skip to main content

Energy Loss Reduction of Distribution Systems Equipped with Multiple Distributed Generations Considering Uncertainty using Manta-Ray Foraging Optimization

1Department of Electrical Engineering, Faculty of Engineering, Aswan University, 81542 Aswan, Egypt

2Department of Electrical Engineering, College of Engineering, Qassim University, 56452 Unaizah , Saudi Arabia

3Faculty of Engineering and Technology, Future University in Egypt, Cairo, Egypt

Received: 27 Mar 2021; Revised: 20 May 2021; Accepted: 28 May 2021; Available online: 10 Jun 2021; Published: 1 Nov 2021.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2021 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
This paper has adopted the new bio-inspired Manta-Ray Foraging Optimization (MRFO) algorithm for optimal allocation of multiple Distributed Generation (DG) units attached to Radial Distribution Systems (RDSs) in order to reduce the total energy loss of the studied system. The DG units are optimized to work with a unity power factor (UPF) and optimal power factor (OPF) during a 24-h time-varying demand. The MRFO algorithm optimized single, two, and three DG units. The total energy loss and energy-saving during the time-varying demand are calculated and compared with the original case. The MRFO algorithm behavior is compared to the Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO) algorithms regarding energy loss and energy-saving values. The standard 69-bus RDS is used as a test system. Considerable improvements in energy saving, loss reduction, and voltage profile are achieved after installing DG units, mainly when operating with optimal power factors. The MRFO algorithm achieves energy losses of 817.91, 751.08, and 730.25 kWh with 1, 2, and 3 DG units with UPF allocations, respectively. On the other hand, when the DG units are optimized to work with OPF, the MRFO achieves energy losses of 233.24, 142.08, and 106.79 kWh with the same number of DG units, respectively. Furthermore, the MRFO algorithm has efficient behavior compared with the PSO, ASO, and other algorithms for different operations and conditions.
Fulltext View|Download
Keywords: MRFO; DG optimal allocations; Time-varying demand; Energy loss; Distribution systems

Article Metrics:

