Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Multi-objective optimal operation of smart reconfigurable distribution grids

Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80210, USA

Topical Section: Smart Grids and Networks

Reconfiguration is a valuable technique that can support the distribution grid from different aspects such as operation cost and loss reduction, reliability improvement, and voltage stability enhancement. An intelligent and efficient optimization framework, however, is required to reach the desired efficiency through the reconfiguration strategy. This paper proposes a new multi-objective optimization model to make use of the reconfiguration strategy for minimizing the power losses, improving the voltage profile, and enhancing the load balance in distribution grids. The proposed model employs the min-max fuzzy approach to find the most satisfying solution from a set of nondominated solutions in the problem space. Due to the high complexity and the discrete nature of the proposed model, a new optimization method based on harmony search (HS) algorithm is further proposed. Moreover, a new modification method is suggested to increase the harmony memory diversity in the improvisation stage and increase the convergence ability of the algorithm. The feasibility and satisfying performance of the proposed model are examined on the IEEE 32-bus distribution system.
  Figure/Table
  Supplementary
  Article Metrics

Keywords reconfiguration strategy; fuzzy interactive method; modified harmony search

Citation: Abdollah Kavousi-Fard, Amin Khodaei. Multi-objective optimal operation of smart reconfigurable distribution grids. AIMS Energy, 2016, 4(2): 206-221. doi: 10.3934/energy.2016.2.206

References

  • 1. Kavousi-Fard A, Rostami MA, Niknam T (2015) Reliability-Oriented Reconfiguration of Vehicle-to-Grid Networks. IEEE T Ind Inform 11: 682–691.    
  • 2. Kavousi-Fard A, Niknam T, Fotuhi-Firuzabad M (2015) Stochastic Reconfiguration and Optimal Coordination of V2G Plug-in Electric Vehicles Considering Correlated Wind Power Generation. IEEE T Sust Energ 6: 822–830.    
  • 3. Baziar A, Kavousi-Fard A (2014) An intelligent multi-objective stochastic framework to solve the distribution feeder reconfiguration considering uncertainty. J Intell Fuzzy Syst 26: 2215–2227.
  • 4. Morton AB, Mareels IMY (2000) An efficient brute-force solution to the network reconfiguration problem. IEEE T Power Deliver 15: 996–1000.    
  • 5. Kim H, Ko Y, Jung KH (1993) Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems. IEEE T Power Deliver 8: 1356–1366.    
  • 6. Goswami SK, Basu SK (1992) A new algorithm for the reconfiguration of distribution feeders for loss minimization. IEEE T Power Deliver 7: 1484–1491.    
  • 7. Taylor T, Lubkeman D (1990) Implementation of heuristic search strategies for distribution feeder reconfiguration. IEEE T Power Deliver 5: 239–245.    
  • 8. Lopez E, Opaso H (2004) Online reconfiguration considering variability demand. Applications to real networks. IEEE T Power Syst 19: 549–553.
  • 9. Su CT, Chang CF, Chiou JP (2005) Distribution network reconfiguration for loss reduction by ant colony search algorithm.Electr Pow Syst Res75: 190–199.
  • 10. Chang CF (2008) Reconfiguration and Capacitor Placement for Loss Reduction of Distribution Systems by Ant Colony Search Algorithm. IEEE T Power Syst 23: 1747–1755.    
  • 11. Shirmohammadi D, Hong HW (1989) Reconfiguration of electric distribution networks for resistive line loss reduction. IEEE T Power Syst 4: 1492–1498.
  • 12. Jeon YJ, Kim JC (2000) Network reconfiguration in radial distribution system using simulated annealing and tabu search. Proc IEEE Power Eng Soc Winter Meeting, 23–27.
  • 13. Niknam T, Kavousifard A, Tabatabaei S, et al. (2011) Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks. J Power Sources 196: 8881–8896    
  • 14. Kavousi-Fard A, Niknam T (2014) Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty. IEEE T Power Deliver 29: 1344–1353.    
  • 15. Amanulla B, Chakrabarti S, Singh SN (2012) Reconfiguration of Power Distribution Systems Considering Reliability and Power Loss. IEEE T Power Deliver 27: 918–926.
  • 16. Kavousi-Fard A, Akbari-Zadeh M-R (2013) Reliability Enhancement Using Optimal Distribution Feeder Reconfiguration. Neuro computing 106: 1–11.
  • 17. Zhou Q, Shirmohammadi D, Liu W (1997) Distribution feeder reconfiguration for service restoration and load balancing. IEEE T Power Syst 2: 724–729.
  • 18. Frank S, Steponavice I, Rebennack S (2012) Optimal Power Flow: A Bibliographic Survey I - Formulations and Deterministic Methods. Energy Systems 3: 221–258.    
  • 19. Frank S, Steponavice I, Rebennack S (2012) Optimal Power Flow: A Bibliographic Survey II - Non-Deterministic and Hybrid Methods. Energy Systems 3: 259–289.    
  • 20. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76: 60–68.    
  • 21. Yadav P, Kumar R, Panda SK, et al. (2011) An Improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms. Energ Convers Manage 52: 893–902.    
  • 22. Niknam T (2009) A new hybrid algorithm for multiobjective distribution feeder reconfiguration. Energ Convers Manage 50: 2074–2082.    
  • 23. Niknam T (2009) An efficient hybrid evolutionary based on PSO and ACO algorithms for distribution feeder reconfiguration. Eur T Electr Power. DOI: 10,1002/etep.339.

 

This article has been cited by

  • 1. Abdollah Kavousi-Fard, Amin Khodaei, Efficient integration of plug-in electric vehicles via reconfigurable microgrids, Energy, 2016, 111, 653, 10.1016/j.energy.2016.06.018
  • 2. Amir Ghaedi, Saeed Daneshvar Dehnavi, Hadi Fotoohabadi, Probabilistic scheduling of smart electric grids considering plug-in hybrid electric vehicles, Journal of Intelligent & Fuzzy Systems, 2016, 31, 3, 1329, 10.3233/IFS-162199

Reader Comments

your name: *   your email: *  

Copyright Info: 2016, Amin Khodaei, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved