2,484 research outputs found
Agent-based decentralized optimal charging strategy for plug-in electric vehicles
This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties
Decentralized Convergence to Nash Equilibria in Constrained Deterministic Mean Field Control
This paper considers decentralized control and optimization methodologies for
large populations of systems, consisting of several agents with different
individual behaviors, constraints and interests, and affected by the aggregate
behavior of the overall population. For such large-scale systems, the theory of
aggregative and mean field games has been established and successfully applied
in various scientific disciplines. While the existing literature addresses the
case of unconstrained agents, we formulate deterministic mean field control
problems in the presence of heterogeneous convex constraints for the individual
agents, for instance arising from agents with linear dynamics subject to convex
state and control constraints. We propose several model-free feedback
iterations to compute in a decentralized fashion a mean field Nash equilibrium
in the limit of infinite population size. We apply our methods to the
constrained linear quadratic deterministic mean field control problem and to
the constrained mean field charging control problem for large populations of
plug-in electric vehicles.Comment: IEEE Trans. on Automatic Control (cond. accepted
Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guarantees
The operation of the power grid is becoming more stressed, due to the
addition of new large loads represented by Electric Vehicles (EVs) and a more
intermittent supply due to the incorporation of renewable sources. As a
consequence, the coordination and control of projected EV demand in a network
of fast charging stations becomes a critical and challenging problem.
In this paper, we introduce a game theoretic based decentralized control
mechanism to alleviate negative impacts from the EV demand. The proposed
mechanism takes into consideration the non-uniform spatial distribution of EVs
that induces uneven power demand at each charging facility, and aims to: (i)
avoid straining grid resources by offering price incentives so that customers
accept being routed to less busy stations, (ii) maximize total revenue by
serving more customers with the same amount of grid resources, and (iii)
provide charging service to customers with a certain level of
Quality-of-Service (QoS), the latter defined as the long term customer blocking
probability. We examine three scenarios of increased complexity that gradually
approximate real world settings. The obtained results show that the proposed
framework leads to substantial performance improvements in terms of the
aforementioned goals, when compared to current state of affairs.Comment: Accepted for Publication in IEEE Transactions on Smart Gri
Regularized Jacobi iteration for decentralized convex optimization with separable constraints
We consider multi-agent, convex optimization programs subject to separable
constraints, where the constraint function of each agent involves only its
local decision vector, while the decision vectors of all agents are coupled via
a common objective function. We focus on a regularized variant of the so called
Jacobi algorithm for decentralized computation in such problems. We first
consider the case where the objective function is quadratic, and provide a
fixed-point theoretic analysis showing that the algorithm converges to a
minimizer of the centralized problem. Moreover, we quantify the potential
benefits of such an iterative scheme by comparing it against a scaled projected
gradient algorithm. We then consider the general case and show that all limit
points of the proposed iteration are optimal solutions of the centralized
problem. The efficacy of the proposed algorithm is illustrated by applying it
to the problem of optimal charging of electric vehicles, where, as opposed to
earlier approaches, we show convergence to an optimal charging scheme for a
finite, possibly large, number of vehicles
A multi-agent based scheduling algorithm for adaptive electric vehicles charging
This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources
Continuous-time integral dynamics for Aggregative Game equilibrium seeking
In this paper, we consider continuous-time semi-decentralized dynamics for
the equilibrium computation in a class of aggregative games. Specifically, we
propose a scheme where decentralized projected-gradient dynamics are driven by
an integral control law. To prove global exponential convergence of the
proposed dynamics to an aggregative equilibrium, we adopt a quadratic Lyapunov
function argument. We derive a sufficient condition for global convergence that
we position within the recent literature on aggregative games, and in
particular we show that it improves on established results
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