Search Results (1,367)

The large-scale integration of variable renewable energy sources (RES) such as solar and wind into transmission networks poses significant challenges to grid stability, operational efficiency, and economic dispatch. Battery Energy Storage Systems (BESS) offer a flexible solution, but their optimal placement remains critical to maximizing technical and economic benefits. This paper presents a multi-index, scenario-based framework for optimal BESS siting in a modified IEEE 118-bus transmission system under high renewable penetration. Six complementary indices are employed: Voltage Deviation Index (VDI), Fast Voltage Stability Index (FVSI), Line Congestion Index (LCI), Bus Congestion Index (BCI), Z-bus Sensitivity Index (ZBSI), and nodal price difference (Δλ). Eight extreme scenarios, combining high/low solar, wind, and hydro generation under peak load, are used to identify vulnerable buses. Five weighting case studies reflect different stakeholder priorities: voltage stability, congestion relief, energy arbitrage, loss reduction, and equal weightage. Results show that the congestion relief case achieves the lowest daily operating cost (approx. $8000 less than the base case) and the highest net economic benefit, while the loss reduction case delivers the greatest reduction in active (34 MW) and reactive (169 MVAr) power losses, compared to the base case. The proposed framework demonstrates that integrating technical and market-based indicators enables more robust and economically attractive BESS placement. This work provides a practical, data-driven planning tool for grid operators and investors aiming to enhance transmission system sustainability under high-RES variability. Full article

The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. This paper proposes a hybrid analytical–machine learning framework for efficient voltage stability assessment and classification. The proposed approach consists of two stages. First, a power flow analysis is performed to compute the Fast Voltage Stability Index (FVSI) and quantify the proximity of the system operating conditions to voltage instability. Then, the FVSI values are converted into binary stability labels to formulate a supervised classification problem. In the second stage, the Extreme Gradient Boosting (XGBoost) algorithm is employed to learn the relationship between system operating variables and the corresponding stability states. The performance of the proposed method is evaluated on the IEEE 30-bus system and compared with that of conventional machine learning and deep learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNNs). The simulation results show that the XGBoost-based framework outperforms the benchmark models in terms of classification accuracy, robustness, and computational efficiency. The proposed method provides a fast, reliable, and interpretable solution for real-time voltage stability monitoring. Therefore, it is suitable for modern smart grid applications. Full article

The development of biodegradable materials from renewable sources represents a promising strategy to reduce the environmental impact associated with conventional plastics. This study aimed to optimize potato starch-based bioplastics incorporated with lemongrass essential oil (Cymbopogon citratus) using Response Surface Methodology (RSM) and to evaluate their application in fresh pineapple (Ananas comosus) preservation. A Box–Behnken experimental design with three factors and three levels was applied, considering potato starch concentration (4–8 g), essential oil content (100–300 µL), and glycerol volume (1–2 mL) as independent variables. The effects of these factors on tensile strength, elongation at break, and Young’s modulus were analyzed using a quadratic model. The optimized formulation exhibited a desirability value of 1.00, consisting of 4.13 g of starch, 131.59 µL of essential oil, and 1.43 mL of glycerol, with predicted values of 2.91 MPa tensile strength, 53.18% elongation at break, and 15.18 MPa Young’s modulus. Experimental validation showed good agreement with model predictions, with relative errors below 20%. The optimized bioplastic was subsequently applied as a coating for fresh-cut pineapple stored under refrigeration (4–8 °C), reducing weight loss and improving the stability of physicochemical and textural properties compared with the control treatment. The results demonstrate that potato starch-based bioplastics containing lemongrass essential oil have potential as active biodegradable coatings for extending the quality preservation of fresh pineapple. Full article

