The adoption of electric buses introduces challenges related to limited driving range, extended charging times, and strict battery management requirements. These requirements — often set by battery leasing companies or warranty providers — typically include recommended ranges for the battery’s state-of-charge (SoC). This study addresses these challenges by proposing a novel chance-constrained model for the electric vehicle scheduling problem that accounts for stochastic energy consumption and battery management guidelines. The model ensures compliance with recommended SoC ranges while optimizing operational costs. A tailored branch-and-price heuristic with stochastic pricing problems is developed. Computational experiments on realistic instances show that the stochastic approach offers good trade-offs between operational costs and compliance with battery management guidelines, reducing costs by up to 10% compared to a deterministic worst-case benchmark. In contrast, we show that relying on expected energy consumption yields solutions with a near-certain probability of violating SoC guidelines, rendering such an approach unsuitable for practical use.
This paper presents and compares two delay-free methods to parallelize Electromagnetic Transient (EMT) simulations when there is no line propagation delay for decoupling. Both methods involve transforming the network matrix into a Bordered-Block Diagonal form, allowing for parallel decoupling. In the first one, BBD (node tearing), the BBD form is obtained with no variable addition. In the second method, the Compensation Method (CM), additional variables, such as compensation currents, are required to achieve a parallelizable BBD form. Graph partitioning techniques (Metis) are used to automate the network decoupling in both methods, which are implemented in a Julia-EMT code. Each BBD-based method achieves substantial performance gains on practical networks.
Highly dynamic fluid-granular flows transitioning from subaerial to submerged conditions exhibit strongly nonlinear behaviour involving large deformations, evolving pore pressure, seepage, and granular dilatancy or compaction, posing significant challenges for computational modelling. This study presents a novel mixture-theory-based two-phase Moving Particle Semi-implicit (MPS) model for transitional fluid-granular dynamics. We develop the first volume-adaptive MPS formulation that explicitly accounts for particle volume variations induced by fluid seepage and granular deformation, enabling consistent modelling of dilatancy, compaction, and dry-to-saturated transitions. The granular phase is represented as a deformable porous medium using a generalized constitutive law that captures solid-and fluid-like behaviour under varying saturation conditions. A unified coupling strategy ensures conservative interphase interactions between overlapping continua with evolving volumes. Stabilization techniques are reformulated to accommodate variable-volume particle behaviour while maintaining numerical stability. The model is validated against benchmark problems involving subaerial granular column collapse, dam-break flows into porous media, and hydrostatic stress development in initially dry granular columns, and is further applied to two-and three-dimensional granular collapses interacting with free-surface flows. The results demonstrate accurate predictions of granular dynamics, pore-pressure evolution, and impulse-wave generation and propagation, establishing a volume-consistent particle framework for transitional fluid-granular flows.
This paper presents a new variable time-step method for the electromagnetic transient (EMT) simulation of power systems. Trapezoidal discretization with a constant time-step is commonly employed in EMT simulations of power systems. During an EMT simulation, power systems typically encounter high-frequency transients that demand small time-steps for accuracy, as well as low-frequency and steady-state conditions that allow larger time-steps for efficiency. With a fixed time-step, either the simulation accuracy or speed is compromised, and achieving a trade-off is not feasible for all cases. Consequently, this paper proposes a novel variable time-step strategy for maintaining both simulation accuracy and speed. The performance of the proposed method is verified through the WECC 240-bus system.
Worldwide, 10 to 40 million tons of microplastics (MPs) are emitted annually into the environment, redistributing across environmental compartments and affecting organisms. Current life cycle impact assessment (LCIA) methods partially account for their impacts on ecosystem quality by considering a limited number of compartments and particles. This study advances the work of the MarILCA group by introducing the PEBBLE method which integrates physical effects of MPs, tire and road wear particles (TRWPs), and cellulosic microfibers across environmental compartments into life cycle assessment (LCA).Characterization factors (CFs) are developed for 10 conventional polymers, 6 biodegradable polymers, 6 cellulosic materials, and TRWPs, for multiple shapes and sizes. Particle fate is modelled using the SimpleBox object-oriented (SBoo) model for microparticles, computing fate factors (FFs) in air, terrestrial, freshwater, and marine environments at continental and global scales, following USEtox regionalization. SBoo was improved to account for size and shape effects, refined degradation data, regional marine currents, and biofouling.FFs are combined with exposure and effect factors in water, sediments, and terrestrial environments to obtain updated CFs. Uncertainty is quantified using Monte Carlo simulation, and time-horizon-dependent CFs are computed. Results are provided for different LCIA methods: for IMPACT World+ and ReCiPe, CFs representing regional species loss are derived, while for GLAM, the Global Extinction Probability is applied to derive CFs representing global species loss. The results support environmental decision-making for plastics and their alternatives, and aim to be the consolidated version of MarILCA CFs for microparticles, ready for implementation in LCA tools and methods.
