Search Results (177)

To address the power and energy balancing challenges faced by high-penetration renewable energy systems under long-term intermittent output conditions, this study proposes a multi-source, multi-timescale collaborative dispatch strategy (2MT-S) integrating wind, solar, hydro, thermal, and hydrogen energy resources. First, a long-term-to-day-ahead coupled scheduling framework is established based on intermittent output duration forecasts (3-day/10-day). By integrating seasonal hydrogen storage and pumped-storage hydroelectric plants, this framework achieves comprehensive coordination among electrochemical storage, thermal power, and other flexible resources. Second, a multi-time-horizon optimization model is developed to simultaneously minimize system operating costs and load curtailment costs. This model dynamically adjusts day-ahead scheduling boundary conditions based on long-term and short-term scheduling results, enabling cross-period resource complementarity during wind and photovoltaic generation troughs. Finally, comparative analysis on an enhanced IEEE 30-bus system demonstrates that compared to traditional day-ahead scheduling, this strategy significantly reduces renewable energy curtailment rates and load curtailment volumes during sustained low-generation periods, fully validating its significant advantages in enhancing power supply reliability and economic benefits. Full article

Despite significant advancement in cancer treatments, therapies with minimal toxicity to healthy cells are still limited. One targetable weakness of cancer cells is their sensitivity to oxidative stress. We find that the combination of two antioxidants—the common food additive tert-butylhydroquinone (tBHQ) and a manganese porphyrin in clinical trials, MnTnBuOE-2-PyP⁵⁺ (MnBuOE)—increases oxidative stress and causes apoptotic death in several cancer cell lines, but not in mouse primary fibroblasts. Investigating the mechanism of cell death, MnBuOE is observed to catalyze the oxidation of tBHQ, producing the electrophilic quinone tert-butylquinone (tBQ). A critical role for tBQ and its electrophilic character was revealed with the observation that di-tert-butylhydroquinone (dtBHQ) in combination with MnBuOE causes no observable oxidative stress and is non-toxic, despite rapid oxidation to di-tert-butylquinone (dtBQ), a non-electrophilic quinone. Cell death from the combination of tBHQ and MnBuOE appears to be completely dependent on the generation of hydrogen peroxide, as shown by the inclusion of catalase. This system, in which two non-toxic molecules in combination cause specific toxicity to cancer cells, is a potential means to kill cancer cells in a targeted manner. Full article

Chennai’s transport sector is undergoing a structural transition as the city seeks to accommodate rapidly growing travel demand while reducing energy consumption and emissions. This study develops a city-scale system dynamics model using STELLA to simulate long-term transitions in bus and Intermediate Public Transport (IPT) systems over the period 2011–2038. Four policy scenarios—Do Minimum, Partial, Desirable, and Ideal—are evaluated to examine how fleet expansion, propulsion technology substitution, and service restructuring influence urban transport energy sustainability. The model integrates demographic growth, service-level fleet benchmarks, and multiple propulsion pathways, including diesel, CNG, LPG, bio-CNG, hydrogen, and battery- and solar-electric technologies. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

Although a large number of copper hydride complexes and clusters have been reported, phosphine-stabilized copper hydrides remain comparatively rare. This is particularly noteworthy given the continuing interest in Stryker’s reagent [HCu(PPh₃)]₆ due to its use as a hydrogenation reagent. In this work, we report on the synthesis and full characterization of a novel copper hydride cluster, [Cu₁₄H₁₂(P^tBu₃)₆Cl₂]. The structure of this copper hydride was determined via SC-XRD. In addition, the reactivity of the hydrides and their position were investigated via a convolutional neural network, quantum chemical calculations, and NMR, and they are compared to the well-known, smaller Stryker’s reagent. Full article

