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A dynamic model for multi-objective feeder reconfiguration in distribution network considering demand response program

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Abstract

The distribution feeder reconfiguration represents a major process of operation in the distribution system utilized to enhance grid performance. Given disparities in the electricity price as well as smart networks’ load pattern, the distribution system’s operational problems are much more time-dependent and more complex than before. For this purpose, the dynamic distribution feeder reconfiguration with diverse objectives such as energy not supplied, energy loss and operational cost is formulated in this research. Time of use service of demand response program is suggested to change customers’ consumption patterns. Given the innate intricacy of this issue, an improved particle swarm optimization (IPSO) algorithm is presented to address the problem of dynamic distribution feeder reconfiguration in the presence of energy storage systems, distributed generation units, and solar photovoltaic arrays. In this paper, the presented algorithm is tested in the IEEE 95-node test system, and discuss its advantages by drawing analogy with other evolutionary algorithms.

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References

  1. Lotfi, H., Ghazi, R.: Optimal participation of demand response aggregators in reconfigurable distribution system considering photovoltaic and storage units. J. Ambient. Intell. Humaniz. Comput. 12(2), 2233–2255 (2021)

    Article  Google Scholar 

  2. Narimani, M.R., et al.: Enhanced gravitational search algorithm for multi-objective distribution feeder reconfiguration considering reliability, loss and operational cost. IET Gener. Transm. Distrib. 8(1), 55–69 (2014)

    Article  Google Scholar 

  3. Mahboubi-Moghaddam, E., et al.: Multi-objective distribution feeder reconfiguration to improve trans,ient stability, and minimize power loss and operation cost using an enhanced evolutionary algorithm at the presence of distributed generations. Int. J. Electr. Power Energy Syst. 76, 35–43 (2016)

    Article  Google Scholar 

  4. Azizivahed, A., et al.: A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration. Energy 138, 355–373 (2017)

    Article  Google Scholar 

  5. López, J.C., Lavorato, M., Rider, M.J.: Optimal reconfiguration of electrical distribution systems considering reliability indices improvement. Int. J. Electr. Power Energy Syst. 78, 837–845 (2016)

    Article  Google Scholar 

  6. Sarma, N., Rao, K.P.: A new 0–1 integer programming method of feeder reconfiguration for loss minimization in distribution systems. Electr. Power Syst. Res. 33(2), 125–131 (1995)

    Article  Google Scholar 

  7. Kashem, M., Ganapathy, V., Jasmon, G.: Network reconfiguration for load balancing in distribution networks. IEE Proc.-Gener. Transm. Distrib. 146(6), 563–567 (1999)

    Article  Google Scholar 

  8. Alonso, F., Oliveira, D., de Souza, A.Z.: Artificial immune systems optimization approach for multiobjective distribution system reconfiguration. IEEE Trans. Power Syst. 30(2), 840–847 (2015)

    Article  Google Scholar 

  9. Nguyen, T.T., et al.: Multi-objective electric distribution network reconfiguration solution using runner-root algorithm. Appl. Soft Comput. 52, 93–108 (2017)

    Article  Google Scholar 

  10. Kaur, M., Ghosh, S.: Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm. Appl. Soft Comput. 49, 868–886 (2016)

    Article  Google Scholar 

  11. Siahbalaee, J., Rezanejad, N., Gharehpetian, G.B.: Reconfiguration and DG sizing and placement using improved shuffled frog leaping algorithm. Electric Power Compon. Syst. 47(17), 1475–1488 (2020)

    Article  Google Scholar 

  12. Singh, J., Tiwari, R.: Real power loss minimisation of smart grid with electric vehicles using distribution feeder reconfiguration. IET Gener. Transm. Distrib. 13(18), 4249–4261 (2019)

    Article  Google Scholar 

  13. Fathi, V., Seyedi, H., Ivatloo, B.M.: Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties. Int. Trans. Electric. Energy Syst. 30(5), e12346 (2020)

    Google Scholar 

  14. Kavousi-Fard, A., Niknam, T., Fotuhi-Firuzabad, M.: Stochastic reconfiguration and optimal coordination of V2G plug-in electric vehicles considering correlated wind power generation. IEEE Trans. Sustain. Energy 6(3), 822–830 (2015)

    Article  Google Scholar 

  15. Guan, W., et al.: Distribution system feeder reconfiguration considering different model of DG sources. Int. J. Electr. Power Energy Syst. 68, 210–221 (2015)

    Article  Google Scholar 

  16. Kavousi-Fard, A., et al.: Optimal distribution feeder reconfiguration for increasing the penetration of plug-in electric vehicles and minimizing network costs. Energy 93, 1693–1703 (2015)

    Article  Google Scholar 

  17. Kaveh, M.R., Hooshmand, R.-A., Madani, S.M.: Simultaneous optimization of re-phasing, reconfiguration and DG placement in distribution networks using BF-SD algorithm. Appl. Soft Comput. 62, 1044–1055 (2018)

    Article  Google Scholar 

  18. Esmaeili, M., Sedighizadeh, M., Esmaili, M.: Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty. Energy 103, 86–99 (2016)

    Article  Google Scholar 

  19. Bayat, A., Bagheri, A., Noroozian, R.: Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method. Int. J. Electr. Power Energy Syst. 77, 360–371 (2016)

    Article  Google Scholar 

  20. Larimi, S.M.M., Haghifam, M.R., Moradkhani, A.: Risk-based reconfiguration of active electric distribution networks. IET Gener. Transm. Distrib. 10(4), 1006–1015 (2016)

