Renewable energy deployment is increasingly shaped not only by conversion efficiency and control stability but also by the quality of supply chain governance and the financial risk environment in which energy infrastructure is produced, financed, and operated. This study develops an integrative research framework that connects (i) advanced control and optimization practices for wind and photovoltaic (PV) energy conversion, particularly maximum power point tracking (MPPT), inverter-based control, and grid-tied multifunctional operation, with (ii) evolving supply chain transparency mechanisms, including blockchain product passports and alliance-level blockchain adoption, and (iii) the macro-financial risk channels that transmit geopolitical shocks, policy uncertainty, and supply chain pressure into energy security and crypto-asset volatility. Using a qualitative meta-synthesis design grounded in systematic interpretive analysis, the study draws strictly on the provided interdisciplinary literature spanning renewable energy control (fuzzy logic, digital inverter control, ANN optimization, FPGA control, and novel control approaches), risk classification in supply chains, blockchain-enabled transparency, corporate fraud mitigation via transparency, and time-varying linkages among geopolitical risk, metals, supply chain pressure, and cryptocurrency market dynamics. The analysis identifies a central governance gap: technical gains in MPPT accuracy, converter resilience, and intelligent control are often pursued without commensurate mechanisms to assure traceability of critical components, legitimacy of environmental claims, or robustness of financing channels under geopolitical stress. The results synthesize evidence into a practical governance architecture comprising four coupled layers—conversion control integrity, cyber-physical assurance, supply chain traceability, and financial risk buffering—designed to increase energy system reliability while reducing informational opacity that may amplify fraud, alliance breakdown, and capital flight during risk episodes. The study concludes that renewable energy resilience requires co-design between engineering control strategies and institutional transparency instruments to improve system performance, accountability, and investment stability across volatile global conditions.

Renewable energy control, MPPT optimization, blockchain transparency, supply chain risk

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), 2689. https://doi.org/10.3390/electronics10212689

Adelopo, I., & Luo, X. (2025). How do cryptocurrency features determine their dynamic volatility and co-movements with stocks? Cogent Business & Management, 12(1), 2461732. https://doi.org/10.1080/23311975.2025.2461732

Baloch, M. H., Kaloi, G. S., & Memon, Z. A. (2016). Current scenario of the wind energy in Pakistan: Challenges and future perspectives—A case study. Energy Reports, 2, 201–210. https://doi.org/10.1016/j.egyr.2016.08.002

Bouri, E., Vo, X. V., & Saeed, T. (2021). Return equicorrelation in the cryptocurrency market: Analysis and determinants. Finance Research Letters, 38, 101497. https://doi.org/10.1016/j.frl.2020.101497

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 and Other Affiliated Events (PerCom Workshops), Biarritz, France. https://doi.org/10.1109/PerComWorkshops59983.2024.10502429

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

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, 102136. https://doi.org/10.1016/j.intfin.2025.102136

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, 1–16. 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: Evidence from the TVP-SV-VAR approach. Economic Analysis and Policy, 85, 1776–1789. https://doi.org/10.1016/j.eap.2025.02.003

Jin, D., & Yu, J. (2023). Predicting cryptocurrency market volatility: Novel evidence from climate policy uncertainty. Finance Research Letters, 58, 104520. https://doi.org/10.1016/j.frl.2023.104520

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? A time and frequency analysis. Energy, 316, 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

Mbarek, M., & Msolli, B. (2025). Assessing linkages between supply chain tokens and other assets: Evidence from a time-frequency quantile connectedness approach. Journal of Behavioral and Experimental Finance, 46, 101029. https://doi.org/10.1016/j.jbef.2025.101029

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

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

Shang, Z., & Chen, H. (2025). Supply chain transparency and corporate financial fraud. Finance Research Letters, 77, 107070. https://doi.org/10.1016/j.frl.2025.107070

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), 18. https://doi.org/10.3390/electronics7020018

Talaat, M., Alblawi, A., Tayseer, M., & Elkholy, M. H. (2022). FPGA control system technology for integrating the PV/wave/FC hybrid system using ANN optimized by MFO techniques. Sustainable Cities and Society, 80, 103825. https://doi.org/10.1016/j.scs.2022.103825

Ullah, M., Sohag, K., & Haddad, H. (2024). Comparative investment analysis between crypto and conventional financial assets amid heightened geopolitical risk. Heliyon, 10(9), e30558. https://doi.org/10.1016/j.heliyon.2024.e30558

Viriyasitavat, W., Hoonsopon, D., & Bi, Z. (2021). Augmenting cryptocurrency in smart supply chain. Journal of Industrial Information Integration, 21, 100188. https://doi.org/10.1016/j.jii.2020.100188

Yan, J., et al. (2025). How does blockchain application impact on supply chain alliance? Technovation, 143, 103199. https://doi.org/10.1016/j.technovation.2025.103199

Yousaf, I., Pham, L., & Goodell, J. W. (2023). Interconnectedness between healthcare tokens and healthcare stocks: Evidence from a quantile VAR approach. International Review of Economics & Finance, 86, 271–283. https://doi.org/10.1016/j.iref.2023.03.013