Search Results (8,202)

Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing to its simple mathematical structure and flexible search capability, SFO has been increasingly applied to various engineering and AI problems. This review study presents a systematic and comprehensive analysis of SFO-based studies published in the literature. The literature search was performed using the Scopus database, and a total of 192 studies were included in the final evaluation process. The reviewed studies were classified into eight major application domains, including engineering design, energy systems, machine learning, image processing, communication systems, robotics, forecasting, and multi-objective optimization. In addition, the distributions of standard, hybrid, and modified SFO approaches were comparatively analyzed. The temporal evolution of SFO studies, hybridization tendencies, application diversity, strengths, limitations, and future research directions were also systematically evaluated. The findings indicate that hybrid and modified SFO structures have become increasingly dominant in recent years, particularly in AI and data-driven optimization applications. Overall, this review provides a broad understanding of the current state and future research potential of SFO-based optimization studies. Full article

Oscillating water column (OWC) wave energy converters equipped with dielectric elastomer generators (DEGs) represent a promising technology for harnessing ocean wave energy. This study emphasises the critical role of functional specifications in guiding the development of these devices from initial concept to full-scale deployment. A comprehensive analysis of key design parameters that influence the performance and efficiency of flexible OWCs with DEG-based power take-off systems is presented. This investigation focuses on the effects of draft, membrane diameter, deformation characteristics, number of layers, and membrane thickness on power output. Utilising a combination of analytical tools, including Wave Venture software, MATLAB, and Abaqus, detailed simulations and analyses are conducted to optimise these parameters. Our results demonstrate that increasing the DEG diameter significantly enhances power output, with diameters between 5 and 12 m showing optimal efficiency. A critical strain threshold of approximately 32% is identified, beyond which power output efficiency diminishes. Furthermore, the study reveals that multi-layer DEG configurations can substantially increase energy production, with thinner membranes generally yielding higher outputs. These findings provide valuable insights for developing functional specifications that balance performance, manufacturability, and long-term reliability in marine environments. This research advances OWC technology by offering a parameter-screening framework to guide device design towards optimised configurations and to accelerate the path to commercial viability in the wave energy sector. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

With the increasing penetration of renewable energy generation (REG) in novel distribution systems, active distribution networks (ADNs) integrated with microgrids (MGs) play a crucial role in enhancing the flexibility of regulation resources and promoting the accommodation of REG. To meet the operational requirements for efficient collaboration between ADNs and MGs under different dispatch time scales, this paper proposes a collaborative optimal dispatch strategy for multi-microgrid active distribution systems based on a hybrid game and variable time scales. Firstly, a transaction operation framework is constructed for the distribution network operator (DNO) and a multi-microgrid alliance (MMA), considering the peer-to-peer (P2P) transaction mode. On this basis, a day-ahead hybrid game model with a two-layer structure is constructed, the upper layer is a master–slave game with the DNO as the leader and the MMA as the follower, while the lower layer is a cooperative game for MGs within the MMA. An asymmetric Nash bargaining strategy based on contribution degree in P2P transactions is introduced to ensure equitable benefit allocation among cooperative MGs. Secondly, an intra-day rolling optimization model for reactive power and voltage based on variable time scales is proposed, which enhances the system’s responsiveness to real-time source–load power fluctuations by dynamically adjusting the dispatch time scale. Finally, the alternating direction method of multipliers (ADMM), integrated with a strategy separation mechanism, is adopted to efficiently solve the hybrid game model involving numerous 0–1 variables. The case study results indicate that, under the proposed strategy, the MMA’s power purchase cost from the DNO and ESS operational cost are decreased by 9.7% and 11.6%, respectively, while the system’s average deviation rate of node voltage decreases by 0.82%. Therefore, the proposed collaborative dispatch strategy can not only effectively reduce the system’s operational cost and ensure voltage stability but also significantly promote the accommodation of REG. Full article

