ORIGINAL PAPER
The impact of selected factors and access to mineral resources on the development of wind energy in Poland
 
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Silesian University of Technology
 
 
Submission date: 2023-09-03
 
 
Final revision date: 2024-01-09
 
 
Acceptance date: 2024-02-18
 
 
Publication date: 2024-03-27
 
 
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2024;40(1):187-212
 
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ABSTRACT
This article presents the results of research on the importance of access to critical raw materials for the development of wind energy in Poland. The authors have built a set of factors that can potentially influence this development. Twenty-four explanatory variables were taken into account, which were assigned to five categories. The amount of demand for mineral resources related to the development of wind technology was determined using a computer programwritten by the authors. The importance of individual factors was verified using the ARMAX model. As a result of this, it was possible to identify the explanatory variables that significantly affect the volume of wind energy production in Poland. The group of mineral resources includes critical metals that are necessary for the production of wind turbines. These are rare earth elements, copper, nickel, boron and manganese. The ARMAX model enables the examination of the relationship between the explained variable and the explanatory variables. Optimization of the model parameters was performed by limiting the mean square error. During the validation of the model, the VIF (variance inflation factor), Dickey-Fuller and Doornik-Hansen tests were used. The ARMAX validation also consisted of selecting the model characterized by the lowest value of information criteria and determining ex post errors, including the mean absolute percentage error (MAPE). In addition, the nature of individual independent variables was determined, i.e. whether they were stimulants, nominants, or destimulants. The forecast made it possible to verify the possibility of meeting the assumptions of the Polish Energy Policy until 2040. It showed that if the development trends of the factors that affect wind energy do not change, it would be possible to meet the assumptions of PEP2040 regarding the dynamic development of wind farms in Poland and double the generation capacity by 2030. Analysis using the ARMAX model showed that access to raw materials such as REE, Cu, Ni, Br and Mn would have a very significant impact on the development of wind energy in Poland. Each factor of the raw material category that was introduced into the model was considered statistically significant at the significance level of α = 0.01, i.e. at the lowest acceptable risk of error. Therefore, the raw material base would be of key importance to ensure access to wind energy at the level adopted in PEP2040.
ACKNOWLEDGEMENTS
The research leading to these results has received funding from the Norway Grants 2014–2021 via the National Centre for Research and Development. Grant number NOR/SGS/MOHMARER/0284/2020-00
METADATA IN OTHER LANGUAGES:
Polish
Wpływ wybranych czynników i dostępu do surowców mineralnych na rozwój energetyki wiatrowej w Polsce
energia wiatrowa, model ARMAX, prognozowanie, metale ziem rzadkich
W artykule zaprezentowano wyniki badań dotyczących znaczenia dostępu do surowców krytycznych dla rozwoju energetyki wiatrowej w Polsce. Autorzy zbudowali zbiór czynników, które potencjalnie mogą wpływać na ten rozwój. Pod uwagę wzięto 24 zmienne objaśniające, które przyporządkowano do pięciu kategorii. Wielkość zapotrzebowania na surowce mineralne w danym roku związane z rozbudową technologii wiatrowej wyznaczono z wykorzystaniem programu napisanego przez autorów. Znaczenie poszczególnych czynników zostało zweryfikowane z wykorzystaniem modelu ARMAX. Dzięki temu możliwe było wskazanie tych zmiennych objaśniających, które istotnie wpływają na wielkość produkcji energetyki wiatrowej w Polsce. Do grupy surowców mineralnych zaliczono metale krytyczne, które są niezbędne do wytwarzania turbin wiatrowych. Są to pierwiastki ziem rzadkich, miedź, nikiel, bor, mangan. Model ARMAX pozwala na zbadanie związku zmiennej objaśnianej ze zmiennymi objaśniającymi. Optymalizacja parametrów modelu była prowadzona na drodze ograniczania wielkości błędu średniokwadratowego. Podczas walidacji modelu posłużono się współczynnikiem VIF – variance inflation factor, testami Dickeya-Fullera oraz Doornika-Hansena. Walidacja ARMAX polegała także na wyborze modelu, których charakteryzuje najniższa wartość kryteriów informacyjnych oraz na wyznaczeniu błędów ex post między innymi błędu Mean Absolute Percentage Error (MAPE). Dodatkowo określono charakter poszczególnych zmiennych niezależnych, czyli ustalono czy są one stymulantami, nominantami, czy destymulantami. Wykonana prognoza umożliwiła zweryfikowanie możliwości spełnienia założeń Polityki Energetycznej Polski do 2040 roku. Prognoza wykazała, że jeśli nie zmienią się trendy rozwojowe czynników wpływających na energetykę wiatrową, możliwe będzie spełnienie założeń PEP2040 dotyczących dynamicznego rozwoju farm wiatrowych w Polsce i podwojenia mocy wytwórczych do 2030 roku. Analiza z wykorzystaniem modelu ARMAX pokazała, że dostęp do surowców takich jak REE, Cu, Ni, Br i Mn będzie miał bardzo istotny wpływ na rozwój energetyki wiatrowej w Polsce. Każdy czynnik kategorii surowców wprowadzony do modelu uznano za istotny statystycznie na poziomie istotności α = 0,01, czyli przy najniższym akceptowalnym ryzyku popełnienia błędu. Dlatego baza surowcowa będzie kluczowa dla zapewnienia dostępu do energetyki wiatrowej na poziomie przyjętym w PEP2040.
 
