Search Results (26,322)

Under the “dual carbon” goals, this study focuses on realizing energy exchange among multiple microgrids via shared energy storage to promote sustainable energy transition. Accordingly, a distributed robust optimwhichization strategy is proposed in this paper. Addressing the uncertainty of distributed renewable energy sources within microgrids, the scenario set generated by the Wasserstein generative adversarial network with gradient penalty and pruned by the K-means++ clustering algorithm serves as the initial renewable energy scenario for the distributed robust optimization set. Combining Nash theory, a cooperative game operation model is constructed. The benefit distribution model based on contribution factors ensures a fair benefit allocation scheme. The parallelizable column and constraint generation algorithm is employed to enhance computational efficiency. Case studies demonstrate that compared to scenes produced by other methods, the proposed model has the lowest alliance operating cost. It more effectively captures renewable energy uncertainty and lowers system operational costs. The respective efficiency improvement rates for each microgrid are as follows: 4.6%, 5.0%, and 4.1%, ensuring a fair profit distribution scheme. This study provides a technical reference for realizing the sustainable development of a multiple microgrid system, contributing to the global goal of low-carbon energy transition and sustainable development. Full article

Suspension architecture has long been a central topic in vehicle chassis research and development. Passive, active, and semi-active suspensions provide different trade-offs in performance, complexity, and energy use. The pseudo-active actuator (PAA) is a newly emerging concept that delivers near active-level performance with semi-active-level energy input, which opens a new direction for suspension architecture design. In this work, a pseudo-active suspension (PAS) based on a PAA is developed. Along with the structural investigation, the corresponding dynamic model and control system are established and experimentally validated. Taking a suspension of a body-on-frame (BoF) vehicle as the application platform, an engineering-feasible PAS configuration is proposed, and design/optimization principles are presented for key geometric parameters and components. A quarter-car three-mass PAS dynamic model is derived, in which the equivalent coupling introduced by the mechanical compensation mechanism is explicitly characterized, leading to a complete state-space representation. To address the multi-objective performance requirements of the PAS, a conventional H_∞ controller and a finite-frequency H_∞ controller with a specified target band are designed, respectively. A quarter-car PAS experimental rig and a real-time control platform are built, and experiments are conducted under various displacement excitation scenarios. Both simulations and experiments demonstrate that the proposed PAS and controllers meet the multi-objective design objectives and provide robust performance, supporting practical implementation. Full article

Used lubricating oils (ULOs) represent one of the largest hazardous liquid waste streams globally and pose significant environmental risks if improperly managed. This study presents a structured review of ULO management pathways, including regeneration, reprocessing, and energy recovery technologies, within a sustainability and circular economy framework. The review systematically categorizes treatment options based on recovery efficiency, waste generation, environmental performance, and technical feasibility. To contextualize environmental risk, a conceptual numerical spill dispersion analysis using the SIMOIL model is included as an illustrative case study under simplified marine conditions. The simulation highlights the rapid dispersion potential of ULOs in coastal environments, reinforcing the need for preventive management strategies. The analysis indicates that refining technologies generally offer higher material circularity potential, while thermochemical processes provide viable alternatives for heavily contaminated oils. The study identifies critical gaps in technoeconomic comparability, regulatory harmonization, and source segregation practices. Strengthening integrated management systems is essential to minimize environmental impact and enhance resource recovery. Full article

The increasing use of integrated renewable energy sources (RESs) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and assets optimization. This paper aims to provide a comprehensive review of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the high penetration of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. Five key areas of the energy sector are identified where AI tools are applied: forecasting electricity generation from RES; forecasting demand and price fluctuations on the electricity spot market; the real-time management of energy flows and assets in active microgrids; and data processing and analyzing, and general industrial direction. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. This digital transformation is a gradual process with consecutive steps. To improve understanding and clarity, the authors present a three-phase roadmap of AI adoption. To develop an adequate AI integration strategy, it is necessary to understand the technologies, algorithms, hierarchical structure, and connections within this structure. Accordingly, the article presents a taxonomy of the hierarchical structure of AI. The subsequent step involves the sequential construction of a digitalization model. Here, the authors consider it necessary to present a 4-layer structure model of AI energy democracy. Finally, through a comparative analysis of different types of intelligent applications for energy problem solving, guidelines are provided for successful decision making in compliance with the specified harmonized standards and protocols. Full article