The transition from conventional silica nanoparticle (SiNP) production based on purified alkoxysilanes and high-temperature flame hydrolysis of silicon tetrachloride to renewable and waste-derived silicon resources requires more than precursor substitution. It requires a mechanistic understanding of how feedstock mineralogy, silicon speciation, impurity chemistry, and processing history propagate through dissolution, nucleation, condensation, gelation, aging, drying, and pore evolution to determine material performance, environmental burden, and manufacturing feasibility. Although previous reviews have established the technical feasibility of producing silica from secondary resources, their predominant organization by feedstock, synthesis route, or application provides limited ability to explain why nominally similar processes generate materials with markedly different structural and functional properties. This review addresses these through a resource-pull, feedstock-to-function framework that links resource chemistry and process design to critical material attributes, application-specific specifications, sustainability, and scale-up requirements. Agricultural residues, industrial by-products, geothermal resources, waste glass, and fluorosilicate streams are critically compared according to silicon form and phase, reactivity, impurity profile, compositional variability, purification demand, and attainable product quality. Particular attention is given to waste-derived alkaline silicate systems, in which molecular, oligomeric, and colloidal silica coexist and therefore require characterization beyond bulk SiO₂ concentration. Established and emerging processing strategies, including controlled combustion and alkaline extraction, alkali fusion, ambient-pressure drying, microwave and mechanochemical activation, biogenic and biomimetic templating, and continuous processing, are evaluated according to their mechanistic effects, technological maturity, structural control, and demands for energy, reagents, water, solvents, effluent treatment, and capital. Across these routes, gelation and aging emerge as critical transfer stages through which feedstock composition is translated into network connectivity, pore architecture, shrinkage behavior, and ultimately functional performance. Evidence from secondary-source aerogels further shows that properly controlled waste-derived systems can attain BET surface areas of approximately 350–500 m² g⁻¹, within the textural range of many alkoxide-derived materials, indicating that feedstock variability, impurity management, and process control are more important constraints than an inherently lower performance ceiling. On this basis, this review proposes a minimum evidence framework comprising feedstock traceability, intermediate-speciation and colloidal characterization, silicon mass balance, gelation and aging metrics, application-specific qualification criteria, performance-normalized life cycle and techno-economic assessment, process analytical control, and staged pilot validation. Collectively, these principles provide a mechanistically grounded basis for moving sustainable silica synthesis beyond isolated proof-of-concept demonstrations toward reproducible, scalable, application-matched, and commercially credible manufacturing platforms. Full article

Poly(β-L-malic acid) (PMLA) is a water-soluble, biodegradable aliphatic polyester whose pendant carboxyl groups support chemical functionalization for biomedical, packaging, and materials applications. Microbial fermentation can use pure sugars and biomass-derived carbon sources under mild conditions, but industrial translation remains constrained by feedstock cost and variability, strain performance, oxygen and pH control, pretreatment-derived inhibitors, and downstream recovery. This review therefore focuses on the fermentative production of PMLA from refined and renewable carbon sources, the microorganisms and metabolic routes involved, and the process variables that govern titer, yield, productivity, molecular weight, and purification. Agricultural and forestry feedstocks are compared according to their actual carbohydrate class and processing requirements. Bamboo shoot shell hydrolysate is treated as an emerging case study rather than an established production platform: one accepted shake-flask study directly demonstrated PMLA production by Aureobasidium pullulans NRRL Y-2311-1, but controlled bioreactor validation, reproducibility, techno-economic analysis, and application-specific product qualification remain to be further investigated. The review also examines autohydrolysis, low-molecular-weight PMLA for biomedical use, furan inhibition, membrane and ion-exchange purification, and the limits of current economic comparisons. This evidence-based framing identifies where bamboo-processing residues may contribute to renewable PMLA production while distinguishing laboratory feasibility from industrial readiness. Full article

With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems. Full article

The massive integration of variable renewable energy sources (vRES) in Central Europe is fundamentally transforming electricity price formation and straining transmission grids. However, existing academic metrics, such as the RES Capture Price, offer only a static view of investor revenues and fail to capture dynamic market rigidity and systemic risks during periods of high instantaneous vRES penetration. This study addresses this literature gap by introducing two novel and transparent methodological parameters: Market Exposure to Green Overproduction (MEGO) and the Price Divergence Coefficient (PDC). Formulated as conditional non-parametric indicators, the MEGO index quantifies the conditional probability of price collapse and the loss of market elasticity during hours when vRES penetration exceeds critical thresholds (α = 0.50 to 0.80) of systemic load. Conversely, the PDC index measures the frequency of substantial price non-convergence across neighbouring bidding zones (CZ, PL, FR) relative to the German reference market (DE). Based on an extensive dataset spanning from 2015 to mid-2026—capturing the transition to 15 min market time units— the empirical results reveal a distinct change in market behaviour. While the frequency of price collapse during high-vRES periods was lower in earlier years and temporarily reduced during the 2022 energy crisis, the post-crisis period (2024–2026) exhibits substantially higher MEGO values, with periods in which wind and solar generation exceeded 80% of instantaneous system load being associated with prices at or below 0 EUR/MWh in up to 60% of the evaluated intervals. Concurrently, the PDC analysis reveals persistent spatial price non-convergence, particularly in France and Poland. These patterns coincided with major changes in European electricity-market conditions, including the implementation of Core Flow-Based Market Coupling, variations in nuclear availability and evolving cross-border network conditions; however, the PDC indicator alone does not permit causal attribution to any individual factor. The proposed MEGO and PDC parameters provide policymakers, transmission system operators (TSOs), and investors with an intuitive diagnostic framework for dimensioning grid flexibility, energy storage, and cross-border infrastructure in the decarbonization era. Full article

