Search Results (914)

The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, reduce greenhouse-gas emissions, and improve adaptability across diverse agricultural environments. Nevertheless, widespread deployment remains constrained by harsh operating conditions, complex duty cycles, and limitations in maintenance capacity and economic feasibility. This review provides a comprehensive synthesis of enabling technologies and application trends in EAM. Performance requirements of electrical subsystems are examined with emphasis on advances in power supply, electric drive, and control systems. The technical characteristics and application scenarios of battery, series hybrid, parallel hybrid, and power-split powertrains are compared. Common EMS approaches (rule-based, optimization-based, and learning-based) are evaluated in terms of design complexity, energy efficiency, adaptability, and computational demand. Representative applications across tillage, seeding, crop management, and harvesting are discussed, underscoring the transformative role of electrification in agricultural production. This review identifies the series hybrid electronic powertrain system and rule-based EMSs as the most mature technologies for practical application in EAM. However, challenges remain concerning operational reliability in harsh agricultural environments and the integration of intelligent control systems for adaptive, real-time operations. The review also highlights key technical bottlenecks and emerging development trends, offering insights to guide future research and support the wider adoption of EAM. Full article

Polyvinyl chloride (PVC), owing to its excellent electrical properties and low cost, is widely applied in the inner insulation and outer sheath of cables. To achieve early fault warning based on characteristic gases, this study integrates experimental testing with molecular simulations to systematically reveal the decomposition and gas generation characteristics of different PVC layers under electrical and thermal stresses. The results indicate that inner-layer PVC under electrical stress predominantly generates small-molecule olefins and halogenated hydrocarbons, while outer-layer PVC during thermal decomposition mainly produces hydrogen chloride, alkanes, and fragments of plasticizers. The surrounding atmosphere significantly regulates the gas generation pathways: air promotes the formation of CO₂ and H₂O, whereas electrical discharges accelerate the release of unsaturated hydrocarbons such as acetylene. By employing TG-FTIR, ReaxFF molecular dynamics, and DFT spectral calculations, a normalized infrared spectral library covering typical products was established and combined with the non-negative least squares method to realize quantitative deconvolution of mixed gases. Ultimately, a diagnostic system was constructed based on the concentration ratios of characteristic gases, which can effectively distinguish the failure modes of inner and outer PVC layers as well as different stress types. This provides a feasible approach for early detection of cable faults and supports intelligent maintenance strategies. Full article

The integration of photovoltaic systems with heat pumps can significantly influence primary energy consumption indicators and therefore plays a particularly important role in the low-energy construction sector. This study provides a simulation-based assessment of the impact of selected photovoltaic panel parameters on the primary energy (PE) index in a low-energy building equipped with an air-source heat pump. The building, located in the relatively cold climate of north-eastern Poland, was analyzed in two insulation variants of the building envelope. In each variant and system configuration, the total amount of energy produced by the panels (E_PV) and used by the system (E_used), as well as the degree to which the system’s electricity demand was covered by the photovoltaic panels (η_cov) and their self-consumption degree (η_self), were assessed. The results showed that, in the baseline scenarios, photovoltaic panels were able to generate 5586 kWh of electricity, covering an average of 60–63% of the system’s demand and achieving a self-consumption of approximately 59%. The E_PV, E_used, and η_cov are inversely proportional to the η_self and PE index. The PE index, η_cov, and η_self ranged from 22.6 to 80 kWh/m², 25.3 to 77.5%, and 23.9 to 100%, respectively, depending on the variant and configuration. The wide range of the obtained results confirms that the analyzed factors have a significant impact on the performance of building-integrated photovoltaic panels. In addition, the use of ASHP and PV instead of a gas boiler and grid electricity reduced both the EP index and CO₂ emissions by 59–67%. Full article

