Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation

Applied Energy

Volumn 185, Issue , 2017, Pages 862-871

  Baležentis, Tomas ^a   Streimikiene, Dalia ^b  

^a Lithuanian Institute of Agrarian Economics   (Lithuania)

^b MYKOLAS ROMERIS UNIVERSITY   (Lithuania)

Author keywords

Energy sector development scenarios; EU energy policy priorities; Integrated assessment models; Multi criteria decision making; Optimization; Sustainability

Indexed keywords

DECISION MAKING; ENERGY EFFICIENCY; ENERGY POLICY; GREENHOUSE GASES; INTELLIGENT SYSTEMS; OPTIMIZATION; SENSITIVITY ANALYSIS; SUSTAINABLE DEVELOPMENT; UNCERTAINTY ANALYSIS;

CONFLICTING OBJECTIVES; DEVELOPMENT SCENARIOS; ENERGY EFFICIENCY IMPROVEMENTS; EU ENERGY POLICIES; INTEGRATED APPLICATIONS; INTEGRATED ASSESSMENT MODELS; MULTI CRITERIA DECISION MAKING; TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTIONS;

MONTE CARLO METHODS;

ALTERNATIVE ENERGY; ASSESSMENT METHOD; DECISION MAKING; EMISSION; ENERGY EFFICIENCY; ENERGY PLANNING; ENERGY POLICY; EUROPEAN UNION; GREENHOUSE GAS; INTEGRATED APPROACH; MONTE CARLO ANALYSIS; SENSITIVITY ANALYSIS; SUSTAINABLE DEVELOPMENT;

EID: 84998538607     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.10.085     Document Type: Article

References (53)

