Search Results (35)

Driven by the global low-carbon transition and the rapid expansion of marine energy development, offshore integrated energy systems are emerging as a critical configuration for coupling offshore renewable resources, geothermal and subsurface thermal resources, oil and gas infrastructure, hydrogen pathways, multi-carrier networks, and offshore loads. Unlike onshore integrated energy systems, offshore systems are constrained by resource intermittency, harsh marine environments, platform space and weight limits, long-distance transmission, operation and maintenance accessibility, safety risks, and cross-regional governance mechanisms. Recent studies have advanced offshore wind-to-hydrogen systems, oil and gas platform electrification, offshore energy hubs, platform repurposing, and offshore geothermal utilization. However, these studies remain fragmented in terms of system boundaries, multi-energy coupling mechanisms, engineering constraints, and optimization methods. This paper reviews offshore integrated energy systems from the perspectives of system configuration, key integration technologies, optimization and assessment methods, and future research needs. Offshore integrated energy systems are first classified into offshore renewable-energy-dominated systems, offshore wind–hydrogen systems, oil and gas platform integrated systems, offshore energy hubs and multi-carrier networks, decommissioned-platform repurposing systems, and offshore geothermal and repurposed-well systems. Resource-side, conversion-side, storage-side, network-side, and load-side integration technologies are then summarized. Capacity configuration, operational scheduling, stochastic and robust optimization, multi-objective optimization, energy, exergy, economic, and environmental (4E) assessment, advanced exergy analysis, and energy-hub modelling are further reviewed. Finally, key research gaps are identified, including resource uncertainty, offshore engineering constraints, multi-carrier network coupling, insufficient demonstration data, and policy and economic uncertainty. This review provides a structured reference for the modelling, integration, optimization, and demonstration of offshore integrated energy systems for low-carbon transition. Full article

The Dynamic Economic Dispatch (DED) problem underpins the cost-efficient and reliable operation of modern power systems, yet valve-point loading, ramp-rate coupling, and the growing share of intermittent wind, photovoltaic, and pumped-storage hydro (PSH) resources render it highly non-convex. Metaheuristic methods typically require large computational budgets and hand-crafted constraint-handling rules, whereas deep reinforcement learning agents rarely guarantee the feasibility of the schedules they produce. To address both limitations, this paper proposes a Two-Stage PPO–RLMPA framework that couples data-driven policy learning with a biomimetic metaheuristic search inspired by marine predator–prey dynamics. In the first stage, a Proximal Policy Optimization (PPO) agent is trained on a Markov Decision Process reformulation of DED in which a deterministic Safety Layer projects every raw action onto the feasible set defined by capacity, ramp-rate, and power-balance constraints, so the policy only observes physically viable transitions. In the second stage, the PPO dispatch is refined by the RLMPA module, a Marine Predators Algorithm (MPA) whose exploration–exploitation balance, Lévy-flight foraging, and Fish Aggregating Devices (FADs) attraction mechanisms emulate strategies documented in marine ecosystems; its step-size factor and FADs probability are further adapted online by a Deep Q-Network. This biomimetics-informed refinement translates predator–prey foraging intelligence into economically efficient thermal dispatch under valve-point non-convexity. Across 30 independent runs on ten- and twenty-unit benchmark systems with wind, PV, and PSH integration, the framework attains best costs of USD 368,763 and USD 737,348 on Test Systems 1 and 2, corresponding to reductions of approximately $1.1\%$ and $4.4\%$ over the CFCEP baseline, with zero post-repair constraint violations in every run. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

The current energy transition has shifted the power system paradigm, including distributed resources (mostly renewables) and energy storage systems, the proper incorporation of which is beneficial for the power system but can also cause issues such as network instability, grid congestion or issues with power quality. Moreover, the exponential electrification of loads, especially ones with dynamic behavior, due to most sectors switching to electric mode, with prominent examples including mobility, heating, hydrogen production and marine applications, can pose challenges for the system operators. The purpose of this paper is to highlight the effects of this transition from the perspective of the distribution and transmission systems in Europe generally, but also in Greece specifically, by presenting key performance indicators (technical, economic, environmental, and social) related to expected EU targets, as well as selected real-life applications, future trends and challenges. Full article