Electrocatalytic water splitting offers a promising route to sustainable H₂, but the oxygen evolution reaction (OER) in alkaline media remains the principal bottleneck for activity and durability. This review focuses on alkaline OER and integrates mechanism, kinetics, materials design, and cell-level considerations. Reaction mechanisms are outlined, including the adsorbate evolution mechanism (AEM) and the lattice oxygen mediated mechanism (LOM), together with universal scaling constraints and operando reconstruction of precatalysts into active oxyhydroxides. Strategies for electronic tuning, defect creation, and heterointerface design are linked to measurable kinetics, including iR-corrected overpotential, Tafel slope, charge transfer resistance, and electrochemically active surface area (ECSA). Representative catalyst families are critically evaluated, covering Ir and Ru oxides, Ni-, Fe-, and Co-based compounds, carbon-based materials, and heterostructure systems. Electrolyte engineering is discussed, including control of Fe impurities and cation and anion effects, and gas management at current densities of 100–500 mA·cm⁻² and higher. Finally, we outline challenges and directions that include operando discrimination between mechanisms and possible crossover between AEM and LOM, strategies to relax scaling relations using dual sites and interfacial water control, and constant potential modeling with explicit solvation and electric fields to enable efficient, scalable alkaline electrolyzers. 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

With growing global demand for sustainable decarbonization, hydrogen energy systems have emerged as a key pillar in achieving carbon neutrality. This study assesses the greenhouse gas (GHG) reduction efficiency of Republic of Korea’s hydrogen ecosystem from a life-cycle perspective, focusing on production and utilization stages. Using empirical data—including the national hydrogen supply structure, fuel cell electric vehicle (FCEV) deployment, and hydrogen power generation records, the analysis compares hydrogen-based systems with conventional fossil fuel systems. Results show that current hydrogen production methods, mainly by-product and reforming-based hydrogen, emit an average of 6.31 kg CO₂-eq per kg H₂, providing modest GHG benefits over low-carbon fossil fuels but enabling up to a 77% reduction when replacing high-emission sources like anthracite. In the utilization phase, grey hydrogen-fueled stationary fuel cells emit more GHGs than the national grid. By contrast, FCEVs demonstrate a 58.2% GHG reduction compared to internal combustion vehicles, with regional variability. Importantly, this study omits the distribution phase (storage and transport) due to data heterogeneity and a lack of reliable datasets, which limits the comprehensiveness of the LCA. Future research should incorporate sensitivity or scenario-based analyses such as comparisons between pipeline transport and liquefied hydrogen transport to better capture distribution-phase impacts. The study concludes that the environmental benefit of hydrogen systems is highly dependent on production pathways, end-use sectors, and regional conditions. Strategic deployment of green hydrogen, regional optimization, and the explicit integration of distribution and storage in future assessments are essential to enhancing hydrogen’s contribution to national carbon neutrality goals. Full article

This review analyzes how recent electric-vehicle LCAs have been carried out, emphasizing goals and scope, functional units, system boundaries (cradle-to-grave and well-to-wheel), and attributional versus consequential modeling rather than reporting outcomes. Using a systematic search of studies mainly from 2018–2025, it maps common tools and data sources (Ecoinvent, GREET, GaBi, and regional inventories) and summarizes LCIA practices, underscoring the need to report versions, regionalization, and assumptions transparently for comparability. Uncertainty studies are uneven: sensitivity and scenario analyses are common, while probabilistic approaches (e.g., Monte Carlo) are less used, indicating room for more consistent, multi-parameter uncertainty analysis. The results show that outcomes are context-dependent: BEVs deliver the largest life-cycle GHG cuts on low-carbon grids with improved battery production and end-of-life management; PHEVs and HEVs act as transitional options shaped by real-world use; and FCEV benefits depend on low-carbon hydrogen. Vehicle-integrated photovoltaics and solar-powered vehicles are promising yet under-studied, with performance tied to local irradiance, design, and grid evolution. Future research suggests harmonized reporting, more regionalized and time-aware modeling, broader probabilistic uncertainty, and comprehensive LCAs of VIPV/SPV and circular pathways to support policy-ready, comparable results. Full article

