Benchmarking Economical Efficiency of Renewable Energy Power Plants: A Data Envelopment Analysis Approach

Abstract:

This paper implements Data Envelopment Analysis (DEA) to calculate an efficiency measure index of 21 energy power plants that use different technologies, including both renewable and conventional types. Super-efficiency measurements are used to generate a ranking of plants based on their efficiency score that can be used to select among alternatives. It is also showed how DEA can also be adopted to estimate the amount of financial subsidies necessary to make a renewable energy plant as efficient as a conventional energy plant, by calculating the extent to which inefficient power plants over-utilize specific inputs or under-produce outputs.

• Periodical: Advanced Materials Research (Volume 772)
• Pages: 699-704
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.772.699
• Online since: September 2013
• Authors: Corrado lo Storto, Gabriella Ferruzzi
• Keywords: Benchmarking, Data Envelopment Analysis (DEA), Efficiency, Ranking, Renewable Energy, Selection, Subsidies

References

[1] T. Melchiora and R. Madlener, Economic evaluation of IGCC plants with hot gas cleaning, Applied Energy, Vol. 97 (2012), p.170–184.

DOI: 10.1016/j.apenergy.2012.02.065

[2] J.A. Matelli, E. Bazzo and J. C. da Silva, Development of a case-based reasoning prototype for cogeneration plant design, Applied Energy, Vol. 88 (2011), p.3030–3041.

DOI: 10.1016/j.apenergy.2011.03.006

[3] H.A. Naim and M.G. Carvalho, Multi-criteria assessment of new and renewable energy power plants, Energy, Vol. 27 (2002), p.739–755.

DOI: 10.1016/s0360-5442(02)00019-1

[4] J.R. San Cristóbal, A multi criteria data envelopment analysis model to evaluate the efficiency of the Renewable Energy technologies, Renewable Energy, Vol. 36 (2011), pp.2742-2746.

DOI: 10.1016/j.renene.2011.03.008

[5] R. Madlener, C.H. Antunes and L. C. Dias, Assessing the performance of biogas plants with multi-criteria and data envelopment analysis, European Journal of Operational Research, Vol. 197 (2009), p.1084–1094.

DOI: 10.1016/j.ejor.2007.12.051

[6] B.A. Akash, R. Mamlook and M. S. Mohsen, Multi-criteria selection of electric power plants using analytical hierarchy process, Electric Power Systems Research, Vol. 52 (1999), p.29–35.

DOI: 10.1016/s0378-7796(99)00004-8

[7] A. Datta, A. Ray, G. Bhattacharya and H. Saha, Green energy sources (GES) selection based on multi-criteria decision analysis (MCDA), International Journal of Energy Sector Management, Vol. 5 (2007), p.271 – 286.

DOI: 10.1108/17506221111146020

[8] J.J. Wang, Y. Y Jing, C.F. Zhang and J.H. Zhao, Review on multi-criteria decision analysis aid in sustainable energy decision-making, Renewable and Sustainable Energy Reviews, Vol. 13 (2009), p.2263–2278.

DOI: 10.1016/j.rser.2009.06.021

[9] A.A. Salo and R.P. Hamalainen, On the measurement of preferences in the analytic hierarchy process, Journal of Multi-Criteria Decision Analysis, Vol. 6 (1997), pp.309-319.

DOI: 10.1002/(sici)1099-1360(199711)6:6<309::aid-mcda163>3.0.co;2-2

[10] W. De Keyser and P. Peeters, A note on the use of PROMETHEE multicriteria methods, European Journal of Operational Research, Vol. 89 (1996), pp.457-461.

DOI: 10.1016/0377-2217(94)00307-6

[11] A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, European Journal of Operational Research, Vol. 2 (1978), pp.429-444.

DOI: 10.1016/0377-2217(78)90138-8

[12] P. Andersen and N.C. Petersen, A Procedure for Ranking Efficient Units in Data Envelopment Analysis, Management Science, Vol. 39 (1993), p.1261–1264.

DOI: 10.1287/mnsc.39.10.1261

[13] A. L'Abbate, G. Fulli., F. Starr. and S.D. Peteves, Distributed Power Generation in Europe, Tech. Rep. JRC41239 (EC Joint Research Centre Institute for Energy, 2008).

[14] F. Rossi, Gestione dei sistemi elettrici nei mercati liberalizzati (ESI, Italy 2007).