Stable operating conditions in electrolyzers are crucial for preserving system durability, ensuring highly pure hydrogen production, and enabling the sustainable utilization of surplus renewable electricity. However, in active distribution networks, the output uncertainty of distributed energy resources, such as renewable energy sources (RES) on the generation side and load demand side, can lead to voltage fluctuations that threaten the operational stability of electrolyzers and limit their contribution to a low-carbon energy transition. This paper proposes a novel framework for optimal electrolyzer placement, tailored to their operational requirements and to the planning of sustainable renewable-integrated distribution systems. First, probabilistic scenario generation is carried out for RES and load to capture the characteristics of their inherent uncertainties. Second, based on these scenarios, continuous power-flow-based P–V (power–voltage) curve analysis is conducted to evaluate voltage stability and identify the loadability and load margin for each bus. Finally, the optimal siting of electrolyzers is determined by analyzing the load margins obtained from the voltage stability assessment and deriving a probabilistic electrolyzer hosting capacity. A case study under various uncertainty scenarios examines how applying this method influences the ability to maintain acceptable voltage levels at each bus in the grid. The results indicate that the method can significantly improve the likelihood of stable electrolyzer operation, support the reliable integration of green hydrogen production into distribution networks, and contribute to the sustainable planning of other voltage-sensitive equipment. Full article

Heavy-duty vehicles cause a significant percentage of the harmful gas emissions from the automotive industry. This article presents the development of a compression system for hydrogen as part of the H2REF-DEMO hydrogen refueling station, joining the European efforts to promote hydrogen (H₂) as a fuel that can play a key role in the energy transition of these types of vehicles. The H2REF-DEMO project, co-funded by the European Union’s “Horizon. Europe” programme under the “Clean Hydrogen Partnership” (grant agreement no. 101101517), involves a partnership between companies and research centers that aims to investigate the possibility of compressing hydrogen through hydraulic power to handle large vehicle refueling applications, such as bus fleet depots, trucks, or trains. The basic principle is the exploitation of hydraulic power to compress hydrogen through hydro-pneumatic bladder accumulators. The hydraulic power units, in fact, pump oil into the accumulators, causing a deformation of the bladder containing H₂ and thus a consequent gas compression. In this article, we focus on the development of the compression system, from the theoretical starting point to the core final layout of the refueling station for large vehicles. We also exploit a lumped parameter numerical model to both support the system design and virtually test its first control logic. The latter, in particular, allows the system to operate in three modes—Bypass, Parallel, and Serial modes—thus leaving room for testing basic and more complex control strategies. The results of numerical simulations demonstrate the effectiveness of this innovative compression technology and its considerable efficiency in terms of refueling time and energy consumption, especially when compared to the standard systems used for this application. These are thus encouraging results that can support the development of an actual H2REF-DEMO hydraulic test rig for hydrogen compression. Full article

The synthesis of a small library of fused Cyclic Aryl Amino Carbon (f-CArAC) carbene precursors in the form of 1,1,2,4-tetraaryl-1H-isoindol-2-ium triflate (6), (7-R) (R = ^tBu, CF₃) or 3,3-dimethyl-2,8-bis-arene-substituted-3,4-dihydro-isoquinolin-2-ium hydrogen-dichloride (8) and 2,4,8-tri(substituted)-isoquinolin-2-ium tosylate salts (12) has been achieved. All of them feature an arene incorporated on the annulated benzene ring of the corresponding heterocycle, introduced at the early stages of their synthesis via the Suzuki cross-coupling reaction between 2,6-dibromo-benzaldehyde and the desired aryl boronic acid. The terphenyl-2′carbaldehyde by-products of this Suzuki reaction are useful starting points for the preparation of two new iminium iodide salts (10-R) (R = H, CF₃) as potential precursors to access ACyclic Amino Carbon (ACAC) carbenes. Compounds (6) and (7-^tBu) react readily with hydroxide either in THF or in a biphasic Et₂O/aqueous OH⁻ solution to produce the substituted isoindolinols (13) and (14), respectively. The thermal dehydration of the former generates the corresponding f-CArAC carbene in situ, which is trapped by Cu(I)Cl furnishing, a rare example of a two-coordinate Cu(I) complex (15) supported by this new ligand scaffold. 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

