Reconfiguration with multi-objective approach in distribution networks using MOEA/D optimization algorithm and multi-criteria decision- making technique
|
Mahmoud Zadehbagheri *1 , Omid Khanzadeh  |
|
|
Abstract: (1357 Views) |
Energy distribution network is the latest interconnection between production and consumer. So, it seems to be of great importance. Several methods have been developed to improve network distribution efficiency. One of the most basic and most commonly used methods to increase the capacity of distribution network operation is the problem of network reconfiguration. One of the most basic and common methods to increase the performance capacity of the distribution network is the problem of network reconfiguring. Reconfiguration is one of the most basic and cost-effective solutions available to reduce distribution network losses. The reconfiguration of distribution networks is actually an optimization problem in which it is tried to achieve an optimal arrangement for the distribution network by using the se in the network, and as a result, a specific objective function is optimized. The reconfiguration of distribution networks is actually an optimization problem in which it is tried to achieve an optimal arrangement for the distribution network by using the existing switches in the network, as a result of which a specific objective function is optimized. In this paper, the topic of reconfiguration in distribution networks will be investigated. This issue will be investigated with two approaches of reducing the active losses of the distribution system and improving the voltage profile. This optimization problem is investigated in both single-objective and multi-objective methods. In the single-objective optimization and multi-objective optimization based on weighted coefficients, the particle swarm optimization (PSO) algorithm is used. Also, the multi-objective optimization based on non-dominated responses will be investigated with the help of multi-objective evolutionary algorithm based on decomposition (MOEA/D). The simulation was done with MATLAB on the IEEE 33 bus system and the simulation results on this system confirm the proper aperformance of these algorithms |
|
Keywords: Reconfiguration, distribution system, active losses, voltage profile, Evolutionary optimization algorithm, MOEA/D. |
|
|
Type of Study: Applicable |
Received: 2023/09/17 | Accepted: 2024/02/12 | Published: 2025/04/6
|
|
|
|
|
References |
1. na 2. T. Sousa, H. Morais, Z. Vale, and R. Castro, (2015) "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, vol. 85, pp. 236-250. [ DOI:10.1016/j.energy.2015.03.077] 3. Fan M, Chen J, Xie Z, Ouyang H, Li S, Gao L. (2022) Improved multi-objective differential evolution algorithm based on a decomposition strategy for multi-objective optimization problems. Sci Rep. 2022 Dec 7;12(1):21176. doi: 10.1038/s41598-022-2544.7. [ DOI:10.1038/s41598-022-25440-7] 4. S. Huang, Q. Wu, L. Cheng and Z. Liu, (2016) "Optimal Reconfiguration-Based Dynamic Tariff for Congestion Management and Line Loss Reduction in Distribution Networks," [ DOI:10.1109/TSG.2015.2419080] 5. IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1295-1303, May 2016. [ DOI:10.1109/TSG.2015.2419080] 6. A. Asrari, T. Wu and S. Lotfifard, (2016). "The Impacts of Distributed Energy Sources on Distribution Network Reconfiguration," IEEE Transactions on Energy Conversion, vol. 31, no. 2, pp. 606-613, June . doi: 10.1109/TEC.2015.2514191 [ DOI:10.1109/TEC.2015.2514191] 7. Chanda, S. & Srivastava, A. K. (2016). Defining and enabling resiliency of electric distribution systems with multiple microgrids. IEEE Transactions on Smart Grid 7 (6), 2859-2868 [ DOI:10.1109/TSG.2016.2561303] 8. Shehadeh, Hisham A., Mohd Yamani Idna Idris, Ismail Ahmedy, Roziana Ramli, and Noorzaily Mohamed Noor. (2018). "The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications" Energies 11, no. 1: 97. [ DOI:10.3390/en11010097] 9. Chen, C., Wang, J., Qiu, F., & Zhao, D. (2015). Resilient distribution system by microgrids formation after natural disasters. IEEE Transactions on Smart Grid, 7(2), 958-966. [ DOI:10.1109/TSG.2015.2429653] 10. Gao, H., Chen, Y., Xu, Y., & Liu, C.