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Managing selection of wind power generation technologies

Abstract

The article presents the principled model for managing the selection of wind power generation technologies enabling business organizations to transform rationally their fossil fuel-based business models towards greater renewable energy reliance. The model is aimed at complex improvement of management of the evaluation, selection processes for public and private organizations keen on switching their business models towards greater use of wind power (including (or) other renewable energy-based technologies). The set of measures proposed has a pivotal focus on the economic utility of the latter with respect to balanced and sustained strategic development of business concerned. Accordingly, the model involves tools for solving the following tasks: setting up an evaluation unit revealing critical factors for rational execution of this task; contributing to situation analysis when determining wind power generation options and assessment criteria. In this respect, besides recommendations on managing data collection, the paper also provides a spectrum of criteria for measuring the attractiveness of wind power generation technologies in terms of economic utility. The latter allow to evaluate, compare possible options in a comprehensive and complex manner; improving assessment and selection task involving and rationally utilizing multi-criteria decision analysis measures including possibilities for combination of MCDA tools if needed. In the context of empirical investigations of the evolution of wind power generation technologies in the EU and globally over the last decade, the paper reveals the benefit of the use of the proposed model specifying all its phases to relevant techniques and actions. Results of its application in practice also confirm the prevailing flexibility when adjusting the model to the specifics of activities of public and private organizations as well as of economic sectors at state, county, and municipal levels.

Keyword : wind power, multi-criteria decision, management, sustainability, business development, model

How to Cite
Tamošiūnas, A. (2018). Managing selection of wind power generation technologies. Business: Theory and Practice, 19, 309-321. https://doi.org/10.3846/btp.2018.31
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Nov 29, 2018
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References

Ackermann F, Eden C (2001) SODA – journey making and mapping in practice. In: Rosenhead J, Mingers J (Eds) Rational analysis for a problematic world revisited: problem structuring methods for complexity, uncertainty and conflict (2nd ed). Chichester: John Wiley & Sons, 43-60.

ARUP (2011) Review of the generation costs and deployment potential of renewable electricity technologies in the UK. Report for UK DECC, 294 pp.

Bana e Costa C, De Corte J, Vansnick J (2012) MACBETH. International Journal of Information Technology and Decision Making 11 (2): 359-387. https://doi.org/10.1142/S0219622012400068

Behzadian MRB, Albadvi KA, Aghdasi M (2010) PROMETHEE: a comprehensive literature review on methodologies and applications. European Journal of Operational Research 200 (1): 198-215. https://doi.org/10.1016/j.ejor.2009.01.021

Brito AJ, Teixeira de Almeida A, Mota CMM (2010) A multi- criteria model for risk sorting of natural gas pipelines based on ELECTRE TRI integrating Utility Theory. European Journal of Operational Research 200 (3): 812-821. https://doi.org/10.1016/j.ejor.2009.01.016

Climate Policy Initiative (CPI) (2016) Policy and investment in Germany renewable energy. CPI, 84 pp.

Corrente S, Rui Figueira J, Greco S (2014) The SMAA-PROMET- HEE method. European Journal of Operational Research 239 (2): 514-522. https://doi.org/10.1016/j.ejor.2014.05.026

DOE (U.S. Department of Energy (DOE)) (2016) 2016 Wind Technologies Market Report. Lawrence Berkeley national laboratory, 2017, 82 pp.

DOE (2014) 2013 Wind Technologies Market Report. Lawrence Berkeley national laboratory, 2014, 82 pp.

DOE (2013) 2012 Wind Technologies Market Report. Lawrence Berkeley national laboratory, 2013, 80 pp.

European Commission (EC) (2011) The Energy Roadmap 2050. (COM (2011)885/2), 24 pp.

Eurostat 2018 News Release: Euro Indicators. Eurostat Press Office, 168/2018 – 30 October 2018. 2 p.

EC (2013) Conclusions on multiannual financial framework. No EUCO 37/13, 48 pp.

European Wind Energy Association (EWEA) (2017a) Financing and investments trends. The European Wind Industry in 2016. EWEA, Wind Europe, 25 pp.

EWEA (2017b) Wind in power. 2016 European statistics. EWEA, Wind Europe, 24 pp.

Gudauskas R, Kaklauskas A, Jokūbauskienė S, Targamadzė V, Budrytė L, Čerkauskas J, Kuzminskė A (2015) Advisory, negotiation and intelligent decision support system for leadership analysis. International Journal of Computers, Communications & Control (IJCCC) 10 (5): 667-677. https:// doi.org/10.1016/j.proeng.2015.10.022

Global Wind Energy Council (GWEC) (2016) Global Wind Energy Outlook. Global Wind Energy Council. GWEC, 44 pp.

GWEC (2015) Global wind report – annual market update 2015. GWEC, 73 pp.

GWEC (2014) Global wind report – annual market update 2014. GWEC, 77 pp.

Hurson Ch, Siskos Y (2014) A synergy of multicriteria techniques to assess additive value models. European Journal of Opera- tional Research 238 (2): 540-551. https://doi.org/10.1016/j.ejor.2014.03.047

International Monetary Fund (IMF) (2018) Commodity Market Monthly. June 2018, IMF, 24 pp.