It is widely acknowledged that universities play a pivotal role in promoting sustainability by encouraging education for durable advancement, carrying out research, and overseeing sustainable campus operations. The present research primarily aims to evaluate the sustainability level of the University of Béjaïa (Algeria) using the international UI GreenMetric model, which is considered the main global framework for assessing the sustainability of a campus. The present work principally intends to assess the environmental and institutional performance of the university, according to international standards, and to propose a methodological framework that is adapted to the Algerian context. To achieve this goal, it was deemed appropriate to apply a mixed-methods approach that combines surveys, field observations, and document analysis through six categories of indicators, namely infrastructure, energy and climate, waste management, water management, transportation, and education and research. The results obtained explicitly indicate that the University of Béjaïa is still in an early phase of transition towards sustainability. This phase is characterized by strong ecological potential but with rather limited systems of governance, energy efficiency, and mobility. Therefore, the study proposes a national model, called AlgéMetric, inspired by the GreenMetric model and the contextualized sustainability assessment tools (SATs) that were developed, in order to address the aforementioned challenges. This hybrid tool includes new indicators that reflect the climatic, institutional, and cultural specificities in Algeria. This research proposes the first systematic application of the UI GreenMetric framework in Algeria. It also contributes to establishing a national network of sustainable campuses aligned with the Sustainable Development Goals (SDGs) and the National Biodiversity Strategy and Action Plan (NBSAP 2016–2030). Full article

Elastomer-based nanocomposites combining polymer flexibility with conductive nanofillers provide lightweight, stretchable systems with tunable electromechanical properties for wearable electronics, soft robotics, and self-powered sensors. However, predicting their nonlinear response remains challenging because the observed piezoelectric-like response arises from strain-dependent interfacial polarization and evolving piezoresistive conduction pathways within heterogeneous microstructures. We introduce a continuum electro-hyperelastic framework combining the Mooney–Rivlin model for large-strain elasticity with a Helmholtz free-energy approach for electrostatic coupling. Analytical expressions for stress, electric displacement, and apparent piezoelectric coefficients are derived and implemented in finite element simulations. The model accurately reproduces the experimental mechanical, dielectric, and electromechanical behaviour of polydimethylsiloxane (PDMS) nanocomposites with 0.1–1 wt% graphene. These show increased stiffness, relative permittivity (from 3.4 to 4.0, ≈18%), and quasi-static d₃₃ coefficients (from −5.6 to −10.0 pC N⁻¹, ≈80% enhancement). Analytical and finite element method (FEM) results show consistent trends across the full deformation range, with Maxwell stress agreement within 10% at lower deformation levels, while deviations of 33–40% for coupled electromechanical quantities at an axial displacement u_z~ = −1 mm (~16.7% compressive strain) are attributable to three-dimensional shear effects absent from the uniaxial analytical assumption. Simulations reveal that graphene boosts Maxwell stress, yielding a four-fold increase at lower stretch ratios. This reframes PDMS–graphene composites as electro-hyperelastic materials, offering a predictive, extensible framework. It highlights apparent piezoelectricity as an emergent, tunable effect from charge redistribution in a compliant hyperelastic matrix—guiding the design of next-generation flexible devices leveraging field-induced coupling over intrinsic polarization. Full article

Most existing studies on the cost competitiveness of self-consumption PV systems fail to jointly consider key technical, economic, and user-specific factors—such as the share of PV electricity self-consumed, energy exported or imported from the grid, and time-of-use electricity pricing—all of which significantly influence investment viability. To address these gaps, this study introduces a novel method based on a new model to calculate the unit cost of electricity consumption from the user’s perspective (C_EC, in €·kWh⁻¹). The array DC power rating is then optimally sized—assuming ideal orientation and tilt—to minimize C_EC. A self-consumption PV system is considered cost-competitive when the annualized minimized C_EC is lower than the applicable regulated electricity tariff. Colombia is selected as a case study to demonstrate the novel method due to the limited deployment and analysis of self-consumption PV systems in the country. The method is applied across residential, commercial, and industrial sectors in various locations. The resulting annualized minimized C_EC values (0.35–8.85 c€/kWh) are consistently below the corresponding regulated tariffs, demonstrating the economic viability of properly sized PV systems. The method’s adaptability to international tariff frameworks makes it a valuable tool for global application and a useful resource for policymakers and stakeholders. Full article

