Search Results (267)

Against the backdrop of China’s integrated rural revitalization and digitalization strategies, advancing the digital transformation of rural emergency management has become crucial for enhancing grassroots governance capabilities. This study aims to systematically examine the underlying mechanisms through which digital technologies empower rural emergency management. By developing an analytical framework that integrates digital infrastructure, collaborative governance networks, emergency response capacity, and comprehensive rural resilience, and applying a multi-criteria decision-making model, we identify the causal structures and driving pathways among key factors. The findings indicate that residents’ safety resilience, the level of digital equipment in rescue teams, and industrial recovery capacity serve as core drivers within the system. Meanwhile, the intelligent dispatch capability of emergency supplies acts as a central hub linking technological application with operational effectiveness. Pathway analysis further reveals a progressive empowerment logic described as “strengthening foundational resilience, enhancing coordinated dispatch, improving industrial recovery.” This study not only deepens the understanding of the complex process of digital empowerment but, more importantly, offers policymakers a clear action plan: in resource allocation and capacity building, priority should be given to synergistically advancing the above key drivers and hub elements to achieve systemic improvement in the effectiveness and resilience of rural emergency management. Full article

Biomass-based energy systems represent a strategic and dispatchable renewable option for sustainable energy transitions in Latin America, where agricultural and agro-industrial residues provide significant potential for circular economy integration. This study presents a PRISMA-compliant systematic literature review synthesizing dominant biomass conversion pathways in the region, with emphasis on biofuels and bioelectricity applications and their reported technical, techno-economic, and environmental indicators. A comprehensive search of Scopus, IEEE Xplore, and ScienceDirect yielded 64 peer-reviewed studies published between 2010 and 2025. Results show a marked growth in scientific output after 2016, although evidence remains concentrated in Brazil, Colombia, and Mexico. Anaerobic digestion emerges as the most frequently assessed route, particularly for agro-industrial effluents, municipal organic waste, livestock residues, and wastewater streams, followed by combustion-based cogeneration linked to sugarcane industries. Electricity generation and biomethane dominate evaluated outputs. Overall, the review highlights technological maturity alongside persistent barriers, including fragmented supply chains, investment constraints, and limited harmonized reporting, underscoring the need for standardized frameworks and system-scale deployment across the region. Full article

Hydrogen, an emerging low-carbon energy carrier, is pivotal for high-penetration renewable energy and integrated energy systems, yet the coupling of hydrogen with electricity and gas for hydrogen production and enriched compression-integrated systems remains a key issue for energy transition. This study establishes the architecture and analyzes the energy flow of an urban hydrogen production and enriched compressing-integrated energy system, as well as models its hydrogen production-enriched compressing, power, and hydrogen-enriched compressed natural gas subsystems based on water electrolysis, hydrogen storage, hydrogen fuel cells (HFCs), and hydrogen-enriched compressed natural gas (HCNG) technology, and develops a low-carbon optimal scheduling model with demand response to minimize intraday economic dispatch costs. Scenario comparisons verify the model’s effectiveness, showing that the system boosts wind-solar utilization by 6.81% and cuts carbon emissions by 1.89%. Full article

Peer-to-peer (P2P) energy-trading has emerged as a promising mechanism for decentralized electricity markets, but its practical deployment is often limited by the difficulty of accounting for physical network constraints and transmission losses in real time. This paper presents a decentralized P2P energy trading mechanism that incorporates network constraints and transmission losses directly into the market-clearing process. The framework combines Power Transfer Distribution Factors (PTDFs) for pre-trade feasibility validation with an Enhanced Least Squares Method (ELSM) for loss estimation, enabling loss-aware settlement without computationally intensive and redundant AC power flow calculations. The mechanism is implemented on Hyperledger Fabric using Attribute-Based Access Control, Access Control Lists and Private Data Collections to ensure privacy and auditability. Numerical studies on a 3-bus and the IEEE 39-bus system show that the proposed approach closely reproduces AC Optimal Power Flow dispatch and cost outcomes, while significantly improving simplified DC-based loss models. The results demonstrate that physically feasible and economically efficient decentralized trading can be achieved in a permissioned blockchain environment. Full article