-C. (2016). Resilience-oriented critical load restoration using microgrids in distribution systems. IEEE Transactions on Smart Grid, 7(6), 2837-2848. [ DOI:10.1109/TSG.2016.2550625] 11. Gautam, P., Piya, P., & Karki, R. (2020). Resilience assessment of distribution systems integrated with distributed energy resources. IEEE Transactions on Sustainable Energy, 12(1), 338-348. [ DOI:10.1109/TSTE.2020.2994174] 12. Liu, X., Shahidehpour, M., Li, Z., Liu, X., Cao, Y., & Bie, Z. (2016). Microgrids for enhancing the power grid resilience in extreme conditions. IEEE Transactions on Smart Grid, 8(2), 589-597. [ DOI:10.1109/TSG.2016.2579999] 13. A. M. Eldurssi and R. M. O'Connell, (2015). "A Fast Nondominated Sorting Guided Genetic Algorithm for Multi-Objective Power Distribution System Reconfiguration Problem," IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 593-601, March [ DOI:10.1109/TPWRS.2014.2332953] 14. Ma, S., Chen, B., & Wang, Z. (2016). Resilience enhancement strategy for distribution systems under extreme weather events. IEEE Transactions on Smart Grid, 9(2), 1442-1451. [ DOI:10.1109/TSG.2016.2591885] 15. Ma, S., Su, L., Wang, Z., Qiu, F., & Guo, G. (2018). Resilience enhancement of distribution grids against extreme weather events. IEEE Transactions on power systems, 33(5), 4842-4853. [ DOI:10.1109/TPWRS.2018.2822295] 16. Liu, D., Bai, T., Deng, M., Huang, Q., Wei, X., & Liu, J. (2023). A parallel approximate evaluation-based model for multi-objective operation optimization of reservoir group. Swarm and Evolutionary Computation, 78, 101288. [ DOI:10.1016/j.swevo.2023.101288] 17. Mousavizadeh, S., Haghifam, M.-R., & Shariatkhah, M.-H. (2018). A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources. Applied energy, 211, 443-460. [ DOI:10.1016/j.apenergy.2017.11.067] 18. Jing, Yuhao, et al.(2023). "Cooperative Deployment Multi-Objective Optimization Approach for High-ResolutionMulti-Airship Earth-Observation Coverage Network." IEEE Transactions on Network Science and Engineering . [ DOI:10.1109/TNSE.2023.3261280] 19. L. Isac Silva, E. Antonio Belati and I. Chaves Silva Junior, (2016)."Heuristic Algorithm for Electrical Distribution Systems Reconfiguration Based on Firefly Movement Equation," IEEE Latin America Transactions, vol. 14, no. 2, pp. 752-758, Feb. [ DOI:10.1109/TLA.2016.7437219] 20. Panteli, M., & Mancarella, P. (2015b). Operational resilience assessment of power systems under extreme weather and loading conditions. 2015 IEEE Power & Energy Society General Meeting, 1-5, Denver, USA. [ DOI:10.1109/PESGM.2015.7286087] 21. Panteli, M., Trakas, D. N., Mancarella, P., & Hatziargyriou, N. D. (2016). Boosting the power grid resilience to extreme weather events using defensive islanding. IEEE Transactions on Smart Grid, 7(6), 2913-2922. [ DOI:10.1109/TSG.2016.2535228] 22. Poudel, S., & Dubey, A. (2018). Critical load restoration using distributed energy resources for resilient power distribution system. IEEE Transactions on power systems, 34(1), 52-63. [ DOI:10.1109/TPWRS.2018.2860256] 23. Chen, L., Cao, L. L., Wen, Y. M., Chen, H., & Jiang, S. L. (2023). A knowledge-based NSGA-II algorithm for multi-objective hot rolling production scheduling under flexible time-of-use electricity pricing. Journal of Manufacturing Systems, 69, 255-270. [ DOI:10.1016/j.jmsy.2023.06.009] 24. Wang, X., Li, Z., Shahidehpour, M., & Jiang, C. (2017). Robust line hardening strategies for improving the resilience of distribution systems with variable renewable resources. IEEE Transactions on Sustainable Energy, 10(1), 386-395. [ DOI:10.1109/TSTE.2017.2788041] 