Intelligent Control of Renewable Energy Systems and Resilient Supply Chain Finance Under Geopolitical Uncertainty: A Digital Integration Perspective
Published 2026-05-01
Keywords
- Renewable energy control,
- supply chain resilience,
- blockchain transparency
How to Cite
Copyright (c) 2026 Dr. Elena V. Markovic

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Background: The contemporary transition toward low-carbon development is no longer shaped by energy engineering alone. It is increasingly influenced by the interaction among renewable energy control systems, digital optimization methods, geopolitical risk, trade policy uncertainty, supply chain resilience, blockchain-enabled transparency, and the growing financialization of digital assets. The references provided for this study span renewable energy conversion control, maximum power point tracking, hybrid solar-wind systems, artificial intelligence-based optimization, supply chain risk classification, trade policy uncertainty, blockchain product passports, supply chain tokens, cryptocurrency dynamics, and connectedness modeling.
Objective: This article develops a publication-ready original research narrative, based strictly on the supplied references, to explain how renewable energy system intelligence and digitally mediated supply chain-financial mechanisms jointly shape resilient energy transitions under uncertainty.
Methodology: A text-based integrative research design was employed. The renewable energy literature was interpreted through the lenses of control architecture, maximum power extraction, hybrid system coordination, and soft-computing optimization. The finance and supply chain literature was interpreted through the lenses of risk transmission, geopolitical uncertainty, blockchain transparency, crypto-linked asset behavior, and dynamic connectedness. These streams were then synthesized into a common framework of techno-financial resilience.
Results: The analysis indicates that renewable energy performance depends not only on resource availability but also on adaptive digital control, especially in variable wind and solar conditions. At the same time, supply chains and energy-linked financial ecosystems are increasingly exposed to geopolitical shocks, trade uncertainty, and volatility spillovers. Blockchain-based transparency, tokenization, and digital asset interconnection offer new opportunities for traceability and financing, but they also introduce fresh channels of contagion and instability.
Conclusion: The study argues that a resilient clean-energy future requires simultaneous progress in three dimensions: intelligent renewable generation control, transparent and sustainable supply chain design, and sophisticated risk management for crypto-financial spillovers under geopolitical pressure. Energy transition resilience therefore emerges as a systems property located at the intersection of engineering control, digital infrastructure, and financial connectedness.
References
- Abdellatif, W. S. E., Mohamed, M. S., Barakat, S., & Brisha, A. (2021). A fuzzy logic controller based MPPT technique for photovoltaic generation system. International Journal on Electrical Engineering and Informatics, 13(2), 394–417. https://doi.org/10.15676/ijeei.2020.13.2.9
- Abdolrasol, M. G. M., et al. (2021). Artificial neural networks based optimization techniques: A review. Electronics, 10(21), Article 2689. https://doi.org/10.3390/electronics10212689
- Abdullah, M., Adeabah, D., Abakah, E. J. A., & Lee, C.-C. (2023). Extreme return and volatility connectedness among real estate tokens, REITs, and other assets: The role of global factors and portfolio implications. Finance Research Letters, 56, Article 104062. https://doi.org/10.1016/j.frl.2023.104062
- Abo-Khalil, A. G., Alharbi, W., Al-Qawasmi, A. R., Alobaid, M., & Alarifi, I. M. (2021). Maximum power point tracking of PV systems under partial shading conditions based on opposition-based learning firefly algorithm. Sustainability, 13(5), Article 2656. https://doi.org/10.3390/su13052656
- Adelopo, I., & Luo, X. (2025). How do cryptocurrency features determine their dynamic volatility and co-movements with stocks? Cogent Business & Management, 12(1), Article 2461732. https://doi.org/10.1080/23311975.2025.2461732
- Aloui, C., Ben Hamida, H., & Yarovaya, L. (2021). Are Islamic gold-backed cryptocurrencies different? Finance Research Letters, 39, Article 101615. https://doi.org/10.1016/j.frl.2020.101615
- Anowar, T., et al. (2016). Fuzzy logic implementation with MATLAB for solar-wind-battery-diesel hybrid energy system. Imperial Journal of Interdisciplinary Research, 2(5), 574–584.
