The contribution of hydropower in meeting electric energy needs of MINT countries

Abstract

Mexico, Indonesia, Nigeria, and Turkey (collectively referred to as MINT) are fast-growing developing countries with the potential to achieve large and technologically advanced economies in the coming decades. Energy policies must be implemented to enable them to meet their increasing energy needs effectively and efficiently without threatening environmental health. Such policies will require realistic energy projections. The main goal of this paper is to develop hydroelectric energy generation (HEG) and total electricity production (TEP) projections for MINT countries to determine the contribution of HEG to the future TEP of MINT countries. Artificial neural networks (ANNs) trained by the arithmetic optimization algorithm (AOA) is used to develop HEG and TEP prediction models. Models were developed based on socio-economic, demographic, and energy indicators. To examine model performance accuracy, results obtained from ANN-AOA models were compared with results from ANN-BP (back propagation) and ANN-Jaya models. According to error criteria, ANN-AOA models outperformed these other models with both training and test sets. Hence, ANN-AOA technique was used to develop HEG and TEP projections. According to the present projections, the total HEG and HEP values of MINT countries in 2040 is expected to increase by 55.6% and 44.4% compared to 2022, reaching 215.8 and 1530.3 TWh, respectively. In addition, while the contribution of HEG to TEP was 56.9% in 2021, this value will decrease to 51.6% in 2040. The findings of this study suggest that current hydroelectric energy investments of MINT countries are not sufficient to achieve renewable energy targets. Consequently, recommendations are included herein for the energy policies of MINT countries.

Keywords

• : MINT countries
• Hydropower generation
• AOA algorithm
• Jaya algorithm
• Neural networks

MINT ülkelerinin elektrik enerjisi ihtiyaçlarının karşılanmasında hidroelektrik enerjinin katkısı

Öz

Meksika, Endonezya, Nijerya ve Türkiye (toplu olarak MINT olarak anılacaktır), önümüzdeki on yıllarda büyük ve teknolojik olarak gelişmiş ekonomilere ulaşma potansiyeline sahip, hızla büyüyen gelişmekte olan ülkelerdir. Çevre sağlığını tehdit etmeden artan enerji ihtiyaçlarını etkili ve verimli bir şekilde karşılayabilmeleri için enerji politikaları uygulanmalıdır. Bu politikalar gerçekçi enerji projeksiyonları gerektirecektir. Bu çalışmanın temel amacı, MINT ülkeleri için hidroelektrik enerji üretimi (HEG) ve toplam elektrik üretimi (TEP) projeksiyonları geliştirerek HEG'nin MINT ülkelerinin gelecekteki TEP'sine katkısını belirlemektir. Aritmetik optimizasyon algoritması (AOA) ile eğitilen yapay sinir ağları (YSA), HEG ve TEP tahmin modellerini geliştirmek için kullanılmıştır. Modeller sosyoekonomik, demografik ve enerji göstergelerine dayalı olarak geliştirilmiştir. Model performans doğruluğunu incelemek için, ANN-AOA modellerinden elde edilen sonuçlar, ANN-BP (geri yayılım) ve ANN-Jaya modellerinden elde edilen sonuçlarla karşılaştırılmıştır. Hata kriterlerine göre, ANN-AOA modelleri hem eğitim hem de test kümelerinde bu diğer modellerden daha iyi performans göstermiştir. Bu nedenle, HEG ve TEP projeksiyonlarını geliştirmek için ANN-AOA tekniği kullanılmıştır. Mevcut projeksiyonlara göre, MINT ülkelerinin toplam HEG ve HEP değerlerinin 2040 yılında 2022'ye kıyasla %55,6 ve %44,4 artarak sırasıyla 215,8 ve 1530,3 TWh'ye ulaşması beklenmektedir. Ayrıca, HEG'nin TEP'e katkısı 2021'de %56,9 iken, bu değer 2040 yılında %51,6'ya düşecektir. Bu çalışmanın bulguları, MINT ülkelerinin mevcut hidroelektrik enerji yatırımlarının yenilenebilir enerji hedeflerine ulaşmak için yeterli olmadığını göstermektedir. Sonuç olarak, MINT ülkelerinin enerji politikalarına yönelik önerilere yer verilmiştir.

