Advertisement

Multi agent system based on law of gravity and fuzzy logic for coalition formation in multi micro-grids environment

  • Didi Omar El Amine
  • Jaouad Boumhidi
Original Research
  • 110 Downloads

Abstract

This paper presents an intelligent multi-agent system based on the law of gravity and fuzzy logic for the optimal management of the intra-MicroGrids energy. The aim is to overcome the weakness of the MicroGrid (MG) regarding the intermittent generation of its renewable source and its dependency to the main grid as the only reliable part in the power exchange. Taking into account that the power loss between each MG and the main grid is larger than that among the MGs, the aforementioned dependency causes high transfer loss. On the other hand, the failure of the single point of common coupling with the main grid makes the MG in a critical situation, especially when there is an internal lack in production. To deal with these problems, the proposed system allows the MGs which may be either generator or load to collaborate together in coalition form. So, the optimal energy distribution and power loss reduction are targeted by dynamically changing the size and the structure of coalitions of all participating MGs in the power exchange. Based on the law of gravity, the system allows the MGs that require energy to possibly specify the resources with the best energy offer and the lowest transfer loss. Besides and in order to deal with the multiple accesses to the same resources, a process based on fuzzy logic is made. Finally the JADE (JAVA Agent DEvelopment Framework) has been used and a comparison is made in order to show the impact of using the proposed system.

Keywords

Multi-agent systems (MAS) Fuzzy Inference System (FIS) Law of gravity Micro-grid (MG) Coalition formation Energy management 