Ships are highly advanced marine structures that incorporate state-of-the-art technologies. Nevertheless, they still depend on outdated systems in certain critical areas, such as escape lighting. Escape lighting systems are vital components of shipboard safety infrastructure. However, conventional systems rely heavily on decentralized battery-powered luminaires and manual testing, leading to high maintenance costs and environmental burdens. This study addresses these challenges through an engineering-driven redesign of escape lighting systems. A novel system architecture was developed, integrating photovoltaic energy sources with centralized battery storage and Automatic Testing Systems (ATSs) compliant with the IEC 62034 standard. The system interfaces with both main and emergency power networks, reducing reliance on fossil fuels and minimizing battery usage. Engineering simulations and operational data indicate a 20% reduction in fuel oil consumption per escape light and a threefold decrease in maintenance costs over a vessel’s lifecycle. For a standard vessel equipped with 350 luminaires, the system demonstrates significant operational efficiency and environmental benefits, including reduced emissions and hazardous waste. This work exemplifies how ocean engineering innovations can enhance vessel safety while promoting sustainability. The integration of renewable energy and automated diagnostics into critical shipboard systems represents a forward-looking approach to marine engineering, aligning with global goals for greener maritime operations. Moreover, the proposed system supports compliance with evolving maritime regulations and offers a scalable solution adaptable to various vessel types and operational profiles. Full article

The growing demand for sustainable and distributed energy solutions has driven increasing interest in triboelectric nanogenerators (TENGs) as platforms for energy harvesting and self-powered sensing. Biowaste-based triboelectric nanogenerators (BW-TENGs) represent an attractive strategy by coupling renewable energy generation with waste valorization under the principles of the circular bioeconomy. This review provides a comprehensive overview of BW-TENGs, encompassing fundamental triboelectric mechanisms, material categories, processing and surface-engineering strategies, device architectures, and performance evaluation metrics. A broad spectrum of biowaste resources—including agricultural residues, food and marine waste, medical plastics, pharmaceutical waste, and plant biomass—is critically assessed in terms of physicochemical properties, triboelectric behavior, biodegradability, biocompatibility, and scalability. Recent advances demonstrate that BW-TENGs can achieve electrical outputs comparable to conventional synthetic polymer TENGs while offering additional advantages such as environmental sustainability, mechanical compliance, and multifunctionality. Key application areas, including environmental monitoring, smart agriculture, wearable and implantable bioelectronics, IoT networks, and waste management systems, are highlighted. The review also discusses major challenges limiting large-scale deployment, such as material heterogeneity, environmental stability, durability, and lack of standardization, and outlines emerging solutions involving material engineering, hybrid energy-harvesting architectures, artificial intelligence-assisted optimization, and life cycle assessment frameworks. Full article

The rapid global expansion of offshore wind energy (OWE) has established it as a critical component of the renewable energy transition; however, this development concurrently introduces significant underwater noise pollution into marine ecosystems. This paper provides a comprehensive review of the acoustic footprint of OWE across its entire lifecycle, rigorously distinguishing between the high-intensity, acute impulsive noise generated during pile-driving construction and the chronic, low-frequency continuous noise associated with decades-long turbine operation. We critically evaluate the engineering capabilities and limitations of current underwater acoustic monitoring architectures, including buoy-based real-time monitoring nodes, cabled high-bandwidth systems (e.g., cabled hydrophone arrays with DAQ/DSP and fiber-optic distributed acoustic sensing, DAS), and autonomous seabed archival recorders (PAM deployment). Furthermore, documented biological impacts are synthesized across diverse taxa, ranging from auditory masking and threshold shifts in marine mammals to the often-overlooked sensitivity of invertebrates and fish to particle motion—a key metric frequently missing from standard pressure-based assessments. Our analysis identifies a fundamental gap in current governance paradigms, which disproportionately prioritize the mitigation of short-term acute impacts while neglecting the cumulative ecological risks of long-term operational noise. This review synthesizes recent evidence on chronic operational noise and outlines a conceptual pathway from event-based compliance monitoring toward long-term, adaptive soundscape management. We propose the implementation of integrated, adaptive acoustic monitoring networks capable of quantifying cumulative noise exposure and informing real-time mitigation strategies. Such a paradigm shift is essential for optimizing mitigation technologies and ensuring the sustainable coexistence of marine renewable energy development and marine biodiversity. Full article