While community energy initiatives and pilot projects have demonstrated technical feasibility and economic benefits, their site-specific nature limits transferability to systematic, scalable investment models. This study addresses this gap by proposing a modular framework for Renewable Energy Valleys (REVs), developed from real-world Community Energy Lab (CEL) demonstrations in Crete, Greece, which is an island with pronounced seasonal demand fluctuation, strong renewable potential, and ongoing hydrogen valley initiatives. Four modular business schemes are defined, each representing different sectoral contexts by combining a baseline of 50 residential units with one representative large consumer (hotel, rural households with thermal loads, municipal swimming pool, or hydrogen bus). For each scheme, a mixed-integer linear programming model is applied to optimally size and operate integrated solar PV, wind, battery (BAT) energy storage, and hydrogen systems across three renewable energy penetration (REP) targets: 90%, 95%, and 99.9%. The framework incorporates stochastic demand modeling, sector coupling, and hierarchical dispatch schemes. Results highlight optimal technology configurations that minimize dependency on external sources and curtailment while enhancing reliability and sustainability under Mediterranean conditions. Results demonstrate significant variation in optimal configurations across sectors and targets, with PV capacity ranging from 217 kW to 2840 kW, battery storage from 624 kWh to 2822 kWh, and hydrogen systems scaling from 65.2 kg to 192 kg storage capacity. The modular design of the framework enables replication beyond the specific context of Crete, supporting the scalable development of Renewable Energy Valleys that can adapt to diverse sectoral mixes and regional conditions. Full article

The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertainty. Conventional rule-based or optimization-based strategies often suffer from limited adaptability, while purely data-driven reinforcement learning approaches risk violating physical feasibility constraints, leading to unsafe or economically inefficient operation. To address this challenge, this paper develops a Physics-Informed Reinforcement Learning (PIRL) framework that embeds first-order physical models and a structured feasibility projection mechanism directly into the training process of a Soft Actor–Critic (SAC) algorithm. Unlike traditional deep reinforcement learning, which explores the state–action space without physical safeguards, PIRL restricts learning trajectories to a physically admissible manifold, thereby preventing battery over-discharge, thermal discomfort, and infeasible hydrogen operation. Furthermore, differentiable penalty functions are employed to capture equipment degradation, user comfort, and cross-domain coupling, ensuring that the learned policy remains interpretable, safe, and aligned with engineering practice. The proposed approach is validated on a modified IEEE 33-bus distribution system coupled with 14 thermal zones and hydrogen facilities, representing a realistic and complex multi-energy microgrid environment. Simulation results demonstrate that PIRL reduces constraint violations by 75–90% and lowers operating costs by 25–30% compared with rule-based and DRL baselines while also achieving faster convergence and higher sample efficiency. Importantly, the trained policy generalizes effectively to out-of-distribution weather conditions without requiring retraining, highlighting the value of incorporating physical inductive biases for resilient control. Overall, this work establishes a transparent and reproducible reinforcement learning paradigm that bridges the gap between physical feasibility and data-driven adaptability, providing a scalable solution for safe, efficient, and cost-effective operation of renewable-rich multi-energy microgrids. Full article