References

  1. Abhilash K, Laura E, Gordon P, Wayne W (2015) Survey of multi-agent systems for microgrid control. Eng Appl Artif Intell 45:192–203. doi: 10.1016/j.engappai.2015.07.005 CrossRefGoogle Scholar
  2. Boltzheim P, Hamori B, Koczy LT (2001) Optimization of trapezoidal membership functions in a fuzzy rule system by the bacterial algorithm approach. Budapest University of Telecommunications and Economics, Department of Telecommunications and Telematics, Budapest, p 2001Google Scholar
  3. Chakraborty S, Nakamura S, Okabe T (2014) Scalable and optimal coalition formation of microgrids in a distribution system. In: IEEE PES Innovative Smart Grid Technologies, pp 1–6Google Scholar
  4. Chakraborty S, Nakamura S, Okabe T (2015) Real-time energy exchange strategy of optimally cooperative microgrids for scale-flexible distribution system. Expert Syst Appl 42:4643–4652. doi: 10.1016/j.eswa.2015.01.017 CrossRefGoogle Scholar
  5. Chao W, Zubair M, Kato N, Takeuchi A (2013) GT-CFS: a game theoretic coalition formulation strategy for reducing power loss in micro grids. IEEE Trans Parallel Distrib Syst 25:2307–2317. doi: 10.1109/TPDS.2013.178 Google Scholar
  6. Daneshvar M (2011) Programmable Trapezoidal and Gaussian membership function generator. J Basic Appl Sci Res 1(11):2073–2079Google Scholar
  7. Dayong Y, Minjie Z, Danny S (2015) Decentralised dispatch of distributed energy resources in smart grids via multi-agent coalition formation. J Parallel Distrib Comput 83:30–43. doi: 10.1016/j.jpdc.2015.04.004 CrossRefGoogle Scholar
  8. Elamine DO, Serraji M, Nfaoui EH, Boumhidi J (2015) Multi-agent system based on fuzzy control and prediction using NN for smart microgrid energy management. In: Intelligent Systems and Computer Vision (ISCV 2015), Fez, pp 1–6Google Scholar
  9. Elamine DO, Serraji M, Nfaoui EH, Boumhidi J (2016) Multi-agent architecture for optimal energy management of a smart micro-grid using a weighted hybrid BP-PSO algorithm for wind power prediction. Int J Technol Intell Plan 11:20–35. doi: 10.1504/IJTIP.2016.074228 Google Scholar
  10. Filippo B, Alessandro F, Meritxell V, Alex (2012) Decentralised stable coalition formation among energy consumers in the smart grid (demonstration). Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, June 04–08. Valencia, Spain, pp 1461–1462Google Scholar
  11. Foo Eddy YS, Gooi HB, Chen SX (2015) Multi-agent system for distributed management of microgrids. IEEE Trans On Power Syst 30:24–34. doi: 10.1109/TPWRS.2014.2322622 CrossRefGoogle Scholar
  12. Fu-Dong L, Min W, Yong H, Xin C (2012) Optimal control in microgrid using multi-agent reinforcement learning. ISA Trans 51:743–751. doi: 10.1016/j.isatra.2012.06.010 CrossRefGoogle Scholar
  13. Hernandez L, Baladron C, Aguiar JM, Carro B, Sanchez-Esguevillas AJ, Lioret J (2013) Short-term load forecasting for micro-grids based on artificial neural networks. Energies 6:1385–1408. doi: 10.3390/en6031385 CrossRefGoogle Scholar
  14. Jiang Q, Xue N, Geng G (2013) Energy management of microgrid in grid-connected and stand-alone modes. IEEE Trans Power Syst 28:3380–3389. doi: 10.1109/TPWRS.2013.2244104 CrossRefGoogle Scholar
  15. Kang D, Yoo W, Won S (2007) Multivariate TS fuzzy model identification based on mixture of Gaussians. In: Proc. of the International Conference on Control,Automation and Systems 2007, October 17–20, Seoul, KoreasGoogle Scholar
  16. Logenthiran T, Srinivasan D, Khambadkone AM, Aung HN (2010) Multi-agent system (MAS) for short-term generation scheduling of a microgrid in sustainable energy technologies. (ICSET) IEEE International Conference on, Sri Lanka, Kandy, pp 1–6Google Scholar
  17. Lu D, Kanchev H, Colas H, Lazarov V, Francois B (2011) Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications. IEEE Trans Ind Electron 58:4583–4592. doi: 10.1109/TIE.2011.2119451 CrossRefGoogle Scholar
  18. Machowski J, Bialek JR, Bumly JR (2008) Power systems dynamics: stability and control. Wiley, New York, p 2008Google Scholar
  19. Nguyen DT, Le LB (2013) Optimal energy management for cooperative microgrids with renewable energy resources. In: Proc. IEEE Smart Grid Com, pp 678–683Google Scholar
  20. Novak V (2005) Are fuzzy sets a reasonable tool for modeling vague phenomena? Fuzzy Sets Syst 156:341–348. doi: 10.1016/j.fss.2005.05.029 MathSciNetCrossRefzbMATHGoogle Scholar
  21. Peng L, Yang B, Chen Y, Abraham A (2009) Data gravitation based classification. Inf Sci 179:809–819. doi: 10.1016/j.ins.2008.11.007 CrossRefzbMATHGoogle Scholar
  22. Piagi P, Lasseter RH (2006) Autonomous control of microgrids. In: Proc. IEEE Power Eng. Soc. General MeetingGoogle Scholar
  23. Rashedi E, Nezamabadi S, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248. doi: 10.1016/j.ins.2009.03.004 CrossRefzbMATHGoogle Scholar
  24. Saad W, Zhu H, Poor H, Basar T (2011) Coalitional game theory for cooperative microgrid distribution networks. In: Proc. IEEE Int. Conf. Communications, Workshop on Smart Grid Communications, pp 1–5Google Scholar
  25. Saad W, Zhu H, Poor H, Basar T (2012) Game-theoretic methods for the smart grid: an overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Process Mag 29:86–105CrossRefGoogle Scholar
  26. Usman A, Shami SH (2013) Evolution of communication technologies for smart grid applications. Renew Sustain Energy Rev 19:191–199. doi: 10.1016/j.rser.2012.11.002 CrossRefGoogle Scholar
  27. Wu J, Guan X (2013) Coordinated multi-microgrids optimal control algorithm for smart distribution management system. IEEE Trans Smart Grid 4:2174–2181. doi: 10.1109/TSG.2013.2269481 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.LIIAN Laboratory, Departement of Computer Sciences, Faculty of Sciences Dhar MehrazSidi Mohammed Ben Abdellah UniversityFezMorocco

Personalised recommendations