This paper presents a power smoothing strategy for wave energy converters (WECs) by means of energy storage systems (ESS) with integrated forecasting filtering algorithms applied to their control. The oscillatory nature of wave energy leads to high variability in power output, posing significant challenges for grid integration. A case study in Tenerife, Spain, was modeled in MATLAB-Simulink (release r2020b) to evaluate the impact of prediction-enhanced smoothing filters on ESS sizing. Various forecasting algorithms were assessed, including Bayesian Neural Networks, ARMA models, and persistence models. The simulation results demonstrate that the use of forecasting algorithms substantially reduces energy storage requirements while maintaining grid stability. Specifically, the application of Bayesian Neural Networks reduced the required ESS energy by up to 36.52% compared to traditional filters. In a perfect prediction scenario, reductions of up to 53.91% were achieved. These results highlight the importance of combining appropriate filtering strategies with advanced forecasting techniques to improve the technical and economic viability of wave energy projects. The paper concludes with a parametric analysis of moving average filter windows and prediction horizons, identifying the optimal combinations for different sea conditions. In summary, this study provides practical information into reducing the storage capacity required for power smoothing in wave energy systems, thereby contributing to the mitigation of grid integration challenges that may arise with the large-scale deployment of marine renewable energy Full article

Ocean energy is an abundant, eco-friendly, and renewable energy resource that is useful for powering sensor networks connected to the maritime Internet of Things (MIoT). These sensor networks can be used to measure different marine environmental parameters that affect ocean infrastructure integrity and harm marine ecosystems. This ocean energy can be harnessed through hybrid nanogenerators that combine triboelectric nanogenerators, electromagnetic generators, piezoelectric nanogenerators, and pyroelectric generators. These nanogenerators have advantages such as high-power density, robust design, easy operating principle, and cost-effective fabrication. However, the performance of these nanogenerators can be affected by the wear of their main components, reduction of wave frequency and amplitude, extreme corrosion, and sea storms. To address these challenges, future research on hybrid nanogenerators must improve their mechanical strength, including materials and packages with anti-corrosion coatings. Herein, we present recent advances in the performance of different hybrid nanogenerators to harvest ocean energy, including various transduction mechanisms. Furthermore, this review reports potential applications of hybrid nanogenerators to power devices in marine infrastructure or serve as self-powered MIoT monitoring sensor networks. This review discusses key challenges that must be addressed to achieve the commercial success of these nanogenerators, regarding design strategies with advanced simulation models or digital twins. Also, these strategies must incorporate new materials that improve the performance, reliability, and integration of future nanogenerator array systems. Thus, optimized hybrid nanogenerators can represent a promising technology for ocean energy harvesting with application in the maritime industry. Full article

As a renewable and clean energy source with abundant reserves, the development of wave energy relies on accurate predictions of significant wave height (Hs). The fluctuation of Hs is a non-stationary process influenced by seasonal variations in marine climate conditions, which poses significant challenges for accurate predictions. This study proposes a deep learning method based on buoy datasets collected from four research locations in China’s offshore waters over three years (2021–2023, 3-hourly). The hybrid modal decomposition CEEMDAN-VMD is employed for reducing non-stationarity of the Hs sequence, with peak information incorporated as a data augmentation strategy to enhance the performance of deep learning. A probabilistic deep learning model, QRCNN-BiLSTM, was developed using quantile regression, achieving 12-, 24-, and 36-h interval predictions of Hs based on 12 days of historical data with three input features (Hs and wave velocities only). Furthermore, an optimization algorithm that integrates the proposed innovative enhancement strategies is used to automatically adjust the network parameters, making the model more lightweight. Results demonstrate that under a 0.95 prediction interval nominal confidence (PINC), the prediction interval coverage probability (PICP) reaches 100% for at least 6 days across all datasets, indicating that the developed system exhibits superior performance in short-term wave forecasting. Full article

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals. Full article

This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution for the Port of Avilés was further developed to evaluate the performance of new tools such as the Energy Management Tool (EMTv1), HYbrid for Renewable Energy Solutions (HY4RES), and a commercial model (Hybrid Optimization of Multiple Energy Resources—HOMER) in optimizing renewable energy and storage management. Seven scenarios were analyzed, integrating different energy sources and storage solutions. Using EMTv1, Scenario 1 showed high surplus energy, while Scenario 2 demonstrated grid independence with Pump-as-Turbine (PAT) storage. The HY4RES model was used to analyze Scenario 3, which achieved a positive grid balance, exporting more than imported, and Scenario 4 revealed limitations of the PAT system due to the low power installed. Scenario 5 introduced a 15 kWh battery, efficiently storing and discharging energy, reducing grid reliance, and fully covering energy needs. Using HOMER modeling, Scenario 6 required 546 kWh of grid energy but sold 2385 kWh back. Scenario 7 produced 3450 kWh/year, covering demand, resulting in 1834 kWh of surplus energy and a small capacity shortage (1.41 kWh/year). AI-based ML analysis was applied to five scenarios (the ones with access to numerical results), accurately predicting energy balances and optimizing grid interactions. A neural network time series (NNTS) model trained on average year data achieved high accuracy (R²: 0.9253–0.9695). The ANN model proved effective in making rapid energy balance predictions, reducing the need for complex simulations. A second case analyzed an increase of 80% in demand, confirming the model’s reliability, with Scenario 3 having the highest MSE (0.0166 kWh), Scenario 2 the lowest R² (0.9289), and Scenario 5 the highest R² (0.9693) during the validation process. This study highlights AI-driven forecasting as a valuable tool for ports to optimize energy management, minimize grid dependency, and enhance their efficiency. Full article