1. [1] Hiremath, R.B., Shikha, S., Ravindranath, N.H., Decentralized energy planning; modeling and application: a review. Renew Sustain Energy Rev 11 (2007), 729–752.
2. [2] Diakoulaki, D., Karangelis, F., Multi-criteria decision analysis and cost–benefit analysis of alternative scenarios for the power generation sector in Greece. Renew Sustain Energy Rev 11:4 (2007), 716–727.
3. [3] Aras, H., Erdoğmuş, Ş., Koç, E., Multi-criteria selection for a wind observation station location using analytic hierarchy process. Renew Energy 29:8 (2004), 1383–1392.
4. [4] Ibáñez-Forés, V., Bovea, M.D., Pérez-Belis, V., A holistic review of applied methodologies for assessing and selecting the optimal technological alternative from a sustainability perspective. J Cleaner Prod 70 (2014), 259–281.
5. [5] Georgiou, D., Mohammed, E.S., Rozakis, S., Multi-criteria decision making on the energy supply configuration of autonomous desalination units. Renew Energy 75 (2015), 459–467.
6. [6] Mardani, A., Jusoh, A., Nor, K.M.D., Khalifah, Z., Zakwan, N., Valipour, A., Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Econ Res – Ekonomska Istrazivanja 28:1 (2015), 516–571.
7. [7] Mardani, A., Jusoh, A., Zavadskas, E.K., Cavallaro, F., Khalifah, Z., Sustainable and renewable energy: an overview of the application of multiple criteria decision making techniques and approaches. Sustainability 7:10 (2015), 13947–13984.
8. [8] Banos, R., Manzano-Agugliaro, F., Montoya, F.G., Gil, C., Alcayde, A., Gómez, J., Optimization methods applied to renewable and sustainable energy: a review. Renew Sustain Energy Rev 15:4 (2011), 1753–1766.
9. [9] Balezentiene, L., Streimikiene, D., Balezentis, T., Fuzzy decision support methodology for sustainable energy crop selection. Renew Sustain Energy Rev 17 (2013), 83–93.
10. [10] Lu, H.-F., Lin, B.-L., Campbell, D.E., Sagisaka, M., Ren, H., Interactions among energy consumption, economic development and greenhouse gas emissions in Japan after World War II. Renew Sustain Energy Rev 54 (2016), 1060–1072.
11. [11] Jansen, J.C., Seebregts, A.J., Long-term energy services security: what is it and how can it be measured and valued?. Energy Policy 38:4 (2010), 1654–1664.
12. [12] Goumas, M.G., Lygerou, V.A., Papayannakis, L.E., Computational methods for planning and evaluating geothermal energy projects. Energy Policy 27 (1999), 147–154.
13. [13] Lootsma, F., Boonekamp, P., Cooke, R., Van Oostvoorn, F., Choice of a long-term strategy for the national electricity supply via scenario analysis and multicriteria analysis. Eur J Oper Res 48 (1990), 189–203.
14. [14] Zhou, P., Ang, B.W., Poh, K.L., Decision analysis in energy and environmental modeling: an update. Energy 31 (2006), 2604–2622.
15. [15] Liu, J.Q.G., Lin, G.H., Huang, Q., Wu, H.P.Li., Energy systems planning and GHG-emission control under uncertainty in the province of Liaoning, China – a dynamic inexact energy systems optimization model. Electr Power Energy Syst 53 (2013), 142–158.
16. [16] Zhu, H., Huang, G.H., Huang, W.W., Planning of regional energy systems: an inexact mixed-integer fractional programming model. Appl Energy 113 (2014), 500–514.
17. [17] Zhang, S., Worrell, E., Crijns-Graus, W., Synergy of air pollutants and greenhouse gas emissions of Chinese industries: a critical assessment of energy models. Energy 93 (2015), 2436–2450.
18. [18] Kriegler, E., Petermann, N., Krey, V., Schwanitz, V.J., Luderer, G., Ashina, S., et al. Diagnostic indicators for integrated assessment models of climate policies. Technol Forecast Soc Chang 9 (2015), 45–61.
19. [19] Syri, S., Lehtilä, A., Ekholm, T., Savolainen, I., Holttinen, H., Peltola, E., Global energy and emissions scenarios for effective climate change mitigation—deterministic and stochastic scenarios with the TIAM model. Int J Greenhouse Gas Control 2:2 (2008), 274–285.
20. [20] Føyn, T.H.Y., Karlsson, K., Balyk, O., Grohnheit, P.E., A global renewable energy system: a modelling exercise in ETSAP/TIAM. Appl Energy 88:2 (2011), 526–534.
21. [21] Labriet, M., Kanudia, A., Loulou, R., Climate mitigation under an uncertain technology future: a TIAM-World analysis. Energy Econ 34 (2012), S366–S377.
22. [22] Gracceva, F., Zeniewski, P., Exploring the uncertainty around potential shale gas development–a global energy system analysis based on TIAM (TIMES Integrated Assessment Model). Energy 57 (2013), 443–457.
23. [23] Gracceva, F., Zeniewski, P., A systemic approach to assessing energy security in a low-carbon EU energy system. Appl Energy 123 (2014), 335–348.
24. 2 emission pathways to 2050: what role can renewables play?. Appl Energy 131 (2014), 79–86.
25. [25] Bosetti, V., Marangoni, G., Borgonovo, E., Anadon, L.D., Barron, R., McJeon, H.C., et al. Sensitivity to energy technology costs: a multi-model comparison analysis. Energy Policy 80 (2015), 244–263.
