Search Results (1,839)

Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units. Full article

By 2050, global population growth will lead to a significant increase in demand for animal-based products, including seafood. Aquaculture is a key solution to meet these needs while reducing pressure on wild aquatic stocks. However, its environmental footprint and energy demand remain open concerns. This study explores the co-location of offshore aquaculture with a wave energy converter—WaveRoller—as a renewable power source. Using a 44-year dataset from the Portuguese coast near Peniche, the analysis evaluates the survivability and operation of the WaveRoller, long-term percentile trends, seasonal energy production, extrapolated extreme events using probabilistic modeling, and confidence intervals for energy costs. A scenario-based range of energy demand is constructed from a baseline blue mussel production of over 400 tons/yr. The K-Means clustering method is applied to reduce data size while maintaining its representativeness. Results show that a 600 kW WaveRoller is similarly suited to operational wave conditions compared to a 1000 kW device, though it excels when aquaculture energy demand peaks in Summertime. The probability that a single WaveRoller fails to meet annual aquaculture energy needs is nearly zero, though, during Summer, it can become statistically significant. The opposite is verified on survivability during Winter, under harsher wave conditions. The Levelized Cost of Energy is calculated for different expenditure scenarios, with minimum values slightly under 200 EUR/MWh being reported only under ideal conditions. Future work should include climate change scenarios and life cycle assessments to better evaluate environmental impacts and techno-economic viability. 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

With rising energy demand and the need for sustainable waste treatment, anaerobic digestion (AD) has emerged as a key technology for converting organic residues into renewable energy. However, predicting methane yield in full-scale facilities remains challenging due to the complexity of AD processes, the variability of feedstocks, and the impracticality of frequent biochemical methane potential (BMP) testing. In this study, we developed a simple, data-driven approach to forecast methane production in a commercial-scale digester co-digesting manure and food waste. The model employs weekly cumulative BMP of feedstock mixtures, calculated from literature values, as the explanatory variable. The model achieved an R² of 0.70 and a forecast mean absolute percentage error (MAPE) of 7.4, indicating its potential for full-scale AD prediction. Importantly, the analysis revealed a long-run equilibrium between BMP and methane yield, with deviations corrected within roughly one month—closely matching the system’s hydraulic retention time. These findings demonstrate that literature-based BMP values can be used to reliably predict methane yield in operating AD systems, offering a low-cost and scalable tool to support decision-making in waste management and biogas plant operations. Full article

The oscillation stability of renewable energy plants under varying grid strengths can be improved through the optimized allocation of grid-following (GFL) and grid-forming (GFM) power converter devices. However, in practical operation, the wide variations in both the output of renewable energy plants and the strength of the grid present significant challenges in simultaneously ensuring stability, economic efficiency, and robustness. To address this, this paper proposes a two-level optimization method for the allocation of GFL and GFM devices, aiming to enhance oscillation stability in renewable energy plants. The method considers the complementary dynamic behaviors of GFL and GFM strategies, whose complementary dynamic behaviors contribute to balanced and stable operation. The upper-level optimization model accounts for the wide range of variability in renewable plant outputs, with the primary objective and constraint being the assurance of oscillation stability under low short-circuit ratio (SCR) conditions at a minimal cost. Based on the GFM configuration determined by the upper-level model, the lower-level optimization model further evaluates the upper SCR limit within which oscillation stability can still be maintained. This prevents instability that may arise from GFM devices operating under high-SCR conditions. By iteratively solving the upper- and lower-level models, an optimized GFL-GFM allocation strategy is obtained, which ensures oscillation stability across a wide SCR range while balancing cost-effectiveness and practical operability. Case studies are also conducted to validate the method. It is indicated that when SCR = 1.5, configuring 15% of the wind generators in the GFM strategy can ensure stability of the wind plant across typical operating scenarios, while when SCR > 3, switching these generators to the GFL strategy can likewise avoid the oscillation issues. Full article

