Search Results (1,992)

With the extensive integration of renewable energy sources (RESs), power systems face challenges in load frequency control (LFC) due to RES intermittency. While energy storage systems (ESSs) aid frequency regulation, existing strategies are limited—single-type ESSs fail in multi-ESS scenarios, and hybrid ESSs lack state-of-charge (SoC) consistency control. This paper proposes an LFC framework combining energy storage aggregators (ESAs), leader–follower finite-time consensus control, and DDPG-RNN (Deep Deterministic Policy Gradient with Recurrent Neural Networks). ESAs aggregate small distributed ESSs for scalable regulation; consensus control ensures finite-time ESS power tracking and SoC balancing; and DDPG-RNN adaptively tunes control gains to handle RES fluctuations and load changes. Simulations on a high-RES power system with hybrid ESSs (SCES, LABES, VRFBES, LIPBES) show that the framework outperforms traditional proportional–integral–derivative (PID) control and basic leader–follower control: it reduces frequency deviation peaks, shortens recovery time, achieves SoC synchronization, and alleviates conventional generator power fluctuations. Full article

Accurate short-term wind speed forecasting is essential for the sustainable operation and planning of coastal wind farms. This study develops an improved BP-PSO hybrid model that integrates particle-swarm optimization, time-ordered walk-forward validation, and uncertainty quantification through block-bootstrap confidence intervals and Monte-Carlo dropout prediction intervals. Using multi-year and seasonal datasets from four coastal stations in China—from Bohai Bay (LHT, XCS, ZFD) to Zhejiang Province (SSN)—the proposed model achieves high predictive accuracy, with RMSE values between 1.09 and 1.54 m/s, MAE between 0.79 and 1.10 m/s, and R² exceeding 0.70 at most sites. The multi-year configuration provides the most stable and robust results, while autumn at ZFD yields the highest errors due to intensified turbulence. XCS and SSN exhibit the most consistent performance, confirming the model’s spatial adaptability across distinct climatic regions. Compared with the ARIMA and persistence baselines, BP-PSO reduces RMSE by over 50%, demonstrating improved efficiency and generalization. These results highlight the potential of intelligent data-driven forecasting frameworks to enhance renewable energy reliability and sustainability by enabling more accurate wind-power scheduling, grid stability, and coastal energy system resilience. Full article

Tambatinga (Colossoma macropomum × Piaractus brachypomus), a hybrid Amazonian fish recognized for its superior growth performance, represents a valuable and sustainable source of collagen-rich raw material. Due to its tropical origin, the species’ skin may contain higher levels of amino acids, which can enhance the functional and structural properties of gelatin derived from it. The valorization of fish processing residues for biopolymer production not only mitigates environmental impacts but also reinforces the principles of the circular economy within aquaculture systems. This study explores the development of biopolymer films from Tambatinga skin, an abundant by-product of Brazilian aquaculture. The skins were cleaned and subjected to a hot water–acid extraction process to obtain gelatin. The extracted gelatin exhibited high proline and hydroxyproline contents (12.47 and 9.84 g/100 g of amino acids, respectively) and a Bloom strength of 263.9 g, confirming its suitability for film formation. Films were prepared using 2 g of gelatin per 100 g of film-forming solution, with glycerol added at 10 and 20 g/100 g of gelatin. The resulting films were transparent, flexible, and showed uniform surfaces. Increasing the glycerol concentration reduced tensile strength (from 59.4 to 37.9 MPa) but improved elongation at break (from 116% to 159.1%) and modified the films’ thermal behavior. Moreover, Tambatinga gelatin films demonstrated excellent UV-blocking performance (below 300 nm) and lower water vapor permeability compared to other gelatin-based films reported in the literature. These findings highlight the potential of fish skin—typically regarded as industrial waste—as a renewable and high-value raw material for the production of sustainable biopolymers. This approach supports resource efficiency, waste reduction, and the broader goals of sustainable development and circular bioeconomy. Full article

Nearshoring in Mexico is expanding rapidly, yet chronic volatility in the national power grid threatens the reliability and cost-competitiveness of relocated manufacturing lines. To inform strategic mitigation, this study presents a hybrid Fuzzy–CES decision-support framework that embeds the Constant-Elasticity-of-Substitution (CES) production function within a Mamdani Fuzzy-Inference Engine, implemented in both Type-1 and Interval Type-2 variants, to evaluate and optimize production adaptability in energy-constrained environments. Using sector-wide data from Mexico’s automotive industry, key input variables (energy reliability, capital intensity, and labor availability) are objectively quantified and normalized to reflect the realities of regional plant operations. The system linguistically classifies each facility’s production elasticity as low, moderate, or high, and generates actionable recommendations for resource allocation, such as targeted investments in renewable microgrids or workforce strategies. Implemented in MATLAB, simulation results confirm that, while high capital and labor inputs are essential, energy reliability remains the primary bottleneck limiting adaptability; only states with all three strong factors achieve maximum resilience. The Type-2 fuzzy approach demonstrates superior robustness to input uncertainty, enhancing managerial decision-making under volatile grid conditions. In addition, a case study regarding the automotive industry is presented to illustrate how the proposed framework is implemented. The same structure can be used to deploy it in another industry. This research offers a transparent, data-driven tool to inform both firm-level investment and regional policy, directly supporting Mexico’s efforts to sustain competitiveness and resilience in the global shift toward nearshoring. Full article

