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Optimal Placement of Photovoltaic Systems and Wind Turbines in Distribution Systems by Using Northern Goshawk Optimization Algorithm

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Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

In this study, the placement of photovoltaic generators (PVGs) and wind power generators (WGs) in distribution networks is optimized in both size and location in order to minimize the power loss as much as possible. An IEEE 85-bus radial distribution power network is employed to simulate the effectiveness of PVGs and WGs. The optimal solutions of the problem are determined by applying three novel metaheuristic methods including Northern Goshawk Optimization (NGO), Bonobo optimizer (BO), and Transient Search Optimization (TSO). By evaluating the results obtained from these methods, NGO proved itself the most effective method and its performance completely superiors both BO and TSO in all compared criteria such as minimum loss value (min. loss), mean loss values (mean loss), maximum loss values (max. loss), and the standard deviation (STD). More importantly, the impacts caused by quantity of PVGs and WGs to the power loss value are clarified in different case studies.

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References

  1. Suyarov, A., Hasanov, M., Boliev, A., & Nazarov, F. (2021). Whale optimization algorithm for intogreting distributed generators in radial distribution network. SSRN Electronic Journal.

    Google Scholar 

  2. Alnabi, L. A., Dhaher, A., & Essa, M. (2022). Optimal allocation of distributed generation with reconfiguration by genetic algorithm using both Newton Raphson and Gauss Seidel methods for power losses minimizing. International Journal of Intelligent Engineering and Systems, 15.

    Google Scholar 

  3. Hemeida, M. G., Ibrahim, A. A., Mohamed, A.-A. A., Alkhalaf, S., & El-Dine, A. M. B. (2021). Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO). Ain Shams Engineering Journal, 12(1), 609–619.

    Article  Google Scholar 

  4. Quoc, S. N., & Ngoc, D. V. (2020). Symbiotic organism search algorithm for power loss minimization in radial distribution systems by network reconfiguration and distributed generation placement. Mathematical Problems in Engineering, 2020, Article ID 1615792, 22 pages.

    Google Scholar 

  5. Sun, B., Li, Y., Zeng, Y., Chen, J., & Shi, J. (2022). Optimization planning method of distributed generation based on steady-state security region of distribution network. Energy Reports, 8, 4209–4222.

    Article  Google Scholar 

  6. Asrari, A., Wu, T., & Lotfifard, S. (2016). The impacts of distributed energy sources on distribution network reconfiguration. IEEE Transactions on Energy Conversion, 31(2), 606–613.

    Article  Google Scholar 

  7. Prakash, P., Meena, D. C., Malik, H., Alotaibi, M. A., & Khan, I. A. (2022). A novel analytical approach for optimal integration of renewable energy sources in distribution systems. Energies, 15(4), 1341.

    Article  Google Scholar 

  8. Eid, A., Kamel, S., & Abualigah, L. (2021). Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Computing and Applications, 33(21), 14327–14355.

    Article  Google Scholar 

  9. Heng, P., Prasatsap, U., Polprasert, J., & Kiravittaya, S. (2019). Optimal placement of distributed generation using analytical approach to minimize losses in a university. GMSARN International Journal, 13(2), 81–85.

    Google Scholar 

  10. Kaewvata, C., Sirisamphanwong, C., & Suriwong, T. (2021). Simulation of the appropriate capacity and mouthing position of distributed battery storage systems for maintaining the power quality in Maesariang Microgrid System. GMSARN International Journal, 15(3), 166–174.

    Google Scholar 

  11. Hao, X., Jiang, C., Wu, L., & Zhang, L. (2015). Based on the power factors of DFIG wind farm for power flow optimization. Lecture Notes in Electrical Engineering, 334, 157–165.

    Article  Google Scholar 

  12. Mokryani, G., & Siano, P. (2015). Evaluating the benefits of optimal allocation of wind turbines for distribution network operators. IEEE Systems Journal, 9, 629–638.

