A generalized framework for optimal sizing of distributed energy resources in micro-grids using an indicator-based swarm approach

IEEE Transactions on Industrial Informatics

Volumn 10, Issue 1, 2014, Pages 152-162

  Di Silvestre, Maria Luisa ^a   Graditi, Giorgio ^b   Riva Sanseverino, Eleonora ^a  

^a UNIVERSITY OF PALERMO   (Italy)

^b Centro Ricerche ENEA Agenzia Nazionale per le Nuove Tecnologie   (Italy)

Author keywords

Glow worm optimization; indicator based evolutionary algorithm; microgrids; NSGA II; planning

Indexed keywords

ALGORITHMS; COSTS; DESIGN; DISTRIBUTED POWER GENERATION; ENERGY RESOURCES; MULTIOBJECTIVE OPTIMIZATION; OPTIMAL SYSTEMS; PLANNING;

APPROXIMATE EVALUATION; DISTRIBUTED ENERGY RESOURCES; DISTRIBUTED ENERGY SOURCES; DISTRIBUTED GENERATORS; MICRO GRID; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; MULTIOBJECTIVE MANAGEMENT; NSGA-II;

QUALITY CONTROL;

EID: 84898465478     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2013.2272945     Document Type: Article

References (30)

1. A. Bertani, A. Borghetti, C. Bossi, L. De Biase, O. Lamquet, S. Massucco, A.Morini, C. A. Nucci, M. Paolone, E. Quaia, and F. Silvestro, Management of low voltage grids with high penetration of distributed generation: Concepts, implementations and experiments presented at the 2007 CIGRE, Paris, France, Session C6-304.
2. R. C. Dugan and T. E. McDermottG. J. Ball, Planning for distributed generation IEEE Ind. Appl. Mag., vol. 7, no. 2, pp. 80-88, 2001. (Pubitemid 32255300)
3. A. Bin Humayd, Distribution system planning with distributed generation: Optimal versus heuristic approach Ph.D. dissertation, Univ. of Waterloo, Waterloo, ON, Canada, 2011[Online]. Available: https://uwspace.uwaterloo.ca/ handle/10012/5917
4. M. F. Shaaban, Y. M. Atwa, and E. F. El-Saadany, DG allocation for benefit maximization in distribution networks IEEE Trans. Power Syst., vol. 28, no. 2, pp. 639-649, May 2013.
5. C. Yammani, S. Maheswarapu, and S. K. Matam, Optimal placement and sizing DER 's with load models using BAT algorithm in Proc. Intl. Conf. Circuits, Power, Comput. Technol., 2013, pp. 394-399.
6. A. Alarcon-Rodriguez, G. W. Ault, and S. Galloway, Multi-objective planning of distributed energy resources: A review of the stat-ofthe- art generation Renew. Sustain. Energy Rev., vol. 14, no. 5, pp. 1353-1366, Jun. 2010.
7. Y. M. Atwa, E. F. El-Saadany, M. M. A. Salama, and R. Seethapathy, Optimal renewable resources mix for distribution system energy loss minimization IEEE Trans. Power Syst., vol. 25, no. 1, pp. 360-370, 2010.
8. G. Celli, E. Ghiani, S. Mocci, and F. Pilo, A multiobjective evolutionary algorithm for the sizing and siting of distributed generation IEEE Trans. Power Syst., vol. 20, no. 2, pp. 750-757, 2005. (Pubitemid 40699704)
9. A. Soroudi and M. Ehsan, A distribution network expansion planning model considering distributed generation options and techno-economical issues Energy, vol. 35, no. 8, pp. 3364-3374, Aug. 2010.
10. M. T.Wishart,M.Dewadasa, I. Ziari, G. Ledwich, and A.Ghosh, Intelligent distribution planning and control incorporating microgrids presented at the IEEEPower Energy Soc. Gen.Meet., Jul. 24-29, 2011.
11. O. Hafez and K. Bhattacharya, Optimal Planning and Design of a Renewable Energy Based Supply System for Mcrogrids Renewable Energy. Amsterdam, TheNetherlands: Elsevier, 2012, vol. 45, pp. 7-15.
12. A. K. Basu, Microgrids: Planning of fuel energy management by strategic deployment of CHP-based DERs - An evolutionary algorithm approach Int. J. Electr. Power Energy Syst., vol. 44, no. 1, pp. 326-336, Jan. 2013.