  1. Abdelaziz, A. Y., Hegazy, Y. G., El-Khattam, W., & Othman, M. M. (2015). Optimal planning of distributed generators in distribution networks using modified firefly method. Electric Power Components and Systems, 43(3), 320–333.
  2. Abdelkader, M. A., Elshahed, M. A., & Osman, Z. H. (2019). An analytical formula for multiple DGs allocations to reduce distribution system losses. Alexandria Engineering Journal.
  3. Abu-Mouti, F. S., & El-Hawary, M. E. (2011). Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Transactions on Power Delivery, 26(4), 2090–2101.
  4. Almabsout, E. A., El-Sehiemy, R. A., An, O. N. U., & Bayat, O. (2020). A Hybrid Local Search-Genetic Algorithm for Simultaneous Placement of DG Units and Shunt Capacitors in Radial Distribution Systems. IEEE Access, 8, 54465–54481.
  5. Ansari, M. M., Guo, C., Shaikh, M. S., Chopra, N., Haq, I., & Shen, L. (2020). Planning for Distribution System with Grey Wolf Optimization Method. Journal of Electrical Engineering and Technology, 15(4), 1485–1499.
  6. Arabi Nowdeh, S., Davoudkhani, I. F., Hadidian Moghaddam, M. J., Najmi, E. S., Abdelaziz, A. Y., Ahmadi, A., … Gandoman, F. H. (2019). Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method. Applied Soft Computing Journal, 77, 761–779.
  7. Atwa, Y. M., El-Saadany, E. F., Salama, M. M. A., & Seethapathy, R. (2010). Optimal renewable resources mix for distribution system energy loss minimization. IEEE Transactions on Power Systems, 25(1), 360–370.
  8. Bai, K., & Yildizbasi, A. (2020). Optimal Siting and Sizing of Battery Energy Storage System for Distribution Loss Reduction Based on Meta-heuristics. Journal of Control, Automation and Electrical Systems, 31(6), 1469–1480.
  9. Dehghani, M., Montazeri, Z., & Malik, O. P. (2020). Optimal Sizing and Placement of Capacitor Banks and Distributed Generation in Distribution Systems Using Spring Search Algorithm. International Journal of Emerging Electric Power Systems, 21(1), 1–9.
  10. Eid, A. (2020). Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations. Alexandria Engineering Journal.
  11. Eid, A. (2021). Performance improvement of active distribution systems using adaptive and exponential PSO algorithms. International Review of Electrical Engineering (IREE), 16(2), 147–157.
  12. Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493–178513.
  13. Elsadd, M. A., Elkalashy, N. I., Kawady, T. A., & Taalab, A. M. I. (2017). Earth fault location determination independent of fault impedance for distribution networks. International Transactions on Electrical Energy Systems, 27(5), 1–16.
  14. Elsadd, M. A., Kawady, T. A., Taalab, A.-M. I., & Elkalashy, N. I. (2021). Adaptive optimum coordination of overcurrent relays for deregulated distribution system considering parallel feeders. Electrical Engineering.
  15. Gkaidatzis, P. A., Bouhouras, A. S., Doukas, D. I., Sgouras, K. I., & Labridis, D. P. (2017). Load variations impact on optimal DG placement problem concerning energy loss reduction. Electric Power Systems Research, 152, 36–47.
  16. Hemeida, M. G., Alkhalaf, S., Mohamed, A. A. A., Ibrahim, A. A., & Senjyu, T. (2020). Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO). Energies, 13(15).
  17. Hung, D. Q., Member, S. S., Mithulananthan, N., Member, S. S., & Lee, K. Y. (2014). Determining PV penetration for distribution systems with time-varying load models. IEEE Transactions on Power Systems, 29(6), 3048–3057.
  18. Hung, D. Q., & Mithulananthan, N. (2013). Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Transactions on Industrial Electronics, 60(4).
  19. Hung, D. Q., Mithulananthan, N., & Bansal, R. C. (2010). Analytical expressions for DG allocation in primary distribution networks. IEEE Transactions on Energy Conversion.
  20. Hung, D. Q., Mithulananthan, N., & Bansal, R. C. (2013). Analytical strategies for renewable distributed generation integration considering energy loss minimization. Applied Energy, 105.
  21. Hung, D. Q., Mithulananthan, N., & Bansal, R. C. (2014). Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability. Applied Energy, 113, 1162–1170.
  22. Kaur, S., Kumbhar, G., & Sharma, J. (2014). A MINLP technique for optimal placement of multiple DG units in distribution systems. International Journal of Electrical Power and Energy Systems, 63, 609–617.
  23. Kennedy, J., & Eberhart, R. (2011). Particle swarm optimization-based feature selection for cognitive state detection. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 6556–6559.
  24. Khalifa, L. S., Elsadd, M. A., Abd El-Aal, R. A., & El-Makkawy, S. M. (2018). Enhancing Recloser-Fuse Coordination Using Distributed Agents in Deregulated Distribution Systems. 2018 Twentieth International Middle East Power Systems Conference (MEPCON).
  25. Khodabakhshian, A., & Andishgar, M. H. (2016). Simultaneous placement and sizing of DGs and shunt capacitors in distribution systems by using IMDE algorithm. International Journal of Electrical Power and Energy Systems, 82, 599–607.
  26. Martín García, J. A., & Gil Mena, A. J. (2013). Optimal distributed generation location and size using a modified teaching-learning based optimization algorithm. International Journal of Electrical Power and Energy Systems, 50(1), 65–75.
  27. Mehta, P., Bhatt, P., & Pandya, V. (2018). Optimal selection of distributed generating units and its placement for voltage stability enhancement and energy loss minimization. Ain Shams Engineering Journal, 9(2), 187–201.
  28. Murthy, V. V. S. N. S. N., & Kumar, A. (2013). Comparison of optimal DG allocation methods in radial distribution systems based on sensitivity approaches. International Journal of Electrical Power and Energy Systems, 53(1), 450–467.
  29. Naderipour, A., Abdul-Malek, Z., Hajivand, M., Seifabad, Z. M., Farsi, M. A., Nowdeh, S. A., & Davoudkhani, I. F. (2021). Spotted hyena optimizer algorithm for capacitor allocation in radial distribution system with distributed generation and microgrid operation considering different load types. Scientific Reports, 11(1), 1–15.
  30. Parihar, S. S., & Malik, N. (2020). Optimal allocation of renewable DGs in a radial distribution system based on new voltage stability index. International Transactions on Electrical Energy Systems, 30(4), 1–19.
  31. Radosavljevic, J., Arsic, N., Milovanovic, M., & Ktena, A. (2020). Optimal Placement and Sizing of Renewable Distributed Generation Using Hybrid Metaheuristic Algorithm. Journal of Modern Power Systems and Clean Energy, 8(3), 499–510.
  32. Sanjay, R., Jayabarathi, T., Raghunathan, T., Ramesh, V., & Mithulananthan, N. (2017). Optimal allocation of distributed generation using hybrid grey Wolf optimizer. IEEE Access, 5(c), 14807–14818.
  33. Selim, A., Kamel, S., & Jurado, F. (2020). Efficient optimization technique for multiple DG allocation in distribution networks. Applied Soft Computing Journal, 86, 105938.
  34. Selim, A., Kamel, S., Jurado, F., Lopes, J. A. P., & Matos, M. (2021). Optimal setting of PV and battery energy storage in radial distribution systems using multi‐objective criteria with fuzzy logic decision‐making. IET Generation, Transmission & Distribution, 15(1), 135–148.
  35. Tolba, M. A., Rezk, H., Tulsky, V., Diab, A. A. Z., Abdelaziz, A. Y., & Vanin, A. (2018). Impact of optimum allocation of renewable distributed generations on distribution networks based on different optimization algorithms. Energies, 11(1), 1–33.
  36. Ullah, Z., Wang, S., & Radosavljević, J. (2019). A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation. IEEJ Transactions on Electrical and Electronic Engineering, 14(12), 1754–1763.
  37. Yuan, Z., Wang, W., Wang, H., & Yildizbasi, A. (2020). A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction. Journal of Energy Storage, 29(January), 101368.
  38. Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283–304.
  39. Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87(December 2018), 103300.