    Article  Google Scholar 

  21. Rahmani-Andebili, M.: Dynamic and adaptive reconfiguration of electrical distribution system including renewables applying stochastic model predictive control. IET Gener. Transm. Distrib. 11(16), 3912–3921 (2017)

    Article  Google Scholar 

  22. Ameli, A., et al.: A dynamic method for feeder reconfiguration and capacitor switching in smart distribution systems. Int. J. Electr. Power Energy Syst. 85, 200–211 (2017)

    Article  Google Scholar 

  23. Azizivahed, A., et al.: Dynamic feeder reconfiguration in automated distribution network integrated with renewable energy sources with respect to the economic aspect. In: 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). IEEE (2019)

  24. Azizivahed, A., et al.: Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage. IEEE Trans. Sustain. Energy 11(2), 662–673 (2019)

    Article  Google Scholar 

  25. Youman, D., Boming, Z., Tian, T.: A fictitious load algorithm and it’s applications to distribution network dynamic optimizations. Chin. Soc. Electric. Eng. 4, (1996)

  26. YIn, L.-Y., Yu, J.-L.: Dynamic reconfiguration (DR) of distribution network with multi-time periods. In: Proceedings of the Csee, p. 7 (2002)

  27. Azizivahed, A., et al.: Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems. Energy 147, 896–914 (2018)

    Article  Google Scholar 

  28. Shariatkhah, M.-H., et al.: Duration based reconfiguration of electric distribution networks using dynamic programming and harmony search algorithm. Int. J. Electr. Power Energy Syst. 41(1), 1–10 (2012)

    Article  Google Scholar 

  29. Milani, A.E., Haghifam, M.R.: An evolutionary approach for optimal time interval determination in distribution network reconfiguration under variable load. Math. Comput. Model. 57(1–2), 68–77 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  30. Lotfi, H.: Optimal sizing of distributed generation units and shunt capacitors in the distribution system considering uncertainty resources by the modified evolutionary algorithm. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03194-w

    Article  Google Scholar 

  31. Niknam, T., Kavousifard, A., Aghaei, J.: Scenario-based multiobjective distribution feeder reconfiguration considering wind power using adaptive modified particle swarm optimisation. IET Renew. Power Gener. 6(4), 236–247 (2012)

    Article  Google Scholar 

  32. Gitizadeh, M., Vahed, A.A., Aghaei, J.: Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms. Appl. Energy 101, 655–666 (2013)

    Article  Google Scholar 

  33. Azizivahed, A., et al.: A new bi-objective approach to energy management in distribution networks with energy storage systems. IEEE Trans. Sustain. Energy 9(1), 56–64 (2018)

    Article  Google Scholar 

  34. Majidi, M., Nojavan, S., Zare, K.: Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program. Energy Convers. Manag. 144, 132–142 (2017)

    Article  Google Scholar 

  35. Aalami, H., Moghaddam, M.P., Yousefi, G.: Evaluation of nonlinear models for time-based rates demand response programs. Int. J. Electr. Power Energy Syst. 65, 282–290 (2015)

    Article  Google Scholar 

  36. Jahani, M.T.G., et al.: Multi-objective optimization model for optimal reconfiguration of distribution networks with demand response services. Sustain. Cities Soc. 47, 101514 (2019)

    Article  Google Scholar 

  37. Niknam, T., et al.: Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Gener. Transm. Distrib. 6(6), 515–527 (2012)

    Article  Google Scholar 

  38. Lotfi, H., Ghazi, R., Bagher Naghibi-Sistani, M.: Multi-objective dynamic distribution feeder reconfiguration along with capacitor allocation using a new hybrid evolutionary algorithm. Energy Syst. 11(3), 779–809 (2020)

    Article  Google Scholar 

  39. Niknam, T., Farsani, E.A.: A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration. Eng. Appl. Artif. Intell. 23(8), 1340–1349 (2010)

    Article  Google Scholar 

  40. Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, US (2010)

    Google Scholar 

  41. Ganguly, S., Sahoo, N., Das, D.: A novel multi-objective PSO for electrical distribution system planning incorporating distributed generation. Energy Syst. 1(3), 291–337 (2010)

    Article  Google Scholar 

  42. Lotfi, H., Samadi, M., Dadpour, A.: Optimal capacitor placement and sizing in radial distribution system using an improved Particle Swarm Optimization algorithm. In: 2016 21st Conference on Electrical Power Distribution Networks Conference (EPDC). IEEE (2016). https://doi.org/10.1109/EPDC.2016.7514799

  43. Zhang, Y., Xiong, X., Zhang, Q.: An improved self-adaptive PSO algorithm with detection function for multimodal function optimization problems. Math. Probl. Eng. (2013). https://doi.org/10.1155/2013/716952

    Article  MathSciNet  MATH  Google Scholar 

  44. Shafiu, A., et al.: Active management and protection of distribution networks with distributed generation. In: IEEE Power Engineering Society General Meeting, 2004. IEEE (2004). https://doi.org/10.1109/PES.2004.1373011

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Funding

This research has not been funded by any individual or organization.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran

    Hossein Lotfi

  2. Department of Electrical Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

    Ali Asghar Shojaei

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  1. Hossein Lotfi
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  2. Ali Asghar Shojaei
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Correspondence to Ali Asghar Shojaei.

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Lotfi, H., Shojaei, A.A. A dynamic model for multi-objective feeder reconfiguration in distribution network considering demand response program. Energy Syst 14, 1051–1080 (2023). https://doi.org/10.1007/s12667-022-00507-6

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