Search Results (3,828)

Stabilizing weak soils is a well-known pavement and geotechnical engineering technique. This technique involves introducing minimal cementitious materials to improve the soil’s geotechnical characteristics. This paper investigates the use of recycled industrial polymer waste (IPW) as a reinforcement material in the presence of cementitious binders to stabilize weak silty sand soil (SM), supporting sustainable engineering practices. The randomly distributed IPW were added as percentages of 0%, 5%, and 10% to a mixture of lime soil and cement soil, with varying amounts of 0% to 6% of lime (L) and 0% to 6% of ordinary Portland cement (OPC), respectively. The laboratory experiments were conducted on natural and stabilized samples in wet (unsoaked) and submerged (soaked) conditions. The experimental program included Proctor compaction, California bearing ratio (CBR), unconfined compressive strength (UCS), durability tests, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction analyses. The resilient modulus (Mr) was estimated using an empirical equation. The outcomes of this experimental study show that adding a combination of IPW shreds with a small amount of L and/or OPC to the SM soil provides a significant increase in the UCS, CBR, durability and Mr values compared with case of SM with only L, which allows for superior characteristics and increases strength and stiffness parameters throughout any phase of earthwork construction design, resulting in stronger and stiffer subgrades. These results were reinforced by microstructural observations from SEM, EDS, and DRX, confirming the formation of cementitious gels and chemical compounds, consistent with the macro-scale mechanical improvements. The expected practical outcomes include potential reductions in pavement thickness, which can help lower pavement stabilization costs and extend its service life. Additionally, the use of waste materials to replace raw materials contributes to decreased energy consumption and emissions, although detailed assessments are needed to quantify these effects. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

The development of offshore wind energy in tropical cyclone-prone regions requires analytical frameworks that capture non-stationary climate dynamics. This study presents a multi-scale spectral approach to characterize Atlantic tropical cyclone variability and assess implications for offshore wind resilience in the Caribbean Basin. The methodology integrates Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to resolve temporal variability in sea surface temperature, cyclone frequency, and intensity, complemented by two-dimensional kernel density estimation (KDE) and non-stationarity analysis. Using NOAA and National Hurricane Center datasets, results identify dominant periodicities at annual and ENSO (2–7 year) scales, a post-1995 spectral energy shift associated with the positive AMO phase, and a thermodynamically consistent energy corridor along 12–16° N. A statistically significant change point in 1987 (Pettitt test, p < 0.05) is detected, although spatial displacement is not significant. An integrated Wind Risk Index highlights the central-western Caribbean as a high-exposure zone overlapping offshore wind development areas. Exceedance analysis shows that 39.8% of observations surpass 25 m/s, 6.0% exceed 50 m/s, and 1.3% approach 70 m/s, indicating relevant design considerations. These findings support the need for non-stationary, multi-scale approaches in offshore wind risk assessment under tropical cyclone influence. Full article

This systematic literature review (SLR) synthesizes current research on integrating machine learning (ML) into robust optimization (RO) frameworks for solving Green Vehicle Routing Problems (Green-VRP) under uncertainty. The key contributions include utilizing the EmbedSLR 2.0 framework for objective screening, establishing a functional ML role taxonomy, and mapping uncertainty sets to computational tractability. Following PRISMA guidelines, searches across Scopus, Sage, and Dimensions identified 82 eligible studies validated through a three-point quality assessment scale. Bibliometric analysis indicates that the VRP has evolved into an interdisciplinary field that combines the power of rigorous RO with the integration capabilities of ML to achieve sustainability and resilience goals. Based on the results of the literature review, it was found that ML plays four crucial functional roles: as an end-to-end problem solver, a tool for predicting input parameters, a guide for search subroutines, and a mechanism for constructing more precise uncertainty sets. Various frameworks such as Adjustable Robust Optimization (ARO), Distributionally Robust Optimization (DRO), and Data-Driven Robust Optimization (DDRO) have been reported in various studies to offer improved cost efficiency and robustness compared to conventional static RO models by utilizing data more dynamically to reduce the level of conservatism. The integration of these environmental factors is carried out through emission and energy consumption parameters, which systematically give rise to operational trade-offs. This SLR has several limitations, including database and language limitations, the absence of cross-reference validation in EmbedSLR 2.0, and limitations in quality assessment. This publication is funded by the Universitas Padjadjaran through the LPDP on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under the EQUITY Program (Contract No. 4303/B3/DT.03.08/2025 and 3927/UN6.RKT/HK.07.00/2025), as well as the Universitas Padjadjaran Research Grant under Research Grant for Graduate Students (Hibah Riset Melibatkan Mahasiswa Pascasarjana - RMMP) with contract number 5598/UN6.3.1/PT.00/2025. This systematic review was registered on the Open Science Framework (OSF) on 8 May 2026. Full article