Ultra-high-voltage direct current (UHVDC) wall bushings are critical components in DC transmission systems, ensuring insulation integrity and operational reliability. In recent years, surface discharge incidents induced by charge accumulation at the gas–solid interface have become increasingly prominent. A comprehensive understanding of the electric field distribution and charge accumulation behavior of wall bushings under UHVDC is therefore essential for improving their safety and stability. In this work, an electrostatic field model of a ±800 kV UHVDC wall bushing core was developed using COMSOL Multiphysics 6.3. Based on this, a geometrically scaled model of the bushing core was further established to investigate charge distribution characteristics along the gas–solid interface under varying voltage amplitudes, application durations, and practical operating conditions. The results reveal that the maximum surface charge density occurs near the geometric corner of the core, with charge accumulation increasing as the applied voltage amplitude rises. Over time, the accumulation exhibits a saturation trend, approaching a steady state after approximately 480 min. Moreover, under actual operating conditions, the charge accumulation at the gas–solid interface increases by approximately 40%. These findings provide valuable insights for the design optimization of UHVDC wall bushings, thereby contributing to improved insulation performance and enhanced long-term operational reliability of DC transmission systems. Full article

Integrated Energy Systems (IESs), which leverage the synergistic coordination of electricity, heat, and gas networks, serve as crucial enablers for a low-carbon transition. Current research predominantly treats energy storage as a subordinate resource in dispatch schemes, failing to simultaneously optimise IES economic efficiency and storage operators’ profit maximisation, thereby overlooking their potential value as independent market entities. To address these limitations, this study establishes an operator-autonomous management framework incorporating electrical, thermal, and hydrogen storage in IESs. We propose a joint optimal dispatch model for hybrid energy storage systems in low-carbon IES operation. The upper-level model minimises total system operation costs for IES operators, while the lower-level model maximises net profits for independent storage operators managing various storage assets. These two levels are interconnected through power, price, and carbon signals. The effectiveness of the proposed model is verified by setting up multiple scenarios, for example analysis. Full article

The large-scale adoption of renewable energy sources, while environmentally beneficial, introduces significant frequency fluctuations due to the inherent variability of wind and solar output. Electric vehicle (EV) integration with substantial battery storage and bidirectional charging capabilities offers potential mitigation for these fluctuations. This study addresses load frequency regulation in multi-area interconnected power systems incorporating diverse generation resources: renewables (solar/wind), conventional plants (thermal/gas/hydro), and EV units. A hybrid controller combining the proportional–integral–derivative with second derivative (PIDD²) and tilted derivative (TD) structures is proposed, with parameters tuned using an innovative optimization method called the Tianji’s Horse Racing Optimization (THRO) technique. The THRO-optimized PIDD²-TD controller is evaluated under realistic conditions including system nonlinearities (generation rate constraints and governor deadband). Performance is benchmarked against various combination structures discussed in earlier research, such as PID-TID and PIDD²-PD. THRO’s superiority in optimization has also been proven against several recently published optimization approaches, such as the Dhole Optimization Algorithm (DOA) and Water Uptake and Transport in Plants (WUTPs). The simulation results show that the proposed controller delivers markedly better dynamic performance across load disturbances, system uncertainties, operational constraints, and high-renewable-penetration scenarios. The THRO-based PIDD²-TD controller achieves optimal overshoot, undershoot, and settling time metrics, reducing overshoot by 76%, undershoot by 34%, and settling time by 26% relative to other controllers, highlighting its robustness and effectiveness for modern hybrid grids. Full article

The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively. Full article

Integrated energy systems (IESs) are increasingly being deployed and expanded, which integrate various energy infrastructures to enable flexible conversion and utilization among different energy forms. To facilitate collaboration among operators of varying scales and fully leverage the economic and environmental benefits of multi-integrated energy systems (MIESs), this study develops a peer-to-peer (P2P) energy sharing framework for MIES based on asymmetric Nash bargaining. First, an IoT-based P2P energy sharing architecture for MIES is proposed, which incorporates coordinated electricity–heat–gas multi-energy synergy within IES models. Carbon capture systems (CCS) and power-to-gas (P2G) units are integrated with carbon trading mechanisms to reduce carbon emissions. Then, an MIES energy sharing operational model is established using Nash bargaining theory, subsequently decoupled into two subproblems: alliance benefit maximization and individual IES benefit distribution optimization. For subproblem 2, an asymmetric bargaining method employing natural exponential functions quantifies participant contributions, enabling fair distribution of cooperative benefits. Finally, the alternating direction method of multipliers (ADMM) is employed to solve both subproblems distributively, effectively preserving participant privacy. The effectiveness of the proposed method is verified by case simulation, demonstrating reduced operational costs across all IESs alongside equitable benefit allocation proportional to energy-sharing contributions. Carbon emission amounts are simultaneously reduced. Full article