A novel reduced-component multilevel inverter (MLI) topology is presented to overcome the limitations of conventional multilevel inverters, such as high switching losses, complex modulation, and excessive semiconductor usage. The proposed triple-source cross-connected configuration minimizes conduction paths and reduces voltage stress across switching devices to approximately 45% of the total DC-link voltage. A hybrid carrier-less pulse-width modulation (PWM) strategy, derived from the equal-area criterion, was developed to generate switching pulses without the need for carriers or reference signals. Analytical and experimental analyses demonstrated a significant improvement in power quality, achieving a total harmonic distortion (THD) of 4.3%, compared with 8.2% in conventional PWM schemes, while enhancing the conversion efficiency from 91.5% to 95.2%. Simulation and hardware validation in a nine-level prototype confirmed the superior efficiency, low harmonic distortion, and compactness of the proposed inverter, making it well-suited for renewable energy integration, electric vehicles, and medium-power industrial systems. Full article

This paper presents a novel, robust, and reliable control strategy for renewable energy production systems, leveraging artificial neural networks (ANNs) to optimize performance and efficiency. Unlike conventional ANN approaches that rely on perturbation-based methods, we develop a fundamentally different ANN model incorporating equilibrium points (EPs) that achieve superior regulation of photovoltaic (PV) systems. The efficacy of the proposed approach is evaluated through comparative analysis against the conventional control strategy based on perturb and observe (MPPT/PO), demonstrating a 3.3% improvement in system efficiency (98.3% vs. 95%), a five times faster response time (6 s vs. 30 s), and six-fold reduction in voltage ripple (1% vs. 5.95%). A critical aspect of ANN-based controller design is the learning phase, which is addressed through the integration of deep reinforcement learning (DRL) for primary PV system control. Specifically, a hybrid control architecture combining the Artificial Neural Network based on Equilibrium Points (ANN/EP) model with DRL (ANN/PE-RL) is introduced, utilizing a synergistic integration of two reinforcement learning agents: Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG). The TD3-based hybrid approach achieves an average reward value of 434.78 compared to 422.767 for DDPG, representing a 2.84% performance improvement in tracking maximum power points under imbalanced conditions. This hybrid approach demonstrates significant potential for improving the overall performance of grid-connected PV systems, reducing energy losses from 1.95% to below 1%, offering a promising solution for advanced renewable energy management. Full article

Under the dual carbon goals, microgrids face significant challenges in managing multi-energy flow coupling and maintaining operational robustness with high renewable energy penetration. This paper proposes a novel dynamic pressure-aware spatiotemporal optimization dispatch strategy. The strategy is centered on intelligent energy storage and enables proactive energy allocation for critical pressure moments. We designed and validated the strategy under an ideal benchmark scenario with perfect foresight of the operational cycle. This approach demonstrates its maximum potential for spatiotemporal coordination. On this basis, we propose a Multi-Objective Self-Adaptive Hybrid Enzyme Optimization (MOSHEO) algorithm. The algorithm introduces segmented perturbation initialization, nonlinear search mechanisms, and multi-source fusion strategies. These enhancements improve the algorithm’s global exploration and convergence performance. Specifically, in the ZDT3 test, the IGD metric improved by 7.7% and the SP metric was optimized by 63.4%, while the best HV value of 0.28037 was achieved in the UF4 test. Comprehensive case studies validate the effectiveness of the proposed approach under this ideal setting. Under normal conditions, the strategy successfully eliminates power and thermal deficits of 1120.00 kW and 124.46 kW, respectively, at 19:00. It achieves this through optimal quota allocation, which involved allocating 468.19 kW of electricity at 13:00 and 65.78 kW of thermal energy at 18:00. Under extreme weather, the strategy effectively converts 95.87 kW of electricity to thermal energy at 18:00. This conversion addresses a 444.46 kW thermal deficit. Furthermore, the implementation reduces microgrid cluster trading imbalances from 1300 kW to zero for electricity and from 400 kW to 176.34 kW for thermal energy, significantly enhancing system economics and multi-energy coordination efficiency. This research provides valuable insights and methodological support for advanced microgrid optimization by establishing a performance benchmark, with future work focusing on integration with forecasting techniques. Full article

