The solubility of hydrogen (H2) was experimentally determined in pure water, NaCl solutions, and deep reservoir brines from the Ketzin site using a high-pressure autoclave setup. Experiments were conducted at temperatures up to 373.15 K and pressures up to 200 bar, covering conditions relevant for underground hydrogen storage. Hydrogen solubility increases with pressure, whereas temperature effects are comparatively moderate and partly non-monotonic. Measurements in pure water reproduce literature data and confirm the characteristic temperature inversion behavior of hydrogen dissolution. In saline systems, a pronounced salting-out effect reduces hydrogen solubility relative to pure water. This effect becomes stronger in the complex Ketzin brines, where increasing ionic strength and multi-ion composition further suppress the temperature sensitivity of hydrogen dissolution. At reservoir-relevant conditions, only a small fraction of injected hydrogen is expected to dissolve into the formation brine. A key outcome of this work is the generation of the first experimentally determined hydrogen solubility dataset for natural multi-ion reservoir brines. The dataset provides an experimental basis for future thermodynamic and reservoir-scale modeling studies, supports the assessment of dissolution-driven hydrogen losses, and contributes to reducing uncertainties in the evaluation of underground hydrogen storage performance.
The escalating penetration of distributed photovoltaics (DPV) frequently triggers voltage violations and degrades the static voltage stability within distribution networks. To effectively mitigate these challenges, this paper proposes a dynamic hosting capacity (DHC) assessment method that incorporates the static voltage stability margin (SVSM). First, a set of key stress scenarios (KSS)—specifically capturing extreme conditions of “maximum reverse power flow” and “maximum forward heavy loading”—is generated utilizing non-parametric probabilistic forecasting. Next, an optimization model designed to maximize PV hosting capacity is formulated, innovatively enforcing the SVSM as a hard constraint to guarantee system robustness during heavy-load transitions. To overcome the intensive computational burden inherent to traditional physical verification, a hybrid solution framework is introduced. This framework integrates a deep neural network (DNN) surrogate model with an advanced particle swarm optimization (PSO) algorithm. By combining the offline learning of nonlinear SVSM boundaries with online rapid predictions and dual physical validation, the method achieves quasi-real-time capacity assessment. Case studies conducted on a modified IEEE 33-bus system demonstrate that the proposed framework not only uncovers the latent limitations imposed by evening peak voltage stability risks—which are typically neglected by conventional approaches—but also significantly accelerates computational efficiency while preserving rigorous evaluation accuracy.
Modern power systems face operational challenges due to the integration of variable renewable energy, necessitating enhanced reactive power support from synchronous generators. In this study, we propose a comprehensive reactive power boosting strategy (Q-strategy) framework, addressing technical and economic challenges via four pillars: (1) Q-boost defines reactive power limits for continuous/dynamic states by leveraging thermal margins under insulation thresholds (155 °C); (2) Q-model, a capacity twin model (CTM) combining a lumped-parameter thermal network and saturated field current models, enables online hot spot temperature management without finite element analysis; (3) Q-energy optimizes reactive energy delivery by integrating thermal endurance models with aging and start–stop cycles; and (4) Q-value formulates costs for reactive power services, accounting for losses beyond grid codes and lifetime depreciation. The results demonstrate a 57% increase in continuous reactive power capacity, with smaller generators incurring higher costs (up to 1.33 $/Mvarh) than larger units. The proposed framework exploits hydrogenerators’ slow thermal time constants by using authentic historical machine parameters and operational statistics from Nordic installations. To isolate the machine’s direct electro-thermal boundaries and localized voltage-support capabilities without confounding multi-bus network variables, system-level validation is conducted on a single-machine, two-bus power system topology subjected to extreme contingency events and localized load profiles. An online temperature controller ensures safe operation under extreme conditions while aligning with real-world load patterns. The Q-strategy offers a cost-effective alternative to FACTS devices, balancing grid resilience with economic feasibility in high-renewable grids.