REFERENCES (64)
1.
Akçay, H. and Filik, T. 2017. Short-term wind speed forecasting by spectral analysis from long-term observations with missing values. Applied energy 191, pp. 653–662, DOI: 10.1016/j.apenergy.2017.01.063.
 
2.
Almorox et al. 2005 – Almorox, J., Benito, M. and Fernandez, C.H. 2005. Estimation of monthly Angström -Prescott equation coefficients from measured daily data in Toledo, Spain. Renewable Energy 30(6), pp. 931–936.
 
3.
Alves Dias et al. 2020 – Alves Dias, P., Bobba, S., Carrara, S. and Plazzotta, B. 2020. The role of rare earth elements in wind energy and electric mobility. DOI: 10.2760/303258.
 
4.
Ardente et al. 2008 – Ardente, F., Beccali, M., Cellura, M. and Brano, V. L. 2008. Energy performances and life cycle assessment of an Italian wind farm. Renewable and Sustainable Energy Reviews 12(1), pp. 200–217, DOI: 10.1016/j.rser.2006.05.013.
 
5.
Azad et al. 2014 – Azad, H.B., Mekhilef, S. and Ganapathy, V.G. 2014. Long-term wind speed forecasting and general pattern recognition using neural networks. IEEE Transactions on Sustainable Energy 5(2), pp. 546–553, DOI: 10.1109/TSTE.2014.2300150.
 
6.
Balaram, V. 2019. Rare earth elements: A review of applications, occurrence, exploration, analysis, recycling, and environmental impact. Geoscience Frontiers 10(4), pp. 1285–1303, DOI: 10.1016/j.gsf.2018.12.005.
 
7.
Ballinger et al. 2020 – Ballinger, B., Schmeda-Lopez, D., Kefford, B., Parkinson, B., Stringer, M., Greig, C. and Smart, S. 2020. The vulnerability of electric-vehicle and wind-turbine supply chains to the supply of rare-earth elements in a 2-degree scenario. Sustainable Production and consumption 22(1), pp. 68–76, DOI: 10.1016/j.spc.2020.02.005.
 
8.
Belsley et al. 1980 – Belsley, D.A., Kuh, E. and Welsh R.E. 1980. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley and Sons, DOI: 10.1002/0471725153.
 
9.
Bianco et al. 2009 – Bianco, V., Manca, O. and Nardini, S. 2009. Electricity consumption forecasting in Italy using linear regression models. Energy 34(9), pp. 1413–1421, DOI: 10.1016/j.energy.2009.06.034.
 
10.
Bliemel, F. 1973. Theil’s Forecast Accuracy Coefficient: A Clarification. Journal of Marketing Research 10(4), pp. 444–46.
 
