Search Results (1,686)

With the increasing penetration of renewable energy, the frequency regulation burden on thermal power units is growing significantly. Among them, combined cycle gas turbine (CCGT) units are playing an increasingly important role in grid ancillary services due to their high efficiency and low emissions. This paper investigates coordinated control strategies to improve the auxiliary frequency regulation capability of CCGTs, addressing the limitations of traditional control approaches where gas turbines dominate while steam turbines respond passively. A decentralized model predictive control (MPC) strategy based on rate-limited signal decomposition is proposed to improve auxiliary frequency regulation. First, a dynamic model of the F-class CCGT systems oriented towards control is established. Then, predictive controllers are designed separately for the top and bottom cycles, with control accuracy improved through a fuzzy prediction model, Kalman filtering and state augmentation. Furthermore, a multi-scale decomposition method for AGC (Automatic Generation Control) signals is developed, separating the signals into load-following and high-frequency components, which are allocated to the gas and steam turbines respectively for coordinated response. Comparative simulations with a conventional MPC strategy demonstrate that the proposed method significantly improves power tracking speed, stability, and overshoot control, with the IAE (Integral of Absolute Error) index reduced by 83.7%, showing strong potential for practical engineering applications. Full article

The increasing penetration of renewable energy and electric vehicles (EVs) has intensified the need for grid-forming (GFM) inverters capable of supporting frequency and voltage stability. Virtual Oscillator Control (VOC) has recently emerged as a promising time-domain GFM strategy due to its fast dynamics and autonomous synchronisation capability. This paper presents a comprehensive analysis of recent VOC developments, focusing on the Andronov–Hopf Oscillator (AHO) and its variants. A comparative overview of different VOC structures highlights their capabilities in providing essential services such as dispatchability, fault ride-through (FRT), virtual inertia, and damping. A generalised small-signal state-space model is developed to assess the influence of virtual inertia, grid impedance, and control parameters on transient performance, which is essential for optimal parameter design and controller tuning in various applications. Experimental validation using a 2.5 kVA single-phase inverter shows excellent agreement with theoretical predictions. The results confirm that while increased virtual inertia enhances frequency stability, it also introduces oscillations that can be effectively mitigated through damping enhancement. Furthermore, the experiments demonstrate that advanced AHO-based strategies successfully deliver vehicle-to-grid (V2G) and vehicle-to-home (V2H) services, confirming their practical applicability in future EV-integrated and renewable-rich power systems. Full article

This study focuses on highway service areas. Building upon prior research that identified key influencing factors through surveys and ISM–MICMAC analysis, it constructs a tripartite evolutionary game model involving the government, service area operators, and carbon reduction technology providers based on stakeholder theory. Combined with MATLAB simulations, the model reveals the dynamic patterns of the carbon reduction system. The results indicate that government strategies exert the strongest influence on the system and catalyze the other two parties, followed by service area operators. Carbon reduction technology providers adopt a more cautious stance in decision-making. Government actions shape system evolution through a “cost-benefit-incentive” triple mechanism, with its strategies exhibiting significant spillover effects on other actors. Enterprise behavior is markedly influenced by Xinjiang’s regional characteristics, where the core barriers to corporate carbon reduction lie in the costs of proactive equipment and technological investments. The willingness of technology providers to cooperate primarily depends on two drivers: incremental baseline benefits and enhanced economies of scale. The core trade-off in government decision-making lies between the cost of strong regulation (Cg1) and the cost of environmental governance under weak regulation (Cg2). An increase in Cg1 prolongs the government’s convergence time by 233.3% and indirectly suppresses the willingness of enterprises and technology providers due to weakened subsidy capacity. Enterprises are relatively sensitive to the investment costs of carbon reduction equipment and technology, with convergence time extending by 120%. Technology providers are highly sensitive to incremental baseline returns (Rt), with stabilization time extending by 500%. Compared to existing research, this model quantitatively reveals the “cost-benefit-incentive” triple transmission mechanism for carbon reduction coordination in “grid-end” regions, identifying key parameters for strategic shifts among stakeholders. Based on this, corresponding policy recommendations are provided for all three parties, offering precise and actionable directions for the sustainable advancement of carbon reduction efforts in service areas. The research conclusions can provide a replicable collaborative framework for decarbonizing transportation infra-structure in grid-end regions with high clean energy endowments. Full article