Demand response (DR) can relieve source-load imbalance in microgrids (MGs), but its practical scheduling value is limited by renewable uncertainty and uncertain user response behavior. This paper proposes a coordinated day-ahead/intraday scheduling framework that combines multi-scenario risk scheduling with fuzzy chance-constrained rolling correction. Specifically, an endowment effect based mechanism is modeled to quantify users’ psychological costs caused by breaking habitual electricity consumption in DR regulation. Differentiated uncertainty handling strategies are adopted across timescales: KDE-Gaussian-copula-based multi-scenario stochastic optimization is used to mitigate long-term source-load stochasticity for risk-aware day-ahead scheduling, while fuzzy chance-constrained rolling optimization addresses short-term forecast deviations for efficient intraday correction. Comparative benchmarks include deterministic, stochastic, CVaR-based, fuzzy chance-constrained, robust, and literature endowment-effect models. Numerical results show that the proposed endowment-effect-based DR model reduces the endowment cost from 14,913.86 to 2,346.73 CNY and limits the DR quantity to 3.33 MW, indicating a lighter and more behaviorally plausible response pattern. Relative to independent KDE/MC sampling, the KDE-Gaussian-copula scenario generator reduces autocorrelation RMSE, cross-correlation RMSE, and ramp-rate Wasserstein distance by 67.6%, 43.1%, and 69.8%, respectively; relative to the Gaussian-copula-normal baseline, it reduces Wasserstein and extreme-quantile errors by 53.1% and 34.4%. Intraday comparisons further show that the proposed hybrid framework reduces online computation time by 84.39% compared with the unified multi-scenario model. Compared with the unified fuzzy model, it increases intraday revenue by 642.35 CNY and reduces DR curtailment by 0.4841 MW. These results indicate that the proposed framework improves the balance among revenue, risk exposure, user-side behavioral cost, real-time tractability, renewable utilization, and computational burden.
Effective thermal management constitutes a critical enabling technology for the safe, efficient, and durable operation of lithium-ion battery packs in electric vehicle applications. The present study presents a reduced-order, lumped thermal-network investigation comparing the thermal performance of staggered (zig-zag) and inline (aligned) cell arrangements in a 13S8P battery pack configuration employing commercial Panasonic NCR18650BD lithium-ion cells under passive natural convection cooling conditions. A reduced-order, node-per-cell thermal model incorporating temperature-dependent thermophysical properties, Arrhenius-type internal resistance behavior, and well-established Churchill-Chu natural convection heat transfer correlations is developed and systematically evaluated across eight discharge rates spanning from 0.20 °C to 1.97 °C, representing the full spectrum of electric vehicle operating conditions from auxiliary loads to peak acceleration demands. The results indicate a consistent but modest thermal benefit for the staggered arrangement. Evaluated at a common depth-of-discharge (15.01 Ah removed at every operating point), the staggered configuration attains an average maximum-temperature reduction of 1.3 °C, equivalent to 3.4% in absolute terms or 14.5% of the temperature rise above ambient, with the reduction increasing monotonically from 0.3 °C at 5A to 2.0 °C at 50A. The mean effective convective heat transfer coefficient is approximately 71% higher for the staggered arrangement, which directly reflects the array correction factors adopted from tube-bank correlations rather than an independently resolved flow field. The model-estimated intra-pack temperature non-uniformity is small for both arrangements (ΔT ≲ 0.35 °C) and is sensitive to the assumed air-mixing parameters; consequently only a weak, qualitative uniformity advantage is claimed, and a quantitative uniformity result would require CFD or experimental validation. Both arrangements maintain maximum cell temperatures below the 45 °C threshold up to 40A (1.57 °C) and become marginal at 50A (1.97 °C); within the range studied the staggered arrangement therefore provides additional thermal margin (approximately 1.9 °C at 40A) rather than an extended current limit. This additional margin is obtained purely through cell arrangement, without active cooling intervention. A paired comparison of the maximum-temperature difference across the eight operating points yields a mean difference of 1.3 °C (paired t-test p = 4.8 × 10−4, Cohen’s d = 2.2, 95% CI [0.81, 1.83] °C); because the comparison is across deterministic model outputs rather than experimental replicates, this is reported as a descriptive measure of the consistency of the advantage rather than inferential proof. Numerical solution convergence is established through a time-step refinement study, the maximum temperature changing by less than 10–3% between successive step halvings. These findings provide actionable design guidelines for passive battery thermal management systems, demonstrating that simple geometric modification through cell arrangement optimization can substantially enhance thermal performance without adding system complexity, weight, parasitic power consumption, or manufacturing cost. The comprehensive dataset and validated methodology presented herein provide a reproducible foundation for continued optimization of cylindrical cell battery pack designs for electric vehicle applications.