The rapid growth of renewable energy is driving a profound transformation of electricity grids toward architectures characterized by high shares of inverter-based generation, increased decentralization, and extensive digitalization. While wind and solar technologies have matured at the component level, their large-scale integration introduces technical, operational, and institutional challenges that extend beyond conventional power-system design paradigms. This review provides an integrated synthesis of the technologies, control strategies, and management processes that enable renewable energy integration into emerging electricity grids. Key challenges are analyzed across multiple timescales: fast frequency and voltage dynamics in low-inertia systems (milliseconds to seconds), forecasting, optimization, and automated control (real-time to near-real-time), and long-term planning of transmission, storage, and flexibility resources (years to decades). The synthesis covers grid-forming and grid-following inverter control, with quantitative comparison across short-circuit-ratio regimes; HVDC and HVAC transmission technologies; energy storage systems, including emerging electrochemical and mechanical solutions; smart-grid digitalization through EMS, SCADA, and digital twins; artificial intelligence and machine-learning deployments at major transmission system operators; sector coupling involving hydrogen and carbon capture; and cybersecurity considerations. Real-world case studies are used to illustrate practical lessons, with explicit attention to the brownfield–greenfield distinction between modernization of legacy systems and the design of new networks in developing regions. The review concludes by identifying key research and development priorities for achieving reliable, resilient, and economically efficient high-renewable energy systems. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Building climate-resilient cities strengthens urban livability and sustainable development levels. This paper constructs a difference-in-differences model to examine the impact of the pilot policy for climate-resilient city construction (CRCC—CRCC is used uniformly in the following text to represent the policy) on green sustainable innovation, using panel data of 260 prefecture-level Chinese cities from 2009 to 2023. The results reveal that CRCC can significantly promote green sustainable innovation in Chinese cities. Additionally, CRCC promotes green sustainable innovation by increasing the level of informatization, improving green total-factor energy efficiency, boosting corporate ESG performance, and alleviating corporate financing constraints. Therefore, it is necessary to further strengthen the implementation and promotion of China’s climate pilot policy. Attention should be paid to optimizing the pathways through which the pilot policy affects green sustainable innovation. Differentiated regional policies should be implemented based on local conditions. A tripartite linkage mechanism involving the government, enterprises, and the public should be established to increase societal awareness and support for climate-resilient city construction. Full article

This paper proposes a contingency-aware, sensitivity-based criterion for the optimal placement of shunt compensation in transmission power systems under N–1 security constraints. Conventional approaches typically rely on post-contingency voltage severity or heuristic optimization techniques, which may fail to capture the system-wide impact of reactive power support during the planning stage. The proposed method integrates contingency severity assessment with a system-wide sensitivity index to support structured and physically interpretable planning decisions. First, a global contingency index is used to identify the most critical operating condition under N–1 scenarios. Based on this condition, a reduced set of candidate buses is selected according to post-contingency voltage magnitudes. These candidates are then ranked using a sensitivity metric defined as the derivative of the contingency index with respect to reactive power injection ($𝜕J/𝜕Q_k$), which quantifies the global effect of local reactive support on system performance. The selected compensation locations are validated through AC optimal power flow simulations, enabling the evaluation of voltage profiles and active power losses under both normal and contingency conditions. The methodology is tested on the IEEE 14-, 30-, and 57-bus transmission systems to assess its scalability and consistency across networks of different sizes. Results show that the bus with the lowest post-contingency voltage is not necessarily the optimal compensation location. Instead, the proposed sensitivity-based criterion identifies buses that provide greater system-wide benefits, including reductions in active power losses and improved voltage recovery. The approach provides a transparent and reproducible planning-oriented decision criterion, supporting improved operational efficiency and aligning with sustainability-oriented objectives in modern power systems. The proposed method provides a reproducible and planning-oriented decision criterion that complements conventional optimization-based approaches. Full article