26. [26] Marcucci, A., Fragkos, P., Drivers of regional decarbonization through 2100: a multi-model decomposition analysis. Energy Econ 51 (2015), 111–124.
27. [27] Huang, J.P., Poh, K.L., Ang, B.W., Decision analysis in energy and environmental modeling. Energy 20:9 (1995), 843–855.
28. [28] Zhou, P., Ang, B.W., Poh, K.L., Decision analysis in energy and environmental modeling: an update. Energy 31:14 (2006), 2604–2622.
29. [29] Løken, E., Use of multicriteria decision analysis methods for energy planning problems. Renew Sustain Energy Rev 11:7 (2007), 1584–1595.
30. [30] Troldborg, M., Heslop, S., Hough, R.L., Assessing the sustainability of renewable energy technologies using multi-criteria analysis: suitability of approach for national-scale assessments and associated uncertainties. Renew Sustain Energy Rev 39 (2014), 1173–1184.
31. [31] Brand, B., Missaoui, R., Multi-criteria analysis of electricity generation mix scenarios in Tunisia. Renew Sustain Energy Rev 39 (2014), 251–261.
32. [32] Şengül, Ü., Eren, M., Shiraz, S.E., Gezder, V., Şengül, A.B., Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renew Energy 75 (2015), 617–625.
33. [33] Franco, C., Bojesen, M., Hougaard, J.L., Nielsen, K., A fuzzy approach to a multiple criteria and Geographical Information System for decision support on suitable locations for biogas plants. Appl Energy 140 (2015), 304–315.
34. [34] Yazdani-Chamzini, A., Majid Fouladgar, M., Zavadskas, E.K., Hamzeh Haji Moini, S., Selecting the optimal renewable energy using multi criteria decision making. J Bus Econ Manage 14:5 (2013), 957–978.
35. [35] Cobuloglu, H.I., Büyüktahtakın, İ.E., A stochastic multi-criteria decision analysis for sustainable biomass crop selection. Expert Syst Appl 42:15 (2015), 6065–6074.
36. [36] Madani, K., Lund, J.R., A Monte-Carlo game theoretic approach for multi-criteria decision making under uncertainty. Adv Water Resour 34 (2011), 607–616.
37. [37] Mateos, A., Jiménez, A., Ríos-Insua, S., Monte Carlo simulation techniques for group decision making with incomplete information. Eur J Oper Res 174:3 (2006), 1842–1864.
38. [38] Jiménez, A., Mateos, A., Ríos-Insua, S., Monte Carlo simulation techniques in a decision support system for group decision making. Group Decis Negot 14:2 (2005), 109–130.
39. [39] Fragkos, P., Kouvaritakis, N., Capros, P., Incorporating uncertainty into World energy modelling: the PROMETHEUS model. Environ Model Assess 20:5 (2015), 549–569.
40. [40] Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A., Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika – Electron Electr Eng 122:6 (2012), 3–6.
41. [41] Turskis, Z., Zavadskas, E.K., A new additive ratio assessment (ARAS) method in multi-criteria decision-making. Technol Econ Dev Econ 16:2 (2010), 159–172.
42. [42] Hwang, C.L., Yoon, K., Multiple attribute decision making methods and applications. 1981, Springer-Verlag, Berlin.
43. [43] Zavadskas, E.K., Turskis, Z., Vilutiene, T., Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method. Arch Civ Mech Eng 10:3 (2010), 123–141.
44. [44] Chakraborty, S., Zavadskas, E.K., Applications of WASPAS method in manufacturing decision making. Informatica 25:1 (2014), 1–20.
45. [45] Zavadskas, E.K., Antucheviciene, J., Hajiagha, S.H.R., Hashemi, S.S., Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF). Appl Soft Comput 24 (2014), 1013–1021.
46. [46] Brauers, W.K.M., Zavadskas, E.K., The MOORA method and its application to privatization in a transition economy. Contr Cybern 35 (2006), 445–469.
47. [47] Brauers, W.K.M., Zavadskas, E.K., Robustness in the Multimoora Model: the example of Tanzania. Transform Bus Econ 9:3 (2010), 67–84.
48. [48] Opricovic, S., Tzeng, G.H., Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156:2 (2004), 445–455.
49. [49] Antuchevičienė, J., Zavadskas, E.K., Zakarevičius, A., Multiple criteria construction management decisions considering relations between criteria. Technol Econ Dev Econ 16 (2010), 109–125.
50. [50] PLANETS. WP5. Final report on regional economic and energy implications of reaching global climate targets – a policy scenario analysis. Richard Loulou, editor; 2009.
51. [51] Streimikiene, D., Assessment of road transport technologies based on GHG emission reduction potential and costs. Transform Bus Econ 12:29 (2013), 138–148.
52. [52] Weyant, J., EMF 19 alternative technology strategies for climate change policy. Energy Econ 26:6 (2004), 501–756.
53. [53] Förster, H., Schumacher, K., De Cian, E., Hübler, M., Keppo, I., Mima, S., et al. European energy efficiency and decarbonization strategies beyond 2030—a sectoral multi-model decomposition. Clim Change Econ, 4(Suppl. 01), 2013, 1340004.