The transition toward decentralized, digitalized power systems creates new opportunities for integrating distributed generation, energy storage, and large-scale electric vehicle (EV) infrastructure within smart grid architectures. This study examines cold ironing, the supply of shore-side electricity to berthed vessels, as a form of large-scale EV charging infrastructure and develops a collaborative port energy community framework for its design, management, and quality governance. The framework integrates ISO 50001 energy management and ISO 37101 community sustainability standards with a genetic algorithm (GA) optimization of photovoltaic (PV) distributed generation and lithium iron phosphate (LFP) battery energy storage systems (BESS). Twelve scenarios are evaluated at the Port of Ancona across four electrification scales (1.7–52 GWh/year) and three technology configurations (grid-only, grid + PV, grid + PV + BESS). Smart energy management dispatch logic prioritizing self-consumption, storage buffering, and grid interaction governs the hourly simulation. The optimal full-electrification configuration (30.2 MWp PV, 18.6 MWh BESS) achieves an LCOE of 0.211 EUR/kWh, an interna rate of return (IRR) of 36.12%, and a payback period of 2.77 years, while the optimal targeted configuration (12.9 MWp PV, 12.4 MWh BESS) achieves the highest carbon footprint reduction of 56.04%. All configurations satisfy the quality criteria derived from the ISO 50001 and ISO 37101 governance frameworks. A technology readiness level (TRL) assessment identifies the integrated system at TRL 6–7, with individual components at TRL 8–9. The findings demonstrate that dock-by-dock clustered electrification outperforms monolithic sizing in decarbonization intensity, and that the energy community governance model provides a viable institutional pathway for multi-stakeholder coordination in port energy infrastructure.
IntroductionWhile grid-forming (GFM) converters can enhance fault-period transient voltage support, they may also increase short-circuit current stress and affect fault-current distribution, making benefit–risk coordination a key challenge in GFM converter siting.MethodsTo address this trade-off, this paper proposes a coordinated evaluation method for the siting of fixed-capacity GFM converters. Static indicators are first used to screen candidate buses, thereby reducing the computational burden of multi-fault electromagnetic transient (EMT) simulations, and the retained siting schemes are then evaluated in terms of transient voltage support benefit and short-circuit current risk cost. Fault scenario weighting, Pareto-front analysis, and the ideal-point distance method are further combined to identify the recommended siting scheme.ResultsThe IEEE 39-bus case study shows that maximizing transient voltage support benefit alone does not necessarily lead to the optimal siting decision. The recommended scheme achieves a better benefit–risk balance.DiscussionThe results demonstrate the effectiveness of the proposed framework for GFM converter siting under short-circuit current constraints.
IntroductionThe South African electricity supply sector is undergoing structural reform, with balancing and ancillary-service markets likely to increase the need for active portfolio scheduling under supply shortages and grid constraints. This paper formulates a day-ahead virtual-grid optimal power flow problem for a geographically dispersed South African corporate portfolio comprising load, co-generation, renewable energy, battery energy storage and flexible demand.MethodsA mixed-integer linear programming/DC optimal power flow benchmark, a genetic algorithm and gorilla troop optimisation are compared under a common cost-and-voltage-stability objective, with all schedules assessed by AC post-validation and Jacobian singular-value/condition-number metrics. The algorithms are first tested through repeated simulations on a modified IEEE 9-bus system and then transferred to a bespoke 35-bus, 39-branch South African-grid-referenced reduced-order application system constructed from selected 400 kV corridors, a 132 kV renewable independent power producer proxy and 66/22 kV load connection proxies. South African load and generation data are used in the simulations.ResultsThe bespoke application converged in all 24 hourly snapshots. The deterministic benchmark gave the lowest true cost, while the genetic algorithm was faster than gorilla troop optimisation and showed marginally stronger stability metrics in the single bespoke run. Gorilla troop optimisation achieved a marginally lower true cost than the genetic algorithm. A flexible-demand-with-storage use case reduced aggregate residual deficit significantly.DiscussionThe results indicate that the reduced-order grid provides fit-for-purpose OPF transferability and supports comparative assessment of deterministic and metaheuristic scheduling methods for South African virtual-grid applications.
