Search Results (2,822)

This study addresses an integrated airport gate assignment and departure scheduling problem under capacity constraints while explicitly accounting for the operational role of apron resources. A bi-objective mixed integer linear programming model is developed to jointly determine gate or apron assignments and departure times by considering passenger transfer times, taxi operations, runway separation, and schedule deviations. The first objective minimizes a normalized composite measure of passenger transfer burden, taxi penalties, and departure schedule deviation, whereas the second objective minimizes apron usage. The epsilon constraint method is used to generate exact Pareto-efficient solutions. Computational experiments on synthetically generated congested hub airport instances with 20 flights show that increasing physical gate capacity from 3 to 5 improves the average value of Objective 1 from 1.37 to 0.92 and reduces average apron usage from 10.00 to 4.00 flights. In the highlighted 20-flight and 5-gate scenario, increasing apron usage from 3 to 5 assignments reduces the standard deviation of departure time deviations from 8.0 to 7.6 min. The results show that selective apron usage improves system-level schedule stability and that gate capacity and apron flexibility should be evaluated jointly in tactical airport planning. Full article

Automated Storage and Retrieval Systems (AS/RSs) are pivotal hubs in modern intelligent logistics, yet their operational efficiency is often constrained by the complex coupling of storage allocation, equipment scheduling, and path planning. This study proposes a systematic optimization framework to address these three critical control challenges. First, a multi-objective mathematical model for storage location allocation is established, considering efficiency, stability, and correlation. To solve this high-dimensional discrete problem, a Tabu Variable Neighborhood Search (TVNS) algorithm is proposed, integrating short-term memory mechanisms with multi-structure exploration to prevent premature convergence. Second, regarding stacker crane and forklift collaborative scheduling, a Pheromone-guided Artificial Hummingbird Algorithm (PT-AHA) is introduced. By incorporating pheromone feedback into foraging behavior, the algorithm significantly enhances global search capability to minimize total task completion time. Third, stacker crane path planning is modeled as a constrained Traveling Salesman Problem (TSP) and solved using a hybrid Simulated Annealing-Whale Optimization Algorithm (SA-WOA). Quantitative simulation results demonstrate that the TVNS algorithm improves storage allocation fitness by 1.1% over standard Genetic Algorithms, while the PT-AHA reduces task completion time (Makespan) by 21.9% for small-scale batches and consistently outperforms ACO by up to 3.6% in large-scale operations. Validation through an Intelligent Warehouse Management System (WMS) confirms that the integrated framework maintains high industrial resilience by triggering fault alarms and initiating recovery within 3.2 s during simulated equipment failures, providing a robust solution for enterprise-level deployments. Full article

The stochastic and intermittent characteristics of renewable energy pose significant challenges to energy utilization and power system stability. The reversible solid oxide cell (RSOC), as an emerging multi-energy conversion technology, exhibits high efficiency in both electrolysis and power generation modes, offering a promising solution to renewable energy integration and energy supply issues. However, RSOC performance degrades over time, and its average efficiency decay rate directly influences capacity investment decisions and day-ahead scheduling strategies. To address this, a comprehensive energy system model considering RSOC capacity is developed, with a detailed representation of each subsystem. A bi-level optimization framework is then proposed, where the upper level minimizes system investment and operation costs, and the lower level optimizes day-ahead scheduling costs. The model explicitly accounts for RSOC efficiency degradation and lifetime attenuation. Particle swarm optimization is applied to determine the optimal capacity configuration. Case studies demonstrate that the proposed model enhances system economics, promotes multi-energy complementarity, and prolongs RSOC lifetime, providing theoretical and technical support for the planning and operation of integrated energy systems with RSOC. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

Against the backdrop of the accelerated development of the low-altitude economy and the structural transformation of modern logistics systems, systematically elucidating the key driving factors and their interaction structure is paramount for optimizing operational efficiency, promoting sustainable industry growth, and enhancing policy effectiveness. Integrating an extensive literature review with expert consultations, this study constructs a comprehensive indicator system of influencing factors for the coordinated development of the low-altitude economy and sustainable modern logistics. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to characterize the causal relationships and influence directions among the factors. Empowered by these findings, an Analytic Network Process (ANP) model is established to calculate refined weights, forming a hybrid DEMATEL–ANP analytical framework. The results indicate that technological factors and institutional factors constitute the primary driving layer of the system. Specifically, System Integration and Operational Technology, Flight Control and Scheduling Capability, as well as the Standardisation of Airspace Management and the Completeness of the Regulatory and Standards Framework, exert pivotal influences on the systemic evolution. Social factors and infrastructure factors primarily function as the outcome and feedback layers, with their effectiveness contingent upon the maturity of the core driving elements. Further hybrid weight analysis demonstrates that the ranking of key influencing factors exhibits high stability and robustness. The coordinated development process presents a progressive transmission characteristic from “technology–institution” to “market–application” providing targeted practical guidance for promoting the sustainable and high-quality synergy between the low-altitude economy and modern logistics. Full article

