Search Results (699)

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

Path planning for large-scale agricultural fields faces challenges such as irregular field shapes, uncertain boundaries, and the need to balance path efficiency, energy consumption, and coverage quality. To address these problems, this research introduces a strategy-aware hierarchical hybrid optimization framework (HANS) for autonomous agricultural operations. This framework introduces a global principal axis extraction method based on Principal Component Analysis (PCA), utilizing the statistical distribution of field boundaries to guide path direction, thereby improving robustness against boundary noise and irregular geometries. The framework integrates Adaptive Large Neighborhood Search (ALNS) for global exploration and Tabu Search (TS) for local optimization, forming a tightly coordinated hybrid structure. The framework further employs a Pareto-set-based decision support selection strategy to solve a multi-objective optimization model encompassing machine kinematics, turning patterns, and energy-aware cost evaluation. This strategy provides three methods: weighted preference-based compromise solution selection, crowding distance-based diversified solution selection, and single-objective extreme value-based dedicated optimization solution selection. To balance the impact of path length, energy consumption, and coverage rate, we assigned equal or nearly equal weights to them (i.e., (0.33, 0.33, 0.34)). Furthermore, the framework incorporates operators and feedback learning mechanisms specific to agricultural coverage path problems to enable adaptive operator selection and reduce reliance on manual parameter tuning. Simulation results under three representative field scenarios show that compared to fixed-direction planning, HANS improves the average coverage rate by 0.51 percentage points and reduces fuel consumption by 4.34%. Compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), and Simulated Annealing (SA), the proposed method shortens the working path length by 0.37–0.83%, improves coverage rate by 0.34–1.11%, and reduces energy consumption by 0.61–1.03%, while maintaining competitive computational costs. These results demonstrate the effectiveness and practicality of HANS in large-scale autonomous farming operations. Full article

To enhance the potential application of ships in energy conservation and emission reduction, a parallel hybrid power system simulation model was developed using Matlab/Simulink based on the operational scenario of a 3300 m³ inland LPG tanker. A rule-based control strategy was employed to simulate and analyze the impact of different power modes on the energy efficiency and emissions of the ship’s power system. An optimization method for an energy management strategy based on the ship’s operating cycle was proposed. The results show that using an LNG engine as the main engine in the hybrid system significantly reduces fuel consumption and pollutant emissions throughout the ship’s operating cycle. When the hybridization ratio is 0.2, the system achieves relatively optimal overall energy consumption and emission levels. Building on this, an Equivalent Consumption Minimization Strategy (ECMS) was introduced, and multi-objective optimization was carried out using an improved particle swarm optimization algorithm. Additionally, reinforcement learning was applied to optimize the energy management strategy, resulting in further reductions in fuel consumption over the operating cycle. Full article

To achieve “carbon peak and carbon neutrality” in manufacturing, this paper tackles high energy consumption in flexible job shop production by developing a low-carbon scheduling optimization model with time-of-use electricity pricing, incorporating a photovoltaic microgrid. The model minimizes makespan, carbon emissions, and costs, considering photovoltaic power uncertainty, energy storage dynamics, and time-of-use pricing. To address coupled scheduling and energy management challenges, a three-stage bilevel collaborative optimization framework is proposed, enhancing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to develop a Collaborative MOPSO (CMOPSO). The improved algorithm features a four-layer encoding mechanism with energy factors, chaotic mapping for better global search, and adaptive mutation for population diversity. Validation using the Brandimarte benchmark demonstrates the algorithm’s robustness. Specifically, comparative experiments reveal that the proposed strategy significantly outperforms the traditional scheduling mode. While maintaining a similar makespan, the proposed method reduces production costs by 44.3% and carbon emissions by 29%. Simulations confirm that the method effectively shifts tasks to low-price periods and leverages photovoltaic energy during peak hours, supporting the manufacturing industry’s green transition. Full article

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles. Full article

As wind power is a core component of sustainable energy systems, ensuring its stable grid integration is critical to advancing the 2030 Agenda for Sustainable Development, particularly in increasing the share of renewable energy, reducing carbon emissions, and promoting energy system sustainability. During the system frequency stability regulation, the active power output of wind farms undergoes continuous changes, which can affect the stable operation of the grid-connected system. To address this issue, this paper proposes an active power optimization allocation strategy of multiple wind turbines considering stability improvement. First, an equivalent impedance model of the wind farm grid-connected system was established, taking into account the differences in active power output and terminal impedance of wind turbines. Based on this model, the mechanisms by which different active power outputs and terminal impedances affect the system’s stability margin were analyzed, revealing the matching mechanism between wind turbine output and terminal impedance required to meet stability requirements; second, with the objective of maximizing the system damping ratio stability margin while balancing power constraints and wind turbine frequency regulation capability constraints, a multi-turbine frequency regulation power optimization model considering stability enhancement was established. The particle swarm optimization algorithm is employed to solve for the optimal frequency regulation power allocation scheme for each wind turbine. Finally, the effectiveness of the proposed strategy in improving the stability of the frequency regulation process in wind farms was verified through simulation examples. The proposed strategy enhances the reliability of wind power integration, reduces the risk of curtailment or disconnection of clean energy, and provides a technical tool for sustainable energy transition. Full article

