Challenges in the wind turbines location process in Central Europe – The use of spatial decision support systems

Renewable and Sustainable Energy Reviews

Volumn 76, Issue , 2017, Pages 425-433

  Kazak, Jan ^a   van Hoof, Joost ^b   Szewranski, Szymon ^a  

^a WROC AW UNIVERSITY OF ENVIRONMENTAL AND LIFE SCIENCES   (Poland)

^b FONTYS UNIVERSITY OF APPLIED SCIENCES   (Netherlands)

Author keywords

Renewable energy location; Spatial decision support system; Spatial econometrics; Wind turbine

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION MAKING; DECISION SUPPORT SYSTEMS; ECONOMICS; ENERGY POLICY; INVESTMENTS; LOCATION; STATISTICS; WIND POWER;

CENTRAL EUROPE; DECISION-MAKING PROCESS; DEVELOPMENT PROGRAMMES; ENERGY PRODUCTIONS; LOCATION FACTORS; RENEWABLE ENERGIES; RENEWABLE ENERGY LOCATION; SPATIAL DECISION SUPPORT SYSTEMS; SPATIAL ECONOMETRICS; WESTERN COUNTRIES;

WIND TURBINES;

EID: 85015794680     PISSN: 13640321     EISSN: 18790690     Source Type: Journal    
DOI: 10.1016/j.rser.2017.03.039     Document Type: Review

References (81)

