13,916 research outputs found
Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid
A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV, in prep. for journal submission. In V3, we add a proof that the
socially-optimal solution can be enforced as a general equilibrium, a
privacy-preserving distributed optimization algorithm, a description of the
receding-horizon implementation and additional numerical results, and proofs
of all theorem
Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators
Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious
targets set for the near future, the management of large EV fleets must be seen
as a priority. Specifically, we study a scenario where EV charging is managed
through self-interested EV aggregators who compete in the day-ahead market in
order to purchase the electricity needed to meet their clients' requirements.
With the aim of reducing electricity costs and lowering the impact on
electricity markets, a centralised bidding coordination framework has been
proposed in the literature employing a coordinator. In order to improve privacy
and limit the need for the coordinator, we propose a reformulation of the
coordination framework as a decentralised algorithm, employing the Alternating
Direction Method of Multipliers (ADMM). However, given the self-interested
nature of the aggregators, they can deviate from the algorithm in order to
reduce their energy costs. Hence, we study the strategic manipulation of the
ADMM algorithm and, in doing so, describe and analyse different possible attack
vectors and propose a mathematical framework to quantify and detect
manipulation. Importantly, this detection framework is not limited the
considered EV scenario and can be applied to general ADMM algorithms. Finally,
we test the proposed decentralised coordination and manipulation detection
algorithms in realistic scenarios using real market and driver data from Spain.
Our empirical results show that the decentralised algorithm's convergence to
the optimal solution can be effectively disrupted by manipulative attacks
achieving convergence to a different non-optimal solution which benefits the
attacker. With respect to the detection algorithm, results indicate that it
achieves very high accuracies and significantly outperforms a naive benchmark
ABSCEV: An agent-based simulation framework about smart transportation for reducing waiting times in charging electric vehicles
[EN] Fuel has been the main source of energy for cars for many years, but the non-renewable resources are limited in the planet. In this context, electric vehicles (EVs) are increasingly replacing the previous kind of cars. However, as the number of EVs increases, some challenges arise such as the reduction of waiting times in the queues of fast charging stations. The current work addresses this challenge by means of social coordination mechanisms. In particular, this work presents an agent-based simulation framework for simulating the effects of different coordination policies in the route planning of EV drivers for charging their vehicles on their trips. In this manner, researchers and professionals can test different coordination mechanisms for this purpose. This framework has been experienced by simulating an adaptive strategy based on the implicit communication through booking systems in the charging stations. This strategy was compared with another common strategy, which was used as the control mechanism. This comparison was done by simulating several scenarios in two Spanish cities (i.e. Madrid and Zaragoza). The experimental results show that the current approach was useful to propose a route planning strategy that had statistically significant improvements in the reduction of waiting times in charging stations and also in the global trip times. In addition, the evolutions of pathfinding execution times and the numbers of interchanged messages did not show any overloading pattern over the time. (C) 2018 Elsevier B.V. All rights reservedWe acknowledge the research project "Construccion de un framework para agilizar el desarrollo de aplicaciones mviles en el ambito de la salud" funded by University of Zaragoza and Foundation Ibercaja with grant reference JIUZ-2017-TEC-03. This work has been supported by the program "Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores" funded by the Spanish Ministry of Education, Culture and Sport with reference CAS17/00005. We also acknowledge support from "Universidad de Zaragoza", "Fundacion Bancaria Ibercaja" and "Fundacion CAI" in the "Programa Ibercaja-CAI de Estancias de Investigacion" with reference IT1/18. This work acknowledges the research project "Desarrollo Colaborativo de Soluciones AAL" with reference TIN2014-57028-R funded by the Spanish Ministry of Economy and Competitiveness. It has also been supported by "Organismo Autonomo Programas Educativos Europeos" with reference 2013-1-CZ1-GRU06-14277. We also acknowledge support from project "Sensores vestibles y tecnologa movil como apoyo en la formacin y practica de mindfulness: prototipo previo aplicado a bienestar" funded by University of Zaragoza with grant number UZ2017-TEC-02.García-Magariño, I.; Palacios-Navarro, G.; Lacuesta Gilaberte, R.; Lloret, J. (2018). ABSCEV: An agent-based simulation framework about smart transportation for reducing waiting times in charging electric vehicles. Computer Networks. 138:119-135. https://doi.org/10.1016/j.comnet.2018.03.01411913513
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