Green hydrogen is a key energy carrier in Power-to-Liquid (PtL) pathways for the production of sustainable synthetic fuels, contributing to the decarbonization of the industrial and transport sectors. However, the intermittent nature of renewable energy sources and the variable hydrogen requirements needed to maintain the appropriate stoichiometric ratio for synthesis processes necessitate regulating hydrogen production according to process demand, rather than maximizing its generation. This article proposes an intelligent control strategy for alkaline water electrolysis, in which the hydrogen production target is determined from the stoichiometric requirements of synthetic methanol production, based on available carbon dioxide. ANN models were developed using the experimental data, incorporating both classical and conformable activation functions in the hidden layer. Based on the selected models, the ANNi was formulated, and PSO and GA were used to determine the required feed current according to hydrogen demand. The proposed methodology was evaluated under a dynamic hydrogen-demand profile derived from the stoichiometric requirements of methanol synthesis. The results show that the proposed controllers closely track changes in hydrogen demand. After each change in the setpoint, the H₂/CO₂ ratio returned to a $\pm 2\%$ band around the stoichiometric setpoint in approximately 0.98 s for ICANNi-PSO and 0.96 s for ICANNi-GA. Furthermore, some conformable activation functions achieved performance comparable to that of classical activation functions while using fewer neurons in the hidden layer. Both optimization algorithms provided comparable tracking performance under the evaluated conditions. Full article

Planning renewable energy bases with chronological source-grid-storage simulation makes exhaustive capacity screening impractical. This study develops a phase-adaptive constrained active-sampling framework whose core mechanism is a simulator-verified phase switch: a probability-of-feasibility-weighted lower confidence bound (PoF-LCB) directs the search until the first verified feasible plan is found, after which constrained expected improvement (CEI) directs economic refinement, supplemented by bounded optimal-neighborhood and constraint-boundary ranking refinements. Gaussian-process surrogates decide only the evaluation order; the reported objective and all four engineering constraints—photovoltaic curtailment, loss-of-load energy, capacity credit, and flexibility scarcity—are verified exclusively by the original 8760 h simulator. In a paired 2 × 2 factorial experiment over 30 common initial designs on a 125-candidate pool, the phase switch raised exact-optimum recovery from 24/30 to 30/30 (exact McNemar p = 0.03125), and the full rule maintained 30/30 under two unseen profile seeds where CEI achieved 24/30 and 22/30. The full rule further recovered the exact optimum of a 1224-candidate pool in 30/30 runs within 20 evaluations and of a six-variable 729-point grid within 75 evaluations, using roughly 2–10% of the exhaustive simulation budget. Constraint-slack and guard-band reporting, candidate-domain audits, and repeated wall-clock measurements turn the recommendation into auditable planning decisions, with all evidence drawn from a reproducible synthetic benchmark. Full article

The growing share of variable renewable energy sources increases the need for operational flexibility in power systems. In regions with cascaded hydropower systems, upgrading existing plants may be a more practical short-term planning option than developing new hydropower facilities. This study employed a multi-criteria decision-making (MCDM) framework based on the VIKOR method to screen and prioritize existing hydropower plants for potential infrastructure upgrading and capacity development in the Skellefte River, located in Northern Sweden. According to this prefeasibility study, six hydropower stations with installed capacities above 50 MW along that river were evaluated using six technical criteria: installed capacity, hydraulic head, efficiency, generation cost, average turbine discharge, and normal annual production; their weights were derived using the Shannon entropy method to minimize subjectivity. The ranking suggests Gallejaur, Kvistforsen, and Bastusel as the most favorable alternatives, mainly due to their strong performance in annual production and hydraulic head. Vargfors, Krångfors, and Selsforsen rank lower because of head and/or production constraints. The proposed pre-feasibility hydropower ranking workflow provides a transparent and reproducible preliminary technical screening tool. More detailed studies, including hydraulic cascade operation, environmental permitting and grid constraints, are required before practical implementation and feasibility assessment studies for future investigations. Full article