1. [1] European Commission. Commission staff working paper. Analysis of options beyond 20% GHG emission reductions: Member state result; February 2012. 〈 http://ec.europa.eu/clima/policies/strategies/2020/docs/swd_2012_5_en.pdf〉. [Access date: 10 December 2015].
2. [2] European Council. Conclusions on 2030 climate and energy policy framework; October 2014. 〈 http://www.consilium.europa.eu/uedocs/cms_data/docs/pressdata/en/ec/145356.pdf〉 [Access date: 10 December 2015].
3. [3] Eurostat. Renewable energy statistics; May 2015. 〈 http://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statistics〉. [Access date: 10 December 2015].
4. [4] Szałata Ł. Odnawialne źródła energii drogą do trwałego ekorozwoju oraz zwiększenia efektywności energetycznej Dolnego Śląska. Wrocław; 2013.
5. [5] Ministerstwo Gospodarki. Prognoza zapotrzebowania na paliwa i energię do 2030 roku; 2009.
6. [6] Kumar, A., Sah, B., Singh, A.R., Deng, Y., He, X., Kumar, P., et al. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain Energy Rev 69 (2017), 596–609.
7. [7] 〈 http://www.eea.europa.eu/data-and-maps/data/urban-atlas〉. [Access date: 30 November 2015].
8. [8] Law, M., Collins, A., Getting to know ArcGIS for desktop, 4th ed., 2015, ESRI Press, Redlands.
9. [9] Walker, D., Daniels, T., The planners guide to CommunityViz: the essential tool for a new generation of planning. 2011, Planners Press, American Planning Association, Chicago.
10. [10] Bayazıt, Y., Bakış, R., Koç, C., An investigation of small scale hydropower plants using the geographic information system. Renew Sustain Energy Rev 67 (2017), 289–294.
11. [11] Lu, X., Wang, S., A GIS-based assessment of Tibet's potential for pumped hydropower energy storage. Renew Sustain Energy Rev 69 (2017), 1045–1054.
12. [12] Abaei, M.M., Arzaghi, E., Abbassi, R., Garaniya, V., Penesis, I., Developing a novel risk-based methodology for multi-criteria decision making in marine renewable energy applications. Renew Energy 102 (2017), 341–348.
13. [13] Kienast, F., Huber, N., Hergert, R., Bolliger, J., Moran, L.S., Hersperger, A.M., Conflicts between decentralized renewable electricity production and landscape services – a spatially-explicit quantitative assessment for Switzerland. Renew Sustain Energy Rev 67 (2017), 397–407.
14. [14] Noorollahi, Y., Arjenaki, H.G., Ghasempour, R., Thermo-economic modeling and GIS-based spatial data analysis of ground source heat pump systems for regional shallow geothermal mapping. Renew Sustain Energy Rev 72 (2017), 648–660.
15. [15] Jahangiri, M., Ghaderi, R., Haghani, A., Nematollahi, O., Finding the best locations for establishment of solar-wind power stations in Middle-East using GIS: a review. Renew Sustain Energy Rev 66 (2016), 38–52.
16. [16] Anwarzai, M.A., Nagasaka, K., Utility-scale implementable potential of wind and solar energies for Afghanistan using GIS multi-criteria decision analysis. Renew Sustain Energy Rev 71 (2017), 150–160, 10.1016/j.rser.2016.12.048 ISSN: 1364-0321 〈http://www.sciencedirect.com/science/article/pii/S1364032116311029〉.
17. [17] European Commission. Communication from the commission to the european parliament and the council The Paris Protocol – A blueprint for tackling global climate change beyond 2020; February 2015. 〈 http://ec.europa.eu/clima/policies/international/paris_protocol/docs/com_2015_81_en.pdf〉. [Access date: 10 December 2015].
18. [18] Lorenc, H., Struktura i zasoby energetyczne wiatru w Polsce. 1996, IMiGW, Warszawa.
19. [19] Adaramola, M.S., Oyewola, O.M., Evaluating the performance of wind turbines in selected locations in Oyo state, Nigeria. Renew Energy 36 (2011), 3297–3304.
20. [20] Katsigiannis, Y., Stavrakakis, G., Estimation of wind energy production in various sites in Australia for different wind turbine classes: a comparative technical and economic assessment. Renew Energy 67 (2014), 230–236.
21. [21] Chandel, S., Ramasany, P., Murthy, K., Wind power potential assessment of 12 locations in western Himalayan region of India. Renew Sustain Energy Rev 39 (2014), 530–545.
22. [22] Ouammi, A., Dagdougui, H., Sacile, R., Mimet, A., Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy). Renew Sustain Energy Rev 14 (2010), 1959–1968.
23. [23] Al-Yahyaia, S., Charabi, Y., Gastli, A., Al.-Alawi, S., Assessment of wind energy potential locations in Oman using data from existing weather stations. Renew Sustain Energy Rev 14 (2010), 1428–1436.
24. [24] Mirhosseini, M., Sharifi, F., Sedaghat, A., Assessing the wind energy potential locations in province of Semnan in Iran. Renew Sustain Energy Rev 15 (2011), 449–459.
25. [25] González, J., Payán, M., Santos, J., González-Longatt, F., A review and recent developments in the optimal wind-turbine micro-siting problem. Renew Sustain Energy Rev 30 (2014), 133–144.
26. [26] Quan, P., Leephakpreeda, T., Assessment of wind energy potential for selecting wind turbines: an application to Thailand. Sustain Energy Technol Assess 11 (2015), 17–26.
27. [27] Wojewódzkie Biuro Urbanistyczne we Wrocławiu. Studium przestrzennych uwarunkowań rozwoju energetyki wiatrowej w województwie dolnośląskim. Wrocław; 2009.
28. [28] Supreme Audit Office. Lokalizacja i budowa lądowych farm wiatrowych. Warszawa; 2014. 〈 https://www.nik.gov.pl/plik/id,7128,vp,9004.pdf〉. [Access date: 10 December 2015].
