Optimal Allocation of Renewable Sources with Battery and Capacitors in Radial Feeders for Reliable Power Supply Using Pathfinder Algorithm

Document Type : Original Article

Authors

1 Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore-560074, Karnataka, India.

2 Dept. of Electrical and Electronics Engineering School of Engineering and Technology Christ (Deemed to be University) Bangalore - 560074. INDIA

10.22059/jser.2023.358718.1299

Abstract

Allocating renewable energy systems (RESs) in an electrical distribution system (EDS) is crucial to achieving various objectives. However, their intermittency presents several challenges. In this connection, an efficient meta-heuristic pathfinder algorithm (PFA) is employed to determine the optimal location and size of photovoltaic (PV) and wind turbine (WT) systems, along with energy storage systems (ESS) and capacitor banks (CB) for both grid and islanding modes of operations. An objective function was formulated for loss reduction, greenhouse gas (GHG) emissions, and voltage profile improvement. The simulation results for the IEEE 33-bus EDS system are shown for two cases: grid-connected and islanding. The computational effectiveness of the PFA was compared with that reported in the literature. The PFA results showed an outstanding ability to resolve difficult optimisation problems. In addition, the optimal size of the RES when the network operates in the grid-connected mode can significantly improve the performance. The real power losses and GHG emissions were reduced by 48.49 % and 67.75% with PV systems and the other, respectively, whereas WT systems they are reduced to 69.68 % and 67.85 %, respectively. However, a combination of ESS, CB, and PV/WT can render the EDN sustainable for the islanding mode of operations.