To address the challenges of real-time control in power systems with high renewable penetration, identifying historical transmission sections similar to future scenarios enables efficient reuse of mature control strategies. However, existing data-driven identification methods exhibit two primary limitations: they typically rely on static Total Transfer Capacity (TTC), ignoring the rapid regulation capability of Energy Storage Systems (ESS) in alleviating congestion; and they employ fixed weights for similarity measurement, failing to distinguish the varying importance of different features (e.g., critical line flows vs. ordinary voltages). To overcome these issues, this paper proposes a similarity identification method for transmission sections considering ESS regulation capabilities and adaptive feature weights. First, a hierarchical decision model is utilized to screen basic grid features. An optimization model incorporating ESS charge/discharge constraints and emergency power support potential is established to calculate the Dynamic TTC, constructing a multi-scale feature set that reflects the real-time safety margin of the grid. Second, a Dispersion-Weighted Fuzzy C-Means (DW-FCM) clustering algorithm is proposed. By introducing a dispersion-weighting mechanism, the algorithm utilizes data distribution characteristics to automatically learn and assign higher weights to key features with high distinguishability during the iteration process, overcoming the subjectivity of manual weighting. Furthermore, fuzzy validity indices (XB, PC, FS) are introduced to adaptively determine the optimal number of clusters. Finally, case studies on the IEEE 39-bus system verify that the proposed method significantly improves identification accuracy compared to traditional methods and provides more reliable references for dispatching decisions. Full article

The increasing penetration of distributed renewable energy resources and the emergence of prosumers are reshaping the operational landscape of distribution grids. This work proposes a comprehensive prosumer-centric control and coordination framework integrated into the IEEE 33-bus radial distribution feeder. Selected buses are modeled as aggregated prosumer nodes equipped with photovoltaic (PV) generation, wind turbines, oncentrated solar power (CSP), a hybrid energy storage system (HESS) including redox flow batteries (RFBs), superconducting magnetic energy storage (SMES), and fuel cells (FCs), as well as electric vehicle (EV) fleets. A hierarchical power management strategy is developed, combining a decentralized fuzzy logic controller for real-time dispatch with a Particle Swarm Optimization (PSO) layer that tunes membership functions and rule weights to enhance system stability and renewable utilization. Time-series simulations are conducted to evaluate the impact of prosumer integration on network performance. The results show a significant improvement in the voltage profile across all buses, particularly at downstream nodes, highlighting the effectiveness of distributed renewable injections and coordinated storage management. The proposed framework illustrates the potential of clustered prosumers to support voltage stability, improve grid operation and enable high-renewable penetration in distribution networks. Full article

Solar-driven hydrogen production is a cornerstone of sustainable energy systems, yet its implementation remains intrinsically constrained by reliance on continuous illumination, limiting temporal control and compatibility with intermittent renewable sources. This perspective articulates the emerging concept of on-demand solar hydrogen generation, in which photon absorption is intentionally decoupled from hydrogen evolution through reversible charge storage and stimuli-responsive catalytic activation. We introduce a systematic classification of on-demand approaches across molecular, semiconductor, and device-level platforms, highlighting how these architectures enable programmable hydrogen release triggered by electrical, chemical, or thermal stimuli and sustained operation beyond illumination periods. Moving beyond a descriptive survey, we propose key performance metrics, including Switching Efficiency, Response Time, and Cycle Fidelity, to enable consistent evaluation and comparison of on-demand systems. Recent advances demonstrate substantial progress in charge storage, catalytic reversibility, and dynamic control, directly addressing the intermittency limitations of conventional photocatalytic and photoelectrochemical technologies. While challenges remain in kinetic synchronization, durability, and scalability, on-demand hydrogen concepts establish a coherent design framework for flexible and dispatchable solar fuels. By enabling integration with variable renewable inputs, this paradigm points toward adaptive and intelligent solar-fuel systems applicable from grid stabilization to off-grid and extraterrestrial environments. Full article