11.
Blissett, R. S. and Rowson N. A. 2012. A review of the multi-component utilization of coal fly ash. Fuel 97, pp. 1–23, DOI: 10.1016/j.fuel.2012.03.024.
 
12.
BP 2023 – BP Statistical Review of World Energy. [Online:] https://www.bp.com/en/global/c... [Accessed: 2023-04-05].
 
13.
Burns, P. 2002. Robustness of the Ljung -Box test and its rank equivalent. Available at SSRN 443560.
 
14.
Carrara et al. 2020 – Carrara, S., Alves Dias, P., Plazzotta, B. and Pavel, C. 2020. Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system. EUR 30095 EN, Luxembourg: Publications Office of the European Union.
 
15.
Chai, T. and Draxler, R.R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development 7, pp. 1247–1250, DOI: 10.5194/gmd-7-1247-2014.
 
16.
COM 2023 – [Online:] COM/2023/160 https://eurlex.europa.eu/legal content/EN/TXT/?uri = CELEX%3A52023 PC0160 [Accessed: 2023-07-19].
 
17.
Crawford, R.H. 2009. Life cycle energy and greenhouse emissions analysis of wind turbines and the effect of size on energy yield. Renewable and Sustainable Energy Reviews 13(9), pp. 2653–2660, DOI: 10.1016/j.rser.2009.07.008.
 
18.
Daigle, B. and DeCarlo, S. 2021. Rare Earths and the US Electronics Sector: Supply Chain Developments and Trends. Office of Industries, US International Trade Commission.
 
19.
Darbellay, G.A. and Slama, M. 2000. Forecasting the short-term demand for electricity: Do neural networks stand a better chance? International Journal of Forecasting 16(1), pp. 71–83.
 
20.
Domański, C. and Szczepocki, P. 2020. Comparison of selected tests for univariate normality based on measures of moments. Statistics in Transition new series 21(5), pp. 151–178.
 
21.
EON – [Online:] https://eon.pl/dla-biznesu/fir... [Accessed: 2023-07-20] (in Polish).
 
22.
Farina, A. and Anctil, A. 2022. Material consumption and environmental impact of wind turbines in the USA and globally. Resources, Conservation and Recycling 176, 105938.
 
23.
Farnum, N.R. and Stanton, W. 1989. Quantitative Forecasting Methods. Boston: PWS-Kent Publishing Company.
 
24.
Franus et al. 2015 – Franus, W., Wiatros-Motyka, M.M. and Wdowin, M. 2015. Coal fly ash as a resource for rare earth elements. Environmental Science and Pollution Research 22, pp. 9464-9474, DOI: 10.1007/s11356-015-4111-9.
 
25.
Forbes, K.F. and Zampelli, E.M. 2019. Wind energy, the price of carbon allowances, and CO2 emissions: Evidence from Ireland. Energy Policy 133, DOI: 10.1016/j.enpol.2019.07.007.
 
26.
Geoportal 2023 – [Online:] http://geoportal.pgi.gov.pl/cs... [Accessed: 2023-11-10].
 
27.
Gil et al. 2010 – Gil, A., De La Torre, M., Domínguez, T. and Rivas, R. 2010. Influence of wind energy forecast in deterministic and probabilistic sizing of reserves. [In:] 9th International Workshop on Large-Scale Integration of Wind Power Into Power Systems As Well As on Transmission Networks for Offshore Wind Power Plants. Quebec, 18–19 October 2010.
 
28.
Greco et al. 2011 – Greco, A., Mistry, K., Sista, V., Eryilmaz, O. and Erdemir, A. 2011. Friction and wear behavior of boron based surface treatment and nano-particle lubricant additives for wind turbine gearbox applications. Wear 271(9–10), pp. 1754–1760, DOI: 10.1016/j.wear.2010.11.060.
 
29.
GS 2023 – [Online:] https://www.goldmansachs.com/i... [Accessed: 2023-11-10].
 
30.
Ha et al. 2019 – Ha, S., Tae, S. and Kim, R. 2019. Energy demand forecast models for commercial buildings in South Korea. Energies 12(12), DOI: 10.3390/en12122313.
 