In new distribution systems with high penetration of renewable energy, inverter-based sources exhibit significant differences in fault characteristics compared to traditional power sources due to the absence of a constant electromotive force and their operation under nonlinear control links, rendering conventional fault current calculation methods inadequate. To address these challenges, a full-time-domain analysis-based method for modelling and calculating fault transient characteristics is proposed. First, a dynamic model of inverter-based sources accounting for current loop saturation effects is established, and phase plane analysis is employed to resolve nonlinear control regions. On this basis, a full-time-domain fault current calculation method is proposed, wherein the steady-state operating point after a fault is determined by iteratively solving the network node voltage equations. By integrating control strategies and derived transient differential equations, the fault current expression across the full-time-domain scope is formulated. Furthermore, a multi-objective optimization control strategy is proposed to achieve effective fault current suppression, and an improved Simulated Annealing-Particle Swarm Optimization (SA-IPSO) hybrid algorithm is adopted for efficient solution. Finally, SIMULINK-based simulation experiments validate the accuracy and effectiveness of the proposed method in transient characteristic analysis and current suppression. Full article

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

This paper presents the optimal design for a 30 kW, 3-phase squirrel-cage induction motor (IM). In this paper, three optimization algorithms are used for design optimization, namely, Particle Swarm Optimization Algorithm (PSO), genetic algorithm (GA), and Sequential Quadratic Programming (SQP). The optimal goals are maximum starting torque, efficiency, and minimum material cost. The result of the IM design optimization using three optimal methods is announced and compared. Additionally, computation time and the number of iterations of each algorithm are compared to find out the most suitable algorithm for the optimal design of an induction motor. In addition, this paper proposes a solution that permits us to find only one solution satisfying all the optimal criteria. Instead of using the conventional multi-objective optimization method that normally leads to a Pareto set with many optimal points at the same optimal level, we propose a hierarchical optimization method that experiences some mono-objective optimization and then builds a function representing the multi-objective optimization. Using this method, having a global optimal point can be obtained. Comparison of the optimal algorithms and multi-objective optimization methods has given broadened insight into optimal techniques for IMs. We have found that PSO is the best method for optimization design of IMs in terms of computation time and finding the global optimal point. Full article

The high penetration of renewable energy brings significant power fluctuations and operational uncertainties to distribution networks. Traditional power allocation methods for hybrid energy storage systems (HESSs) exhibit strong parameter dependency, limited frequency-domain recognition accuracy, and poor dynamic coordination capability. To overcome these limitations, this study proposes an adaptive power allocation strategy for HESSs under renewable energy integration scenarios. The proposed method employs the Grey Wolf Optimizer (GWO) to jointly optimize the mode number and penalty factor of the Variational Mode Decomposition (VMD), thereby enhancing the accuracy and stability of power signal decomposition. In conjunction with the Hilbert transform, the instantaneous frequency of each mode is extracted to achieve a natural allocation of low-frequency components to the battery and high-frequency components to the supercapacitor. Furthermore, a multi-objective power flow optimization model is formulated, using the power commands of the two storage units as optimization variables and aiming to minimize voltage deviation and network loss cost. The model is solved through the Particle Swarm Optimization (PSO) algorithm to realize coordinated optimization between storage control and system operation. Case studies on the IEEE 33-bus distribution system under both steady-state and dynamic conditions verify that the proposed strategy significantly improves power decomposition accuracy, enhances coordination between storage units, reduces voltage deviation and network loss cost, and provides excellent adaptability and robustness. Full article

This study employs a multi-objective particle swarm optimization (MOPSO) algorithm to address the dual-objective challenge in the robotic polishing of Ti-6Al-4V. The aim is to determine optimal parameters that minimize surface roughness while maximizing the material removal rate (MRR), thereby improving both surface quality and processing efficiency. First, a material removal depth model for end-face polishing is established based on Preston’s equation and theoretical analysis, from which the MRR model is derived. Subsequently, orthogonal experiments are conducted to investigate the influence of process parameters and their interactions on surface roughness, followed by the development of a quadratic polynomial roughness prediction model. Analysis of variance (ANOVA) and model validation confirm the model’s reliability. Finally, the MOPSO algorithm is applied to obtain the Pareto optimal solution set, yielding the optimal parameter combination. Experimental results demonstrate that at a normal contact force of 7.58 N, a feed rate of 4.52 mm/s, and a spindle speed of 5851 rpm, the achieved MRR and Ra values are 0.2197 mm³/s and 0.291 μm, respectively. These results exhibit errors of only 5.64% and 2.65% compared to model predictions, validating the proposed method’s effectiveness. Full article