Globally, the energy supply systems (ESSs) of buildings are increasingly integrating clean energy technologies to reduce energy costs while supporting the transition to a net-zero-emission future. In this regard, the role of energy use in buildings in the European Union (EU) has already been established. Norway also has ambitious climate goals, with improvements to energy use in buildings representing a significant part of the effort. There is consensus among Norwegian energy experts, consumers, and prosumers regarding the need for greater consumer involvement in ESSs and financial incentives to support it. However, the necessary support levels and supported actions have not been explored deeply in regard to sustainability of energy supply and flexibility, both of which are recognised as important factors for future energy systems. Therefore, we analysed the impacts of financial support relative to the living area on the sustainability and flexibility achieved by retrofitting ESSs in buildings and by considering different support priorities. The genetic algorithm (GA) was utilised to derive an optimal equipment set for each sub-scenario and perform scheduling with a hybrid (optimisation and rule-based) control for three buildings in Norway. To better evaluate the flexibility of energy use, we devised a new parameter called the energy cost flexibility improvement factor (ECFIF) . The results indicate that support focused primarily toward improvements in sustainability provide the most benefits overall, with indications that a support strategy that does not enforce equipment selection could be even more successful. For the studied Norwegian buildings, a support of 10–40 EUR/m2 represents the highest increase in profitability while a support of 20–35 EUR/m2 achieves the highest impact on ESS performance. However, separate determination of the support level may be necessary for high-cost and high-impact projects, such as rebuilding commonly used electric-based space heating to hot-water-circulation-based systems, to achieve the envisioned climate goals.
Dark fermentation is the conversion of various biomass wastes into biohydrogen. It occurs in the absence of light. The purpose of this review is to provide the latest advances in biohydrogen production via dark fermentation, focusing on feedstock characteristics, microbial community dynamics, and approaches to system integration. Carbohydrate-based substrates are ideal for the production of biohydrogen due to their high fermentative potential. In contrast, lignocellulosic biomass requires pretreatment to enhance its susceptibility to microbial degradation. Hydrogen-producing microorganisms such as Clostridium spp were considered, focusing on metabolic processes, changes in the microbial communities involved, and process stability at different process parameters. The hydrogen partial pressure, byproducts, and inhibition due to competition between methanogens, lactic acid bacteria, and hydrogen-utilizing organisms were reviewed along with control mechanisms. The use of fermentation by-products, volatile fatty acids, and acidified slurry has been considered within biorefinery concepts. This approach addresses the economic challenges associated with the biohydrogen production technology. Although some success has been achieved, several issues remain. These include scalability and feedstock variation. These factors present important bottlenecks for future commercial applications of this technology.
Typhoons pose significant threats to power infrastructure, often leading to data center outages and critical data loss. To enhance power supply resilience, this paper proposes a refined spatiotemporal risk probability distribution prediction method based on the coupling model of WRF and FEDformer, as well as differentiated collaborative supply measures considering risk levels. First, a fault probability matrix is generated using the WRF physical model and the FEDformer temporal encoder to construct a power supply risk profile under complex typhoon scenarios. Then, a differentiated collaborative supply mechanism is proposed, based on non-critical load transfer and dedicated mobile energy storage, with an optimized two-stage robust optimization model constructed by classifying non-critical loads and optimizing transfer paths. Compared to traditional methods, the proposed approach can forecast the spatiotemporal distribution characteristics of data center risk probabilities under typhoon impact, and provide targeted differentiated supply measures according to prediction results. The accuracy of the proposed prediction model is verified using the “Yangliu” typhoon case. Simulation results indicate that the proposed strategy reduces voltage sag switching to only 3.8%, stabilizes frequency deviation within 0.12 Hz, improves power supply availability from 76.3% to 100%, enhances capacity margin by 250% through non-critical load transfer, and ensures power supply availability during extreme disasters using dedicated mobile energy storage.