This study investigates how institutional quality conditions the relationship between energy transition and environmental sustainability in nine CIS economies over the period 1996–2024, drawing on annual panel data sourced from the World Development Indicators. In the empirical framework, carbon dioxide emissions are specified as the dependent variable, while industrial output, foreign direct investment (FDI), renewable energy consumption, economic growth, trade openness, overall energy use, and an institutional quality index are included as key determinants of environmental pressure. Methodologically, the paper employs second-generation panel econometric techniques, commencing with cross-sectional dependence diagnostics and panel unit root tests, and proceeding to long-run estimation through FMOLS and CCR. The robustness of these estimates is reinforced using Driscoll-Kraay standard errors, while the System-GMM estimator is applied to address heteroskedasticity, serial correlation, cross-sectional dependence, and endogeneity concerns. The results indicate that industrial activity, energy consumption, and FDI significantly increase CO₂ emissions, whereas greater reliance on renewable energy and stronger institutional quality help to alleviate environmental degradation. Under more rigorous specifications, trade openness and economic growth are found to reduce emissions, pointing to emerging decoupling patterns within CIS countries. Importantly, the interaction between renewable energy and institutional quality reveals a pronounced complementary effect, suggesting that stronger governance frameworks amplify the environmental benefits of energy transition. Taken together, the findings underscore that environmental sustainability across CIS economies is jointly determined by structural, economic, and institutional factors, with institutional quality serving as a critical lever for advancing progress toward SDG 13. Full article

Accurate identification of customer transformer relationships in distribution sub-zones is a fundamental prerequisite for the refined management of low-voltage distribution networks and the integration of distributed generation sources. Addressing current issues such as missing records, non-standard wiring and unclear boundaries between multiple sub-zones, this paper proposes an identification method based on the Power Admittance Regression Algorithm (PARA). Based on the fundamental laws of electrical circuits, this method constructs a regressible model of the linear relationship between the total admittance at the transformer end and the admittances at each consumer end. By utilising electrical data collected simultaneously from smart metres and distribution transformer terminals, it formulates the identification of consumer transformer relationships as a problem of minimising regression residuals. For three typical operating conditions—pure residential load, mixed residential and commercial load, and photovoltaic connection at the feeder terminus—constrained least-squares regression models and binary regression models incorporating PV variables were established respectively; ridge regression regularisation was introduced to suppress multicollinearity and enhance model robustness. Simulation tests were conducted using a dataset comprising 150 consecutive time sections and 70 test nodes (of which 60 were customers within the local substation area and 10 were interference nodes from other substation areas) for validation. The results indicate that, under the three conditions described above, in engineering simulations accounting for three-phase imbalance, random perturbations in line parameters and measurement noise, the average accuracy of this method, as determined by 100 Monte Carlo simulations, was 86.2 percent, 92.8 percent and 93.1 percent respectively, with standard deviations ranging from 1.6% to 1.9%, thereby validating its effectiveness and superiority in scenarios involving complex load structures and the integration of renewable energy. As this work is based on simulation data, further online validation using actual feeder data from electricity consumption data acquisition systems is required. Full article

The transition toward lower-carbon polyolefin manufacturing requires evaluating whether renewable hydrogen can be used in industrial polypropylene production while maintaining acceptable process operation and product quality. In this study, an industrial gas-phase polypropylene production campaign that used electrolytic hydrogen was assessed using statistical and multivariate analyses. A dataset comprising 1441 process observations and more than 100 laboratory measurements was analyzed to characterize process variability, catalyst-feed stability, fouling behavior, and polypropylene quality. The monitored variables included the H₂/C₃, triethylaluminum-to-titanium selectivity-control-agent-to-titanium (TEAL/Ti), SCA/Ti, and TEAL/SCA ratios, production rate, reactor pressure, distributor-plate pressure drop, recycle-system variables, and fouling indicators. Product quality was evaluated through melt flow index, xylene solubles, bulk density, and residual catalyst species. Descriptive statistics, temporal analysis of variance, Pearson correlation analysis, and principal component analysis were applied to identify the main sources of operational variability and their relationships with product quality. During the evaluated campaign, the process maintained an average production rate of 30.62 ± 0.69 t h⁻¹, with low variability in the principal catalyst-feed ratios. The polypropylene exhibited an average melt flow index of 2.10 ± 0.11 g/10 min and a xylene-soluble content of 1.19 ± 0.08 wt.%, both within the specifications considered for the commercial grade produced. Temporal analysis of variance identified catalyst ratios, hydrogen utilization, and production rate as the variables with the largest temporal effects, whereas the distributor-plate fouling factor showed comparatively limited variation. The first two principal components explained 58.99% of the total process variance, with hydrogen utilization, catalyst-related variables, reactor pressure, and space–time yield among the dominant contributors. These results provide industrial-scale evidence that electrolytic hydrogen can be integrated into the investigated polypropylene process while maintaining stable operation and specification-compliant product quality during the evaluated period. However, because no parallel or matched campaign using fossil-derived hydrogen was available under the same plant, catalyst, grade, and operating conditions, the present results should not be interpreted as demonstrating full equivalence or direct replacement of conventional hydrogen. Instead, the study establishes an operational baseline and a multivariate monitoring framework for future comparative validation of the use of renewable hydrogen in polyolefin manufacturing. Full article