- Baloch, M. H., Kaloi, G. S., & Memon, Z. A. (2016a). Current scenario of the wind energy in Pakistan: Challenges and future perspectives. Energy Reports, 2, 201–210. https://doi.org/10.1016/j.egyr.2016.08.002
- Baloch, M. H., Wang, J., & Kaloi, G. S. (2016b). A review of the state of the art control techniques for wind energy conversion system. International Journal of Renewable Energy Research, 6(4), 1276–1295.
- Belmili, H., Boulouma, S., Boualem, B., & Fayçal, A. M. (2017). Optimized control and sizing of standalone PV-wind energy conversion system. Energy Procedia, 107, 76–84. https://doi.org/10.1016/j.egypro.2016.12.134
- Bollerslev, T., & Wooldridge, J. M. (1992). Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews, 11(2), 143–172. https://doi.org/10.1080/07474939208800229
- Bouri, E., Vo, X. V., & Saeed, T. (2021). Return equicorrelation in the cryptocurrency market: Analysis and determinants. Finance Research Letters, 38, Article 101497. https://doi.org/10.1016/j.frl.2020.101497
- Caldara, D., Iacoviello, M., Molligo, P., Prestipino, A., & Raffo, A. (2020). The economic effects of trade policy uncertainty. Journal of Monetary Economics, 109, 38–59. https://doi.org/10.1016/j.jmoneco.2019.11.002
- Canciani, A., Felicioli, C., Severino, F., & Tortola, D. (2024). Enhancing supply chain transparency through blockchain product passports. In IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). https://doi.org/10.1109/PerComWorkshops59983.2024.10502429
- Chen, K.-S., & Yang, J. J. (2024). Asymmetric dynamic correlations and portfolio management between Bitcoin and stablecoins. Applied Economics. https://doi.org/10.1080/00036846.2024.2408034
- Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrency as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182–199. https://doi.org/10.1016/j.irfa.2018.09.003
- Demiralay, S., & Golitsis, P. (2021). On the dynamic equicorrelations in cryptocurrency market. Quarterly Review of Economics and Finance, 80, 524–533. https://doi.org/10.1016/j.qref.2021.04.002
- Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
- Engle, R., & Kelly, B. (2012). Dynamic equicorrelation. Journal of Business & Economic Statistics, 30(2), 212–228. https://doi.org/10.1080/07350015.2011.652048
- Fang, Y., Tang, Q., & Wang, Y. (2024). Geopolitical risk and cryptocurrency market volatility. Emerging Markets Finance and Trade, 60(14), 3254–3270. https://doi.org/10.1080/1540496X.2024.2343948
- Feng, P., Zhou, X., Zhang, D., Chen, Z., & Wang, S. (2022). The impact of trade policy on global supply chain network equilibrium: A new perspective of product-market chain competition. Omega, 109, Article 102612. https://doi.org/10.1016/j.omega.2022.102612
- Gao, J., Qin, Q., & Zhou, S. (2025). Spillover effects of US economic policy uncertainty on emerging markets: Evidence from transnational supply chains. Journal of International Financial Markets, Institutions and Money, 100, Article 102136. https://doi.org/10.1016/j.intfin.2025.102136
- Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
- Jemaa, A., Zarrad, O., Hajjaji, M. A., & Mansouri, M. N. (2018). Hardware implementation of a fuzzy logic controller for a hybrid wind-solar system in an isolated site. International Journal of Photoenergy. https://doi.org/10.1155/2018/5379864
- Jia, Y., Liu, Y., & Taghizadeh-Hesary, F. (2025). The nexus among geopolitical risk, metal prices, and global supply chain pressure. Economic Analysis and Policy, 85, 1776–1789. https://doi.org/10.1016/j.eap.2025.02.003
- Joshi, A., Wazid, M., & Goudar, R. H. (2015). An efficient cryptographic scheme for text message protection against brute force and cryptanalytic attacks. Procedia Computer Science, 48, 360–366. https://doi.org/10.1016/j.procs.2015.04.194
- Kaloi, G. S., Wang, J., & Baloch, M. H. (2016). Active and reactive power control of the doubly fed induction generator based on wind energy conversion system. Energy Reports, 2, 194–200. https://doi.org/10.1016/j.egyr.2016.08.001