Installing offshore wind jackets faces increasing risks from dynamic marine conditions and is challenged by trajectory deviations due to coupled hydrodynamic and environmental factors. To address the limitations of software, such as long simulation times and tedious parameter adjustments, this study develops a rapid prediction model combining Radial Basis Function (RBF) and Backpropagation (BP) neural networks. The model is enhanced by incorporating both numerical simulation data and real-world measurement data from the launching operation. The real-world data, including the barge attitude before launching, jacket weight distribution, and actual environmental conditions, are used to refine the model and guide the development of a fully parameterized adaptive controller. This controller adjusts in real time, with its performance validated against simulation results. A case study from the Pearl River Mouth Basin was conducted, where datasets—capturing termination time, six-degrees-of-freedom motion data for the barge and jacket, and actual environmental conditions—were collected and integrated into the RBF and BP models. Numerical models also revealed that wind and wave conditions significantly affected lateral displacement and rollover risks, with certain directions leading to heightened operational challenges. On the other hand, operations under more stable environmental conditions were found to be safer, although precautions were still necessary under strong environmental loads to prevent collisions between the jacket and the barge. This approach successfully reduces weather-dependent operational delays and structural load peaks. Hydrodynamic analysis highlights the importance of directional strategies in minimizing environmental impacts. The model’s efficiency, requiring a fraction of the time compared to traditional methods, makes it suitable for real-time applications. Overall, this method provides a scalable solution to enhance the resilience of marine operations in renewable energy projects, offering both computational efficiency and high predictive accuracy. Full article

Integrating renewable energy sources with aquaculture systems on floating multi-use platforms presents an innovative approach to developing sustainable and resilient offshore infrastructure, utilizing the ocean’s considerable potential. From March 2021 to January 2022, a 1:15-scale prototype was tested in Reggio Calabria, Italy, which gave crucial insights into how these structures behave under different wave conditions. This study investigates the application of Artificial Neural Networks (ANNs) to predict changes in mooring loads, particularly at key points of the structure. By analyzing metocean data, several ANN models and optimization techniques were evaluated to identify the most accurate predictive model. With a Normalized Root Mean Square Error (NRMSE) of 1.7–4.7%, the results show how ANNs can effectively predict offshore platform dynamics. This research highlights the potential of machine learning in developing and managing sustainable ocean systems, setting the stage for future advancements in data-driven marine resource management. Full article

European Union (EU) energy markets are changing rapidly. After the recent turmoil, a new wave of EU legislation is once again reshaping the way energy should be used in the EU, emphasizing not only the increasing importance of using renewable and local energy sources but also highlighting the importance of energy efficiency and decarbonization of high to abate sectors (including aviation and marine fuels). Heating and cooling account for about half of the total gross final energy consumption in the EU. This article explores the novel concept of using waste heat from the flexible Fischer–Tropsch (FT) process (FLEXCHX) in the existing district heating network, resulting in tri-generation: FT C5+ liquids, heat, and electricity. FLEXCHX provides operation flexibility and combines advanced biomass gasification, catalytic liquefaction, electrolysis, and waste heat recovery, allowing use of biomass residues in a more sustainable way. Our results, based on the Kaunas district heating (DH) system, show that this process could be integrated into the existing district heating network in Northern Europe and successfully compete with existing heat-only boilers and CHPs using biomass or municipal waste, resulting in more efficient use of biomass and savings accumulated up to EUR 200 million over the study period in the analysis (2020–2050), supplying up to 30% of the heat in the Kaunas DH system. Enriching the FT process with hydrogen (using electrolysis) could result in additional FLEXCHX utilization benefits by creating demand for cheap excess electricity that might otherwise be curtailed. Full article