Accelerating decarbonization in hot-climate buildings requires integrated retrofit strategies that address energy performance, environmental impact, thermal comfort, and economic feasibility within a unified decision framework. This study develops and validates a simulation-driven multi-criteria approach to evaluate retrofit packages across three representative ASHRAE hot sub-climates (1B, 2B, 2A). An academic building was modeled using DesignBuilder (Stroud, UK) and validated in accordance with ASHRAE Guidelines. The retrofit analysis integrates envelope enhancements (insulation and reflective coatings), glazing-integrated photovoltaics (GIPV), rooftop photovoltaics (RTPV), and a Dedicated Outdoor Air System (DOAS). The performance evaluation incorporates dynamically simulated energy consumption, operational CO₂ emissions, thermal comfort indicators (PMV and DCH), and techno-economic metrics (IRR, ROI, PBP). Weighting factors were derived from a structured stakeholder consultation to reflect context-sensitive sustainability priorities. The results indicate energy reductions of approximately 51–57% and carbon emission reductions of 40–53% across the examined zones, while discomfort hours decreased by roughly 42–46%. This demonstrates significant improvements in thermal comfort under integrated retrofit strategies, particularly with DOAS integration, highlighting the importance of ventilation-driven comfort enhancement. Economic feasibility was climate-dependent; envelope-focused solutions yielded high returns, while integrated strategies delivered balanced environmental and economic performance. The proposed framework enables systematic, climate-specific prioritization of retrofit alternatives and supports scalable, economically viable NZEB transitions in rapidly expanding hot-climate educational infrastructure. Full article

To enhance the market-oriented operation capability of integrated energy retailers and improve the synergy and economic efficiency of complex microgrids, this paper constructs a bi-level optimization model of “upper-level price optimization, lower-level multi-energy flow scheduling” under the background of multi-energy coupling of electricity, heat, gas, and hydrogen. The upper level optimizes electricity and heat price signals using the APSO and IGWO algorithms, while the lower level realizes coordinated multi-energy flow scheduling based on these signals. The operational performance of the two algorithms is compared across four scenarios. The results show that the scenario with multi-energy storage (Scenario 3) is the optimal adaptive scenario: the charge–discharge regulation of energy storage interacts with price guidance, and the peak-shaving and valley-filling characteristics significantly improve the system’s energy utilization efficiency. This scenario can fully unlock the value of bi-level optimization and meet the operational requirements of complex multi-energy coupling. In the algorithm comparison, the APSO algorithm presents distinct advantages, outperforming the IGWO algorithm in the precise regulation of upper-level electricity and heat prices, lower-level multi-energy flow balance, total operation cost control, and convergence stability. It provides an effective technical solution for the economic and stable operation of integrated energy systems. Full article

This survey is an integrated and complete summary of the strategies and technological systems of surveying environmental hazard in marine, freshwater, and brackish environments. Contrary to the previous articles where the separate parts of the monitoring chain are investigated or certain environments/enabling technologies are considered, the given work has a cross-domain approach that unites sensing modalities, data acquisition schemes, communication schemes, operational platforms, data analytics, energy management schemes, and regulatory compliance into one consistent framework. The survey systematically examines the entire sensing-to-cloud pipeline, which includes sensor technologies, data acquisition systems, telecommunication infrastructures, and a variety of monitoring platforms such as buoy-based systems, Unmanned Surface Vehicles (USVs), Autonomous Underwater Vehicles (AUVs), and Unmanned Aerial Vehicles (UAVs). In addition, it touches on the administration and examination of mass environmental data, including cloud-based systems and AI-based methods of automated feature identification, anomaly recognition and predictive modeling. The key points of the autonomy of the system, including power supply solutions and energy-conscious management, are also mentioned, as well as the relevant regulations on the environmental monitoring nationally, at the European level, and globally. This paper presents a systematic six-step design process of aquatic environmental monitoring systems: (1) risk categorization, (2) physical data acquisition systems, (3) monitoring platforms, (4) data management & analytics, (5) energy autonomy strategies, and (6) regulatory compliance. The systematic framework offers researchers and practitioners practical guidelines to follow when designing end-to-end systems, thus completing the gaps in the historically disjointed research strands and going beyond the traditional domain- and technology-based studies. Full article

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, grid stability, and resilient operation in renewable-integrated power systems. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for intelligent joint demand–supply forecasting in smart grids. The model was developed and implemented in MATLAB using real-world datasets comprising electricity consumption, photovoltaic (PV) generation, temperature, and irradiance variables. Comparative evaluations demonstrate that the hybrid CNN–GRU outperforms single-model approaches, including Long Short-Term Memory (LSTM), GRU, and eXtreme Gradient Boosting (XGBoost), based on Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. On a 14-day test set, the proposed model achieves RMSE values of approximately 34 kW for demand and 28 kW for PV generation, with MAPE of approximately 4% and 6%, respectively. Furthermore, average net-load RMSE is reduced by approximately 15–25% relative to GRU/LSTM baselines, while maintaining controlled errors of approximately 35–40 kW during sharp ≥100 kW/15 min ramp events. By reducing net-load uncertainty and improving forecasting precision, the proposed framework enhances renewable energy utilization, supports more efficient reserve allocation and storage scheduling, and provides a quantitative tool for sustainability-oriented energy management. Consequently, the study contributes to the advancement of sustainable smart grid operation and the broader transition toward low-carbon and resilient energy systems. Full article