Electric vehicles (EVs) are emerging as cost-effective and eco-friendly alternatives to gasoline cars, but widespread adoption still faces hurdles, notably the scarcity of public fast-charging stations. This paper proposes an optimal method to locate and size a fast-charging station in Barcelona, integrating solar photovoltaics (PV) and a battery energy storage system (BESS). The goal is to reduce range anxiety, cut investment costs, and minimize environmental impact. We introduce a modular, scalable station design compatible with second-life batteries and PV panels. Our methodology is twofold: first, determining the optimal charging infrastructure configuration; second, calculating financial viability via net present value (NPV) and internal rate of return (IRR). Results indicate that PV and BESS installation represents the largest cost component, yet energy independence enables rapid capital recovery, with payback in around four years. Selling surplus energy can generate an additional ~4% profit. NPV and IRR values confirm feasibility for scenarios using PV, BESS, or both. Particularly in the highway deployment scenario, combining PV and BESS yields a 72% reduction in greenhouse gas emissions. Overall, our study demonstrates that integrating renewable generation and storage into fast-charging infrastructure in Barcelona is both economically viable and environmentally beneficial. Full article

The rapid adoption of electric vehicles (EVs) requires efficient charging infrastructure planning. This study proposes a multi-objective optimization model for siting and capacity planning of EV charging stations, distinguishing between fast and slow chargers. The model integrates investment, dynamic electricity costs, and user experience, factoring in congestion-adjusted travel distances, time-of-use pricing, and queuing delays using an enhanced M/M/c approach. A comparison of algorithm reveals that the simulated annealing (SA) algorithm outperforms the genetic algorithm (GA) and ant colony optimization (ACO) in minimizing total costs. A case study in Changchun’s urban core demonstrates the model’s applicability, resulting in an optimal plan of 15 stations with 110 fast and 40 slow chargers, providing 11,544 kVA capacity at an annual cost of 38.2651 million yuan. Compared to traditional models that ignore charger types and simplify delays, the proposed model reduces total system costs by 4.31%, investment costs by 5.31%, and user costs by 3%, while easing delays in high-demand areas. This framework provides practical insights for urban planners and policymakers, helping balance investment and user satisfaction, and promoting sustainable EV mobility. Full article

Integrated energy load forecasting plays a crucial role in optimizing the operation and economic dispatch of integrated energy systems. Its forecasting accuracy is not only time-dependent but also influenced by the coupling characteristics among energy sources. Solely relying on time-scale training methods cannot adequately capture the strong correlations among multiple energy sources. To address challenges in extracting coupled load forecasting features, obtaining periodic characteristics, and setting model network structures, this paper proposes an Integrated Energy Short-Term Adaptive Load Forecasting Method Based on Coupled Feature Extraction (AP-CFE). This approach integrates high-dimensional coupling features and periodic temporal features effectively using ensemble algorithms. To prevent overfitting or underfitting issues, an Adaptive learning algorithm (AP) is introduced. The load demonstrates highly stochastic behavior in response to external factors, resulting in rapid, volatile fluctuations in grid demand. The strategy of employing sparse self-attention to approximate the residual terms effectively mitigates this issue. Simulation results using comprehensive energy load data from Australia demonstrate that the proposed model outperforms existing models, achieving better capture of energy coupling characteristics with average absolute percentage errors reduced by 20.75%, 28.48%, and 21.64% for electricity, heat, and gas loads, respectively. Full article