- Khan, K. (2025). How do supply chain and geopolitical risks threaten energy security? Energy, 316, Article 134501. https://doi.org/10.1016/j.energy.2025.134501
- Kumar, D., & Chatterjee, K. (2016). A review of conventional and advanced MPPT algorithms for wind energy systems. Renewable and Sustainable Energy Reviews, 55, 957–970. https://doi.org/10.1016/j.rser.2015.11.013
- Long, H., Demir, E., Będowska-Sójka, B., Zaremba, A., & Shahzad, S. J. H. (2022). Is geopolitical risk priced in the cross-section of cryptocurrency returns? Finance Research Letters, 49, Article 103131. https://doi.org/10.1016/j.frl.2022.103131
- Mbarek, M., & Msolli, B. (2025). Assessing linkages between supply chain tokens and other assets. Journal of Behavioral and Experimental Finance, 46, Article 101029. https://doi.org/10.1016/j.jbef.2025.101029
- Memon, B., Baloch, M. H., Memon, A. H., Qazi, S. H., Haider, R., & Ishak, D. (2019). Assessment of wind power potential based on Raleigh distribution model. Engineering, Technology & Applied Science Research, 9(1), 3721–3725. https://doi.org/10.48084/etasr.2381
- Prasad, D., Kumar, N., Sharma, R., Malik, H., Garcia Márquez, F. P., & Pinar-Pérez, J. M. (2023). A novel ANROA based control approach for grid-tied multifunctional solar energy conversion system. Energy Reports, 9, 2044–2057. https://doi.org/10.1016/j.egyr.2023.01.039
- Qin, M., Su, C.-W., Umar, M., Lobonţ, O.-R., & Manta, A. G. (2023). Are climate and geopolitics the challenges to sustainable development? Economic Analysis and Policy, 77, 748–763. https://doi.org/10.1016/j.eap.2023.01.002
- Rezvani, A., Izadbakhsh, M., & Gandomkar, M. (2015). Enhancement of hybrid dynamic performance using ANFIS for fast varying solar radiation and fuzzy logic controller in high speeds wind. Journal of Electrical Systems, 11(1), 11–26.
- Shahbaz, M. S., Sohu, S., Khaskhelly, F. Z., Bano, A., & Soomro, M. A. (2019). A novel classification of supply chain risks: A review. Engineering, Technology & Applied Science Research, 9(3), 4301–4305. https://doi.org/10.48084/etasr.2781
- Tahir, S., Wang, J., Baloch, M., & Kaloi, G. (2018). Digital control techniques based on voltage source inverters in renewable energy applications: A review. Electronics, 7(2), Article 18. https://doi.org/10.3390/electronics7020018
- Ullah, M., Sohag, K., & Haddad, H. (2024). Comparative investment analysis between crypto and conventional financial assets amid heightened geopolitical risk. Heliyon, 10(9), Article e30558. https://doi.org/10.1016/j.heliyon.2024.e30558
- Urom, C., Ndubuisi, G., & Guesmi, K. (2022). Dynamic dependence and predictability between volume and return of non-fungible tokens (NFTs): The roles of market factors and geopolitical risks. Finance Research Letters, 50, Article 103188. https://doi.org/10.1016/j.frl.2022.103188
- Urom, C., Ndubuisi, G., & Guesmi, K. (2024). Global macroeconomic factors and the connectedness among NFTs and conventional assets. Research in International Business and Finance, 71, Article 102429. https://doi.org/10.1016/j.ribaf.2024.102429
- Viriyasitavat, W., Hoonsopon, D., & Bi, Z. (2021). Augmenting cryptocurrency in smart supply chain. Journal of Industrial Information Integration, 21, Article 100188. https://doi.org/10.1016/j.jii.2020.100188
- Wang, J. (2025). Enhancing global supply chain resilience under trade uncertainties. In Reference Module in Social Sciences. Elsevier. https://doi.org/10.1016/B978-0-443-28993-4.00140-2
- Yan, J., et al. (2025). How does blockchain application impact on supply chain alliance? Technovation, 143, Article 103199. https://doi.org/10.1016/j.technovation.2025.1031 99
- Zhao, Q., Wang, W., & Tao, Y. (2025). Supply chain sustainability and its impact on firm market competitiveness: A perspective based on ESG practices. International Review of Economics & Finance, 101, Article 104236. https://doi.org/10.1016/j.iref.2025.104236