As renewable energy is increasingly integrated via high-voltage direct current (HVDC) transmission, hybrid multi-infeed receiving-end grids containing both line-commutated converters (LCC) and modular multilevel converters (MMC) have become common, and wideband resonance problems in power-electronized networks are growing more prominent. This paper proposes an active resonance analysis and suppression strategy for such systems. First, a wideband current source converter model and a wideband voltage source converter model are adopted to describe the LCC and MMC, respectively, and a positive-sequence s-domain model of the system is established. A two-stage s-domain nodal admittance matrix method is then applied to efficiently determine the wideband resonance modes and the corresponding mode shape eigenvectors. A dual criterion combining the matching degree between resonance frequencies and LCC characteristic harmonics with the modal damping ratio identifies high-risk resonance modes. On this basis, an active damping strategy that realizes a parallel virtual resistance on the AC side through MMC supplementary control is proposed, together with a quantitative design method for the virtual conductance. At the control implementation level, a modulation wave reconstruction bypass injection scheme superimposes the high-frequency damping command directly in the $\alpha \beta$ stationary reference frame, thereby bypassing the PI controller and reducing the amplitude attenuation and phase distortion caused by the high-frequency limitation of the integral path. PSCAD/EMTDC simulation results on an IEEE 9-bus test system demonstrate that the proposed strategy effectively suppresses resonance amplification and wideband power oscillations excited by LCC characteristic harmonics without affecting the fundamental power transmission. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

The digital transformation of tourist facilities requires careful selection of technologies that can provide secure, stable and scalable network infrastructure. Due to the possibility of application in different sectors with different specificities, the focus of the research was placed on the implementation of smart tourist services. A hybrid multi-criteria decision-making model based on PIPRECIA and MVA models was applied for the research. Based on the literature and the opinions of experts in the field, evaluation criteria such as bandwidth, latency, energy efficiency, security and privacy, scalability, costs and interoperability were defined, and internet technologies such as Li-Fi, Wi-Fi 7, Wi-Fi 6, private 5G networks, Ethernet-over-Power (EoP), NB-IoT and LoRaWAN were defined. The results obtained put the security and privacy criterion at the top (0.2253), followed by scalability (0.1952) and bandwidth (0.1624). The obtained results indicate that Wi-Fi 7 achieved the highest weighted score (4.2247), followed closely by Li-Fi (4.2177) and Wi-Fi 6 (4.0771). Wi-Fi 7 demonstrated particularly strong performance in scalability, interoperability and bandwidth, making it highly suitable for environments with high user density. Li-Fi achieved very high scores in security and latency, which makes it particularly appropriate for security-sensitive smart tourism environments. Lower-ranked technologies such as NB-IoT and LoRaWAN proved valuable for supporting IoT and monitoring functions, rather than as primary communication infrastructure. The proposed model has proven to be a flexible, transparent and practical tool for strategic decision-making in the field of smart tourism. In addition to the basic application presented in the paper, the model has the potential to be adapted to different contexts and expanded with additional criteria or new technologies. The proposed hybrid approach can serve as a useful decision-making tool for tourism managers, system engineers and urban planners who are looking for optimal solutions for the development of digital infrastructure. Full article

Energy storage systems (ESSs) installed alongside traditional photovoltaic (PV) power plants are primarily used to track planned output, which often results in low utilization rates and extended payback periods. Moreover, existing research inadequately addresses actual grid frequency fluctuation characteristics and lacks multi-timescale optimization frameworks. To address these issues, this paper proposes a day-ahead and intraday multi-market coordinated rolling optimization strategy that integrates energy market trading with Automatic Generation Control (AGC) frequency regulation services through a zone-based frequency regulation control strategy. The strategy first defines distinct regulation zones based on regional control deviations, enabling a dynamic power allocation approach for the energy storage system. Recognizing that conventional constant power control can lead to battery overcharging, over-discharging, and reduced cycle life, the strategy introduces state of charge (SOC)-based variable power charging and discharging constraint coefficients. These constraints ensure the battery operates safely within its optimal range. Furthermore, an electrochemical energy storage life decay model is developed to quantify battery degradation. To accommodate the uncertainty in PV output, Latin hypercube sampling is employed. A day-ahead dispatch model is established to maximize the system’s total daily operating revenue, and rolling optimization is applied during the intraday phase to correct deviations from the day-ahead forecast. Finally, simulation studies using actual data from a PV power plant demonstrate that the proposed strategy achieves a total daily revenue of 107,477 ¥, representing a 24.6% improvement over energy market-only participation; battery aging costs are reduced by 11.1% compared to the scenario without zone-based frequency regulation control. Results indicate that the proposed strategy effectively balances battery life degradation against market revenue, significantly improving the overall operational efficiency and economic viability of PV-storage hybrid systems. Full article

Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts–Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to $1.8\times$ higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet. Full article

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids. Full article

This study investigates the impact of internal muffler geometry on flow-related dissipation characteristics potentially relevant to acoustic behavior using steady-state Computational Fluid Dynamics (CFD) simulations. Four variants were analyzed: an empty tube, considered to be a baseline model, a three-chamber baffle system, a single spiral channel, and a complex multi-channel insert manufacturable only via advanced additive technologies. Simulations were conducted in SimScale using a compressible flow model with the k-ω SST turbulence formulation. Key outputs included static pressure distribution and turbulent kinetic energy (TKE), both of which were evaluated as qualitative surrogate indicators associated with flow-induced energy dissipation phenomena. The results indicate that geometries incorporating spiral features modify flow redistribution patterns, pressure gradients and localized turbulence intensity, suggesting potential applicability for future acoustic optimization studies. The study highlights how additive manufacturing enables the integration of geometrically complex internal structures otherwise unattainable through conventional methods. By comparing pressure drop and TKE patterns with internal design features, the research offers a preliminary CFD-based framework for geometry screening and conceptual evaluation of muffler insert designs for automotive exhaust systems. This approach provides computational support for rapid comparative assessment prior to experimental validation and detailed acoustic analysis. Full article

Modern power grids with high renewable energy penetration are vulnerable to fast voltage disturbances caused by grid faults. Among these, voltage sags are critical because they develop within milliseconds and require rapid reactive current support to maintain grid stability and power reliability. Reinforcement learning has previously shown potential for reactive current injection control during voltage sag events due to its fast response and adaptability to changing system conditions. However, existing approaches rely on separate policies for specific subsets of the operating space, which limits their ability to provide optimal actions when the system operates across broader or combined state regions. To address this limitation, this paper proposes a unified Soft Actor–Critic (SAC) target policy trained over the full state and action space by integrating multi-source transfer learning with potential-based reward shaping approach. Results show that the proposed multi-source transfer approach enables the target agent to converge faster and reach a higher reward solution than the baseline SAC and single-source transfer approach. The trained policy also improved prediction accuracy, achieving reactive-current errors below 0.2 A with respect to the ground-truth reference generated through extensive simulations over the full observation and action space. The reference follows the grid-code requirement for minimum reactive current injection during faults and provides a benchmark for evaluating prediction accuracy. This can help distributed generation sources respond more effectively during severe perturbations such as voltage sags, support voltage recovery, and reduce the risk of cascaded disconnections that could lead to unwanted blackouts. Additionally, the inference execution time is also sufficiently fast to satisfy the response-time requirement of voltage sag events, confirming the real-time feasibility of the proposed controller. Full article

Optimal sizing of hybrid renewable energy systems for desert camps is a multi-objective problem that must account for cost, reliability, component degradation, and uncertainty. This paper introduces an improved multi-objective neural architecture search (IMONAS) framework for hybrid nanogrid sizing in the desert environment of Hafr Al-Batin, Saudi Arabia. The framework combines neural optimization, stochastic uncertainty modeling, and explicit battery degradation modeling, a combination not addressed in the reviewed studies for this application. Six test cases are examined by varying uncertainty assumptions, battery degradation, and the annual duration of uncertain operation. For each case, IMONAS provides Pareto-front solutions that specify the photovoltaic, diesel generator, battery autonomy, and inverter choices while minimizing the cost of energy (COE) and the loss of power supply probability (LPSP). IMONAS is compared with the original MONAS and five other multi-objective optimization methods. In addition to visual Pareto-front comparisons, the assessment uses Pareto-dominance indicators, namely the C-metric and an aggregated score derived from pairwise C-metric comparisons across the algorithms and cases. The results provide a validated sizing framework for remote arid-region nanogrids under uncertainty and battery degradation. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article