Optimizing the allocation of emergency vehicles is essential for enhancing route-planning efficiency and ensuring road safety during traffic incidents. Traditional dispatch methods often struggle with complex scenarios due to their inability to integrate and balance multiple conflicting factors. This study proposes a multi-objective dispatch framework for emergency vehicles that integrates regression analysis, deep learning, and an enhanced ant colony algorithm. Key environmental factors (e.g., weather, visibility) are selected through logistic regression, and a BP neural network predicts the impact ranges of accidents. The adaptive ant colony algorithm optimizes dynamic routing through innovations such as adjusting state transition probability and implementing pheromone reward—penalty strategies. It achieves faster convergence (with a comprehensive index of 86 in 8 iterations compared to 158 in 20 iterations) and superior path quality (a 9% reduction in rescue time and a 12% decrease in costs). Compared with existing hybrid frameworks, this study is the first to integrate logistic regression-selected environmental factors with BP neural network-predicted accident impact ranges, and further proposes adaptive state transition and pheromone reward-penalty update mechanisms, thereby achieving faster convergence speed and superior path quality in dynamic multi-objective rescue route planning. Full article

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

The decarbonization of electricity supply has intensified interest in standalone microgrids capable of achieving high renewable penetration while maintaining strict reliability. This study addresses the research questions of how cost-optimal standalone hybrid microgrids emerge under near-zero unmet-load constraints, how renewable variability and storage dynamics influence system behavior, and how cost-optimal designs compare with emissions-minimizing alternatives. A hybrid photovoltaic–wind–battery microgrid with dispatchable generation supplying a hospital facility in Carbondale, Illinois, USA, is analyzed under islanded operation. Site-specific data are combined with a constrained techno-economic optimization framework implemented in the Hybrid Optimization Model for Electric Renewables (HOMER) to minimize net present cost (NPC) while enforcing hourly power balance and battery state-of-charge constraints. Sensitivity analysis on photovoltaic derating evaluates robustness under performance uncertainty. Results show that the cost-optimal hybrid configuration achieves a renewable fraction of 74.6%, with a renewable utilization index of approximately 0.78 and excess electricity of 22.4%. Limited and intermittent use of dispatchable generation reduces lifecycle cost to approximately $38.2 M. In contrast, a diesel-free configuration nearly doubles net present cost to $71 M under identical reliability constraints. The findings demonstrate that economically viable decarbonization of standalone microgrids is best achieved through diversified hybrid architectures rather than fully renewable extremes. Full article

This study examines the sectoral dynamics of natural gas consumption in Colombia by applying additive and multiplicative Holt–Winters exponential smoothing models. The analysis covers the main demand segments (Thermal Generation, Industrial, Residential, Refinery, Compressed Natural Gas for Vehicles (GNVC), Commercial, Petrochemical, and SNT Compressor Stations) using official monthly data from the Colombian Mercantile Exchange for the period April 2020 to July 2025. Model configurations were optimized by minimizing the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE) to identify the most appropriate structure for each sector. The results confirm that natural gas consumption in Colombia does not follow a uniform seasonal pattern. Instead, each segment exhibits distinct dynamics shaped by operational conditions, production schedules, mobility-related behavior, or logistical planning. The Thermal Generation sector was best represented by the multiplicative model, reflecting proportional variability associated with electricity dispatch and system-level operational changes. In contrast, the Industrial, Residential, GNVC, Commercial, and SNT Compressor Stations sectors showed superior performance under the additive model, consistent with relatively stable or constant-magnitude seasonal effects. The Petrochemical and Refinery sectors displayed short-term cyclical behavior, with model accuracy depending on the performance metric prioritized. These findings demonstrate that energy forecasting must incorporate the structural heterogeneity of demand systems rather than treating natural gas consumption as a homogeneous aggregate. Practically, the results provide insights for improving supply planning, contract allocation, and regulatory segmentation. The study also offers a replicable methodological basis for forecasting in emerging economies characterized by diverse consumption profiles. Full article