To address the challenges posed to the secure and reliable operation of the power grid under the “dual-carbon” goals, an optimal planning and investment return analysis method for grid-side energy storage system (GSESS) is proposed, with multi-dimensional grid security requirements being considered. By this method, a decision-making framework for the scientific planning of GSESS is provided, through which both technical and economic viability are balanced. Firstly, an evaluation indicator system for GSESS demand is established, in which loading stress, voltage quality, and renewable energy accommodation capacity are comprehensively considered. The candidate sites are then prioritized by a hybrid subjective-objective weighting method combined with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Subsequently, the top 10% most severe scenarios are identified from historical operational data, and a set of typical extreme scenarios is extracted using an improved K-means clustering algorithm. Based on these scenarios, an optimal capacity planning model incorporating multi-dimensional security constraints is formulated, and the final planning scheme is thereby determined. Furthermore, with the objective of maximizing net revenue from multiple application scenarios, an optimal operational model for GSESS is established. The life-cycle costs and benefits are quantified, and a comprehensive investment return analysis is conducted accordingly. Finally, the proposed methodology is validated through a case study based on the 220 kV substations in QZ City of China. It is demonstrated by the results that through the application of the derived planning scheme, the operational security of the power grid is significantly enhanced, and a promising outlook for investment returns is also exhibited. Full article

Cryptocurrencies utilize blockchain technology to ensure transparency, decentralization, and immutability in financial transactions. It is expected that blockchain applications will significantly impact renewable energy markets. However, there is a lack of studies addressing the energy requirements of digital currencies. This research proposes optimizing a hybrid energy system consisting of distributed renewable and non-renewable energy sources, focusing on cryptocurrency mining. Although previous studies have not yet addressed energy system optimization considering cryptocurrency mining farms, the increasing prominence of such farms highlights the growing need for research in this area. The primary renewable sources in the proposed hybrid system include photovoltaic (PV) panels and wind turbines. We employ diesel generators as backup systems to compensate for the intermittent nature of solar and wind energy production. Besides meeting the demands of urban loads, cryptocurrency mining devices will be considered a major energy consumer. In this article, the optimal configuration of the energy system will be determined based on technical and economic indicators. Additionally, economic evaluations will be conducted to assess the income generated from cryptocurrency mining farms, and appropriate approaches will be identified from both technical and financial perspectives, focusing on return on investment (ROI). Full article

Floating and offshore photovoltaic (FPV) installations present a promising solution for addressing land-use conflicts while enhancing renewable energy production. With an estimated global offshore PV potential of 4000 GW, FPV systems offer unique advantages, such as increased efficiency due to water cooling effects and synergy with other offshore technologies. However, challenges related to installation costs, durability, environmental impacts, and regulatory gaps remain. This review provides a comprehensive and critical analysis of FPV advancements, focusing on inland, nearshore, and offshore applications. A systematic evaluation of recent studies is conducted to assess technological innovations, including material improvements, mooring strategies, and integration with hybrid energy systems. Furthermore, the economic feasibility of FPVs is analysed, highlighting cost–benefit trade-offs, financing strategies, and policy frameworks necessary for large-scale deployment. Environmental concerns, such as biofouling, wave-induced stress, and impacts on aquatic ecosystems, are also examined. The findings indicate that while FPV technology has demonstrated significant potential in enhancing solar energy yield and water conservation, its scalability is hindered by high capital costs and the absence of standardised regulations. Future research should focus on developing robust offshore floating photovoltaic (OFPV) designs, optimising material durability, and establishing regulatory guidelines to facilitate widespread adoption. By addressing these challenges, FPVs can play a critical role in achieving global climate goals and accelerating the transition to sustainable energy systems. Full article

The elevated needs for high-performance energy storage, dictated by electrification, renewable sources integration, and the global increase in interconnected devices, have placed batteries to the forefront of technological research. Additive manufacturing is increasingly recognized as a compelling approach to advance battery research and application by enabling tailored control over design, pore geometry, materials, and integration. This perspective work examines the opportunities and challenges associated with utilizing additive manufacturing as an enabling battery manufacturing technology. Recent advances in the additive fabrication of electrodes, electrolytes, separators, and integrated devices are examined, exhibiting the potential to acheive electrochemical performance, design adaptability, and sustainability. At the same time, key challenges—including materials formulation, reproducibility, economic feasibility, and regulatory uncertainty—are discussed as limiting factors that must be addressed for achieving the expected results. Rather than being viewed as a replacement for conventional gigafactory-scale production, additive manufacturing is positioned as a complementary fabrication technique that can deliver value in niche, distributed, and application-specific contexts. This work concludes by outlining research and policy priorities that could accelerate the maturation of 3D-printed batteries, stressing the importance of hybrid manufacturing, multifunctional printable materials, circular economy integration, and carefully phased timelines for deployment. Moreover, by enabling customized form factors, improved device–user interfaces, and seamless integration into smart, automated environments, additive manufacturing has the potential to significantly enhance user experience across emerging battery applications. In this context, this perspective provides a grounded assessment of how additive fabrication methods may contribute to next-generation battery technologies that not only improve electrochemical performance but also enhance user interaction, reliability, and seamless integration within automated and control-driven systems. Full article