29. [29] Morton, M.S., Management decision systems; computer-based support for decision making. 1971, Harvard University, Boston.
30. [30] Power DJ. Decision support systems: a historical overview. In: Handbook on decision support systems 1, International handbooks information system. Springer Berlin Heidelberg; 2008. p. 121–40.
31. [31] Tripathi, K.P., Decision support system is a tool for making better decisions in the organization. Indian. J Comput Sci Eng 2 (2011), 112–117.
32. [32] Druzdzel, M., Flynn, R., Decision support systems. 2002, Marcel Dekker, New York [Access date: 10 December 2015] http://www.pitt.edu/~druzdzel/psfiles/dss.pdf.
33. [33] Keenan, P.B., Spatial decision support systems. Mora, M., Forgionne, G., Gupta, J.N.D., (eds.) Decision making support systems: achievements and challenges for the new decade, 2003, Idea Group, 28–39.
34. [34] Crossland, M.D., Wynne, B.E., Perkins, W.C., Spatial decision support systems: an overview of technology and a test of efficacy. Decis Support Syst 14:3 (1995), 219–235.
35. [35] Pontius, R. Jr., Si, K., Spatial decision support systems. Wright, J.D., (eds.), Second edition International encyclopedia of the social & behavioral sciences, 2015, 2015, Elsevier, Oxford, 136–141.
36. [36] Kim, H.Y., Lee, H.K., Enhanced validity and reliability of spatial decision support systems (SDSS) for sustainable transportation decision-making. Appl Geogr 51 (2014), 65–71.
37. [37] Coutinho-Rodrigues, J., Simão, A., Antunes, C.H., A GIS-based multicriteria spatial decision support system for planning urban infrastructures. Decis Support Syst 51 (2011), 720–726.
38. [38] Mokgonyana, L., Zhang, J., Li, H., Hu, Y., Optimal location and capacity planning for distributed generation with independent power production and self-generation. Appl Energy 188 (2017), 140–150.
39. [39] Silva, S., Alcada-Almeida, L., Dias, L.C., Development of a web-based multi-criteria spatial decision support system for the assessment of environmental sustainability of dairy farms. Comput Electron Agric Comput Electron Agric 108 (2014), 46–57.
40. [40] Bottero, M., Comino, E., Duriavig, M., Ferretti, V., Pomarico, S., The application of a multicriteria spatial decision support system (MCSDSS) for the assessment of biodiversity conservation in the Province of Varese (Italy). Land Use Policy 30 (2013), 730–738.
41. [41] Cheng, C., Multi-choice goal programming model for the optimal location of renewable energy facilities. Renew Sustain Energy Rev 41 (2015), 379–389.
42. [42] Sánchez-Lozano, J., Teruel-Solano, J., Soto-Elvira, P., García-Cascales, M., Geographical information systems (GIS) and multi-criteria decision making (MCDM) methods for the evaluation of solar farms locations: case study in south-eastern Spain. Renew Sustain Energy Rev 24 (2013), 544–556.
43. [43] Wanderer, T., Herle, S., Creating a spatial multi-criteria decision support system for energy related integrated environmental impact assessment. Environ Impact Assess Rev 2015:52 (2015), 2–8.
44. [44] Cristóbal, J., A goal programming model for the optimal mix and location of renewable energy plants in the north of Spain. Renew Sustain Energy Rev 16 (2012), 4461–4464.
45. [45] Gorsevski P, Cathcart S, Mirzaei G, Jamali M, Ye X, Gomezdelcampo E. A group-based spatial decision support system for wind farm site selection in Northwest Ohio. Energy policy, special section: Long run transitions to sustainable economic structures in the European Union and beyond. 55; 2013. p. 374–85.
46. [46] Azadeh, A., Rahimi-Golkhandan, A., Moghaddam, M., Location optimization of wind power generation–transmission systems under uncertainty using hierarchical fuzzy DEA: a case study. Renew Sustain Energy Rev 30 (2014), 877–885.
47. [47] Baležentis, T., Streimikiene, D., Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation. Appl Energy 185 (2017), 862–871.
48. [48] Skiba, M., Mrówczyńska, M., Bazan-Krzywoszańska, A., Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: the town of Zielona Góra. Appl Energy 188 (2017), 356–366.
49. [49] Wu, Q., Ren, H., Gao, W., Ren, J., Benefit allocation for distributed energy network participants applying game theory based solutions. Energy 119 (2017), 384–391.
50. [50] Alipour, M., Alighaleh, S., Hafezi, R., Omranievardi, M., A new hybrid decision framework for prioritizing funding allocation to Iran's energy sector. Energy 121 (2017), 388–402.
51. [51] Omrani, H., Drobinski, P., Arsouze, T., Bastin, S., Lebeaupin-Brossier, C., Mailler, S., Spatial and temporal variability of wind energy resource and production over the North Western Mediterranean Sea: sensitivity to air-Sea interactions. Renew Energy 101 (2017), 680–689.
52. [52] Lunney, E., Ban, M., Duic, N., Foley, A., A state-of-the-art review and feasibility analysis of high altitude wind power in Northern Ireland. Renew Sustain Energy Rev 68 (2017), 899–911.
53. [53] Mohamed, B., Fadela, B., Mounir, K., Optimization of the wind turbines location in Kaberten wind farm in Algeria. Energy Procedia 74 (2015), 122–129.
54. [54] Ali, E.S., Abd Elazim, S.M., Abdelaziz, A.Y., Ant lion optimization algorithm for optimal location and sizing of renewable distributed generations. Renew Energy 101 (2017), 1311–1324.