Joint Research Centre (JRC) (2015) 2014 JRC wind status report. Technology, market and economic aspect of wind energy in Europe. Joint Research Centre, European Union, 92 pp.

Macharis C, Springael J, De Brucker Kl, Verbeke Al (2004) PRO- METHEE and AHP: the design of operational synergies in multicriteria analysis. Strengthening PROMETHEE with ideas of AHP. European Journal of Operational Research 153 (2): 307-317. https://doi.org/10.1016/S0377-2217(03)00153-X

Marková V, Lesníková P, Kaščáková A, Vinczeová M (2017) The present status of sustainability concept implementation by businesses in selected industries in the Slovak Republik. E&M Ekonomie a management = E&M Economics and Management 20 (3): 101-117. Liberec: Technická univerzita v Liberci. ISSN 1212-3609.

Masri HFBA, Houda A (2016) A recourse stochastic goal pro- gramming approach for the multi-objective stochastic vehicle routing problem. Journal of Multi-Criteria Decision Analysis 23 (1-2): 3-14. https://doi.org/10.1002/mcda.1563

Norese MF (2016) A model-based process to improve robustness in Multicriteria Decision Aiding interventions. Journal of Multi-Criteria Decision Analysis 23 (5-6): 183-196. https://doi.org/10.1002/mcda.1597

Omar MN, Fayek AR (2016) A topsis-based approach for priori- tized aggregation in multi-criteria decision-making problems. Journal of Multi-Criteria Decision Analysis 23 (5-6): 197-209. https://doi.org/10.1002/mcda.1561

Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research 156 (2): 445-455. https://doi.org/10.1016/S0377-2217(03)00020-1

Opricovic S, Tzeng G-H (2007) Extended VIKOR method in com- parison with outranking methods. European Journal of Operational Research 178 (2): 514-529. https://doi.org/10.1016/j.ejor.2006.01.020

Opricovic S (2011) Fuzzy VIKOR with an application to water resources planning. Expert Systems with Application 38 (10): 12983-12990. https://doi.org/10.1016/j.eswa.2011.04.097

Scholten L, Schuwirth N, Reichert P, Lienert J (2015) Tackling uncertainty in multi-criteria decision analysis – an application to water supply infrastructure planning. European Journal of Operational Research 242 (1): 243-260. https://doi.org/10.1016/j.ejor.2014.09.044

Statista (2016) Renewable energy worldwide. Statista, 49 pp.

Statista (2017a) Off shore wind power globally. Statista, 52 pp.

Statista (2017b) Renewable energy in Europe. Statista, 114 pp.

Statista (2017c) Wind energy industry in Europe. Statista, 75 pp.

Statista (2017d) Wind power in United States. Statista, 55 pp.

Statista (2017e) Renewable energy sources in United States. Statista, 63 pp.

Tamošiūnas A (2017) The integrative management model for restructuring Small and Medium-sized Enterprises (SME). E&M Ekonomie a management=E&M Economics and Mana- gement 20 (3): 36-51. Liberec: Technická univerzita v Liberci. ISSN 1212-3609. https://doi.org/10.15240/tul/001/2017-3-003

The United Nations Framework Convention on Climate Change (UNFCC) (2016) Paris Agreement. 2016.11.04, 27 pp.

US Bureau of Economic Analysis (2018) News Release, 30 August 2018, 11 pp.

Wind Power Database (WPD) 2017 Wind Power Market Update 2016. Wind Power Database, April 1st, 2017. France, 31 pp.

Winter M, Andersen ES, Elvin R, Levene R (2006) Focusing on business projects as an area for future research an explora- tory discussion of four different perspectives. International Journal of Project Management 24 (8): 699-709. https://doi. org/10.1016/j.ijproman.2006.08.005

Winter M, Szczepanek T (2008) Projects and programmes as value creation processes: a new perspective and some practical implications. International Journal of Project Management 26 (1): 95-103. https://doi.org/10.1016/j.ijproman.2007.08.015

Winter M, Szczepanek T (2009) Images of Projects. Gower Publishing, 264 pp.

Yang RJ, Zou PXW, Wang J (2016) Modelling stakeholder-as- sociated risk networks in green building projects. International Journal of Project Management 34: 66-81. https://doi.org/10.1016/j.ijproman.2015.09.010

Zeleny M (2010) Multiobjective optimization, systems design and De Novo programming. In: Zopounidis C, Pardalos PM (Eds) Handbook of Multicriteria Analysis. Berlin: Springer. https://doi.org/10.1007/978-3-540-92828-7_8

Zeleny, M (2011) Multiple criteria decision making (MCDM): from paradigm lost to paradigm regained? Journal of multi-criteria decision analysis 18: 77-90. https://doi.org/10.1002/ mcda.473

Zolfani SH, Maknoon R, Zavadskas EK (2014) Multiple nash equilibriums and evaluation of strategies. New application of MCDM methods. Journal of Business Economics and Management 16 (2): 290-306. https://doi.org/10.3846/16111699.2014.967715