To facilitate easy accessibility to the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) results for the geoscientific community, multiple institutions have successively developed mass anomaly grid products including mass concentration (mascon) grids; these were provided at the Gravity Information Service (GravIS) portal. However, an assessment of their consistency for studying large-scale mass redistribution and transport in Earth’s system is still not available. Here, we compare three major mascon solutions separately from the Center for Space Research (CSR), the Jet Propulsion Laboratory (JPL), the Goddard Space Flight Center (GSFC) and GravIS products based on the Combination Service for Time-variable Gravity fields (COST-G) by analyzing the Antarctic Ice Sheet (AIS) mass changes in four aspects. Our results demonstrate that: (1) the four datasets exhibit strong consistency on the entire AIS mass change time series, with the largest difference occurring in the Antarctic Peninsula; (2) mass trend estimates show better agreement over longer periods and larger regions, but differences with a percentage of 20–40 exist during the late stage of GRACE and the whole GRACE-FO timespan; (3) notable discrepancies arise in the annual statistics of the Eastern AIS in 2016, leading to inconsistency on the sign of annual AIS mass change; (4) good agreement can be seen among these interannual mass variations over the AIS and its three subregions during 2003–2023, excluding the period from mid-2016 to mid-2018. These findings may provide key insights into improving algorithms for mascon solutions and grid products towards refining their applications in ice mass balance studies. Full article

In disaster relief operations, Unmanned Aerial Vehicles (UAVs) equipped with base stations (UAV-BS) are vital for re-establishing communication networks where conventional infrastructure has been compromised. Optimizing their trajectories and coverage to ensure equitable service delivery amidst obstacles, wind effects, and energy limitations remains a formidable challenge. This paper proposes an innovative reinforcement learning framework leveraging a Long Short-Term Memory (LSTM)-based Advantage Actor–Critic (A2C) model enhanced with an attention mechanism. Operating within a grid-based disaster environment, our approach seeks to maximize fair coverage for randomly distributed ground users under tight energy constraints. It incorporates a nine-direction movement model and a fairness-focused communication strategy that prioritizes unserved users, thereby improving both equity and efficiency. The attention mechanism enhances adaptability by directing focus to critical areas, such as clusters of unserved users. Simulation results reveal that our method surpasses baseline reinforcement learning techniques in coverage fairness, Quality of Service (QoS), and energy efficiency, providing a scalable and effective solution for real-time disaster response. Full article

This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks. Full article

This article presents and discusses the role of the Virtual Islanding (VI) control strategy in decentralizing flexibility services for distribution networks (DNs). VI services are provided by Renewable Energy Communities (RECs) through a suitable coordination of available local flexible resources. The impact of the proposed VI service is assessed by investigating its ability in mitigating security violations during DN operations. The VI service’s efficacy is tested considering the model of a realistically sized DN and developing a scenario of a high-RES penetration scenario, as foreseen by the Italian Energy Plan (PNIEC). The robustness of the approach was challenged applying a randomized association of flexible resources to the RECs. The aim is to prove that VI can substantially support DN operations, regardless the distribution of flexible resources among feeders and energy communities. To make a comparison with an ideal centralized control made by the Distribution System Operator, a Distribution Optimal Power Flow (OPF) formulation is also developed. The simulations are performed in a Python (v. 3.12.7)/OpenDSS (v. 9.6.1.1) open-source programming environment. Full article

