arXiv subjects

Johann Hurink

Publications and source records attributed to Johann Hurink.

4 recordsLinked to original sources

arXiv2025

Relating Electric Vehicle Charging to Speed Scaling with Job-Specific Speed Limits

Due to the ongoing electrification of transport in combination with limited power grid capacities, efficient ways to schedule the charging of electric vehicles (EVs) are needed for the operation of, for example, large parking lots. Common approaches such as model predictive control repeatedly solve a corresponding offline problem. In this work, we first present and analyze the Flow-based Offline Charging Scheduler (FOCS), an offline algorithm to derive an optimal EV charging schedule for a fleet of EVs that minimizes an increasing, convex and differentiable function of the corresponding aggregated power profile. To this end, we relate EV charging to processor speed scaling models with job-specific speed limits. We prove our algorithm to be optimal and derive necessary and sufficient conditions for any EV charging profile to be optimal. Furthermore, we discuss two online algorithms and their competitive ratios for a specific class objective functions. In particular, we show that if those algorithms are applied and adapted to the presented EV scheduling problem, the competitive ratios for Average Rate and Optimal Available match those of the classical speed scaling problem. Finally, we present numerical results using real-world EV charging data to put the theoretical competitive ratios into a practical perspective.

math.OC↗

arXiv2025

Carbon, Cost and Capacity: Multi-objective Charging of Electric Buses

The public transport sector is in the process of decarbonizing by electrifying its bus fleets. This results in challenges if the high electricity demand resulting from battery charging demand is confronted with limited grid capacity and high synchronicity at bus charging sites. In this paper, we explore multi-objective scheduling for bus charging sites to minimize the emissions associated with charging processes and to aid the operation of the electricity grid by mitigating peak consumption. In particular, we discuss and validate optimization approaches for those objectives, as well as their weighted combination, based on data from a real-life bus charging site in the Netherlands. The simulation results show that compared to uncontrolled charging, power peaks can be reduced by up to 57%, while time-of-use emissions associated with the charging of electric buses are also reduced significantly. Furthermore, by using a synthetic baseload, we illustrate the flexibility potential offered by bus charging sites, and advocate that such sites should share a grid connection with other high-load assets.

math.OC↗

arXiv2024

A flexible numerical tool for large dynamic DC networks

DC networks play an important role within the ongoing energy transition. In this context, simulations of designed and existing networks and their corresponding assets are a core tool to get insights and form a support to decision-making. Hereby, these simulations of DC networks are executed in the time domain. Due to the involved high frequencies and the used controllers, the equations that model these DC networks are stiff and highly oscillatory differential equations. By exploiting sparsity, we show that conventional adaptive time stepping schemes can be used efficiently for the time domain simulation of very large DC networks and that this scales linearly in the computational cost as the size of the networks increase.

eess.SY↗

arXiv2024

Improving the Optimization in Model Predictive Controllers: Scheduling Large Groups of Electric Vehicles

In parking lots with large groups of electric vehicles (EVs), charging has to happen in a coordinated manner, among others, due to the high load per vehicle and the limited capacity of the electricity grid. To achieve such coordination, model predictive control can be applied, thereby repeatedly solving an optimization problem. Due to its repetitive nature and its dependency on the time granularity, optimization has to be (computationally) efficient. The work presented here focuses on that optimization subroutine, its computational efficiency and how to speed up the optimization for large groups of EVs. In particular, we adapt FOCS, an algorithm that can solve the underlying optimization problem, to better suit the repetitive set-up of model predictive control by adding a pre-mature stop feature. Based on real-world data, we empirically show that the added feature speeds up the median computation time for 1-minute granularity by up to 44%. Furthermore, since FOCS is an algorithm that uses maximum flow methods as a subroutine, the impact of choosing various maximum flow methods on the runtime is investigated. Finally, we compare FOCS to a commercially available solver, concluding that FOCS outperforms the state-of-the-art when making a full-day schedule for large groups of EVs.

math.OC↗