Energy storage systems (ESSs) play a crucial role in mitigating the intermittency and variability of renewable energy sources (RESs) and enhancing the stability and reliability of modern power systems. However, the inherent limitations of individual storage technologies, particularly the trade-off between energy density and power density, restrict their ability to satisfy diverse operational requirements. In this context, hybrid energy storage systems (HESSs), which combine complementary storage technologies, such as batteries, supercapacitors, and flywheels, have emerged as an effective solution capable of simultaneously delivering high-energy and high-power performance. This paper presents a comprehensive review of HESS architectures, converter topologies, energy management strategies (EMSs), and applications. The EMS taxonomy is organized into classical and intelligent control. Classical EMS approaches are categorized into filtration-based, rule-based, deadbeat, droop, sliding mode, and fuzzy logic control, whereas intelligent EMS approaches encompass optimization-based methods, including model predictive control, as well as learning-based techniques such as supervised and reinforcement learning. Moreover, HESS applications are examined across grid-scale systems, microgrids, renewable energy systems, transportation, power quality improvement, frequency regulation, peak shaving, and uninterruptible power supply systems. Representative implementations are also reviewed to identify current technological trends, operational challenges, and performance trade-offs. Finally, future research directions are outlined, with emphasis on digital twins, privacy-preserving and explainable learning frameworks, cyber–physical security, and adaptive and scalable EMSs. Full article

Energy planning should focus on actions to save energy and reduce consumption while also implementing renewable energy sources to support sustainable urban development. In addition to global regulations, individual countries also have documents that facilitate energy management at the local level. This is a particularly valuable source of information about resources that influence energy efficiency at the national level. This article analyzes the Low-Emission Economy Plans (LEEPs) developed for selected cities in Poland. Based on selected provisions, fuzzy modeling solutions are proposed to support energy decisions in municipalities. The research thesis assumes that the appropriate selection of criteria for emission reduction, as well as their objectification and hierarchization, when supported by fuzzy logic modeling and multi-criteria analysis, enables local governments to identify key variables and structures and compare decision scenarios relevant to local energy policies. To verify this thesis, an analysis of the LEEP provisions of four city municipalities located in the Kuyavian–Pomeranian Voivodeship was conducted. Based on these criteria, a set was identified, and diagrams were developed to identify variables and concepts that occupy key positions in the modeled pathways leading to emission reductions. Fuzzy logic modeling and multi-criteria analysis were used as decision-support tools in the research process. A comparison of the applied approaches enables the identification and prioritization of variables of greatest importance within the adopted set of criteria. The obtained results allow us to determine how the adopted energy strategies are linked to the implementation of local policy objectives and which relationships play a key role in the modeled decision-making structure. The analysis indicates that the decision-making variants differ in their impact on the paths leading to reduced final energy consumption and greenhouse gas emissions. Variant W1, which is based on investments in renewable energy sources, is strongly associated with the path leading to reduced greenhouse gas emissions, while increased public awareness and acceptance also play a significant role in the model’s structure. The strongest relationships were identified between increased energy efficiency and building energy standards, between building energy standards and reduced final energy consumption, and between reduced final energy consumption and reduced greenhouse gas emissions. The reasoning map thus highlighted the particular importance of the sequence of relationships linking energy efficiency, building energy standards, and reduced final energy consumption. The proposed approach can also provide a basis for further comparisons with solutions used in other countries, thus expanding the possibilities of analyzing low-emission policies at the local and national levels. Full article