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system integrates solar and wind energy, V2G capabilities, and time-of-use (ToU) tariffs. The DHO strategy dynamically adjusts control horizons based on forecasted load, generation, and electricity prices, while considering battery health. A PEV-specific pricing scheme couples ToU tariffs with system marginal prices. Case studies on a microgrid with four heterogeneous EV charging stations show that the proposed method reduces peak load by 23.5%, lowers charging cost by 12.6%, and increases average final SoC by 12.5%. Additionally, it achieves a 6.2% reduction in carbon emissions and enables V2G revenue while considering battery longevity. Full article

The widespread adoption of electric vehicles (EVs) is contingent on high-power fast-charging infrastructure that can also provide grid stabilization services through bidirectional power flow. While the constituent power stages of such off-board chargers are well-known, a critical research gap exists in their system-level integration, where sub-optimal dynamic interaction between independently controlled stages often leads to DC-link instability and poor transient performance. This paper presents a rigorous, system-level study to address this gap by developing and optimizing a unified control framework for a high-power bidirectional EV fast-charging system. The system integrates a three-phase active front-end rectifier with an LCL filter and a four-phase interleaved bidirectional DC/DC converter. The methodology involves a holistic dynamic modeling of the coupled system, the design of a hierarchical control strategy augmented with a battery current feedforward scheme, and the system-wide optimization of all Proportional–Integral (PI) controller gains using the Artificial Bee Colony (ABC) algorithm. Comprehensive simulation results demonstrate that the proposed optimized control framework achieves a critically damped response, significantly outperforming a conventionally tuned baseline. Specifically, it reduces the DC-link voltage settling time during charging-to-discharging transitions by 74% (from 920 ms to 238 ms) and eliminates voltage undershoot, while maintaining excellent steady-state performance with grid current total harmonic distortion below 1.2%. The study concludes that system-wide metaheuristic optimization, rather than isolated component-level design, is key to unlocking the robust, high-performance operation required for next-generation EV fast-charging infrastructure, providing a validated blueprint for future industrial development. Full article

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations. Full article

This study aims to quantify the spatiotemporal evolution of ecosystem service value (ESV) in the Zhengzhou Metropolitan Area from 2010 to 2022. We employed an improved equivalent factor method to calculate ESV and used Geodetector analysis to identify its key driving factors. Over this 12-year period, the total ESV exhibited a spatial decreasing pattern from west to east, with farmland and forestland contributing the most to total ESV. Geodetector results across four grid scales indicate that vegetation cover (Fractional Vegetation Cover, FVC) and slope Digital Elevation Model (DEM) are the primary natural drivers; notably, the optimal model fit was achieved at finer grid scales. These findings provide a scientific basis for promoting coordinated eco-economic development and formulating conservation strategies during the urbanization process of the Zhengzhou Metropolitan Area. Full article

Grid-scale lithium-ion storage must deliver fast, reliable thermal control during dynamic grid services, yet high-fidelity thermal models are too slow for real-time use and inefficient cooling inflates energy and safety costs. This study develops and validates a reduced-order thermal modeling framework for grid-scale lithium-ion battery energy storage, targeting real-time thermal management. The framework uses proper orthogonal decomposition to capture dominant thermal dynamics across frequency regulation, peak shaving, and fast charging. Across scenarios, it delivers 15.2–22.3× computational speedups versus a detailed model while maintaining RMS temperature errors of 7.8 °C (frequency regulation), 34.4 °C (peak shaving), and 23.3 °C (fast charging). Spatial analysis identifies inter-zone temperature gradients up to 1.0 °C under severe loading, motivating targeted cooling strategies. Cooling energy scales nonlinearly with load intensity, from 5.44 kWh in frequency regulation to over 300 kWh in peak shaving, with cooling efficiencies spanning 17.27% to 8.94%. The reduced-order model achieves sub-0.1 s computational solve time per control cycle, suggesting feasibility for real-time integration into industrial battery-management systems under the tested simulation settings. Collectively, the results show that reduced-order thermal models can balance accuracy and computational efficiency for several grid services in the simulated scenarios, while high-power operation benefits from scenario-specific calibration and controller tuning. Practically, the benchmarks and workflow support decisions on predictive cooling schedules, temperature limits, and service prioritization to minimize parasitic energy. Full article