Keywords

References

  1. Haes Alhelou, H., Hamedani-Golshan, M. E., Njenda, T. C., & Siano, P. (2019). A survey on power system blackout and cascading events: Research motivations and challenges. Energies, 12(4), 682. DOI: 10.3390/en12040682
  2. Li, M. J., Tse, C. K., Liu, D., & Zhang, X. (2023). Cascading Failure Propagation and Mitigation Strategies in Power Systems. IEEE Systems Journal. DOI: 10.1109/JSYST.2023.3248044
  3. Streimikiene, D., Balezentis, T., Alisauskaite-Seskiene, I., Stankuniene, G., & Simanaviciene, Z. (2019). A review of willingness to pay studies for climate change mitigation in the energy sector. Energies, 12(8), 1481. DOI: 10.3390/en12081481
  4. Das, H. S., Rahman, M. M., Li, S., & Tan, C. W. (2020). Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renewable and Sustainable Energy Reviews, 120, 109618. DOI: 10.1016/j.rser.2019.109618
  5. Ghaffarzadeh, N., & Faramarzi, H. (2022). Optimal Solar plant placement using holomorphic embedded power Flow Considering the clustering technique in uncertainty analysis. Journal of Solar Energy Research, 7(1), 997-1007. DOI: 10.22059/JSER.2022.330961.1221
  6. Aryan Nezhad, M. (2021). Economic Impacts of Long-Term Wind Speed Changes on Optimal Planning of a Hybrid Renewable Energy System (HRES). Journal of Solar Energy Research, 6(1), 656-663. DOI: 10.22059/JSER.2021.315440.1186
  7. Hassan, A. S., Othman, E. A., Bendary, F. M., & Ebrahim, M. A. (2020). Optimal integration of distributed generation resources in active distribution networks for techno-economic benefits. Energy Reports, 6, 3462-3471. DOI: 10.1016/j.egyr.2020.12.004
  8. Khasanov, M., Kamel, S., Halim Houssein, E., Rahmann, C., & Hashim, F. A. (2023). Optimal allocation strategy of photovoltaic-and wind turbine-based distributed generation units in radial distribution networks considering uncertainty. Neural Computing and Applications, 35(3), 2883-2908. DOI: 10.1007/s00521-022-07715-2
  9. Selim, A., Kamel, S., Mohamed, A. A., & Elattar, E. E. (2021). Optimal allocation of multiple types of distributed generations in radial distribution systems using a hybrid technique. Sustainability, 13(12), 6644. DOI: 10.3390/su13126644
  10. Ali, M. H., Kamel, S., Hassan, M. H., Tostado-Véliz, M., & Zawbaa, H. M. (2022). An improved wild horse optimization algorithm for reliability based optimal DG planning of radial distribution networks. Energy Reports, 8, 582-604. DOI: 10.1016/j.egyr.2021.12.023
  11. McIlwaine, N., Foley, A. M., Morrow, D. J., Al Kez, D., Zhang, C., Lu, X., & Best, R. J. (2021). A state-of-the-art techno-economic review of distributed and embedded energy storage for energy systems. Energy, 229, 120461. DOI: 10.1016/j.energy.2021.120461
  12. Worku, M. Y. (2022). Recent advances in energy storage systems for renewable source grid integration: a comprehensive review. Sustainability, 14(10), 5985. DOI: 10.3390/su14105985
  13. Venkateswaran, V. B., Saini, D. K., & Sharma, M. (2020). Approaches for optimal planning of energy storage units in distribution network and their impacts on system resiliency. CSEE Journal of power and energy systems, 6(4), 816-833. DOI: 10.17775/CSEEJPES.2019.01280
  14. Das, C. K., Bass, O., Mahmoud, T. S., Kothapalli, G., Mousavi, N., Habibi, D., & Masoum, M. A. (2019). Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks. Applied Energy, 252, 113468. DOI: 10.1016/j.apenergy.2019.113468
  15. Zheng, Y., Song, Y., Huang, A., & Hill, D. J. (2019). Hierarchical optimal allocation of battery energy storage systems for multiple services in distribution systems. IEEE Transactions on Sustainable Energy, 11(3), 1911-1921. DOI: 10.1109/TSTE.2019.2946371
  16. Lei, J., Gong, Q., Liu, J., Qiao, H., & Wang, B. (2019). Optimal allocation of a VRB energy storage system for wind power applications considering the dynamic efficiency and life of VRB in active distribution networks. IET Renewable Power Generation, 13(4), 563-571. DOI: 10.1049/iet-rpg.2018.5619
  17. Al-Ghussain, L., Samu, R., Taylan, O., & Fahrioglu, M. (2020). Sizing renewable energy systems with energy storage systems in microgrids for maximum cost-efficient utilization of renewable energy resources. Sustainable Cities and Society, 55, 102059. DOI: 10.1016/j.scs.2020.102059
  18. Kiptoo, M. K., Lotfy, M. E., Adewuyi, O. B., Conteh, A., Howlader, A. M., & Senjyu, T. (2020). Integrated approach for optimal techno-economic planning for high renewable energy-based isolated microgrid considering cost of energy storage and demand response strategies. Energy Conversion and Management, 215, 112917. DOI: 10.1016/j.enconman.2020.112917
