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Hussain, Refat Taleb
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Deep Reinforcement Learning-Based Control Architectures for Autonomous Maritime Renewable Energy Platforms Sabah, Sura; Hussain, Refat Taleb; Mohammed, Ismail Abdulaziz; Jawad, Haider Mahmood; Abbas, Intesar; Hariguna, Taqwa
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1209

Abstract

Autonomous vessels driven by renewable energy are increasingly envisioned as vital for sustainable ocean?operations such as environmental monitoring, offshore power generation, and long-haul unmanned surface vehicles. Implementing fine-scale control of these systems has proven challenging however,?due to time-varying sea-state dynamics, sporadic energy inputs, the possibility of failure at the component level, and the requirement for coordination between multiple agents. In the article, an end-to-end deep reinforcement learning-based hierarchical control solution with real-time navigation and?its synthesis for energy optimization is proposed. It combines high-level energy regulation with low-level actuator scheduling so as to react to the variations of?the environment and internal perturbations. Simulations using actual wave realizations, sensor failures, actuator outages, and network communication variation were used?to demonstrate the performance of the control system in the following 5 performance aspects: energy saving, navigation accuracy, communication reliability, fault tolerant and multi-agent coordination. Results indicate that the architecture sustained over 80% of the performance and achieved energy efficiencies up to 54.5% in the?best case under failure scenarios. Performance-measures demonstrated reasonable scalability?up to 5–7 agents without significant communication overhead. The findings support the applicability of deep reinforcement learning for real-time maritime control under uncertainty, offering a viable alternative to conventional rule-based or predictive control strategies. The framework’s modular design allows for future integration with federated learning, hybrid control models, or autonomous deployment. The article contributes to the growing field of intelligent marine systems by providing a robust and adaptable control strategy for sustainable and scalable operations in autonomous maritime environments.
Blockchain Technology for Renewable Energy Transactions and Grid Management Mousa, Sura Hamed; Hussain, Refat Taleb; Hassan, Zahraa Mohammed; Qasim, Nameer Hashim; Mahdi, Akram Fadhel; Batumalay, M.
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1731

Abstract

The transition to renewable energy sources necessitates novel solutions for decentralized energy management, secure transactions, and transparent regulatory compliance. This paper presents the design and evaluation of a blockchain-based system addressing these challenges through peer-to-peer (P2P) energy trading, dynamic smart grid coordination, and automated Renewable Energy Certificate (REC) lifecycle management. Employing a hybrid methodology that combined qualitative stakeholder interviews with a six-month quantitative simulation of 50 prosumers, our Ethereum Proof-of-Stake (PoS) platform was assessed for efficiency, latency, and stability. The results indicate superior performance over traditional models, revealing significant gains in energy transfer efficiency, marked reductions in transaction latency under various network loads, near-elimination of REC fraud, and enhanced grid frequency stability. This study empirically confirms that decentralized architectures can augment or replace centralized utility models, establishing blockchain as a viable infrastructure for future smart grids and informing policy decisions needed to create a more resilient and equitable energy market for energy efficiency.
Co-Authors Abbas, Intesar Batumalay, M. Hassan, Zahraa Mohammed Jawad, Haider Mahmood Mahdi, Akram Fadhel Mohammed, Ismail Abdulaziz Mousa, Sura Hamed Qasim, Nameer Hashim Sabah, Sura Taqwa Hariguna