31.
Hickey et al. 2012 – Hickey, E., Loomis, D.G., and Mohammadi, H. 2012. Forecasting hourly electricity prices using ARMAX-GARCH models: An application to MISO hubs. Energy Economics 34(1), pp. 307–315, DOI: 10.1016/j.eneco.2011.11.011.
 
32.
Huang et al. 2011 – Huang, C.Y., Liu, Y.W., Tzeng, W.C., and Wang, P.Y. 2011. Short term wind speed predictions by using the gray prediction model based forecast method. [In:] IEEE Green Technologies Conference (IEEE-Green) (pp. 1–5).
 
33.
IBM 2023 – [Online:] https://www.ibm.com/docs/pl/sp... = sales-collinearity-diagnostics [Accessed: 2023-11-15].
 
34.
Imholte et al. 2018 – Imholte, D.D., Nguyen, R.T., Vedantam, A., Brown, M., Iyer, A., Smith, B.J., Collins, J.W., Anderson, C.G. and O’Kelley, B. 2018. An assessment of US rare earth availability for supporting US wind energy growth targets. Energy Policy 113, pp. 294–305, DOI: 10.1016/j.enpol.2017.11.001.
 
35.
Jaroni et al. 2019 – Jaroni, M.S., Bernd, F. and Peter, L. 2019. Economical feasibility of rare earth mining outside China. Minerals 9(10), DOI: 10.3390/min9100576.
 
36.
Kalvig, P. and Machacek, E. 2018. Examining the rare-earth elements (REE) supply–demand balance for future global wind power scenarios. GEUS Bulletin 41, pp. 87–90, DOI: 10.34194/geusb.v41.4350.
 
37.
Kamani, D. and Ardehali, M.M. 2023. Long-term forecast of electrical energy consumption with considerations for solar and wind energy sources. Energy 268, DOI: 10.1016/j.energy.2023.126617.
 
38.
KGHM 2021 – [Online:] https://kghm.com/pl/wstepne-wy... [Accessed: 2023-11-11].
 
39.
Kramer, O., and Gieseke, F. 2011. Short-term wind energy forecasting using support vector regression. [In:] Soft computing models in industrial and environmental applications. 6th International Conference SOCO 2011. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 271–280.
 
40.
Kufel, T. 2004. Econometrics. Solving problems using the Gretl program (Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL). Warszawa: PWN (in Polish).
 
41.
Kumari, S.S. 2008. Multicollinearity: Estimation and elimination. Journal of Contemporary research in Management 3(1), pp. 87–95.
 
42.
Lee, C.K. 2008. Corrosion and wear-corrosion resistance properties of electroless Ni-P coatings on GFRP composite in wind turbine blades. Surface and Coatings Technology 202(19), pp. 4868–4874, DOI: 10.1016/j.surfcoat.2008.04.079.
 
43.
Li et al. 2022– Li, C., Mogollón, J. M., Tukker, A., Dong, J., von Terzi, D., Zhang, C. and Steubing, B. 2022. Future material requirements for global sustainable offshore wind energy development. Renewable and Sustainable Energy Reviews 164, DOI: 10.1016/j.rser.2022.112603.
 
44.
Mehedintu et al. 2018 – Mehedintu, A., Sterpu, M. and Soava, G. 2018. Estimation and forecasts for the share of renewable energy consumption in final energy consumption by 2020 in the European Union. Sustainability 10(5), DOI: 10.3390/su10051515.
 
45.
Mesa-Jiménez et al. 2023 – Mesa-Jiménez, J.J., Tzianoumis, A.L., Stokes, L., Yang, Q., and Livina, V.N. 2023. Long-term wind and solar energy generation forecasts, and optimization of Power Purchase Agreements. Energy Reports 9(2), pp. 292–302, DOI: 10.1016/j.egyr.2022.11.175.
 
46.
Miles, J. 2014. Tolerance and variance inflation factor. DOI: 10.1002/9781118445112.STAT06593.
 
47.
Niu, M. and Li, G. 2022. The Impact of Climate Change Risks on Residential Consumption in China: Evidence from ARMAX Modeling and Granger Causality Analysis. International Journal of Environmental Research and Public Health 19(19), DOI: 10.3390/ijerph191912088.
 