Integrated energy systems (IES) are central to sustainable energy transitions because sector coupling can raise renewable utilization and cut greenhouse gas emissions. Yet, traditional optimizers often become trapped in local optima and struggle with multi-objective trade-offs between economic and environmental goals. This study applies the hippopotamus optimization algorithm (HOA) to the sustainability-oriented, multi-objective operation of an electricity–gas–heat IES that incorporates power-to-gas (P2G), photovoltaic generation, and wind power. We jointly minimize operating cost and carbon emissions while improving renewable energy utilization. In comparative tests against pigeon-inspired optimization (PIO) and particle swarm optimization (PSO), HOA achieves superior Pareto performance, lowering operating costs by ~1.5%, increasing energy utilization by 16.3%, and reducing greenhouse gas emissions by 23%. These gains stem from HOA’s stronger exploration–exploitation balance and the flexibility introduced by P2G, which converts surplus electricity into storable gas to support heat and power demands. The results confirm that HOA provides an effective decision tool for sustainable IES operation, enabling deeper variable-renewable integration, lower system-wide emissions, and improved economic outcomes, thereby offering practical guidance for utilities and planners pursuing cost-effective decarbonization. Full article

This work presents a preliminary “Strengths, Weaknesses, Opportunities, and Threats” (SWOT) analysis followed by a “Correct, Adapt, Maintain, and Explore” (CAME) analysis on wood sawdust biofuel. New designs of sawdust biofuels boilers and reactors require gathering relevant information on the main characteristics of sawdust biofuels. Optimisation algorithms require not only the numerical parameters needed to find optimal solutions but also the consideration of scenarios related to the use of this type of biofuel. This work provides complementary information to create a comprehensive framework for assessing the viability and sustainability of integrating wood sawdust into diverse energy production systems. This includes an examination of the current state of sawdust utilisation, its environmental implications, and the potential of valorising this abundant biomass resource. This review further delves into the technical aspects of converting sawdust into biofuel pellets, examining various technical processes involved in its physical analysis. The intended audience of this review encompasses researchers, mechanical designers, policymakers, and industry strategists and stakeholders interested in sustainable energy solutions and waste management strategies, providing a holistic perspective on the opportunities presented by wood sawdust as a renewable energy source. Full article

The proliferation of artificial intelligence (AI) and online services has significantly escalated the demand for computing and storage resources, which are fundamentally enabled by cloud computing infrastructure. As the backbone of cloud computing services, data centers have undergone continuous expansion in scale, consequently leading to significant energy consumption and a significant carbon footprint. To effectively mitigate the environmental impact, the strategy should prioritize the integration of renewable energy, while simultaneously minimizing other contributing factors such as energy consumption. Achieving both objectives simultaneously requires a fine-grained, dynamic approach to workload management. To this end, this study proposes a comprehensive container placement strategy that integrates a dynamic priority-based container selection algorithm with a multi-factor single-objective container placement algorithm based on the Dream Optimization Algorithm (DOA). The placement algorithm converts multiple factors—including load balancing in multi-data center environments, energy consumption, renewable energy utilization rate, carbon emissions, Service Level Agreement Violation (SLAV), and container migration costs—into a comprehensive fitness metric. Experimental results on Google and Alibaba datasets show our method consistently achieves the highest renewable energy utilization rates (up to 92.08%) and the lowest carbon emissions. Furthermore, our integrated strategy demonstrates a superior trade-off, reducing migration counts by up to 16.3% and SLAV by 12.4% compared to baselines, while maintaining excellent green performance. This establishes our method as a practical and effective solution for sustainable cloud computing. Full article