To address the issues that carbon trading fails to cover the full life cycle and that traditional demand response achieves poor emission reduction due to a lack of accurate carbon-intensity feedback in park integrated energy systems (PIESs) during low-carbon transition, this study proposes a two-layer optimal scheduling method synergizing life-cycle stepwise carbon trading and low-carbon demand response (LCDR) to balance low-carbon performance and economic efficiency. Firstly, based on life cycle theory, embodied carbon from new energy equipment manufacturing and transportation is incorporated into accounting, with a stepwise carbon trading mechanism designed. Secondly, corrected dynamic carbon emission factors for power and heating networks are constructed to quantify real-time carbon intensity. A dual-driven LCDR model (electricity price and carbon factor) is established to coordinate shiftable and sheddable electric-thermal loads and is combined with a two-layer scheduling model (pre-scheduling and re-scheduling) targeting the minimal total operation cost. Simulation results of a South China park show that life-cycle stepwise carbon trading reduces emissions by 16.7%, and LCDR further cuts 4.05%. Their synergy achieves significant carbon reduction with a slight cost increase, while supplementary sensitivity analyses further confirm the scalability and robustness of the proposed framework under varying load levels and demand response capabilities. Full article

High-speed railway corridors operating under dense traffic conditions often face capacity limitations and operational conflicts caused by minimum headway constraints and heterogeneous train services. Differences in running times and stopping patterns between fast and slow trains may lead to overtaking conflicts and inefficient infrastructure utilization. This study investigates a multi-objective timetable optimization problem for high-speed railways under minimum headway constraints. A timetable optimization framework is established for high-speed railways under dense heterogeneous operations. The core mathematical formulation explicitly models timetable variables and basic temporal bounds, including sectional running-time limits, dwell-time bounds, and operating time-window constraints. Additional engineering feasibility requirements, such as minimum headway, station-capacity restrictions, and in-station overtaking feasibility, are enforced through the BS-FGS feasibility-scheduling procedure and the repair-based constraint-handling mechanism in the improved MOPSO stage. A hierarchical solution framework is proposed in which a Binary Search–Feasibility-Guided Greedy Scheduling (BS-FGS) method first evaluates the maximum feasible train number and generates an initial feasible timetable, followed by an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to obtain Pareto-optimal solutions within the feasible region. A case study on the Shanghai–Hangzhou High-Speed Railway corridor shows that system utilization can reach approximately 0.93–0.94 when in-station overtaking is allowed. Robustness simulations further demonstrate that the optimized timetables maintain stable train intervals and exhibit strong disturbance resistance. These results indicate that the proposed framework provides effective support for capacity evaluation and timetable optimization in high-density high-speed railway operations. Full article

This paper presents an engineering framework for smart grid virtual power plant (VPP) day-ahead scheduling using fractional calculus-enhanced particle swarm optimization, targeting practical deployment in energy management systems. A fractional calculus-enhanced particle swarm optimization algorithm was developed and validated for day-ahead scheduling in virtual power plants using authentic market data and rigorous statistical analysis. The algorithm incorporates Grünwald–Letnikov fractional derivatives with adaptive memory into particle velocity updates, enabling trajectory-aware search that leverages historical exploration patterns. A factorial experiment across 500 independent test cases (50 dates × 10 trials) with controlled random seeds demonstrated that fractional particle swarm optimization increased mean daily profit by $205, representing a 4.1% improvement over standard particle swarm optimization. Wilcoxon signed-rank tests confirmed statistical significance (p < 0.0001, Cohen’s d = 1.08), with superior performance observed in 89.4% of cases. The factorial design identified fractional calculus as the primary performance driver, while advanced scenario generation provided no significant additional benefit. Sensitivity analysis indicated that wind generation variability was the primary predictor of performance variance, with profit difference standard deviations ranging from $34 to $325 depending on meteorological conditions, supporting the use of adaptive computational strategies. Computation required approximately two minutes per optimization on standard hardware. These findings establish fractional calculus as a credible enhancement for operational energy systems and demonstrate that the quality of optimization algorithms outweighs the complexity of forecast uncertainty modeling. The results extend fractional calculus applications from benchmark functions to practical infrastructure scheduling, with projected annual value exceeding $74,000 for a 50-megawatt system. The three-stage optimization architecture is designed for integration with standard energy management systems and SCADA platforms, offering a deployable pathway for smart grid operators. Full article

The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within an energy community (EC). Battery aging is accounted for in the optimization model through the state of health (SOH). The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS. Full article

