26 research outputs found
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Stochastic modelling of the effects of interdependencies between critical infrastructure
An approach to Quantitative Interdependency Analysis, in the context of Large Complex Critical Infrastructures, is presented in this paper. A Discrete state–space, Continuous–time, Stochastic Process models the operation of critical infrastructure, taking interdependencies into account. Of primary interest are the implications of both model detail (that is, level of model abstraction) and model parameterisation for the study of dependencies. Both of these factors are observed to affect the distribution of cascade–sizes within and across infrastructure
Cascade Failure in a Phase Model of Power Grids
We propose a phase model to study cascade failure in power grids composed of
generators and loads. If the power demand is below a critical value, the model
system of power grids maintains the standard frequency by feedback control. On
the other hand, if the power demand exceeds the critical value, an electric
failure occurs via step out (loss of synchronization) or voltage collapse. The
two failures are incorporated as two removal rules of generator nodes and load
nodes. We perform direct numerical simulation of the phase model on a
scale-free network and compare the results with a mean-field approximation.Comment: 7 pages, 2 figure
Self-Control of Traffic Lights and Vehicle Flows in Urban Road Networks
Based on fluid-dynamic and many-particle (car-following) simulations of
traffic flows in (urban) networks, we study the problem of coordinating
incompatible traffic flows at intersections. Inspired by the observation of
self-organized oscillations of pedestrian flows at bottlenecks [D. Helbing and
P. Moln\'ar, Phys. Eev. E 51 (1995) 4282--4286], we propose a self-organization
approach to traffic light control. The problem can be treated as multi-agent
problem with interactions between vehicles and traffic lights. Specifically,
our approach assumes a priority-based control of traffic lights by the vehicle
flows themselves, taking into account short-sighted anticipation of vehicle
flows and platoons. The considered local interactions lead to emergent
coordination patterns such as ``green waves'' and achieve an efficient,
decentralized traffic light control. While the proposed self-control adapts
flexibly to local flow conditions and often leads to non-cyclical switching
patterns with changing service sequences of different traffic flows, an almost
periodic service may evolve under certain conditions and suggests the existence
of a spontaneous synchronization of traffic lights despite the varying delays
due to variable vehicle queues and travel times. The self-organized traffic
light control is based on an optimization and a stabilization rule, each of
which performs poorly at high utilizations of the road network, while their
proper combination reaches a superior performance. The result is a considerable
reduction not only in the average travel times, but also of their variation.
Similar control approaches could be applied to the coordination of logistic and
production processes
An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations
Transportation electrification is a valid option for supporting decarbonization efforts but, at the same time, the growing number of electric vehicles will produce new and unpredictable load conditions for the electrical networks. Accurate electric vehicle load forecasting becomes essential to reduce adverse effects of electric vehicle integration into the grid. In this paper, a methodology dedicated to probabilistic electric vehicle load forecasting for different geographic regions is presented. The hierarchical approach is applied to decompose the problem into sub-problems at low-level regions, which are resolved through standard probabilistic models such as gradient boosted regression trees, quantile regression forests and quantile regression neural networks, coupled with principal component analysis to reduce the dimensionality of the sub-problems. The hierarchical perspective is then finalized to forecast the aggregate load at a high-level geographic region through an ensemble methodology based on a penalized linear quantile regression model. This paper brings, as relevant contributions, the development of hierarchical probabilistic forecasting framework, its comparison with non-hierarchical frameworks, and the assessment of the role of data dimensionality refduction. Extensive experimental results based on actual electric vehicle load data are presented which confirm that the hierarchical approaches increase the skill of probabilistic forecasts up to 9.5% compared with non-hierarchical approaches
Critical behaviour in charging of electric vehicles
The increasing penetration of electric vehicles over the coming decades,
taken together with the high cost to upgrade local distribution networks and
consumer demand for home charging, suggest that managing congestion on low
voltage networks will be a crucial component of the electric vehicle revolution
and the move away from fossil fuels in transportation. Here, we model the
max-flow and proportional fairness protocols for the control of congestion
caused by a fleet of vehicles charging on two real-world distribution networks.
We show that the system undergoes a continuous phase transition to a congested
state as a function of the rate of vehicles plugging to the network to charge.
We focus on the order parameter and its fluctuations close to the phase
transition, and show that the critical point depends on the choice of
congestion protocol. Finally, we analyse the inequality in the charging times
as the vehicle arrival rate increases, and show that charging times are
considerably more equitable in proportional fairness than in max-flow.Comment: 19 pages, 6 figure