The growing penetration of renewable energy sources requires resilient microgrids capable of providing stable and continuous operation. Hybrid energy storage systems (HESS), which integrate hydrogen-based storage systems (HBSS), battery storage systems (BSS), and supercapacitor banks (SCB), are essential to ensuring the flexibility and robustness of these microgrids. Accurate modelling of these microgrids is crucial for analysis, controller design, and performance optimization, but the complexity of HESS poses a significant challenge: simplified linear models fail to capture the inherent nonlinear dynamics, while nonlinear approaches often require excessive computational effort for real-time control applications. To address this challenge, this study presents a novel state space model with linear variable parameters (LPV), which effectively balances accuracy in capturing the nonlinear dynamics of the microgrid and computational efficiency. The research focuses on a high-voltage DC bus microgrid architecture, in which the BSS and SCB are connected directly in parallel to provide passive DC bus stabilization, a configuration that improves system resilience but has received limited attention in the existing literature. The proposed LPV framework employs recursive linearisation around variable operating points, generating a time-varying linear representation that accurately captures the nonlinear behaviour of the system. By relying exclusively on directly measurable state variables, the model eliminates the need for observers, facilitating its practical implementation. The developed model has been compared with a reference model validated in the literature, and the results have been excellent, with average errors, MAE, RAE and RMSE values remaining below 1.2% for all critical variables, including state-of-charge, DC bus voltage, and hydrogen level. At the same time, the model maintains remarkable computational efficiency, completing a 24-h simulation in just 1.49 s, more than twice as fast as its benchmark counterpart. This optimal combination of precision and efficiency makes the developed LPV model particularly suitable for advanced model-based control strategies, including real-time energy management systems (EMS) that use model predictive control (MPC). The developed model represents a significant advance in microgrid modelling, as it provides a general control-oriented approach that enables the design and operation of more resilient, efficient, and scalable renewable energy microgrids. Full article

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper, we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources, including remote-controlled switches (RCSs), mobile hydrogen emergency resources (MHERs), and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling, enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages, including prevention, fault isolation, and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events. Full article

The transportation industry significantly contributes to greenhouse gas (GHG) emissions. Federal and provincial governments have implemented strategies to decrease dependence on gasoline and diesel fuels. This encompasses promoting the adoption of electric cars (EVs) and biofuel alternatives, investing in renewable energy sources, and enhancing public transit systems. There is a growing focus on enhancing infrastructure to facilitate active transportation modes like cycling and walking, which provide the combined advantages of decreasing emissions and advancing public health. In this paper, we propose a System Dynamics simulation model for evaluating factors for the adoption of alternative-fuel vehicles such as EVs, biofuel vehicles, bus, bikes, and hydrogen vehicles. Five factors—namely, customer awareness, government initiatives, cost of vehicles, cost of fuels, and infrastructure developments—to increase the adoption of alternative-fuel vehicles are studied. Two scenarios are modeled: A baseline scenario that follows the existing trends in transportation (namely the use of gasoline vehicles), Scenario 1, which prioritizes greater adoption of electric vehicles (EVs) and biofuel-powered vehicles, and Scenario 2, which prioritizes hydrogen fuel-based vehicles and improves biking culture. The simulation findings show that all scenarios achieve reductions in GHG emissions compared to the baseline, with Scenario 2 showing the lowest emissions. The proposed work is useful for transport decision makers and municipal administrators in devising policies for reducing overall GHG emissions, and this also aligns with Canada’s net zero goals. Full article

To achieve deep decarbonization in the transportation sector, this study employs life cycle assessment (LCA) and the GREET model to construct baseline and low-carbon scenarios. It simulates the evolution of emissions and energy consumption within Inner Mongolia’s public transportation energy system (including diesel buses (DBs), electric buses (EBs), and hydrogen fuel cell buses (HFCBs)) from 2022 to 2035, while exploring synergistic pathways for its low-carbon transition. Results reveal that under the baseline scenario, reliance on industrial by-product hydrogen causes fuel cell bus emissions to increase by 3.64% in 2025 compared to 2022, with system energy savings below 10%, and decarbonization potential will be constrained by scale limitations and storage/transportation losses in cold regions. Under the low-carbon scenario, deep grid decarbonization, vehicle structure optimization, and green hydrogen integration reduced system emissions and energy consumption by 66.86% and 40.44%, respectively, compared to 2022. The study identifies a 15% green hydrogen penetration rate as the critical threshold for resource misallocation and confirms grid decarbonization as the top-priority policy tool, yielding marginal benefits 1.43 times greater than standalone hydrogen policies. This study underscores the importance of multi-policy coordination and ‘technology-supply chain’ synergy, particularly highlighting the critical threshold of green hydrogen penetration and the primacy of grid decarbonization, offering insights for similar coal-dominated, cold-region transportation energy transitions. Full article