Alternative Energy Sources and Their Analysis as Investment Opportunities: A Case of the Czech Republic

Document Type : Review Paper

Authors

Department of Econometrics, University of Economics, Prague, Czech Republic

Abstract

Alternative energy sources and their efficient usage are currently a significant and widely discussed problem in all countries. The paper deals with the analysis of possible energy sources and their evaluation using multiple criteria decision making (MCDM) methods in specific conditions of the Czech Republic. The analysis aims at decision what alternative energy sources are suitable best for possible investments. The analysis uses TESES (technical, economic, social, ecological, strategic) classification of criteria. The total number of criteria considered in evaluation is 16, and five alternative renewable sources are defined. The evaluation is based on the crisp data set that describes the current situation in the Czech Republic. The most often applied MCDM methods (analytic hierarchy process, TOPSIS, and PROMETHEE class method, and the weighted sum approach) are used to compare the results. The differences in the obtained results by all methods are compared and discussed in detail. The main contribution of the study consists of a demonstration of applicability various decision-making techniques in the analysis of alternative energy sources. The results can be generalized not only for the specific conditions of the Czech Republic.

Keywords


Ahmad, S. and Tahar, R.M., (2014). Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: A case of Malaysia. Renewable Energy, Vol. 63, pp. 458-466.
Amini, A. and Alinezhad, A., 2017. Integrating DEA and Group AHP for Efficiency Evaluation and the Identification of the Most Efficient DMU. International Journal of Supply and Operations Management, Vol. 4 (4), pp. 318-327.
Amini, A., Alinezhad, A. and Salmanian, S., 2016. The Development of Data Envelopment Analysis for the Performance Evaluation of Green Supply Chain with Undesirable Outputs. International Journal of Supply and Operations Management, Vol. 3 (2), pp.1267-1283.
Barahona, I., Cavazos, J. and Yang, J.-B., 2014. Modelling the level of adoption of analytical tools. An implementation of multicriteria evidential reasoning. International Journal of Supply and Operations Management, Vol. 1(2), pp. 129-151.
Barros C.P., 2008. Efficiency analysis of hydro-electric generating plants: A case study for Portugal. Energy Economics, Vol. 30 (1), pp. 59-75.
Beranovsky, J., 2002.  Application of multiple criteria decision-making in system planning of renewable energy sources (in Czech).  Ekowatt, Praha.
Beynon M.J. and Wells P., 2008. The lean improvement of the chemical emissions of motor vehicles based on preference ranking: a PROMETHEE uncertainty analysis. Omega, Vol. 36 (3), pp. 384-394.
Brans J.P., Vincke P. and Mareschal B., 1986. How to select and how to rank projects: The PROMETHEE method. European Journal for Operational Research, Vol. 24 (2), pp. 228-238.
HakimiAsl, M., Amalnick, M., Zorriassatine, F. and HakimiAsl, A., 2016. Green Supplier Evaluation by Using an Integrated Fuzzy AHP- VIKOR Approach. International Journal of Supply and Operations Management, Vol. 3(2), pp. 1284-1300.
Chatzimouratidis A.I. and Pilavachi P.A., 2007. Objective and subjective evaluation of power plants and their non-radioactive emissions using analytic hierarchy process. Energy Policy, Vol. 35(8), pp. 4027-4037.
Chatzimouratidis A.I. and Pilavachi P.A., 2008. Multicriteria evaluation of power plants impact on the living standard using the analytic hierarchy process. Energy Policy, Vol. 36(3), pp. 1074-1089.
Chatzimouratidis A.I. and Pilavachi P.A., 2009a. Technological, economic and sustainability evaluation of power plants using the analytic hierarchy process. Energy Policy, Vol. 37, pp. 778-787.
Chatzimouratidis A.I. and Pilavachi P.A., 2009b. Sensitivity analysis of technological, economic and sustainability evaluation of power plants using the analytic hierarchy process. Energy Policy, Vol. 37, pp. 788-798.
Chatzimouratidis A.I. and Pilavachi P.A., 2012. Decision support systems for power plants impact on the living standard. Energy conversion and management, Vol. 64, pp. 182-198.
Dombi M., Kuti I. and Balogh P., 2014. Sustainability assessment of renewable power and heat generation technologies. Energy Policy, Vol. 67, pp. 264-271.
Ghasempour, R.,  Alhuyi Nazari, M., Ebrahimi, M., Ahmadi, M. H. and Hadiyanto, H. (2019). Multi-Criteria Decision Making (MCDM) Approach for Selecting Solar Plants Site and Technology: A Review. International Journal of Renewable Energy Development, Vol. 8(1), pp. 15-25.
HakimiAsl, M., Amalnick, M.S., Zorriassatine, F. and HakimiAsl, A., 2016. Green Supplier Evaluation by Using an Integrated Fuzzy AHP- VIKOR Approach. International Journal of Supply and Operations Management, Vol. 3(2), pp. 1284-1300.
Hwang C.L. and Yoon K., 1981. Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
