1,965 research outputs found
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
Engineering User-Centric Smart Charging Systems
Die Integration erneuerbarer Energiequellen und die Sektorenkopplung erhöhen den Bedarf an FlexibilitĂ€t im ElektrizitĂ€tssystem. Elektrofahrzeuge koordiniert zu Laden bietet die Chance solche FlexibilitĂ€t bereitzustellen. Allerdings hĂ€ngt das FlexibilitĂ€tspotential von Elektrofahrzeugen davon ab in welchem Umfang sich die Nutzer der Fahrzeuge dazu entschlieĂen intelligentes Laden zu nutzen.
Ziel dieser Dissertation ist es Lösungen fĂŒr intelligente Ladesysteme zu entwickeln, welche die Nutzer zu flexiblerem Laden anreizen und diese dabei zu unterstĂŒtzen. Anhand eines LiteraturĂŒberblicks und einer Expertenbefragung werden zunĂ€chst Ziele identifiziert, welche Nutzer zu einer flexiblen Ladung motivieren können.
Die Ergebnisse zeigen, dass neben finanziellen Anreizen auch die Integration erneuer-barer Energien und die Vermeidung von NetzengpĂ€ssen einen Anreiz fĂŒr das flexible La-den darstellen können. In der Folge wird untersucht, ob das Framing der Ladesituation hinsichtlich dieser Ziele die LadeflexibilitĂ€t von Elektrofahrzeugnutzern beeinflussen kann. Hierzu wird ein Online-Experiment mit Elektrofahrzeugnutzern evaluiert.
Das sich ein Teil der Nutzer bei einem Umwelt-Framing flexibler verhĂ€lt, macht Feedback darĂŒber, wie die CO2-Emissionen von der bereitgestellten FlexibilitĂ€t abhĂ€ngen zu einem vielversprechenden Anreiz intelligentes Laden zu nutzen. Um solches Feedback zu er-möglichen werden als NĂ€chstes die CO2-Einsparpotenziale eines optimierten Ladens im Vergleich zu unkontrolliertem Laden untersucht. Dazu werden die marginalen Emissions-faktoren im deutschen Stromnetz mithilfe eines regressionsbasierten Ansatzes ermittelt. Um Echtzeit-Feedback in realen Systemen zu ermöglichen wird darauf aufbauend eine Prognosemethode fĂŒr Emissionsfaktoren entwickelt.
Die Zielerreichung intelligenten Ladens hĂ€ngt hauptsĂ€chlich von der zeitlichen und energetischen FlexibilitĂ€t der Elektrofahrzeuge ab. Damit Nutzer diese Ladeeinstellungen nicht bei jeder Ankunft an der Ladestation von Hand eingeben zu mĂŒssen, könnten sie durch intelligente Assistenten unterstĂŒtzt werden. HierfĂŒr werden probabilistische Prognosen fĂŒr die FlexibilitĂ€t einzelner LadevorgĂ€nge basierend auf historischen LadevorgĂ€ngen und MobilitĂ€tsmustern entwickelt. DarĂŒber hinaus zeigt eine Fallstudie, dass probabilistische Prognosen besser als Punktprognosen dazu geeignet sind die Ladung mehrerer Elektrofahrzeuge zu koordinieren
Charging electric vehicles in the smart city: A survey of economy-driven approaches
International audienceElectric vehicles (EVs), as their penetration increases, do not only challenge the sustainability of the power grid but also stimulate and promote its upgrading. Indeed, EVs can actively reinforce the development of the smart grid if their charging processes are properly coordinated through two-way communications, possibly benefiting all types of actors. Because grid systems involve a large number of actors with nonaligned objectives, we focus on the economic and incentive aspects, where each actor behaves in its own interest. We indeed believe that the market structure will directly impact the actors' behaviors, and as a result, the total benefits that the presence of EVs can earn in the society, hence the need for a careful design. This survey provides an overview of economic models considering unidirectional energy flows and bidirectional energy flows, i.e., with EVs temporarily providing energy to the grid. We describe and compare the main approaches, summarize the requirements on the supporting communication systems, and propose a classification to highlight the most important results and lacks
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