13. A. Alarcon-Rodriguez, E. Haesen, G. W. Ault, J. Driesen, and R. Belmans, Multiobjective planning framework for stochastic and controllable distributed energy resources IET J. Renew. PowerGenerat., vol. 3, no. 2, pp. 227-238, 2009.
14. A. Alarcon-Rodriguez and G. W. Ault, Multi-objective planning of distributed energy resources with probabilistic constraints in Proc. 2010 IEEE Power Energy Soc. General Meet.
15. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA, USA: Addison-Wesley, 1989.
16. F. Herrera, M. Lozano, and J. L. Verdegay, Tackling real coded genetic algorithms: Operators and tools for behavioural analysis in Artificial Intelligence Review. Norwell, MA, USA: Kluwer, 1998, vol. 12, pp. 265-319. (Pubitemid 128514089)
17. M. L. Di Silvestre, G. F. Scimemi,M. G. Ippolito, E. Riva Sanseverino, and G. Zizzo, A double-shell design approach for multiobjective optimal design of microgrids in Proc. 2nd KES Int. Symp. 2010 Adv. Intell. Dec. Technol., Inner Harbor, Baltimore, MD, USA, pp. 65-73.
18. E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach IEEE Trans. Evol. Comput., vol. 3, no. 4, pp. 257-271, 1999. (Pubitemid 30544879)
19. Directorate-General for Research Sustainable Energy Systems, European Commission, European SmartGrids Technology Platform: Vision and Strategy for Europe's Electricity Networks of the Future, Apr. 2006[Online]. Available: http://ec.europa.eu/research/energy/pdf/smartgrids-en.pdf.
20. N. P. Padhy, Unit commitment - A bibliographical survey IEEE Trans. Power Syst., vol. 19, no. 2, pp. 1196-205, 2004.
21. A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagaska, Multiobjective intelligent energy management for a microgrid IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1688-1699, 2013.
22. P. Siano, C. Cecati, Y. Hao, and J. Kolbusz, Real time operation of smart grids via FCN networks and optimal power flow IEEE Trans. Ind. Informatics, vol. 8, no. 4, pp. 944-952, 2012.
23. X. Zhang, R. Sharma, and Y. He, Optimal energy management of a rural microgrid system using multi-objective optimization in Proc. 2012 IEEE Innovative Smart Grid Technol. (ISGT).
24. M. L. Di Silvestre, R. Gallea, and E. Riva Sanseverino, Pareto-optimal Glowworm Swarms optimization for smart grids management in Lecture Notes in Computer Science. New York, NY, USA: Springer, 2013, vol. 7835, pp. 22-31.
25. A. K. Basu, S. Chowdhury, and S. P. Chowdhury, Operational management of CHP-based microgrid in Proc. Intl. Conf. Power Syst. Technol., 2010, pp. 1-5.
26. K. Deb, S. Agrawal, A. Pratap, and T. A. Meyarivan, Fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II in Parallel Problem Solving from Nature VI - Lecture Notes in Computer Science 2000. Paris, France: Springer, pp. 849-858.
27. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. New York, NY, USA: Wiley, 2001.
28. K. Deb, An efficient constraint-handling method for geneticalgorithms Comput. Methods Appl. Mechan. Eng., vol. 186, no. 2-4, pp. 311-338, 2000.
29. K. N. Krishnanand and D. Ghose, Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions Swarm Intell., vol. 3, pp. 87-124, Jun. 2009.
30. A. Borghetti, M. Bosetti, C. Bossi, S. Massucco, E. Micolano, A. Morini, C. A. Nucci, M. Paolone, and F. Silvestro, An energy resource scheduler implemented in the automatic management system of a microgrid test facility in Proc. Int. Conf. Clean Elect. Power, Capri, Italy, May 21-23, 2007.