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into renewable energy systems (RES) is increasingly recognized as a practical pathway for improving operational efficiency, reliability, and sustainability. Renewable sources, such as solar, wind, and hydropower, as well as hybrid configurations, are inherently intermittent and operationally complex, creating persistent challenges for grid stability, asset reliability, and energy optimization. Conventional maintenance strategies, whether reactive or preventive, often lead to unplanned downtime, inefficient inspections, and increased lifecycle costs. In response, AI/ML-enabled predictive maintenance (PdM) leverages high-frequency sensor data, SCADA streams, and historical performance records to detect anomalies, diagnose faults, and estimate remaining useful life (RUL), enabling proactive maintenance interventions. Beyond maintenance, AI/ML supports operational optimization through energy generation forecasting, load prediction, grid integration, storage scheduling, and adaptive control, thereby strengthening system resilience and lowering operational costs. Unlike prior reviews that treat PdM and RES optimization as separate topics, this work provides a unified, decision-oriented synthesis that explicitly links (i) maintenance outcomes (fault detection/diagnosis/RUL) and (ii) operational outcomes (forecasting, dispatch, storage scheduling, and grid control) through shared data pipelines and coupled decision trade-offs. The review further provides a structured mapping that connects AI/ML methods to (a) RES asset types (solar, wind, hydropower, hybrid, and emerging marine systems), (b) decision objectives (fault/RUL, forecasting, dispatch and control), and (c) deployment settings (IoT/SCADA, edge-cloud, and digital-twin-enabled monitoring). Emerging technologies, including digital twins, edge AI, federated learning, and explainable AI (XAI), are discussed as enabling mechanisms for real-time monitoring, privacy-preserving learning, adaptive decision-making, and transparency in critical infrastructure. Cybersecurity risks including vulnerabilities arising from expanded IoT/edge/cloud connectivity and adversarial threats to AI-driven control are highlighted as a critical adoption barrier alongside data quality, interoperability with legacy systems, scalability, and model interpretability. By consolidating fragmented evidence across maintenance and optimization and highlighting deployment trade-offs, this review provides an implementation-oriented reference for AI/ML-enabled operation of modern renewable energy systems.
This work reviews microtubular solid oxide fuel cells (MT-SOFCs) and their application in high-performance electrochemical energy systems. The demand for MT-SOFCs is rising because of their sustainable characteristics, such as high power density (400–600 mW/cm2), low operational temperature (700 °C–850 °C), high thermal resistance (>40 thermal cycles), and suitability for compact, portable small-scale power supply applications. Therefore, this review critically evaluates the synthesis process to produce MT-SOFCs that have better performance than regular SOFCs. The synthesis process is required for micro-stack assembly, sealing technologies, thermal management, current collection, and system-level integration for practical deployment. The focus is on the method used to synthesize hollow-fiber ceramic material that has a high surface area, a short distance for mass transport, enhanced gas diffusion, and adaptability for use in fuel cell stacks. This review also talks about co-sintering as an alternative production method for microtubular cells because the regular method still has drawbacks, including poor thermal expansion, shrinkage, interlayer adhesion, and poor density densification. This article is intended to give new perspectives for researchers to design the MT-SOFC synthesis process from the laboratory to the industrial scale.
With the increasing penetration of distributed renewable energy resources, distribution networks are gradually evolving into interconnected systems composed of multiple microgrids. Microgrids are typically equipped with controllable reactive power resources and can provide voltage support to the distribution network through points of common coupling. However, directly dispatching microgrid reactive resources without explicitly quantifying boundary support responsibilities and local dispatch costs may result in inefficient reactive power allocation and excessive utilization of certain microgrid-side resources. To address this issue, this paper proposes a cost-aware distributed reactive power dispatch method for voltage optimization in active distribution networks with multiple microgrids. First, a PCC reactive-power reference model is established to quantify each microgrid’s baseline reactive support responsibility under rated-voltage operating conditions. Second, a unified reactive dispatch cost model is developed that considers the fixed, operating, and opportunity costs of reactive power resources. Based on these models, a multi-agent distributed reactive power scheduling framework is formulated, in which the distribution network and microgrids independently optimize their local objectives while coordinating through PCC boundary variables. The coupled optimization problem is solved using an adaptive consensus alternating direction method of multipliers (ADMM) algorithm combined with continuous relaxation and integer recovery for discrete voltage regulation devices. Case studies on a modified IEEE 69-bus distribution system demonstrate that the proposed method can effectively improve voltage profiles, reduce network losses and comprehensive operating costs, and enhance the coordinated utilization of microgrid-side reactive resources while preserving microgrid operational autonomy.