Silicon carbide (SiC) power converters offer superior switching performance but generate severe broadband electromagnetic interference (EMI) that challenges regulatory compliance. Existing prediction methods face a fundamental trade-off between physical fidelity and computational efficiency, while conventional suppression strategies lack adaptability to varying operating conditions. This paper proposes a frequency-band-aware physics-informed generative adversarial network (FBA-PIGAN) that integrates electromagnetic domain knowledge into data-driven generative modeling for joint EMI prediction and adaptive suppression in SiC power converters. The framework employs a Wasserstein GAN with gradient penalty as the adversarial backbone and introduces feature-wise linear modulation (FiLM) to inject converter operating parameters into the generator through learned affine transformations. A hierarchical physics-informed loss function enforces three frequency-dependent constraints, namely, harmonic structure consistency, parasitic resonance characterization, and high-frequency envelope regularization, coordinated by a curriculum-based weight-scheduling strategy. An end-to-end differentiable suppression module maps predicted spectra to optimal passive filter parameters through an analytically embedded transfer function. Experimental validation on a 10 kW SiC inverter platform with 5120 measured spectra across 32 operating conditions demonstrates that FBA-PIGAN achieves a mean spectral error of 2.1 dB, 93.8% peak frequency accuracy, and a physical consistency score of 0.93, improving prediction accuracy by 56% over conventional conditional GANs while maintaining sub-millisecond inference latency. The integrated suppression pipeline attains 19.2 dB average attenuation with 98.5% CISPR 25 compliance, and the framework generalizes to unseen operating conditions with only 19% performance degradation, compared with 56% for data-driven baselines. Full article

This study presents a techno-economic and environmental assessment of a photovoltaic–diesel–grid hybrid renewable energy system (SHER) applied to a university campus, with the aim of reducing monetary costs by implementing a methodology to mitigate energy consumption during peak hours, controlling the output of the diesel generator, and determining greenhouse gas emissions. Hourly load profiles are incorporated using billing data, local solar resource data, and grid connection rate schedules. The HOMER Pro v3.14.2 software is used to simulate and identify an off-grid scenario during peak hours, sizing the photovoltaic system at 30%, 50%, 70%, and 100% to compare the investment cost of the SHER. System performance is evaluated using key indicators, including net present cost ($6.96 million), levelized cost of energy (LCOE, $0.707/kWh), and CO₂ emissions (101,311 kg/yr.), among others. The results indicate that photovoltaic generation can cover approximately 80% of annual electricity demand, while the diesel generator operates only during critical periods, contributing to reduced operating costs and emissions. The optimal configuration has a lower LCOE than conventional supply, a renewable fraction of close to 80%, and an investment payback period of approximately five years, demonstrating the technical, economic, and environmental viability of the proposed system. Full article

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, $D=100$), 1.000 (CEC2020, $D=10$), and 2.208 (CEC2022, $D=10$); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant ($p<0.05$). Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

The offshore wind energy sector requires efficient logistics to retrieve generated electricity using maritime mobile energy storage systems. This study addresses the maritime mobile energy storage scheduling problem to maximise the total net energy delivered to the onshore grid. The proposed approach utilises a mixed-integer linear programming framework. The mathematical formulation integrates a replicated port node mechanism to plan multi-trip operations over a continuous planning horizon. Additionally, the model accounts for energy transfer loss coefficients and incorporates a speed discretisation strategy to balance propulsion consumption against retrieved electricity. Numerical experiments based on simulated operational scenarios demonstrate the effectiveness of this method. The results indicate that expanding vessel storage capacity from 500 to 600 megawatt-hours eliminates the necessity for multi-stop trips, thereby reducing propulsion energy consumption from 270.79 to 73.65 megawatt-hours. Furthermore, increasing the fleet size from five to six vessels enables the full retrieval of available offshore electricity while decreasing fleet propulsion consumption to 91.08 megawatt-hours. The solver consistently achieves optimal solutions within an average of 0.88 s. Consequently, this framework provides operators with precise decision support for determining fleet capacity and configuring offshore energy retrieval networks. Full article

To address the problem of renewable energy curtailment and the need for operational economic optimization in integrated energy systems with high penetration of wind and solar power, a coordinated optimization method integrating thermoelectric decoupling, ammonia-blended combustion technology, and energy storage capacity leasing is proposed. First, a chaotic-improved Latin Hypercube Sampling (C-LHS) method, combined with an improved K-means clustering algorithm, is employed to generate representative wind–solar–load scenarios. This approach improves the efficiency of uncertainty scenario generation while reducing computational burden and maintaining solution accuracy. Secondly, by coordinating the operation of thermal energy storage and electric boilers, the “heat-led power generation” constraint is relaxed, and, in combination with ammonia-blended combustion in combined heat and power (CHP) units, the system’s flexibility and renewable energy accommodation capability are enhanced. Finally, with the objective of minimizing total operating cost, a day-ahead scheduling model incorporating electrical energy storage (EES) leasing optimization is established. For EES, under a shared energy storage market mechanism, the golden section search (GSS) algorithm is employed to optimize the day-ahead leasing capacity. The simulation results demonstrate that the proposed method improves renewable energy accommodation while maintaining economic performance, and effectively reduces the overall operating cost of the system. These findings confirm the effectiveness of the proposed strategy in enhancing both system flexibility and economic performance. Full article