Inglesi-Lotz R., 2016. The impact of renewable energy consumption to economic growth: A panel data application. Energy Economics, Vol. 53, pp. 58-63.   
Iskin I., Daim T., Kayakutlu G. and Altuntas M., 2012. Exploring renewable energy pricing with analytic network process — Comparing a developed and a developing economy. Energy Economics, Vol. 34(4), pp. 882-891.   
Jablonsky J., 2014. MS Excel based software support tools for decision problems with multiple criteria. Procedia Economics and Finance, Vol. 12, pp. 251-258.
Khalili, J. and Alinezhad, A., 2018. Performance evaluation in green supply chain using BSC, DEA, and Data Mining. International Journal of Supply and Operations Management, Vol. 5(2), pp. 182-191. 
Lee S.K., Mogi G. and Kim J.W., 2008. The competitiveness of Korea as a developer of hydrogen energy technology: the AHP approach. Energy Policy, Vol. 36(4), pp. 1284-1291.  
Li Ch., Hayes D.J. and Jacobs K.L., 2018. Biomass for bioenergy: Optimal collection mechanisms and pricing when feedstock supply does not equal availability. Energy Economics, Vol. 76, pp. 403-410.
Li H. and Sun J., 2009. Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II. European Journal of Operational Research, Vol. 197(1), pp. 214-224.
Mai T., Bistline J., Sun Y., Cole W. and Young D., 2018. The role of input assumptions and model structures in projections of variable renewable energy: A multi-model perspective of the U.S. electricity system. Energy Economics, Vol. 76, pp. 313-324.
Madhuri, Yadav, S. and Hiwarkar, A.D., 2017. Selection of Appropriate Renewable Energy Resources for Uttar Pradesh by using Analytical Hierarchy Process (AHP). International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6(2), pp. 
Mattmann M., Logar I. and Brouwer R., 2016. Hydropower externalities: A meta-analysis. Energy Economics, Vol. 57, pp. 66-77.
Nazam, M., Xu, J., Tao, Z., Ahmad, J. and Hashim, M. (2015). A fuzzy AHP-TOPSIS Framework for the Risk Assessment of Green Supply Chain Implementation in the Textile Industry. International Journal of Supply and Operations Management, Vol. 2(1), pp. 548-568.
Neto D.P., Domingues E.G., Coimbra A.P., de Almeida A.T. and Calixto W.P., 2017. Portfolio optimization of renewable energy assets: Hydro, wind, and photovoltaic energy in the regulated market in Brazil. Energy Economics, Vol. 64, pp. 238-250.   
Papalexandrou M.A., Pilavachi P.A. and Chatzimouratidis A.I., 2008. Evaluation of liquid biofuels using the analytic hierarchy process. Safety and Environmental Protection, Vol. 86, pp. 360-374.
Pilavachi P.A., Chatzipanagi A.I. and Spyropoulou A.I., 2009. Evaluation of hydrogen production methods using the Analytic Hierarchy Process. International Journal of Hydrogen Energy, Vol. 34, pp. 5294-5303.
Reboredo J.C. and Ugolini A., 2018. The impact of Twitter sentiment on renewable energy stocks. Energy Economics, Vol. 76, pp. 153-169.   
Saaty T.L., 1990. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. RWS Publications, Pittsburgh.
Satapathy, S. (2019). An Analysis of Sustainable Supply Chain Management in Thermal Power Plants: Sustainable Supply Chain Management for Customer Satisfaction in the Thermal Power Sector. Managing Operations Throughout Global Supply Chains, Jean C. Essila (ed.), Northern Michigan University.
Smith, A.D. 2013. Successful green-based initiatives among large corporate entities: a case study from a stakeholder perspective. International Journal of Services and Operations Management, Vol. 14, pp. 95-114.
Sueyoshi T. and Goto M., 2014. Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis. Energy Economics, Vol. 42, pp. 271-288.   
Talinli I., Topuz E., Aydin E. and Kabakcı S., 2011. A Holistic Approach for Wind Farm Site Selection by Using FAHP. Wind Farm – Technical Regulations, Potential Estimation and Siting Assessment, Gaston Orlando Suvire (Ed.), InTech. 
Tavana M., Behzadian M., Pirdashti M. and Pirdashti H., 2013. A PROMETHEE-GDSS for oil and gas pipeline planning in the Caspian Sea basin. Energy Economics, Vol. 36, pp. 716-728.   
Troster V., Shahbaz M. and Uddin G.S., 2018. Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis. Energy Economics, Vol. 70, pp. 440-452.   
Vego G., Kucar-Dragicevic S. and Koprivana N., 2008. Application of multi-criteria decision-making on strategic municipal solid waste management in Dalmatia. Croatia Waste Management, Vol. 28 (11), pp. 2192-2201.
Wang B., Kocaoglu D.F., Daim T.U. and Yang J., 2010. A decision model for energy resource selection in China. Energy Policy, Vol. 38, pp. 7130-7141.
Wang B., Nistor I., Murty T. and Wei Y., 2014. Efficiency assessment of hydro-electric power plants in Canada: A multi criteria decision making approach. Energy Economics, Vol. 46, pp. 112-121.   
Winebrake J.J. and Creswick B.P., 2003. The future of hydrogen fuelling systems for transportation: an application of perspective-based scenario analysis using the analytic hierarchy process. Technology Forecasting and Social Change, Vol. 70(4), pp. 359-384.