55. [55] Parkinson, S.C., Makowski, M., Krey, V., Sedraoui, K., Almasoud, A.H., Djilali, N., A multi-criteria model analysis framework for assessing integrated water-energy system transformation pathways. Appl Energy, 2017, 10.1016/j.apenergy.2016.12.142 ISSN: 0306-2619 〈http://www.sciencedirect.com/science/article/pii/S0306261916319250〉.
56. [56] Crichton, F., Petrie, K., Health complaints and wind turbines: the efficacy of explaining the nocebo response to reduce symptom reporting. Environ Res 140 (2015), 449–455.
57. [57] Danish Energy Agency. Wind turbines in Denmark. 2009. 〈 http://www.ens.dk/sites/ens.dk/files/dokumenter/publikationer/downloads/wind_turbines_in_denmark.pdf〉. [Access date: 11 December 2015].
58. [58] Vilar, C., Amarıs, H., Usaola, J., Assessment of flicker limits compliance for wind energy conversion system in the frequency domain. Renew Energy 31 (2006), 1089–1106.
59. [59] Girbau-Llistuella, F., Sumper, A., Díaz-González, F., Galceran-Arellano, S., Flicker mitigation by reactive power control in wind farm with doubly fed induction generators. Int J Electr Power 55 (2014), 285–296.
60. [60] Onakpoya, I., O'Sullivan, J., Thompson, M., Heneghan, C., The effect of wind turbine noise on sleep and quality of life: a systematic review and meta-analysis of observational studies. Environ Int 82 (2015), 1–9.
61. [61] Hanning C. Sleep disturbance and wind turbine noise. June; 2009.
62. [62] Himri, Y., Rehman, S., Draoui, B., Himri, S., Wind power potential assessment for three locations in Algeria. Renew Sustain Energy Rev 12 (2008), 2495–2504.
63. [63] Shaahid, S., Al-Hadhrami, L., Rahman, M., Economic feasibility of development of wind power plants in coastal locations of Saudi Arabia – a review. Renew Sustain Energy Rev 19 (2013), 589–597.
64. [64] Ministry of the Environment. Polish national strategy for adaptation to climate change 2020 (NAS 2020) with the perspective by 2030. Warsaw: 2013. 〈 https://klimada.mos.gov.pl/wp-content/uploads/2014/12/ENG_SPA2020_final.pdf〉. [Access date: 10 December 2015].
65. [65] Al Zohbi, G., Hendrick, P., Bouillard, P., Evaluation of the impact of wind farms on birds: the case study of Lebanon. Renew Energy 80 (2015), 682–689.
66. [66] Marques, A.T., Batalha, H., Rodrigues, S., Costa, H., Pereira, M.J.R., Fonseca, C., et al. Understanding bird collisions at wind farms: an updated review on the causes and possible mitigation strategies. Biol Conserv 179 (2014), 40–52.
67. [67] Wang, S., Wang, S., Smith, P., Ecological impacts of wind farms on birds: questions, hypotheses, and research needs. Renew Sustain Energy Rev 44 (2015), 599–607.
68. [68] Millon, L., Julien, J., Julliard, R., Kerbiriou, C., Bat activity in intensively farmed landscapes with wind turbines and offset measures. Ecol Eng 75 (2015), 250–257.
69. [69] Jameson, J., Willis, C., Activity of tree bats at anthropogenic tall structures: implications for mortality of bats at wind turbines. Anim Behav 97 (2014), 145–152.
70. [70] 〈 http://www.eea.europa.eu/themes/biodiversity/interactive/natura-2000-european-protected-areas〉. [Access date: 10 December 2015].
71. [71] Firestone, J., Bates, A., Knapp, L.A., See me, feel me, touch me, heal me: wind turbines, culture, landscapes, and sound impressions. Land Use Policy 46 (2015), 241–249.
72. [72] Van der Horst D, Toke D. Exploring the landscape of wind farm developments; local area characteristics and planning process outcomes in rural England. Land use policy, forest transitions wind power planning, landscapes and publics. 27; 2010. p. 214–21.
73. [73] Betakova, V., Vojar, J., Sklenicka, P., Wind turbines location: how many and How far?. Appl Energy 151 (2015), 23–31.
74. [74] Maehr, A., Watts, G., Hanratty, J., Talmi, D., Emotional response to images of wind turbines: a psychophysiological study of their visual impact on the landscape. Landsc Urban Plan 142 (2015), 71–79.
75. [75] Gigović, L., Pamučar, D., Božanić, D., Ljubojević, S., Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: a case study of Vojvodina, Serbia. Renew Energy 103 (2017), 501–521.
76. [76] Dalabeeh, A.S.K., Techno-economic analysis of wind power generation for selected locations in Jordan. Renew Energy 101 (2017), 1369–1378.
77. [77] Jalali, L., Bigelow, P., McColl, S., Majowicz, S., Gohari, M., Waterhouse, R., Changes in quality of life and perceptions of general health before and after operation of wind turbines. Environ Pollut 216 (2016), 608–615.
78. [78] Mittal, P., Mitra, K., Kulkarni, K., Optimizing the number and locations of turbines in a wind farm addressing energy-noise trade-off: a hybrid approach. Energy Convers Manag 132 (2017), 147–160.
79. [79] Zohbi, G.A., Hendrick, P., Bouillard, P., Evaluation of the impact of wind farms on birds: the case study of Lebanon. Renew Energy 80 (2015), 682–689.
80. [80] Griffiths, S., A review and assessment of energy policy in the Middle East and North Africa region. Energy Policy 102 (2017), 249–269.
81. [81] Ke, M.-T., Yeh, C.-H., Su, C.-J., Cloud computing platform for real-time measurement and verification of energy performance. Appl Energy 188 (2017), 497–507.