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Multi-objective optimization in active distribution networks with consumer-producer characteristics: Reconfiguration, demand response, and microgrid models using particle swarm optimization.
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Mohamad Mehdi Khademi1   , Mahmoud Samiei Moghaddam *2 , Reza Davarzani1 , Azita Azarfar1 , Mohamad Mehdi Hoseini1 |
1- Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran
2- Department of Electrical Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran |
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Abstract: (336 Views) |
| This paper presents an optimization model for active distribution networks with consumer-producer characteristics and optimal operation of microgrids. The proposed model includes network reconfiguration, demand response programs, and distributed generation resources (fossil, photovoltaic, and wind) and uses an advanced particle swarm algorithm to solve complex optimization problems. The model aims to minimize a multi-objective function by optimizing the charging and discharging schedules of electric vehicles and energy storage systems, along with the use of distributed flexible variable power transmission devices. Simulations were performed on 33-bus and 69-bus networks using Julia software. The results show that demand response programs reduce network losses by 12%, improve voltage stability by 8%, and reduce energy purchase costs. Also, the comparison of robust and deterministic optimization showed that robust optimization provides more reliable power supply under uncertainty despite a slight increase in losses and costs. The findings confirm the superior performance of the proposed model and algorithm in improving the efficiency and effectiveness of microgrid and active distribution network management and demonstrate the high potential of this approach in the face of increasing complexity. |
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| Keywords: Optimization, Microgrids Operation, Evolutionary Method, Renewable Resources, Presence of Electric Vehicles |
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Type of Study: Research |
Received: 2024/12/27 | Accepted: 2025/01/17 | Published: 2025/04/6
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Fan, Z., Fan, B., Peng, J., & Liu, W. (2021). Operation loss minimization targeted distributed optimal control of DC microgrids. IEEE Systems Journal, 15(4), 5186-5196. [ DOI:10.1109/JSYST.2020.3035059] 30. Al-Ismail, F. S. (2021). DC microgrid planning, operation, and control: A comprehensive review. IEEE Access, 9, 36154-36172. [ DOI:10.1109/ACCESS.2021.3062840] 31. Jia, Y., Wen, P., Yan, Y., & Huo, L. (2021). Joint operation and transaction mode of rural multi microgrid and distribution network. IEEE Access, 9, 14409-14421. [ DOI:10.1109/ACCESS.2021.3050793] 32. Nguyen, T. -T., Dao, T. -K., Nguyen, T. -T. -T., & Nguyen, T. -D. (2022). An optimal microgrid operations planning using improved Archimedes optimization algorithm. IEEE Access, 10, 67940-67957. [ DOI:10.1109/ACCESS.2022.3185737] 33. Wang, C., Wang, A., Chen, S., Zhang, G., & Zhu, B. (2022). Optimal operation of microgrids based on a radial basis function metamodel. IEEE Systems Journal, 16(3), 4756-4767. [ DOI:10.1109/JSYST.2021.3130760] 34. Li, Z., Wu, L., Xu, Y., Moazeni, S., & Tang, Z. (2022). Multi-stage real-time operation of a multi-energy microgrid with electrical and thermal energy storage assets: A data-driven MPC-ADP approach. IEEE Transactions on Smart Grid, 13(1), 213-226. [ DOI:10.1109/TSG.2021.3119972] 35. Fallahi, F., Yildirim, M., Lin, J., & Wang, C. (2021). Predictive multi-microgrid generation maintenance: Formulation and impact on operations & resilience. IEEE Transactions on Power Systems, 36(6), 4979-4991. [ DOI:10.1109/TPWRS.2021.3066462] 36. Chamana, M., et al. (2022). Buildings participation in resilience enhancement of community microgrids: Synergy between microgrid and building management