IntroductionIn recent years, the hybridization of metaheuristic algorithms has been widely recognized as an effective strategy for overcoming the limitations of conventional optimization approaches, particularly their slow convergence behavior and susceptibility to premature convergence in complex search spaces.MethodsA novel hybridization of the Grey Wolf Optimizer (GWO) and Arithmetic Optimization Algorithm (AOA), called H-GWOAOA, is proposed. The method selectively incorporates the arithmetic operators of AOA into GWO’s hierarchical leadership model by embedding AOA operators into the α-agents’ updating phase to improve the balance between exploration and exploitation without increasing algorithmic complexity.DiscussionThe effectiveness of the proposed approach is experimentally confirmed through testing on 23 benchmark functions, achieving values of 0.1887 (F1) and 0.0331 (F2) and demonstrating improved accuracy, stability, and robustness compared with GWO, AOA, PSOGWO, SSA, and SCA. Its generalization ability is further evaluated on four biomedical datasets (XOR, Iris, Breast Cancer, and Heart), achieving accuracy rates of 100% for XOR and 99.14% for Breast Cancer. Moreover, H-GWOAOA is applied to parameter estimation for the Single-Diode Model (SDM), Double-Diode Model (DDM), Triple-Diode Model (TDM), and Photovoltaic Model (PMM), achieving a minimum Root Mean Square Error of 5.51 × 10−4 for SDM.DiscussionThe results demonstrate that the proposed H-GWOAOA provides competitive optimization accuracy, convergence stability, and robustness while preserving the computational efficiency of the original GWO framework.
Hydro-power is the backbone of the Swiss electricity system, accounting for approximately two-thirds of the country’s electricity production. Cleverly managing these plants is essential to maximizing profit while balancing demand and production at any time. However, that strategy is often compromised by its dependence on uncertain future inflows, electricity prices, and demand. To help operators in the decision-making process and to simulate the storage hydro-power behavior in the grid, we propose a generic model for the short-to long-term (up to 1 year) using Switzerland as an example. The model relies on a rolling-horizon optimization approach, continuously updating decisions as new forecasts become available. This framework aims to reproduce observed hydro-power operations under realistic conditions. In this study, we evaluate its ability to simulate the Swiss electricity system without using perfect foresight, but solely seasonal and sub-seasonal weather forecasts. Results show that the framework successfully captures both long- and short-term hydrological dynamics. Despite relying exclusively on seasonal and sub-seasonal forecasts, the model reproduces monthly turbine production with a Mean Absolute Percentage Error of 18% relative to actual observed production. While most existing models rely on perfect foresight or simplified representations of hydro-power, we show how such a management would work when only forecasts are available. Spillage only increases from 0.18% to 0.64% of total inflows without perfect foresight, while total hydro-power revenue decreases by 6%. This comparison with an hypothetic perfect situation highlights the impact of forecasting error on the short-and long-term planning of hydro-power operations.
The increasing demand for energy has heightened the importance of optimal power flow (OPF) in achieving robust planning and cost-effective operation of power systems. OPF primarily seeks to reduce total generation costs while adhering to system constraints. Growing environmental challenges and the declining availability and escalating costs of fossil fuels necessitate the integration of renewable energy sources (RESs) into the power grid. Classical OPF, a non-convex and non-linear optimization problem, traditionally focuses on thermal generators. The complexity increases significantly when incorporating uncertain RESs. This article proposes the electromagnetic (EM) wave propagation algorithm (EMWPA) for single-objective OPF and extends it to multi-objective OPF with RESs. Stochastic RES outputs are represented by appropriate probability density functions with associated reserve and penalty costs explicitly embedded in the objective functions. Multi-objective EMWPA (MOEMWPA) augments the base algorithm through an archive-free, in-population, non-dominated sorting and crowding distance mechanism. Validation on the IEEE modified 30-bus system across three bi-objective and one tri-objective cases demonstrates that MOEMWPA achieves the highest normalized hypervolume (HV) indicator in all multi-objective cases, with superior convergence and evenly distributed non-dominated solutions. The tri-objective case additionally shows the lowest execution time, confirming computational efficiency. Scalability assessment on the IEEE 57-bus system confirms that EMWPA achieves the lowest single-objective generation cost among all competing algorithms. In three bi-objective cases on the 57-bus system, MOEMWPA achieves the highest mean HV in all cases and demonstrates statistically significant superiority over NSGA-II across all three. Notably, multi-objective particle swarm optimization (MOPSO) exhibits severe convergence failures on the larger network, whereas MOEMWPA maintains consistent robustness. These results establish MOEMWPA as an effective, scalable, and computationally efficient solver for multi-objective OPF problems in renewable-integrated power systems.