Anahtar Kelimeler

• MINT ülkeleri
• Hidroelektrik üretimi
• AOA algoritması
• Jaya algoritması
• Yapay sinir ağları

References

1. [1] Karakurt I, Aydin G. Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries. Energy. 2023;263:125650.
2. [2] British Petroleum Energy Outlook 2023. Published by British Petroleum. Retrieved March 2024, from https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2023.pdf
3. [3] Adebayo TS, Rjoub H. Assessment of the role of trade and renewable energy consumption on consumption-based carbon emissions: evidence from the MINT economies. Environmental Science and Pollution Research. 2021;28:58271–58283.
4. [4] Adebayo TS, Awosusi AA, Rjoub H, Agyekum EB, Kirikkaleli D. The influence of renewable energy usage on consumption-based carbon emissions in MINT economies. Heliyon. 2022; 8:e08941.
5. [5] Adebayo TS, Ağa M, Agyekum EB, Kamel S, El-Naggar MF. Do renewable energy consumption and financial development contribute to environmental quality in MINT nations? Implications for sustainable development. Frontiers in Environmental Science. 2022;10:234–245.
6. [6] Akram R, Umar M, Xiaoli G, Chen F. Dynamic linkages between energy efficiency, renewable energy along with economic growth and carbon emission. A case of MINT countries an asymmetric analysis. Energy Reports. 2022; 8:2119–2130.
7. [7] Aziz N, Sharif A, Raza A, Jermsittiparsert K. The role of natural resources, globalization, and renewable energy in testing the EKC hypothesis in MINT countries: new evidence from method of moments quantile regression approach. Environmental Science and Pollution Research. 2021;28:13454–13468.
8. [8] Erinofiardi, Gokhale P, Date A, Akbarzadeh A, Bismantolo P, Suryono AF, Mainil AK, Nuramal A. A review on micro hydropower in Indonesia. Energy Procedia. 2017;110:316–321.