A microgrid is a localized distribution network composed of electricity users who have access to local renewable and other energy sources. While the utility grid plays a critical role in the nation’s economy, security, and the well-being of its residents, connecting microgrids to the wider network via utility substations can introduce significant cybersecurity risks. Unlike most existing studies that rely on simulation, this research designs and implements a physical microgrid testbed to examine cybersecurity vulnerabilities in microgrid systems. We examine the impact of various cyberattacks—including denial of service (DoS) and communication hijacking—on microgrid operations, with a particular focus on system stability and communication networks. The findings reveal critical weaknesses within the existing communication infrastructure, providing valuable insights for designing more resilient and secure microgrids. This work offers a practical framework for addressing cybersecurity challenges in real-world industrial utility networks. Full article

Utility disruptions may stem from insufficient power generation, inferior infrastructure, or secondary weather perils (e.g., tornadoes, floods, snowstorms) that take energy infrastructure offline. The latter present a unique risk that not all existing power options can mitigate. Regardless of their origin, power disruptions have the potential to cripple food supply chains and undermine food system sustainability. To prepare for managing future disruptions, food and beverage manufacturers may couple electrical microgrid and thermal district heating infrastructure with small modular reactors (SMRs) or smaller microreactor systems to form low-carbon power islands. Although SMR technology is a somewhat new source of energy and has not yet achieved commercial viability, it provides the potential to make food and beverage manufacturing more resilient and sustainable when it becomes broadly available. To assess the potential cost–benefit of activating such technology as a sustainability-oriented resilience investment, we conducted a technoeconomic downtime threshold analysis. The case assumes that the technology is the full-time power source and the SMR yields stronger returns as facility downtime or downtime costs rise. The analysis found the breakeven point to range from 12.3 h down to 613.2 h down annually for a 5 MW system, depending on facility scale and assumed downtime costs. At a representative downtime opportunity cost of $10,000/h, SMR adoption requires approximately 61.3 h (5 MW) of annual outages to break even, highlighting scale effects on feasibility. Incorporating a 20% thermal energy credit reduces required outage thresholds by roughly 20%, lowering the breakeven level to 49.1 h. These results highlight the potential role of SMR-enabled power islanding in supporting sustainable food manufacturing through improved energy resilience, low-carbon power, and thermal energy recovery. Full article

The frequent occurrence of extreme climate events poses a severe challenge to the reliability of building energy systems. Hydrogen energy, with its long-term storage capacity, has become a key technology carrier for enhancing building resilience. This study constructs a resilience–environment–economy co-optimization framework that couples dynamic simulation and emergy analysis. Through a five-in-one approach of physical modeling, climate scenario generation, resilience quantification, emergy accounting, and multi-objective optimization, the resilience performance of building hydrogen energy systems under the scenario of extreme heat waves combined with grid failure is evaluated. The results show that the thermal time constant deviation of the electrolyzer is 4.06%, the correlation coefficient between the generated heat wave scenario sequence and the historical measured data is 0.94, the prediction deviation of the once-in-a-century extreme temperature is 0.5%, the environmental load rate is 4.33, the Pareto front contains 127 non-dominated solutions, and the comprehensive performance of the co-optimal solution is improved by 42% to 88%. Engineering suggestions: For public buildings in hot summer and cold winter regions, the hydrogen energy system should adopt a configuration of 50–60 kW electrolyzers and 50–70 kg hydrogen storage tanks, with a key load guarantee rate of no less than 95%, and the ecological cost is 35% lower than that of diesel backup. This study provides a quantitative decision-making tool for the resilience planning of building hydrogen energy systems under extreme climate conditions and can be extended to other high climate risk areas. Full article

As climate change worsens, irrigation modernization has become critical for better water distribution and maintaining rice production in the face of increasing water constraints. However, there remains a gap in quantification regarding the environmental trade-offs between pump-managed and gravity-based irrigation systems, especially in integrated assessments that relate economic performance, carbon emissions, and water use. This study used an integrated framework of water productivity (WP), consumptive water footprint (WF), carbon footprint, and eco-efficiency to compare gravity-based and pump-managed systems in the Don Chedi Operation and Maintenance Project, Thailand, from 2021 to 2023. The results showed no significant differences in WP and WF between systems. WP averaged 0.39 kg m⁻³ during the wet seasons and 0.54 kg m⁻³ during the dry seasons, while the WF averaged 2517 m³ t⁻¹ and 1854 m³ t⁻¹, respectively. These findings indicate that pump-managed irrigation enhanced operational flexibility and yield stability but did not substantially improve water use efficiency. However, compared with the gravity-based system, the pump-managed system produced much greater carbon emissions, with total carbon footprints ranging from 1.252 to 1.333 tCO₂eq t⁻¹, or five times higher in the irrigation process. Eco-efficiency metrics rose by up to 8.11% despite this environmental burden, indicating enhanced economic resilience amid fluctuating water conditions. These results show a recurring trade-off between low-carbon agricultural development and irrigation modernization. The study therefore emphasizes the importance of integrating renewable energy and low-carbon technologies into pump-based irrigation systems to support climate-resilient and sustainable agricultural transitions. Full article