  19. Salman, U. T., Al-Ismail, F. S., & Khalid, M. (2020). Optimal sizing of battery energy storage for grid-connected and isolated wind-penetrated microgrid. IEEE Access, 8, 91129-91138. DOI: 10.1109/ACCESS.2020.2992654
  20. Javed, M. S., Ma, T., Jurasz, J., Canales, F. A., Lin, S., Ahmed, S., & Zhang, Y. (2021). Economic analysis and optimization of a renewable energy based power supply system with different energy storages for a remote island. Renewable Energy, 164, 1376-1394. DOI: 10.1016/j.renene.2020.10.063
  21. Xie, C., Wang, D., Lai, C. S., Wu, R., Wu, X., & Lai, L. L. (2021). Optimal sizing of battery energy storage system in smart microgrid considering virtual energy storage system and high photovoltaic penetration. Journal of Cleaner Production, 281, 125308. DOI: 10.1016/j.jclepro.2020.125308
  22. Memon, S. A., Upadhyay, D. S., & Patel, R. N. (2021). Optimal configuration of solar and wind-based hybrid renewable energy system with and without energy storage including environmental and social criteria: A case study. Journal of Energy Storage, 44, 103446. DOI: 10.1016/j.est.2021.103446
  23. Janamala, V., & Reddy, D. S. (2021). Coyote optimization algorithm for optimal allocation of interline–Photovoltaic battery storage system in islanded electrical distribution network considering EV load penetration. Journal of Energy Storage, 41, 102981. DOI: 10.1016/j.est.2021.102981
  24. Janamala, V. (2022). Optimal siting of capacitors in distribution grids considering electric vehicle load growth using improved flower pollination algorithm. SJEE, 19(3), 329-349. DOI: 10.2298/SJEE2203329J
  25. Giridhar, M. S., Rani, K. R., Rani, P. S., & Janamala, V. (2022). Mayfly Algorithm for Optimal Integration of Hybrid Photovoltaic/Battery Energy Storage/D-STATCOM System for Islanding Operation. International Journal of Intelligent Engineering & Systems, 15(3), 225-232. DOI: 10.22266/ijies2022.0630.19
  26. Inkollu, S. R., Anjaneyulu, G. V., NC, K., & CH, N. K. (2022). An Application of Hunter-Prey Optimization for Maximizing Photovoltaic Hosting Capacity Along with Multi-Objective Optimization in Radial Distribution Network. International Journal of Intelligent Engineering & Systems, 15(4), 575-584. DOI: 10.22266/ijies2022.0831.52
  27. Aryan Nezhad, M. (2022). Frequency control and power balancing in a hybrid renewable energy system (HRES): Effective tuning of PI controllers in the secondary control level. Journal of Solar Energy Research, 7(1), 963-970. DOI: 10.22059/JSER.2022.330109.1219
  28. Khasanov, M., Kamel, S., Rahmann, C., Hasanien, H. M., & Al‐Durra, A. (2021). Optimal distributed generation and battery energy storage units integration in distribution systems considering power generation uncertainty. IET Generation, Transmission & Distribution, 15(24), 3400-3422. DOI: 10.1049/gtd2.12230
  29. Adam, S. P., Alexandropoulos, S. A. N., Pardalos, P. M., & Vrahatis, M. N. (2019). No free lunch theorem: A review. Approximation and optimization: Algorithms, complexity and applications, 57-82. DOI: 10.1007/978-3-030-12767-1_5
  30. Kumar, A., Nadeem, M., & Banka, H. (2023). Nature inspired optimization algorithms: a comprehensive overview. Evolving Systems, 14(1), 141-156. DOI: 10.1007/s12530-022-09432-6
  31. Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied soft computing, 78, 545-568. DOI: 10.1016/j.asoc.2019.03.012
  32. 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), 118. DOI: 10.1007/s42452-020-04044-8
  33. Dolatabadi, S. H., Ghorbanian, M., Siano, P., & Hatziargyriou, N. D. (2020). An enhanced IEEE 33 bus benchmark test system for distribution system studies. IEEE Transactions on Power Systems, 36(3), 2565-2572. DOI: 10.1109/TPWRS.2020.3038030
  34. Zimmerman, R. D., Murillo-Sánchez, C. E., & Thomas, R. J. (2010). MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on power systems, 26(1), 12-19. DOI: 10.1109/TPWRS.2010.2051168
  35. Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE. DOI: 10.1109/CEC.2018.8477769
  36. Zervoudakis, K., & Tsafarakis, S. (2020). A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559. DOI: 10.1016/j.cie.2020.106559
  37. Naruei, I., Keynia, F., & Sabbagh Molahosseini, A. (2022). Hunter–prey optimization: Algorithm and applications. Soft Computing, 26(3), 1279-1314. DOI: 10.1007/s00500-021-06401-0
  38. Nguyen, T. T., Nguyen, T. T., Duong, L. T., & Truong, V. A. (2021). An effective method to solve the problem of electric distribution network reconfiguration considering distributed generations for energy loss reduction. Neural Computing and Applications, 33, 1625-1641. DOI: 10.1007/s00521-020-05092-2
Journal of Solar Energy Research
Volume 8, Issue 4
October 2023
Pages 1651-1662

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