In micro gas turbines, electrical power from the high-speed generator is delivered to the grid through a converter that influences overall efficiency and energy quality. This subsystem is often overlooked in efforts to improve turbine performance, which have traditionally focused on combustors and turbomachinery. This study investigates how replacing conventional silicon switching devices in the converter with silicon carbide technology can directly reduce greenhouse gas emissions from micro gas turbines. Although silicon carbide is widely used in electric vehicles and distributed energy systems, its emission reduction impact has not been assessed in micro gas turbines. A MATLAB-based model of a 100 kW Ansaldo Energia micro gas turbine was used to compare the performance of silicon and silicon carbide converters across the 20–100 kW operating range. Silicon carbide reduced total converter losses from 4.316 kW to 3.426 kW at full load, a decrease of 0.889 kW. This improvement lowered carbon dioxide emissions by 5.7 g/kWh and increased net electrical efficiency from 30.03% to 30.29%. Each turbine can therefore avoid about 1.53 tonnes of carbon dioxide annually, or 11.61 tonnes over a 50,000 h service life, without altering turbine design, combustor geometry, or fuel composition. This work establishes the first quantitative link between wide-bandgap semiconductor performance and direct greenhouse gas mitigation in micro gas turbines, demonstrating that upgrading converter technology from silicon to silicon carbide offers a deployable pathway to reduce emissions from micro gas turbines and, by extension, lower the carbon intensity of distributed generation systems. Full article

Location-based services (LBSs) are a crucial element of the Internet of Things (IoT) and have garnered significant attention from both researchers and users, driven by the rise of wireless devices and a growing user base. However, the use of LBS-enabled applications carries several risks, as users must provide their real locations with each query. This can expose them to potential attacks from the LBS server, leading to serious issues like the theft of personal information. Consequently, protecting location privacy is a vital concern. To address this, location dummy-based methods are employed to safeguard the location privacy of LBS users. However, location dummy-based approaches also suffer from problems such as low resistance against inference attacks and the generation of strong dummy locations, an issue that is considered an open problem. Moreover, generating many location dummies to achieve a high privacy protection level leads to high network overhead and requires high computational capabilities on the mobile devices of the LBS users, and such devices are limited. In this paper, we introduce the Caching-Aware Double-Dummy Selection (CaDDSL) algorithm to protect the location privacy of LBS users against homogeneity location and semantic location inference attacks, which may be applied by the LBS server as a malicious party. Then, we enhance the CaDDSL algorithm via encapsulation with agents to solve the tradeoff between generating many dummies and large network overhead by proposing the Cache-Aware Overhead-Aware Dummy Selection (CaOaDSL) algorithm. Compared to three well-known approaches, namely GridDummy, CirDummy, and Dest-Ex, our approach showed better performance in terms of communication cost, cache hit ratio, resistance against inference attacks, and network overhead. Full article

The article presents an overview of knowledge in the field of energy microgrids as smart structures enabling energy self-sufficiency, with particular emphasis on decarbonisation. Based on a review of the literature and technical solutions, the characteristics have been classified and, emphasising the potential for integrating different technologies within microgrid structures, the role that microgrids and their users can play in the functioning of the energy system has been defined. Energy microgrids can be the pillar on which smart energy structures and smart grids, including energy systems using multiple energy carriers, will be based. Microgrids can guarantee energy self-sufficiency within their area of operation and support the entire energy system in this respect. Sensors that respond to both electrical and non-electrical quantities must play a special role in such structures, as they form the technical basis for the functioning of the smart energy sector. Full article