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A comprehensive techno-economic analysis for optimally placed wind farms

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Abstract

Wind power project development investment is based on the separate technical and financial analyses. Based on the actual wind data, data-based wind distribution map and wake effect model, a combined techno-economic analysis is proposed in this paper. Starting from deriving the wind distribution map, a comprehensive analysis extending to the feasibility assessment of the project is presented here. The problem is formulated as the maximization of net present value of the project subject to the specified initial investment cost within a fixed area and turbine spacing constraints. Simultaneous optimization of the wind turbine size, hub height and placement is realized with BPSO-TVAC. Sensitivity analysis and Monte Carlo simulation are used to investigate the feasibility of the project, against various parameters, imposed on by the techno-economic constraints. Hypothesis testing with a confidence level of 99.99% corroborates the results obtained from Monte Carlo simulation. With scenario analysis, a positive NPV is identified even in the worst-case scenario, an attractive trait for investors. An ideal decision-making tool considering technical efficiency and profitability simultaneously is presented.

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References

  1. Patel MR (2006) Wind and solar power system, 2nd edn. Taylor & Francis, New York, pp 81–82

    Google Scholar 

  2. Pookpunt S, Ongsakul W (2016) Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand. Energy Convers Manag 108:160–180

    Article  Google Scholar 

  3. Emami A, Noghreh P (2010) New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renew Energy 35:1559–1564

    Article  Google Scholar 

  4. Khanali M, Ahmadzadegan S, Omid M, Nasab F Keyhani, Chau KW (2018) Optimizing layout of wind farm turbines using genetic algorithms in Tehran province, Iran. Int J Energy Environ Eng 9(4):399–411

    Article  Google Scholar 

  5. Evangelopoulos VA, Georgilakis PS (2014) Optimal distributed generation placement under uncertainties based on point estimate method embedded genetic algorithm. IET Gen Transm Distrib 8(3):389–400

    Google Scholar 

  6. Kirchner-Bossi N, Porté-Agel F (2018) Realistic wind farm layout optimization through genetic algorithms using a Gaussian wake model. Energies 11(12):1

    Article  Google Scholar 

  7. Chen Y, Li H, He B, Wang P, Jin K (2015) Multi-objective genetic algorithm based innovative wind farm layout optimization method. Energy Convers Manag 105:1318–1327

    Article  Google Scholar 

  8. Pookpunt S, Ongsakul W (2013) Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients. Renew Energy 55:266–276

    Article  Google Scholar 

  9. Guirguis D, Romero DA, Amon CH (2016) Toward efficient optimization of wind farm layouts: utilizing exact gradient information. Appl Energy 179:110–123

    Article  Google Scholar 

  10. Park J, Law KH (2015) Layout optimization for maximizing wind farm power production using sequential convex programming. Appl Energy 151:320–334

    Article  Google Scholar 

  11. Pillai AC, Chick J, Khorasanchi M, Barbouchi S, Johanning L (2017) Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm. Ocean Eng 139:287–297

    Article  Google Scholar 

  12. MirHassani S, Yarahmadi A (2017) Wind farm layout optimization under uncertainty. Renew Energy 107:288–297

    Article  Google Scholar 

  13. Wang L, Cholette ME, Zhou Y, Yuan J, Tan AC, Gu Y (2018) Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization. Renew Energy 126:819–829

    Article  Google Scholar 

  14. Amaral L, Castro R (2017) Offshore wind farm layout optimization regarding wake effects and electrical losses. Eng Appl Artif Intell 60:26–34

    Article  Google Scholar 

  15. Zeljko D, Milulovic J (2012) Assessment of the wind energy resource in the South Banat region, Serbia. Renew Sustain Energy Rev 16:3014–3023

    Article  Google Scholar 

  16. Shata A S Ahmed, Hanitsch R (2006) Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt. Renew Energy 31:1183–1202

    Article  Google Scholar 

  17. Serrano-Canalejo C, Sarrias-Mena R, García-Triviño P, Fernández-Ramírez LM (2019) Energy management system design and economic feasibility evaluation for a hybrid wind power/pumped hydroelectric power plant. IEEE Lat Am Trans 17(10):1686–1693