9. [9] Syahputra R, Soesanti I. Renewable energy systems based on micro-hydro and solar photovoltaic for rural areas: A case study in Yogyakarta, Indonesia. Energy Reports. 2021;7: 472–490.
10. [10] Susilowati Y, Irasari P, Susatyo A. Study of hydroelectric power plant potential of Mahakam River Basin East Kalimantan Indonesia. 2019 International Conference on Sustainable Energy Engineering and Application: Innovative Technology Toward Energy Resilience. 2019; 207–213.
11. [11] Marliansyah R, Putri DN, Khootama A, Hermansyah H. Optimization potential analysis of micro-hydro power plant (mhpp) from river with low head. Energy Procedia. 2018;153:74–79.
12. [12] Langer J, Quist J, Blok K. Review of renewable energy potentials in Indonesia and their contribution to a 100% renewable electricity system. Energies. 2021 14:7033.
13. [13] Castelán E. Role of large dams in the socio-economic development of Mexico. International Journal of Water Resources Development. 2002;18:163–177.
14. [14] Zarco-Gonzalez Z, Monroy-Vilchis O, Antonio-Nemiga X, Endara-Agramont AR. Land use change around hydroelectric dams using landsat multi-temporal data: a challenge for a sustainable environment in Mexico. Geocarto International. 2022;37(21):6375–6390.
15. [15] Silber-Coats N. Clean energy and water conflicts: contested narratives of small hydropower in Mexico’s Sierra Madre Oriental. Water Alternatives. 2017;10(2):578–601.
16. [16] Aliyu AS, Dada JO, Adam IK. Current status and future prospects of renewable energy in Nigeria. Renewable and Sustainable Energy Reviews. 2015;48:336–346.
17. [17] Chioma O, Thomas S, Hussein SU, Aboi G, Oshiga O, Ahmed AA. Hydro power generation in Nigeria: impacts and mitigation. 2019 15th International Conference on Electronics, Computer and Computation. 2019; 1–5. doi:10.1109/ICECCO48375.2019.9043184.
18. [18] Ijeoma RC, Briggs I. Hydro power generation in Nigeria, environmental ramifications. IOSR Journal of Electrical and Electronics Engineering. 2018;13(5):01–09.
19. [19] Osokoya OO, Ojikutu AO, Olayiwola OO, Chinedum CW. Enhancing small hydropower generation in Nigeria. Journal of Sustainable Energy Engineering. 2013;1(2): 113–126.
20. [20] Olukanni DO, Adejumo TA, Salami AW, Adedeji AA. Optimization-based reliability of a multipurpose reservoir by genetic algorithms: Jebba hydropower dam, Nigeria. Cogent Engineering. 2018;5(1):1438740.
21. [21] Ugwu CO, Ozor PA, Mbohwa C. Small hydropower as a source of clean and local energy in Nigeria: Prospects and challenges. Fuel Communications. 2022;10:100046.
22. [22] Abdulkadir TS, Salami AW, Anwar AR, Kareem AG. Modelling of hydropower reservoir variables for energy generation: neural network approach. Ethiopian Journal of Environmental Studies and Management. 2013; 6(3):310–316.
23. [23] Fasipe OA, Izinyon OC, Ehiorobo JO. Hydropower potential assessment using spatial technology and hydrological modelling in Nigeria river basin. Renewable Energy. 2021;178:960–976.
24. [24] Uzlu E, Akpınar A, Kömürcü Mİ. Restructuring of Turkey’s electricity market and the share of hydropower energy: The case of the Eastern Black Sea Basin. Renewable Energy. 2011;36(2):676–688.
25. [25] Cinar D, Kayakutlu G, Daim T. Development of future energy scenarios with intelligent algorithms: case of hydro in Turkey. Energy. 2010;35(4):1724–1729.
26. [26] Ceylan H, Ozturk HK. Modeling hydraulic and thermal electricity production based on genetic algorithm-time series (gats). International journal of green energy. 2004;1(3): 393–406.
27. [27] Uzlu E. Estimates of hydroelectric energy generation in Turkey with Jaya algorithm-optimized artificial neural networks. Gazi University Journal of Science Part C: Design and Technology. 2021;9(3):446–462.
28. [28] Uzlu E. Application of Jaya algorithm-trained artificial neural networks for prediction of energy use in the nation of Turkey. Energy Sources, Part B: Economics, Planning, and Policy. 2019;14(5):183–200.
29. [29] Altunkaynak A, Nigussie TA. Monthly water consumption prediction using season algorithm and wavelet transform–based models. Journal of Water Resources Planning and Management. 2017;143(6):04017011.
30. [30] Uzlu E. Estimates of hydroelectric energy generation in BRICS-T countries using a new hybrid model. Energy Sources, Part B: Economics, Planning, and Policy. 2024;19(1): 2310094.
31. [31] Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH. The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering. 2021;376: 113609.
32. [32] Zheng R, Jia H, Abualigah L, Liu Q, Wang S. Deep ensemble of slime mold algorithm and arithmetic optimization algorithm for global optimization. Processes. 2021;9(10): 1774.
33. [33] Shi X, Yu X, Esmaeili-Falak M. Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation. Composite Structures. 2023;306:116599.
34. [34] Çelik E. IEGQO-AOA: information-exchanged gaussian arithmetic optimization algorithm with quasi-opposition learning. Knowledge-Based Systems. 2023;260:110169.
35. [35] Liu Q, Li N, Jia H, Qi Q, Abualigah L, Liu Y. A hybrid arithmetic optimization and golden sine algorithm for solving industrial engineering design problems. Mathematics. 2022;10(9):1567.
36. [36] International Monetary Fund. World Economic Outlook. Published by the IMF. Retrieved March 2024, from https://www.imf.org/-/media/Files/Publications/WEO/2024/April/English/text.ashx
37. [37] Gryczka M. MINT countries as possible rising stars in the global economy–benchmarking with BRICS countries. Acta Scientiarum Polonorum Oeconomia. 2018;17(3):23-31.
38. [38] World Bank Indicators. Retrieved March 2024, from https://data.worldbank.org/indicator
39. [39] International Monetary Fund. Retrieved March 2024, from https://www.imf.org/en/Data.
40. [40] Akın F. An assessment on the macroeconomic performance and the ınsurance sector development of the MINT economies. Journal of Finance Letters. 2018;110:71–94.
41. [41] Dincer H, Karakus H. The effect of renewable energy on sustainable economic development: a comparative analysis on BRICS and MINT countries. The International Journal of Economic and Social Research. 2020;1(1):100–123.
42. [42] International Energy Agency. Retrieved March 2024, from https://www.iea.org/countries
43. [43] Statistical Review of World Energy. Published by British Petroleum. Retrieved March 2024, from https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf
44. [44] Qamruzzaman M, Karim S. Clarifying the relationship between green investment, technological innovation, financial openness, and renewable energy consumption in MINT. Heliyon. 2023;9:e21083.
45. [45] Uzlu E, Akpınar A, Öztürk HT, Nacar S, Kankal M. Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy. 2014; 69:638–647.
46. [46] Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533–536.
47. [47] Aslay SE, Dede T. 3D cost optimization of 3 story RC constructional building using Jaya algorithm. Structures. 2022;40:803–811.
48. [48] Rao R. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations. 2016;7:19–34.
49. [49] Dhal KG, Sasmal B, Das A, Ray S, Rai R. A comprehensive survey on arithmetic optimization algorithm. Archives of Computational Methods in Engineering. 2023;30(5):3379–3404.
50. [50] Agushaka JO, Ezugwu AE. Evaluation of several initialization methods on arithmetic optimization algorithm performance. Journal of Intelligent Systems. 2022;31(1):70–94.
51. [51] Yıldız BS, Kumar S, Panagant N, Mehta P, Sait SM, Yildiz AR, Pholdee N, Bureerat S, Mirjalili S. A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems. Knowledge-Based Systems. 2023;271:110554.
52. [52] Republic of Turkey Ministry of Trade. Retrieved March 2024, from https://ticaret.gov.tr/blog/ulkelerden-ticari-haberler/meksika/meksikada-enerji-sektorundeki-gelismeler
53. [53] World Bank. Retrieved March 2024, from https://archive.doingbusiness.org/en/data/exploreeconomies/nigeria
54. [54] Dai H, Xie X, Xie Y, Liu J, Masui T. Green growth: The economic impacts of large-scale renewable energy development in China. Applied Energy. 2016;162:435–449.
55. [55] Inglesi-Lotz R. The impact of renewable energy consumption to economic growth: A panel data application. Energy Economics. 2016;53:58–63.