systems. IEEE Access, 10, 100922-100938. [ DOI:10.1109/ACCESS.2022.3207772] 37. Karimianfard, H., & Haghighat, H. (2019). Generic resource allocation in distribution grid. IEEE Transactions on Power Systems, 34(1), 810-813. [ DOI:10.1109/TPWRS.2018.2867170] 38. Zhang, Z., Wang, Z., Wang, H., Zhang, H., Yang, W., & Cao, R. (2021). Research on bi-level optimized operation strategy of microgrid cluster based on IABC algorithm. IEEE Access, 9, 15520-15529. [ DOI:10.1109/ACCESS.2021.3053122] 39. Wang, C., Yu, H., Chai, L., Liu, H., & Zhu, B. (2021). Emergency load shedding strategy for microgrids based on dueling deep Q-learning. IEEE Access, 9, 19707-19715. [ DOI:10.1109/ACCESS.2021.3055401] 40. Lee, J., Lee, S., & Lee, K. (2021). Multistage stochastic optimization for microgrid operation under islanding uncertainty. IEEE Transactions on Smart Grid, 12(1), 56-66. [ DOI:10.1109/TSG.2020.3012158] 41. Zhao, Z., et al. (2022). Distributed robust model predictive control-based energy management strategy for islanded multi-microgrids considering uncertainty. IEEE Transactions on Smart Grid, 13(3), 2107-2120. [ DOI:10.1109/TSG.2022.3147370] 42. Harasis, S., Sozer, Y., & Elbuluk, M. (2021). Reliable islanded microgrid operation using dynamic optimal power management. IEEE Transactions on Industry Applications, 57(2), 1755-1766. [ DOI:10.1109/TIA.2020.3047587] 43. Salehi, N., Martínez-García, H., Velasco-Quesada, G., & Guerrero, J. M. (2022). A comprehensive review of control strategies and optimization methods for individual and community microgrids. IEEE Access, 10, 15935-15955. [ DOI:10.1109/ACCESS.2022.3142810] 44. Karimianfard, H., Salehizadeh, M. R., & Siano, P. (2022). Economic profit enhancement of a demand response aggregator through investment of large-scale energy storage systems. CSEE Journal of Power and Energy Systems, 8(5), 1468-1476. 45. Vilaisarn, Y., Rodrigues, Y. R., Abdelaziz, M. M. A., & Cros, J. (2022). A deep learning based multiobjective optimization for the planning of resilience-oriented microgrids in active distribution system. IEEE Access, 10, 84330-84364. [ DOI:10.1109/ACCESS.2022.3197194] 46. Reiz, C., & Leite, J. B. (2022). Optimal coordination of protection devices in distribution networks with distributed energy resources and microgrids. IEEE Access, 10, 99584-99594. [ DOI:10.1109/ACCESS.2022.3203713] 47. Vilaisarn, Y., Moradzadeh, M., Abdelaziz, M., & Cros, J. (2022). An MILP formulation for the optimum operation of AC microgrids with hierarchical control. International Journal of Electrical Power & Energy Systems, 137, 107674. [ DOI:10.1016/j.ijepes.2021.107674] 49. Singh, A., & Nguyen, H. D. (2022). A two-layer framework for optimal control of battery temperature and microgrid operation. Journal of Energy Storage, 50, 104057. [ DOI:10.1016/j.est.2022.104057] 50. Lakouraj, M. M., Shahabi, M., Shafie-khah, M., & Catalão, J. P. S. (2022). Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources. Energy, 239, 122156. [ DOI:10.1016/j.energy.2021.122156] 52. Akulker, H., & Aydin, E. (2023). Optimal design and operation of a multi-energy microgrid using mixed-integer nonlinear programming: Impact of carbon cap and trade system and taxing on equipment selections. Applied Energy, 330, 120313. [ DOI:10.1016/j.apenergy.2022.120313] 54. Shokouhmand, E., & Ghasemi, A. (2022). Stochastic optimal scheduling of electric vehicles charge/discharge modes of operation with the aim of microgrid flexibility and efficiency enhancement. Sustainable Energy, Grids and Networks, 32, 100929. [ DOI:10.1016/j.segan.2022.100929] |
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Khademi M M, Samiei Moghaddam M, Davarzani R, Azarfar A, Hoseini M M. Multi-objective optimization in active distribution networks with consumer-producer characteristics: Reconfiguration, demand response, and microgrid models using particle swarm optimization.. ieijqp 2024; 13 (3) URL: http://ieijqp.ir/article-1-1022-en.html
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