25. Wang, Y., Chen, C., Wang, J., & Baldick, R. (2015). Research on resilience of power systems under natural disasters-A review. IEEE Transactions on power systems, 31(2), 1604-1613. [ DOI:10.1109/TPWRS.2015.2429656] 26. Yuan, C., Illindala, M. S., & Khalsa, A. S. (2016). Modified Viterbi algorithm based distribution system restoration strategy for grid resiliency. IEEE Transactions on Power Delivery, 32(1), 310-319. [ DOI:10.1109/TPWRD.2016.2613935] 27. Singh, J., Fatima, S., & Chauhan, A. S. (2023). Multi-Objective Travel Route Optimization Using Non-Dominated Sorting Genetic Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 785-794. 28. Zhou, W., Liu, Y., Li, M., Wang, Y., Shen, Z., Feng, L., & Zhu, Z. (2023). Dynamic Multi-Objective Optimization Framework With Interactive Evolution for Sequential Recommendation. IEEE Transactions on Emerging Topics in Computational Intelligence. [ DOI:10.1109/TETCI.2023.3251352] 29. Rohman, F. S., Muhammad, D., Zahan, K. A., & Murat, M. N. (2023). Operation and Design Optimisation of Industrial Low-Density Polyethylene Tubular Reactor for Multiple Objectives Using an Evolutionary Algorithm-Based Strategy. Process Integration and Optimization for Sustainability, 1-18. [ DOI:10.1007/s41660-023-00308-z] 30. Zhang, Qingfu, and Hui Li. (2007) "MOEA/D: A multiobjective evolutionary algorithm based on decomposition." IEEE Transactions on evolutionary computation11.6: 712-731. [ DOI:10.1109/TEVC.2007.892759] 31. Yuan, W., Wang, J., Qiu, F., Chen, C., Kang, C., & Zeng, B. (2016). Robust optimization-based resilient distribution network planning against natural disasters. IEEE Transactions on Smart Grid, 7(6), 2817-2826. [ DOI:10.1109/TSG.2015.2513048] 32. Zhang, B., Dehghanian, P., & Kezunovic, M. (2017). Optimal allocation of PV generation and battery storage for enhanced resilience. IEEE Transactions on Smart Grid, 10(1), 535-545. [ DOI:10.1109/TSG.2017.2747136] 33. Ye, Q., Wang, Z., Zhao, Y., Dai, R., Wu, F., & Yu, M. (2023). A clustering-based competitive particle swarm optimization with grid ranking for multi-objective optimization problems. Scientific Reports, 13(1), 11754. [ DOI:10.1038/s41598-023-38529-4] 34. Tahboub, A.M.; Pandi, V.R.; Zeineldin, H.H., "Distribution System Reconfiguration for Annual Energy Loss Reduction Considering Variable Distributed Generation Profiles," IEEE Transactions on Power Delivery, vol.30, no.4, pp.1677-1685, Aug. 2015 [ DOI:10.1109/TPWRD.2015.2424916] 35. T. T. Nguyen and A. V. Truong, "Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm," International Journal of Electrical Power & Energy Systems, vol. 68, pp. 233-242, Jun. 2015. [ DOI:10.1016/j.ijepes.2014.12.075] 36. Alonso, F.R.; Oliveira, D.Q.; Zambroni de Souza, A.C., "Artificial Immune Systems Optimization Approach for Multiobjective Distribution System Reconfiguration," IEEE Transactions on Power Systems, vol.30, no.2, pp.840-847, March 2015 [ DOI:10.1109/TPWRS.2014.2330628] 37. M. Lavorato, J. F. Franco, M. J. Rider, and R. Romero, "Imposing Radiality Constraints in Distribution System Optimization Problems," IEEE Transactions on Power Systems, vol. 27, pp. 172-180, 2012. [ DOI:10.1109/TPWRS.2011.2161349] 38. Z. Tian, W. Wu, B. Zhang and A. Bose, "Mixed-integer second-order cone programing model for VAR optimisation and network reconfiguration in active distribution networks," IET Generation, Transmission & Distribution, vol. 10, no. 8, pp. 1938-1946, 5 19 2016. [ DOI:10.1049/iet-gtd.2015.1228] 39. C. Liu, S. Mehrotra and Z. Bie, "Robust Distribution Network Reconfiguration," IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 836-842, March 2015. [ DOI:10.1109/TSG.2014.2375160]
|
|
Zadehbagheri M, Khanzadeh O. Reconfiguration with multi-objective approach in distribution networks using MOEA/D optimization algorithm and multi-criteria decision- making technique. ieijqp 2024; 13 (1) URL: http://ieijqp.ir/article-1-970-en.html
|