48.
Paul, R.K. 2006. Multicollinearity: Causes, effects, and remedies. IASRI, New Delhi 1(1), pp. 58–65.
 
49.
PEP 2040 – The Energy Policy of Poland until 2040. [Online:] https://www.gov.pl/web/klimat/... [Accessed: 2023-0401].
 
50.
Piłatowska, M. 2010. Information Criteria in Model Selection (Kryteria informacyjne w wyborze modelu ekonometrycznego). Studies and Works of the Cracow University of Economics 10, pp. 25–37. (in Polish).
 
51.
Ren et al. 2021 – Ren, K., Tang, X., Wang, P., Willerström, J. and Höök, M. 2021. Bridging energy and metal sustainability: insights from China’s wind power development up to 2050. Energy 227, DOI: 10.1016/j.energy.2021.120524.
 
52.
RNP 2023. [Online:] https://zpp.net.pl/wpcontent/u... [Accessed: 2023-04-10].
 
53.
Rybak, A. and Rybak, A.2021. Characteristics of some selected methods of rare earth elements recovery from coal fly ashes. Metals 11(1), DOI: 10.3390/met11010142.
 
54.
Safi, S. and Zeroual, A. 2002. Prediction of global daily solar radiation using higher order statistics. Renewable Energy 27(4), pp. 647–666, DOI: 10.1016/S0960-1481(01)00153-7.
 
55.
Schader et al. 2003 – Schader, M., Gaul, W.A. and Vichi, M. 2003. Between Data Science and Applied Data Analysis. Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation, Springer Berlin.
 
56.
Shabbir et al. 2019 – Shabbir, N., AhmadiAhangar, R., Kütt, L., Iqbal, M.N. and Rosin, A. 2019. Forecasting short term wind energy generation using machine learning. [In:] IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) (pp. 1–4). IEEE.
 
57.
Shammugam et al. 2019 – Shammugam, S., Gervais, E., Schlegl, T. and Rathgeber, A. 2019. Raw metal needs and supply risks for the development of wind energy in Germany until 2050. Journal of Cleaner Production 221(9), pp. 738–752, DOI: 10.1016/j.jclepro.2019.02.223.
 
58.
Tao et al. 2022 – Yue Tao, Lu Shen, Chong Feng, Rongyi Yang, Jianhua Qu, Hanxun Ju, Ying Zhang. 2022. Distribution of rare earth elements (REEs) and their roles in plant growth: A review. Environmental Pollution 298, DOI: 10.1016/j.envpol.2021.118540.
 
59.
USAID 2021 – [Online:] https://www.land-links.org/wp-... [Accessed: 2023-07-25].
 
60.
Verma et al. 2022 – Verma, S., Paul, A.R. and Haque, N. 2022. Assessment of materials and rare earth metals demand for sustainable wind energy growth in India. Minerals 12(5), DOI: 10.3390/min12050647.
 
61.
Wetherill et al. 1986 – Wetherill, G.B., Duncombe, P., Kenward, M., Köllerström, J., Paul, S.R., Vowden, B.J. and Vowden, B.J. 1986. Heteroscedasticity and serial correlation. Regression Analysis with Applications, pp. 199–214.
 
62.
Willmott, C.J. and Matsuura, K. 2005. Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research 30(1) , pp. 79–82, DOI: 10.3354/cr030079.
 
63.
Witkowska-Kita et al. 2016 – Witkowska-Kita, B., Blaschke, W., Biel, K. and Orlicka, A. 2016. Management of the Non-Energy Raw Materials in Poland; Critical, Strategic and Deficit Natural Resources (Gospodarka surowcami nieenergetycznymi w Polsce – surowce mineralne krytyczne, strategiczne i deficytowe). Przegląd Górniczy 72(3), pp. 76–84 (in Polish).
 
64.
Yousefi et al. 2019 – Yousefi, H., Abbaspour, A. and Seraj, H. 2019. Worldwide development of wind energy and co2 emission reduction. Environmental Energy and Economic Research 3(1), pp. 1–9, DOI: 10.22097/EEER.2019.164295.1064.
 
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