Achieving Egypt’s 2035 renewable electricity targets presents substantial operational and institutional challenges, compounded by limited electricity trade across the Middle East and North Africa (MENA) region. This study applies a PLEXOS-based simulation framework that integrates short-term economic dispatch with the Projected Assessment of System Adequacy (PASA) to evaluate the system-level impacts of economically dispatched cross-border interconnections with Saudi Arabia, Libya, Jordan, and Sudan. The analysis also incorporates domestic flexibility measures, including five-minute dispatch, dynamic reserve requirements, and battery energy storage systems (BESS). Scenarios with renewable energy penetration levels of up to 50% are assessed using Egypt’s 2023 power system as the baseline. The results demonstrate that transitioning from a static, hourly, standalone operating framework to an integrated flexibility configuration—combining five-minute dispatch, 8 GW of economically dispatched cross-border interconnection capacity, and 8 GWh of BESS—yields substantial system-wide benefits at 50% renewable penetration. Loss-of-Load Probability declines from 96.48% to zero, ensuring full system adequacy, while total operational costs decrease by more than 45%, corresponding to annual savings of approximately USD 1.04 billion. Renewable energy curtailment is reduced by over 98%, enabling nearly 15 TWh of additional clean electricity generation, and CO₂ emissions fall by 11.6 million tons (≈40%). In addition, the operating-reserve shadow price—an indicator of reserve scarcity—declines to near zero, underscoring the effectiveness of coordinated regional dispatch and domestic flexibility in mitigating scarcity conditions. These findings provide robust evidence that integrated operational, temporal, and spatial flexibility can significantly accelerate renewable energy integration while strengthening system adequacy. The proposed framework offers an actionable and scalable blueprint for policy coordination and market reform in Egypt, with broader relevance for emerging power systems across the MENA region. Full article

The increasing penetration of renewable energy introduces significant challenges to grid stability and economic performance due to the intermittent and non-dispatchable nature of solar and wind generation. These fluctuations contribute to grid congestion, frequency control issues, and price volatility, reducing revenue predictability for renewable producers. It is then clear that the challenge of energy transition can be addressed by making the introduction of renewable sources into the electricity grid sustainable. Battery Energy Storage Systems (BESSs) have emerged as a flexibility resource providing time-shifting, frequency and voltage support, congestion management, and energy arbitrage. In response, several Transmission System Operators (TSOs), such as Terna in Italy in cooperation with photovoltaic (PV) and wind power producers, have initiated flexibility projects. However, these projects are limited and should be accompanied by liberalization measures that allow BESSs to be economically sustainable only under market conditions. This study evaluates the techno-economic feasibility of utility-scale BESSs either integrated into large PV/wind farms or stand-alone for providing grid flexibility services and profit increase for the producers. Both market conditions and TSO incentives will be considered. A two-step mixed integer linear (MILP) optimization approach is employed: first, an optimization schedules BESS charge and discharge operations based on historical generation and market data; second, the Net Present Value (NPV) is maximized to determine optimal system sizing and profit. The model is validated through real case studies and sensitivity analyses including BESS degradation, market volatility, and regulatory factors. The developed model is ultimately applied to compare the study cases, and the analysis shows that, under specific conditions, the arbitrage of a stand-alone BESS can be as profitable as the incentives offered by TSOs. Full article

The demand for computing power has increased at a rate never seen before due to the quick development of artificial intelligence (AI) technologies and applications. Consequently, AI data centers, referring to computing facilities specifically designed for large-scale artificial intelligence workloads, have become one of the fastest-growing electricity consumers globally. Therefore, it is essential to understand the load characteristics of AI data centers and their impact on the grid. This paper provides a comprehensive review of the evolving energy landscape of AI data centers. Specifically, this paper (i) presents the energy consumption structure in AI data centers and analyzes the key workload features and patterns in four stages, emphasizing how high power density, temporal variability, and cooling requirements shape total energy use, (ii) examines the impacts of AI data centers for power systems, including impacts on grid stability, reliability and power quality, electricity markets and pricing, economic dispatch and reserve scheduling, and infrastructure planning and coordination, (iii) presents key technological, operational and sustainability challenges for AI data centers, including renewable energy integration, waste heat utilization, carbon-neutral operation, and water–energy nexus constraints, (iv) evaluates emerging solutions and opportunities, spanning grid-side measures, data-center-side strategies, and user-side demand-flexibility mechanisms, (v) identifies future research priorities and policy directions to enable the sustainable co-evolution of AI infrastructure and electric power systems. The review aims to support utilities, system operators, and researchers in maintaining reliable, resilient, and sustainable grid operation in the context of the rapid development of AI data centers. Full article

Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration ($\gamma =1$) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling. Full article