Soil organic matter (SOM) is a key indicator for evaluating soil fertility and ecological functions, and hyperspectral technology provides an effective means for its rapid and non-destructive estimation. However, in practical soil systems, the spectral response of SOM is often highly covariant with mineral composition, moisture conditions, and soil structural characteristics. Under small-sample conditions, hyperspectral SOM modeling results are usually highly sensitive to spectral preprocessing methods, sample perturbations, and model architecture and parameter configurations, leading to fluctuations in predictive performance across independent runs and thereby limiting model stability and practical applicability. To address these issues, this study proposes a multi-strategy collaborative deep learning modeling framework for small-sample conditions (SE-EDCNN-DA-LWGPSO). Under unified data partitioning and evaluation settings, the framework integrates spectral preprocessing, data augmentation based on sensor perturbation simulation, multi-scale dilated convolution feature extraction, an SE channel attention mechanism, and a linearly weighted generalized particle swarm optimization algorithm. Subtropical red soil samples from Guangxi were used as the study object. Samples were partitioned using the SPXY method, and multiple independent repeated experiments were conducted to evaluate the predictive performance and training consistency of the model under fixed validation conditions. The results indicate that the combination of Savitzky–Golay filtering and first-derivative transformation (SG–1DR) exhibits superior overall stability among various preprocessing schemes. In model structure comparison and ablation analysis, as dilated convolution, data augmentation, and channel attention mechanisms were progressively introduced, the fluctuations of prediction errors on the validation set gradually converged, and the performance dispersion among different independent runs was significantly reduced. Under ten independent repeated experiments, the final model achieved R² = 0.938 ± 0.010, RMSE = 2.256 ± 0.176 g·kg⁻¹, and RPD = 4.050 ± 0.305 on the validation set, demonstrating that the proposed framework has good modeling consistency and numerical stability under small-sample conditions. Full article

To address the challenges of low precision, slow convergence, and poor anti-interference in traditional Particle Swarm Optimization (PSO) for Permanent Magnet Synchronous Motor (PMSM) speed control, a new Adaptive Hybrid Particle Swarm Optimization (AM-PSO) algorithm is proposed. This algorithm integrates adaptive dynamic inertia weight, hybrid local search mechanisms, neural network-based adjustments, multi-stage optimization, and multi-objective optimization. The adaptive dynamic inertia weight improves the balance, boosting both convergence speed and accuracy. The inclusion of Simulated Annealing (SA) and Differential Evolution (DE) strengthens local search and avoids local optima. Neural network adjustments improve search flexibility by intelligently modifying search direction and step size. Additionally, the multi-stage strategy allows broad exploration initially and refines local searches as the solution approaches, speeding up convergence. The multi-objective optimization further ensures the simultaneous improvement of key performance metrics like precision, response time, and robustness. Experimental results demonstrate that AM-PSO outperforms traditional PSO in PMSM speed control, achieving a 40% reduction in speed error, 25% faster convergence, and enhanced robustness. Notably, the speed error increased only marginally from 0.03 RPM to 0.05 RPM, showcasing the algorithm’s superior ability to reject disturbances. Full article

With the increasing penetration of renewable energy, power systems are facing greater uncertainty and volatility, which poses significant challenges for Virtual Power Plant scheduling. Existing research mainly focuses on optimizing economic efficiency but often overlooks system reliability and the impact of forecasting deviations on scheduling, leading to suboptimal performance. Thus, this paper presents a reliability-cost bi-objective cooperative optimization model based on a dual-swarm particle swarm algorithm: it introduces positive and negative imbalance price penalty factors to explicitly describe the economic costs of forecast deviations, constructs a reliability evaluation system covering PV, EVs, air-conditioning loads, electrolytic aluminum loads, and energy storage, and solves the multi-objective model via algorithm design of “sub-swarms specializing in single objectives + periodic information exchange”. Simulation results show that the method ensures stable intraday operation of VPPs, achieving 6.8% total cost reduction, 12.5% system reliability improvement, and 14.8% power deviation reduction, verifying its practical value and application prospects. Full article

This paper analyzes the functional feasibility and strategic value of hybrid hydrogen storage and photovoltaic (PV) energy systems at isolated areas, specifically at Egypt’s Shalateen station. The paper is significant as it formulates a solution to the energy independence coupled with economic feasibility issue in regions where the basic energy infrastructure is non-existent or limited. Through the integration of a portfolio of advanced optimization algorithms—Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Multi-Objective Genetic Algorithm (MOGA), Pattern Search, Sequential Quadratic Programming (SQP), and Simulated Annealing—the paper evaluates the performance of two scenarios. The first evaluates the PV system in the absence of hydrogen production to demonstrate how system parameters are optimized by Pattern Search and PSO to achieve a minimum Cost of Energy (COE) of 0.544 USD/kWh. The second extends the system to include hydrogen production, which becomes important to ensure energy continuity during solar irradiation-free months like those during winter months. In this scenario, the same methods of optimization enhance the COE to 0.317 USD/kWh, signifying the economic value of integrating hydrogen storage. The findings underscore the central role played by hybrid renewable energy systems in ensuring high resilience and sustainability of supplies in far-flung districts, where continued enhancement by means of optimization is needed to realize maximum environmental and technological gains. The paper offers a futuristic model towards sustainable, dependable energy solutions key to the energy independence of the future in such challenging environments. Full article