    Article  Google Scholar 

  18. Gul M, Tai N, Huang W, Nadeem MH, Yu M (2019) Assessment of wind power potential and economic analysis at Hyderabad in Pakistan: Powering to local communities using wind power. Sustain 11(5):1

    Article  Google Scholar 

  19. Chaurasiya PK, Kumar VK, Warudkar V, Ahmed S (2019) Evaluation of wind energy potential and estimation of wind turbine characteristics for two different sites. Int J Ambient Energy 1:1–11

    Article  Google Scholar 

  20. Brogna R, Feng J, Sørensen JN, Shen WZ, Porté-Agel F (2020) A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain. Appl Energy 259:114189

    Article  Google Scholar 

  21. Song D et al (2020) Optimal design of wind turbines on high-altitude sites based on improved Yin–Yang pair optimization. Energy 193:116794

    Article  Google Scholar 

  22. SW, PDJ, HT (1995) A Manual for the Economic Evaluation of Energy Efficiency and Renewable Energy Technologies. NREL Tech Rep NREL/TP-462-5173

  23. González JS, Rodríguez ÁGG, Mora JC, Payán M Burgos, Santos JR (2011) Overall design optimization of wind farms. Renew Energy 36:1973–1982

    Article  Google Scholar 

  24. Mora EB, Spelling J, van der Weijde AH, Pavageau E-M (2019) The effects of mean wind speed uncertainty on project finance debt sizing for offshore wind farms. Appl Energy 252:113419

    Article  Google Scholar 

  25. Shin H, Baldick R (2018) Mitigating market risk for wind power providers via financial risk exchange. Energy Econ 71:344–358

    Article  Google Scholar 

  26. Judge F et al (2019) A lifecycle financial analysis model for offshore wind farms. Renew Sustain Energy Rev 103:370–383

    Article  Google Scholar 

  27. Afanasyeva S, Saari J, Kalkofen M, Partanen J, Pyrhönen O (2016) Technical, economic and uncertainty modelling of a wind farm project. Energy Convers Manag 107:22–33

    Article  Google Scholar 

  28. Pookpunt S, Ongsakul W (2016) Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand. Energy Convers Manag 108:160–180

    Article  Google Scholar 

  29. Jensen NO (1983) A Note on Wind Generator Interaction. Riso National Laboratory, Riso National Laboratory RISO-M-2411, Nov 1983

  30. Sun H, Yang H (2020) Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model. Renew Energy 147:192–203

    Article  Google Scholar 

  31. Pierrot M (2005) Wind turbines and wind farms database. http://www.thewindpower.net/manuturb_turbines_en.php

  32. Ananjavanich P (2015) Thailand: Renewable Energy Policy Update. In: Chrometzka T (ed) New Power Development Plan announced in May (Status May 2015). German Federal Ministry for Economic Affairs and Energy: German Federal Ministry for Economic Affairs and Energy, p 4

  33. Service D. o. t. T. I. R. (2015) How To Depreciate Property. vol Publication 946, D. o. t. T. I. R. Service, Ed., ed. Department of the Treasury Internal Revenue Service: Department of the Treasury Internal Revenue Service, p 114

  34. Saeed MA, Ahmed Z, Yang J, Zhang W (2020) An optimal approach of wind power assessment using Chebyshev metric for determining the Weibull distribution parameters. Sustain Energy Technol Assessm 37:100612

    Google Scholar 

  35. Eurek K, Sullivan P, Gleason M, Hettinger D, Heimiller D, Lopez A (2017) An improved global wind resource estimate for integrated assessment models. Energy Econ 64:552–567

    Article  Google Scholar 

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Correspondence to Weerakorn Ongsakul.

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Pookpunt, S., Ongsakul, W. & Madhu, N. A comprehensive techno-economic analysis for optimally placed wind farms. Electr Eng 102, 2161–2179 (2020). https://doi.org/10.1007/s00202-020-01014-6

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  • DOI: https://doi.org/10.1007/s00202-020-01014-6

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