Renewable wind energy systems widely employ doubly fed induction generators (DFIGs), where efficient converter control ensures grid-integrated power system stability and reliability. Conventional proportional–integral (PI) controller tuning methods often encounter challenges with nonlinear dynamics and parameter variations, resulting in reduced adaptability and efficiency. To address this, we present an owl search optimization (OSO)-based tuning strategy for PI controllers in DFIG back-to-back converters. Inspired by the hunting behavior of owls, OSO provides robust global search capabilities and resilience against premature convergence. The proposed method is evaluated in MATLAB/Simulink and benchmarked against particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) under step wind variations, turbulence, and grid disturbances. Simulation results demonstrate that OSO achieves superior performance, with 96.4% efficiency, reduced power losses (~40 kW), faster convergence (<400 ms), shorter settling time (<345 ms), and minimal oscillations (0.002). These findings establish OSO as a robust and efficient optimization approach for DFIG-based wind energy systems, delivering enhanced dynamic response and improved grid stability. Full article

In this study, the authors demonstrate the development and evaluation of an optimal frequency control controller for an interlinked two-area power system that incorporates Renewable Energy Sources (RESs). In decentralized power grids, the Load Frequency Control (LFC) system allows scheduled tie-line power as well as system frequency to be reimposed to their nominal values. Designing an advanced controller might enhance the functionality of the LFC mechanism. This article illustrates the possible impacts of converter capacitors using the new High-Voltage Direct Current (HVDC) tie-line model as well as the Inertia Emulation Technique (IET). This paper suggests a new adaptive control procedure for the expected LFC mechanism: an ANN-based (PI^λ + PI^λf) controller. The authors evaluate which control parameters are most effective using a modified version of the Quasi-Opposition-learning-based Reptile Search Algorithm (QORSA) method. Software called MATLAB/Simulink-2015 is used to create this arrangement. The use of established techniques for handling step as well as random load disturbances has enabled an evaluation of the suggested LFC architecture’s efficacy. An IET-based HVDC tie-line reduces overshoot by 100% in Areas 1 and 2 (Area 1 frequency deviation, i.e., ∆f₁, as well as Area 2 frequency deviation, i.e., ∆f₂). When considering SLD, the suggested controller outperforms the most widely used alternative settings. The IEEE-39 bus system has been changed by the addition of RESs. The IEEE-39 bus system is composed of three control areas. It is confirmed how the IEEE-39 bus system reacts to changes in frequency in Areas 1, 2, and 3. It is illustrated how to use the suggested controller in the modified IEEE-39 bus system, accompanied by real-time load variations. Recent research indicates that the suggested control method is better and more efficient due to its 100% decrease in overshoot in Areas 1 and 2 and quick response time. Full article

With the increasing penetration of renewable energy generation, large-scale voltage dips may cause significant active power deficits and threaten system frequency stability. To address the issue, this article proposes a two-stage control strategy to enhance the frequency support capability of renewable energy plants by maximizing converter utilization during asymmetric grid voltage dips. First, a qualitative analysis of converter active power capacity considering current capacity constraints under grid faults is conducted to establish the basis for mitigating system-wide active power deficits. Second, individual phase current constraints are formulated for converters under asymmetric voltage conditions to achieve full utilization of converter capacity. Based on this, a two-stage control strategy for renewable energy plants is proposed, where plant-level convex optimization models for both pre-fault and post-fault conditions are established. By optimally allocating current references of converters within the plants, the requirement of grid codes is satisfied, and the overall frequency support capability of plants is effectively improved. Simulation results demonstrate that the proposed strategy raises the system frequency nadir from 49.58 Hz to 49.66 Hz under a minor fault and from 49.06 Hz to 49.11 Hz under a severe fault, confirming its effectiveness in enhancing the frequency support capability of renewable energy plants. Full article