The high penetration of renewable energy is becoming an important feature of new power systems. However, the power grid is facing greater threats of failures with the increasing frequency of extreme weather, making it necessary to enhance the resilience of power systems. In this paper, a multi-time-scale energy storage planning system is proposed for power system resilience improvement. Firstly, the characteristics of multi-time-scale energy storage are analyzed, and models of battery energy storage and hydrogen energy storage are established. Secondly, based on an analysis of random extreme weather scenarios, a bi-level stochastic programming model for multi-energy storage aimed at enhancing the resilience of power systems is constructed. Finally, based on the modified IEEE-24 node system, the model solution and example analysis are carried out, and the optimal configuration scheme for multi-energy storage is obtained. The results show that multi-energy storage is able to adjust more flexibly and effectively improve the resilience of the power system. Compared with the configurations of short-term and long-term energy storage systems, adopting multi-timescale energy storage reduces the total cost by 22.77% and 14.08%, respectively, and improves resilience by 4.33% and 0.67%, respectively. Full article

Off-grid wind-to-hydrogen systems are considered a promising solution for sustainable, large-scale green hydrogen production in remote areas. However, under the combined effects of highly fluctuating wind generation and stochastic load variations, existing energy management methods still face a challenge: in off-grid wind-to-hydrogen systems, intelligent energy management studies that jointly address economic performance and operational stability are still limited. To address these issues, this paper develops a mathematical model for an off-grid wind-to-hydrogen system to reveal the coupling characteristics of the wind–electricity–hydrogen conversion process. Building on this model, a deep-reinforcement-learning-based energy management strategy is proposed. By formulating objectives that simultaneously capture economic benefits and stability requirements, the proposed strategy enables adaptive power flow allocation and dynamic optimization under uncertainty. Case studies demonstrate that, while fully satisfying load demand, the proposed strategy can significantly improve renewable energy utilization and hydrogen production, thereby increasing profit and ensuring stable and sustainable system operation. Full article

This study investigates the integration of a molten salt thermal energy storage (TES) system into a 330 MW coal-fired power unit to enhance its operational flexibility and exergy-based performance. Using EBSILON Professional (version 13) software, several heat storage and heat release schemes were modeled and analyzed to assess their effects on turbine performance, coal consumption rate, heat rate, and exergy losses under various load conditions. The results reveal that coupling TES with conventional thermal units can effectively decouple heat and power generation, enabling deep peak-shaving operation while maintaining system efficiency. The six heat storage schemes and seven heat release schemes considered in this study were selected based on the physical characteristics of the 330 MW reheat-steam cycle and the practical constraints of integrating a molten salt TES system into an existing coal-fired unit. Specifically, the schemes were designed to represent all feasible pathways for redirecting thermal energy within the boiler–turbine system, including steam extraction from different turbine stages, reheater-side interventions, and electric-heating-assisted charging options. These schemes also reflect the operational boundaries of the unit, such as allowable extraction fractions, steam temperature limits, and turbine safety margins. The findings demonstrate that molten salt TES can serve as a feasible and efficient pathway for retrofitting existing coal-fired power units to improve load-following capability, reduce fuel consumption, and support grid flexibility under renewable-dominated energy scenarios. Full article

A crucial problem for autonomous aerial operations is to provide dependable and strong control of unmanned aerial vehicles (UAVs) in adverse environmental circumstances. The current paper provides an extensive analysis of the vehicle dynamics and control of drones in strong wind fields with altitude-dependent wind shear, wind gusts, and turbulence. A comparative evaluation of sliding mode control (SMC), linear quadratic regulator (LQR), model predictive control (MPC), adaptive constrained adaptive linear control (ACALC), and higher-order control barrier function (HOCBF)-based control in the context of trajectory tracking performance, control effort, and robustness is carried out. Simulation outcomes show that SMC exhibits superior robustness to sudden wind disturbances and the most consistent tracking accuracy under stochastic variations; HOCBF and ACALC provide comparable high precision with added constraint enforcement and adaptive capability, respectively; MPC has smooth control and minimal energy consumption; and LQR has a high level of computational efficiency with significantly tolerable tracking performance. Monte Carlo calculations are conducted to measure tracking errors and control energy under the stochastic wind variations, and the capability of the proposed control strategies to remain resilient in uncertain conditions is brought to light. The results provide useful information about the architecture of effective controllers used in UAVs during severe weather conditions and underline the compromises between the accuracy of tracking, the control effort, and the energy consumption. The suggested framework offers an effective and scalable system suitable for reliable autonomous drone activity in complicated reality settings. Full article