Advertisement

Comparison of Bat and Fuzzy Clusterization for Identification of Suitable Locations for a Small-Scale Hydropower Plant

  • Mrinmoy MajumderEmail author
Chapter

Abstract

Hydroelectric plants are an environmentally friendly renewable energy source, but, due to uncertainties in flow patterns, often such energy generation projects fail. Also, deliberations from displaced people and environmental activists (due to large-scale disturbances to the natural ecosystems of adjacent areas) make some highly efficient hydropower projects unfeasible. That is why the success of hydropower projects depends largely on the selection of location. Currently, the efficiency of selecting the ideal locations depends mainly on expert opinion or linear models and other decision-making methodologies where human judgment and opinion play a major role in the reliability of the selection. But, as usual, the error rate in such procedures is generally unsatisfactory. The present study tries to apply clusterization algorithms to identify ideal locations for small hydropower plants in such a way that the need for expert or opinion can be reduced. In the clusterization of a suitable hydropower location, the food foraging behavior of bats and fuzzy-logic-based theory of maximization were applied to a sample population of locations available for hydropower generation including a on where a hydropower plant had already been installed and was operating at rated capacity. The efficiency of the algorithm in identifying this location was analyzed to determine the suitability of the algorithms in estimating the ideal location for hydropower plants. The results showed that both approaches were able to identify the most suitable location, but when the time taken to make the identification was taken into account, fuzzy logic was found to perform better than the bat algorithm as the former took only one iteration to identify the location, whereas the latter needed six iterations but the sensitivity with which the algorithms identified the ideal location was better for bat than fuzzy.

Keywords

Clusterization Hydropower location selection Fuzzy logic Bat algorithm 

References

  1. Cyr JF, Landry M, Gagnon Y (2011) Methodology for the large-scale assessment of small hydroelectric potential: application to the province of New Brunswick (Canada). Renew Energy 36(11):2940–2950. ISSN 0960-1481,  10.1016/j.renene.2011.04.003. http://www.sciencedirect.com/science/article/pii/S0960148111001753
  2. Dudhani S, Sinha AK, Inamdar SS (2006) Assessment of small hydropower potential using remote sensing data for sustainable development in India. Energy Policy 34(17):3195–3205. ISSN 0301-4215,  10.1016/j.enpol.2005.06.011. http://www.sciencedirect.com/science/article/pii/S0301421505001667
  3. Fang Y, Deng W (2011) The critical scale and section management of cascade hydropower exploitation in Southwestern China. Energy 36(10):5944–5953. ISSN 0360-5442,  10.1016/j.energy.2011.08.022. http://www.sciencedirect.com/science/article/pii/S0360544211005524
  4. Ghadimi AA, Razavi F, Mohammadian B (2011) Determining optimum location and capacity for micro hydropower plants in Lorestan province in Iran. Renew Sustain Energy Rev 15(8):4125–4131. ISSN 1364-0321,  10.1016/j.rser.2011.07.003. http://www.sciencedirect.com/science/article/pii/S1364032111002371
  5. Kusre BC, Baruah DC, Bordoloi PK, Patra SC (2010) Assessment of hydropower potential using GIS and hydrological modeling technique in Kopili River basin in Assam (India). Appl Energy 87(1):298–309. ISSN 0306-2619,  10.1016/j.apenergy.2009.07.019. http://www.sciencedirect.com/science/article/pii/S0306261909003109
  6. Larentis DG, Collischonn W, Olivera F, Tucci CEM (2010) Gis-based procedures for hydropower potential spotting. Energy 35(10):4237–4243. ISSN 0360-5442,  10.1016/j.energy.2010.07.014. http://www.sciencedirect.com/science/article/pii/S0360544210003786 Google Scholar
  7. Nunes V, Genta JL (1996) Micro and mini hydroelectric power assessment in Uruguay. Renew Energy 9(1–4):1235–1238. ISSN 0960-1481,  10.1016/0960-1481(96)88499-0. http://www.sciencedirect.com/science/article/pii/0960148196884990
  8. REN21 (2011a) Renewables 2011: global status report. p 17. http://www.ren21.net/Portals/97/documents/GSR/GSR2011_Master18.pdf
  9. REN21 (2011b) Renewables 2011: global status report. p 18. http://www.ren21.net/Portals/97/documents/GSR/GSR2011_Master18.pdf
  10. Rojanamon P, Chaisomphob T, Bureekul T (2009) Application of geographical information system to site selection of small run-of-river hydropower project by considering engineering/economic/environmental criteria and social impact. Renew Sustain Energy Rev 13(9):2336–2348. ISSN 1364-0321,  10.1016/j.rser.2009.07.003. http://www.sciencedirect.com/science/article/pii/S1364032109001373
  11. Supriyasilp T, Pongput K, Boonyasirikul T (2009) Hydropower development priority using MCDM method. Energy Policy 37(5):1866–1875. ISSN 0301-4215,  10.1016/j.enpol.2009.01.023. http://www.sciencedirect.com/science/article/pii/S0301421509000391 Google Scholar
  12. World Energy Assessment, UNDP (1998) http://www.urjaglobal.in/indian_energy.html
  13. Yang X-S (2010) A new Metaheuristic Bat-Inspired Algorithm. In: Gonzalez JR et al (eds) Nature inspired cooperative strategies for optimization (NISCO 2010), Studies in computational intelligence, vol 284. Springer, Berlin, pp 65–74. http://arxiv.org/abs/1004.4170
  14. Yi CS, Lee JH, Shim MP (2010) Site location analysis for small hydropower using geo-spatial information system. Renew Energy 35(4):852–861. ISSN 0960-1481,  10.1016/j.renene.2009.08.003. http://www.sciencedirect.com/science/article/pii/S0960148109003462

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Hydro-Informatics EngineeringNational Institute of Technology Agartala, BarjalaJiraniaIndia

Personalised recommendations