Last update:

  1. Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

    Yingying Liao, Weiguo Zhao, Liying Wang. Mathematics, 9 (18), 2021. doi: 10.3390/math9182230
  2. Manta Ray Foraging Optimization Algorithm: Modifications and Applications

    Mohammed Abdullahi, Ibrahim Hayatu Hassan, Muhammad Dalhat Abdullahi, Ibrahim Aliyu, Jinsul Kim. IEEE Access, 11 , 2023. doi: 10.1109/ACCESS.2023.3276264
  3. An enhanced equilibrium optimizer for strategic planning of PV-BES units in radial distribution systems considering time-varying demand

    Ahmad Eid, Salah Kamel, Essam H. Houssein. Neural Computing and Applications, 34 (19), 2022. doi: 10.1007/s00521-022-07364-5
  4. Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

    Ashraf Ramadan, Mohamed Ebeed, Salah Kamel, Mohamed I. Mosaad, Ahmed Abu-Siada. Electronics, 10 (23), 2021. doi: 10.3390/electronics10233035
  5. A Comparison Study of Multi-Objective Bonobo Optimizers for Optimal Integration of Distributed Generation in Distribution Systems

    Ahmad Eid, Salah Kamel, Mohamed H. Hassan, Baseem Khan. Frontiers in Energy Research, 10 , 2022. doi: 10.3389/fenrg.2022.847495
  6. Grey wolf optimisation algorithm for solving distribution network reconfiguration considering distributed generators simultaneously

    Harish Kumar Pujari, Mageshvaran Rudramoorthy. International Journal of Sustainable Energy, 41 (11), 2022. doi: 10.1080/14786451.2022.2134383

Last update: 2024-03-04 03:12:00

No citation recorded.