    Article  Google Scholar 

  13. Nayak, M., Behura, D., & Kasturi, K. (2021). Optimal allocation of energy storage system and its benefit analysis for unbalanced distribution network with wind generation. Journal of Computational Science, 51, 101319.

    Article  Google Scholar 

  14. Datta, T., & Bajpai, P. (2021). Steady-state modeling of DFIG-based wind energy system for unbalanced operation. In Handbook of renewable energy technology & systems (pp. 3–35). World Scientific (Europe).

    Google Scholar 

  15. Chen, P., Siano, P., Chen, Z., & Bak-Jensen, B. (2010). Optimal allocation of power-electronic interfaced wind turbines using a genetic algorithm – Monte Carlo hybrid optimization method. Presented at the June.

    Google Scholar 

  16. Pazouki, S., Mohsenzadeh, A., Haghifam, M.-R., & Talebian, M. E. (2013). Optimal allocation of wind turbine in multi carrier energy networks improving loss and voltage profile. In ELECO 2013 - 8th International Conference on Electrical and Electronics Engineering (pp. 67–71).

    Google Scholar 

  17. Naderipour, A., Abdul-Malek, Z., Mustafa, M., & Guerrero, J. (2021). A multi-objective artificial electric field optimization algorithm for allocation of wind turbines in distribution systems. Applied Soft Computing, 105, 107278.

    Article  Google Scholar 

  18. Yao, F., Dong, Z. Y., Meng, K., Xu, Z., Iu, H., & Wong, K. (2012). Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia. IEEE Transactions on Industrial Informatics, 8, 880–888.

    Article  Google Scholar 

  19. Calero, I., Canizares, C., Bhattacharya, K., & Baldick, R. (2021). Duck-curve mitigation in power grids with high penetration of PV generation. IEEE Transactions on Smart Grid, 13, 314–329.

    Article  Google Scholar 

  20. Adeagbo, A. P., Ariyo, F. K., et al. (2022). Integration of solar photovoltaic distributed generators in distribution networks based on site’s condition. Solar, 2, 52–63.

    Article  Google Scholar 

  21. Montoya Giraldo, O., Grisales-Noreña, L., Alvarado-Barrios, L., Arias-Londoño, A., & Alvarez, C. (2021). Efficient reduction in the annual investment costs in AC distribution networks via optimal integration of solar PV sources using the Newton Metaheuristic Algorithm. Applied Sciences, 11, 11525.

    Article  Google Scholar 

  22. Montoya Giraldo, O., Grisales-Noreña, L., & Giral-Ramírez, D. (2022). Optimal placement and sizing of PV sources in distribution grids using a modified gradient-based metaheuristic optimizer. Sustainability, 14, 3318.

    Article  Google Scholar 

  23. Janamala, V. (2021). A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience. SN Applied Sciences, 3, 1–17.

    Article  Google Scholar 

  24. Dash, S., & Mishra, S. (2021). Optimal allocation of photo-voltaic units in radial distribution networks using a new student psychology based optimization algorithm. International Journal on Electrical Engineering and Informatics, 13, 318–335.

    Article  Google Scholar 

  25. Alhayali, R., Hasan, G., Salih, Z., Mohammed, M., Klib, M., & Ali, A. (2019). Study the effect of integrating the solar energy source on stability of electrical distribution system. Presented at the June.

    Google Scholar 

  26. Augusteen, W., Geetha, S., & Rengaraj, R. (2016). Economic dispatch incorporation solar energy using particle swarm optimization. Presented at the June.

    Google Scholar 

  27. Samala, R., & Kotapuri, M. (2020). Optimal allocation of multiple photo-voltaic and/or wind-turbine based distributed generations in radial distribution system using hybrid technique with fuzzy logic controller. Journal of Electrical Engineering & Technology, 16, 1–13.