Carbon dioxide (CO₂), the primary anthropogenic greenhouse gas, drives significant and potentially irreversible impacts on ecosystems, biodiversity, and human health. Achieving the Paris Agreement target of limiting global warming to well below 2 °C, ideally 1.5 °C, requires rapid and substantial global emission reductions. While recent decades have seen advances in clean energy technologies, carbon capture, utilization, and storage (CCUS) remain essential for deep decarbonization. Despite proven technical readiness, large-scale carbon capture and storage (CCS) deployment has lagged initial targets. This review evaluates CCS technologies and their contributions to net-zero objectives, with emphasis on sector-specific applications. We found that, in the iron and steel industry, post-combustion CCS and oxy-combustion demonstrate potential to achieve the highest CO₂ capture efficiencies, whereas cement decarbonization is best supported by oxy-fuel combustion, calcium looping, and emerging direct capture methods. For petrochemical and refining operations, oxy-combustion, post-combustion, and chemical looping offer effective process integration and energy efficiency gains. Direct air capture (DAC) stands out for its siting flexibility, low land-use conflict, and ability to remove atmospheric CO₂, but it’s hindered by high costs (~$100–1000/t CO₂). Conversely, post-combustion capture is more cost-effective (~$47–76/t CO₂) and compatible with existing infrastructure. CCUS could deliver ~8% of required emission reductions for net-zero by 2050, equivalent to ~6 Gt CO₂ annually. Scaling deployment will require overcoming challenges through material innovations aided by artificial intelligence (AI) and machine learning, improving capture efficiency, integrating CCS with renewable hybrid systems, and establishing strong, coordinated policy frameworks. Full article

With a large number of traditional thermal power units being replaced by inverter-based resources, the system inertia and regulation capability have significantly decreased in certain countries, exposing a critical gap in traditional generation-side-dominated frequency regulation strategies. The decline in system inertia deteriorates frequency dynamics, creating a critical need for load-side regulation. To enhance frequency stability in low-inertia power systems, this paper proposes a frequency regulation strategy for thermostatically controlled loads (TCLs). The strategy incorporates a differential deadband that adjusts response thresholds based on frequency deviation, along with dynamic droop coefficients that self-adapt according to real-time TCL capacity. First, the operational principles of TCLs and the frequency response characteristics of thermal power units are analyzed to establish the foundation for load-side frequency regulation. Second, building upon the spatiotemporal distribution characteristics of system frequency, the nodal frequency under high renewable energy penetration is derived, and a differential dead zone setting method for TCLs is proposed. Then, a dynamic droop coefficient tuning method is developed to enable adaptive parameter adjustment according to the real-time regulation capacity of TCLs. Finally, these key elements are integrated within a hybrid control framework to formulate the complete TCL frequency regulation strategy. Simulation results demonstrate a 0.342% improvement in frequency nadir and 0.253% reduction in settling time compared to conventional methods, while ensuring reliable TCL operation. This work presents a validated solution for enhancing frequency stability in renewable-rich power systems, where the proposed framework with nodal frequency-based deadbands and adaptive droop coefficients demonstrates effective regulation capability under low-inertia conditions. Full article

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations. Full article

Under the drive of the “dual carbon” goals, the insufficient frequency regulation capability of nuclear power as a baseload source and the dynamic demand of integrating a high proportion of renewable energy into the grid have increasingly highlighted conflicts. The inherent minute-level regulation inertia of nuclear power units struggles to cope with second-level frequency fluctuations in the grid, leading to an increased risk of system instability. There is an urgent need for energy storage technologies to fill the millisecond-level power support gap for nuclear power frequency regulation. This paper, focusing on nuclear power frequency regulation scenarios, constructs a “Technology–Economy–Policy” multidimensional energy storage evaluation system for the first time. Through a systematic analysis of 11 key indicators, such as response time and safety, the paper selects energy storage technologies suitable for nuclear power frequency regulation scenarios and proposes a hybrid energy storage optimization strategy. The research provides a systematic evaluation framework and empirical support for the selection of energy storage for nuclear power frequency regulation, with significant practical value in improving grid dynamic stability and promoting the construction of new power systems under the “dual carbon” goals. Full article