Day-ahead wind power forecasting is often limited to on-site Supervisory Control and Data Acquisition (SCADA) datasets without Numerical Weather Prediction (NWP) information. In this regime, practitioners extend autoregressive windows over many variables, so the input size grows with both features and lags. Many lag–feature pairs are redundant, increasing the training time and overfitting risk. A lightweight, differentiable joint gate over the lag–feature plane trained with a temperature-annealed sigmoid is proposed. Sparsity is induced by capped penalties that (i) bound the total open mass to the top-M features and (ii), within each selected feature, bound the mass to the top-k lags. An additional budget-aware off-state term pushes unused logits negative in proportion to the excess density over the $(M\times k)$ budget. A lightweight, per-feature softmax pooling head supplies the forecasting loss during selection. After training, the learned probabilities are converted into compact, non-contiguous lag–feature subsets (top-M features; per-feature top-k lags) and reused by downstream predictors. Tests on the Offshore Renewable Energy (ORE) Catapult Platform for Operational Data (POD) from the Levenmouth Demonstration Turbine (LDT) dataset show that the joint gate reduces the input dimensionality and training time while improving accuracy and stability relative to Pearson’s correlation, mutual information, and cross-correlation function selectors. Full article

The ocean offers abundant wind and wave energy resources. This paper proposes an integrated concept that co-locates a semi-submersible floating wind platform with wave energy converters (WECs) to exploit the geographical consistency of these resources. By sharing the platform foundation and power transmission infrastructure, this integrated system enhances the utilization efficiency of marine space and renewable energy. Inspired by the principles of the Tuned Mass Damper (TMD) and leveraging mature hydraulic technologies from wave energy conversion and offshore drilling heave compensation systems, this study introduces a novel scheme. This scheme integrates a heave plate with a hydraulic Power Take-Off (PTO) system, functionally acting as a wave energy converter, to the floating platform. The primary objective is to mitigate the platform’s motion response while simultaneously generating electricity. The research investigates the motion performance improvement of this integrated platform under South China Sea conditions. The results demonstrate that the proposed WEC–PTO system not only improves the platform’s wave resistance and adaptability to deep-sea environments but also increases the overall efficiency of marine energy equipment deployment. Full article

Floating offshore wind–wave hybrid systems, as a novel structural form integrating floating wind turbine foundations and WECs, can effectively enhance the efficiency of renewable energy utilization when properly designed. A numerical model is established to investigate the dynamic responses of a wind–wave hybrid system comprising a semi-submersible FOWT and PA wave energy converters. The optimal damping values of the PTO system for the wind–wave hybrid system are determined based on an NSGA-II. Subsequently, a comparative analysis of dynamic responses is carried out for the PTO system with different states: latching, fully released, and optimal damping. Under the same extreme irregular wave conditions, the pitch motion of the FOWT with optimal damping is reduced to 71% and 50% compared to the latching and fully released states, respectively. The maximum mooring line tension in the optimal damping state is similar to that in the fully released state, but nearly 40% lower than in the latching state. This optimal control strategy not only sustains power generation but also enhances structural stability and efficiency compared to traditional survival strategies, offering a promising approach for cost-effective offshore wind and wave energy utilization. Full article

The renewable-energy-powered electrocatalytic CO₂ reduction reaction (CO₂RR) efficiently converts CO₂ into high-value chemicals and fuels, offering a promising approach to addressing environmental and energy sustainability challenges. This process is of immense significance for constructing a sustainable artificial carbon cycle. Cu-based catalysts exhibit remarkable catalytic activity and broad product selectivity in CO₂RR, which can be attributed to their excellent electrical conductivity, moderate adsorption energy, and unique electronic structure. This review comprehensively summarizes the advantages, practical applications, and mechanistic insights of Cu-based catalysts in CO₂RR, with a systematic based on recent advances in tuning strategies via electronic effects and structural design. Specifically, it emphasizes the influence of electronic structure tuning (electron-donating/-withdrawing effects and steric hindrance effects), active center tuning (single-atom catalysts, heterogeneous synergetic effects, and polymer modification), and surface structure (morphology effect, valence-state effect, and crystalline-facet effect) influences on catalytic performance. By rationally designing the catalyst structure, the adsorption behavior of reaction intermediates can be effectively regulated, thereby enabling the highly selective generation of target products. The objective of this paper is to provide a theoretical framework and actionable strategies for the structural design and catalytic performance optimization of Cu-based catalysts, with the ultimate goal of promoting the development and practical application of efficient CO₂RR catalytic systems. Full article