    Google Scholar 

  28. Arasteh, A., Alemi, P., & Beiraghi, M. (2021). Optimal allocation of photovoltaic/wind energy system in distribution network using meta-heuristic algorithm. Applied Soft Computing, 109, 107594.

    Article  Google Scholar 

  29. Rawat, T., Niazi, K., Gupta, N., & Sharma, S. (2020). Impact analysis of demand response on optimal allocation of wind and solar based distributed generations in distribution system. Energy sources. Part B Economics, Planning and Policy, 16, 75–90.

    Article  Google Scholar 

  30. Taha, H., Alham, M., & Youssef, H. (2022). Multi-objective optimization for optimal allocation and coordination of wind and solar DGs, BESSs and capacitors in presence of demand response. IEEE Access, 10, 16225–16241.

    Article  Google Scholar 

  31. Fathi, R., Tousi, B., & Galvani, S. (2021). A new approach for optimal allocation of photovoltaic and wind clean energy resources in distribution networks with reconfiguration considering uncertainty based on info-gap decision theory with risk aversion strategy. Journal of Cleaner Production, 295, 125984.

    Article  Google Scholar 

  32. Aliabadi, M., & Radmehr, M. (2021). Optimization of hybrid renewable energy system in radial distribution networks considering uncertainty using meta-heuristic crow search algorithm. Applied Soft Computing, 107, 107384.

    Article  Google Scholar 

  33. Farzamnia, A., Marjani, S., Galvani, S., & Kin, K. (2022). Optimal allocation of soft open point devices in renewable energy integrated distribution systems. IEEE Access, 10, 9309–9320.

    Article  Google Scholar 

  34. Dehghani, M., Hubalovsky, S., & Trojovsky, P. (2021). Northern Goshawk optimization: A new swarm-based algorithm for solving optimization problems. IEEE Access, 9, 162059–162080.

    Article  Google Scholar 

  35. Das, A., & Pratihar, D. (2022). Bonobo Optimizer (BO): An intelligent heuristic with self-adjusting parameters over continuous spaces and its applications to engineering problems. Applied Intelligence, 52, 2942–2974.

    Article  Google Scholar 

  36. Qais, M., Hasanien, H., & Alghuwainem, S. (2020). Transient search optimization: A new meta-heuristic optimization algorithm. Applied Intelligence, 50, 3926–3941.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Bach Hoang Dinh

  2. Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Thuan Thanh Nguyen

  3. Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Thang Trung Nguyen

Authors
  1. Bach Hoang Dinh
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  2. Thuan Thanh Nguyen
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  3. Thang Trung Nguyen
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Corresponding author

Correspondence to Thang Trung Nguyen .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Punjabi University Patiala, Patiala, Punjab, India

    Mukhdeep Singh Manshahia

  2. Federal Scientific Agroengineering Center VIM, Moscow, Russia

    Valeriy Kharchenko

  3. Institute of Applied Mathematics, Middle East Technical University, Ankara, Türkiye

    Gerhard-Wilhelm Weber

  4. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Pandian Vasant

Appendix

Appendix

To simulate the impact of PVGs and WGs on the power loss of the IEEE 85-node distribution network, solar radiation and wind speeds are used as input data for 24 hours. The solar radiation data and the wind speed data are given in Tables A.1 and A.2. Finally, the obtained results including position and size of both PVG and WG, and power loss of the system are reported in Table A.3.

Table A1 The radiation data in 24 h within a day
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Table A2 The wind speed data in 24 h within a day
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Table A3 Optimal solutions given reported by three applied methods in Case 1
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Table A4 Optimal solutions in Case 2 and Case 3
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Dinh, B.H., Nguyen, T.T., Nguyen, T.T. (2023). Optimal Placement of Photovoltaic Systems and Wind Turbines in Distribution Systems by Using Northern Goshawk Optimization Algorithm. In: Manshahia, M.S., Kharchenko, V., Weber, GW., Vasant, P. (eds) Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26496-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-26496-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26495-5

  • Online ISBN: 978-3-031-26496-2

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