Phase change materials (PCMs) serve as the functional medium in latent heat thermal energy storage (LHTES) systems, where their thermophysical properties directly dictate system performance. However, most commercial PCMs exhibit low thermal conductivity (0.2–5.0 W/m·K), which limits heat transfer rates and reduces charging–discharging efficiency. In addition, modest specific and latent heat capacities, together with issues related to supercooling, phase segregation, and stability, further constrain their practical deployment. Nano-composite PCMs, formed by dispersing nanoscale additives in the base material, have emerged as a promising route to overcome these limitations. Reported enhancements in thermal properties can exceed three orders of magnitude compared to pristine PCMs. Nevertheless, significant discrepancies remain among experimental observations, theoretical predictions, and reported enhancement mechanisms, indicating an incomplete understanding of the factors that govern nanocomposite behavior. This review presents a unified critical assessment of more than 230 studies covering organic, inorganic, eutectic, and solid–solid PCM nanocomposites. It systematically evaluates the effects of nanoparticle type, morphology, concentration, fabrication route, and processing conditions on thermal conductivity, latent heat capacity, specific heat capacity, and thermal stability. In doing so, it critically examines the contradictory findings reported in the literature and synthesizes the underlying mechanisms that are responsible for both the enhancement and eventual degradation of performance. Based on these insights, key research gaps are identified and recommendations are provided for the development of predictive models and the future design of high-performance PCM nanocomposites.
Multiphysics models are often utilized for structural design, condition assessment, and response prediction of a system, such as the support structure of offshore wind turbines. Although multiphysics models are considered to be of high fidelity, discrepancies are often observed between the model predictions and actual data measurements from the real structure. The model updating process reduces the model parameter uncertainty by minimizing the differences between the model and the measured data through an optimization procedure. In this paper, such a procedure is applied to an instrumented GE Haliade 6-MW jacket-supported offshore wind turbine by updating the tower and substructure modulus of Elasticity (E) to minimize differences between modal parameters identified from 1 month of measurements and those obtained from the OpenSees model or predicted from surrogate models. Surrogate models, including a neural network and a polynomial regression fit, are used to replace the OpenFAST simulations. The results indicate that the fully coupled OpenFAST model provides a more physically consistent representation in the medium power range (0.5–4.5 MW) by reducing variability in the updated E, whereas the OpenSees model is more suitable for low and high categories, where pitch control and reduced aerodynamic loading limit the influence of aero-hydro-servo-elastic effects.
Motivated by the need for pumped-storage planning under high renewable energy penetration, this study proposes a two-stage stochastic planning and layout model for pumped-storage power stations. The model considers hydrometeorological factors, source-load uncertainty, candidate-site heterogeneity, and multi-scenario operational verification. First, a meteorology-source-load–regulation-demand mapping mechanism is constructed to convert multi-source uncertainties associated with wind power, photovoltaic generation, and electric vehicles into regional regulation capacity requirements. Second, a candidate-site parameter database is established by considering reservoir capacity, hydraulic head, efficiency, cost, grid-connection conditions, and regional attributes. Furthermore, a compact reformulation and hierarchical warm-start solution strategy is proposed to improve the solution efficiency of the large-scale stochastic mixed-integer model. The strategy integrates single-mode variable reformulation, online-unit-number binding, safe upper-bound tightening, redundant-constraint reduction, and warm-start mechanisms. Case study results show that the proposed model can effectively characterize the planning layout and operational adaptability of pumped-storage power stations under uncertainty, while improving the solution efficiency and stability of large-scale planning models, thereby providing decision support for the scientific allocation of pumped-storage resources in new-type power systems.
