution Feeder for Techno Economic in DERs Based on ANN and Chameleon Optimization Algorithms”. Politeknik Dergisi 26/3 (October 2023), 1285-1297. https://doi.org/10.2339/politeknik.1348672.
JAMA Bojod J, Erkal B. Practical Radial Distribution Feeder for Techno Economic in DERs based on ANN and Chameleon Optimization Algorithms. Politeknik Dergisi. 2023;26:1285–1297.
MLA Bojod, Jemaa and Bilgehan Erkal. “Practical Radial Distribution Feeder for Techno Economic in DERs Based on ANN and Chameleon Optimization Algorithms”. Politeknik Dergisi, vol. 26, no. 3, 2023, pp. 1285-97, doi:10.2339/politeknik.1348672.
Vancouver Bojod J, Erkal B. Practical Radial Distribution Feeder for Techno Economic in DERs based on ANN and Chameleon Optimization Algorithms. Politeknik Dergisi. 2023;26(3):1285-97.
 
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