To mitigate large-scale blackout risks in urban distribution systems under typhoon-induced extreme weather and to reduce post-disaster restoration costs, this study proposes a resilience-oriented spatiotemporal co-optimization framework integrating transportation networks, power grids, and distributed energy resources. First, a city-scale typhoon spatiotemporal model is established, integrating static wind field, dynamic evolution, and trajectory-based mobility with urban-geometry-driven wind speed correction to characterize the spatiotemporal progression of extreme wind hazards. Second, the time-varying failure rates of distribution network components are quantified by explicitly accounting for network topology correlations, while the spatiotemporal dispatchability and output characteristics of distributed resources under disaster conditions are systematically modeled. Third, a pre-disaster proactive deployment model is formulated to minimize load curtailment costs and resource allocation expenditures. The model integrates active network reconfiguration with coordinated placement of distributed generation (DG) and mobile energy storage systems (MESSs), enabling resilience-enhancing pre-positioning strategies. Subsequently, a post-disaster restoration scheduling model is developed with the objective of minimizing unserved load. By embedding traffic flow constraints and optimal path computation under disrupted transportation conditions, the proposed framework realizes spatiotemporal coordination among MESSs, DG, and electric vehicles (EVs), thereby accelerating system-level recovery. Finally, the effectiveness of the proposed strategy is validated on a 51-node urban distribution system located in eastern coastal China, demonstrating significant improvements in restoration performance and resilience enhancement. Full article

The rapid advancement of photovoltaic (PV) technologies has transformed solar energy systems into intelligent, high-efficiency platforms. This review systematically examines next-generation PV materials, hybrid system architectures, and intelligent control strategies. Key technologies include perovskite-based tandem cells, N-type TOPCon, bifacial, heterojunction (HJT), and photovoltaic-thermal (PVT) systems. These innovations overcome the intrinsic limitations of conventional P-type silicon panels by reducing recombination losses, mitigating light- and temperature-induced degradation, and enhancing energy yield under real-world operating conditions. At the system level, AI-enabled inverters, adaptive maximum power point tracking (MPPT), predictive maintenance, and real-time grid interaction enable dynamic optimization under variable irradiance, thermal stress, and load fluctuations. A critical comparison across diverse deployment environments highlights current challenges, including manufacturing complexity, material stability, and AI data-quality limitations. Despite higher upfront costs and system complexity, these advanced PV systems offer superior long-term performance, improved reliability, and reduced levelized cost of electricity through lower degradation rates and enhanced operational resilience. Collectively, intelligent, material-optimized PV technologies represent a scalable, sustainable, and grid-compatible solution for solar energy deployment across diverse climates, supporting the global transition toward low-carbon energy infrastructures. Full article

Urban heating systems continue to rely heavily on fossil fuels, driving significant CO₂ emissions and underscoring the need for scalable renewable alternatives. This study evaluates a solar-assisted aquifer thermal energy storage (ATES) system for sustainable urban heating, operating within a relatively deep aquifer. A numerical model of the Mannville aquifer is developed to simulate charge–discharge cycles in a relatively deep open-loop ATES system, examining subsurface temperature evolution, storage efficiency, and long-term thermal stability under Canadian climatic conditions. Modeling results indicate that such aquifers act as an effective thermal buffer for solar energy storage operations, smoothing seasonal temperature fluctuations and stabilizing heat production. Surplus solar thermal energy injected during low-demand periods significantly reduces long-term temperature decline and preserves thermal availability for winter extraction. Balancing contributions from solar and aquifer storage maintains system efficiency during peak demand while improving overall thermal management. The integrated approach enhances renewable energy utilization, reduces reliance on conventional heating systems, and strengthens the resilience of urban energy networks. Our findings demonstrate that coupling solar thermal input with geothermal heat storage in relatively deep aquifers offers a practical pathway for advancing sustainable urban heating in cold-climate regions. The modeling framework provides a foundation for optimizing seasonal storage strategies and guiding the design of hybrid solar–geothermal systems for large-scale urban applications. Full article