65,941 research outputs found
Optimal Charging of Electric Vehicles in Smart Grid: Characterization and Valley-Filling Algorithms
Electric vehicles (EVs) offer an attractive long-term solution to reduce the
dependence on fossil fuel and greenhouse gas emission. However, a fleet of EVs
with different EV battery charging rate constraints, that is distributed across
a smart power grid network requires a coordinated charging schedule to minimize
the power generation and EV charging costs. In this paper, we study a joint
optimal power flow (OPF) and EV charging problem that augments the OPF problem
with charging EVs over time. While the OPF problem is generally nonconvex and
nonsmooth, it is shown recently that the OPF problem can be solved optimally
for most practical power networks using its convex dual problem. Building on
this zero duality gap result, we study a nested optimization approach to
decompose the joint OPF and EV charging problem. We characterize the optimal
offline EV charging schedule to be a valley-filling profile, which allows us to
develop an optimal offline algorithm with computational complexity that is
significantly lower than centralized interior point solvers. Furthermore, we
propose a decentralized online algorithm that dynamically tracks the
valley-filling profile. Our algorithms are evaluated on the IEEE 14 bus system,
and the simulations show that the online algorithm performs almost near
optimality ( relative difference from the offline optimal solution) under
different settings.Comment: This paper is temporarily withdrawn in preparation for journal
submissio
Distributed Partitioned Big-Data Optimization via Asynchronous Dual Decomposition
In this paper we consider a novel partitioned framework for distributed
optimization in peer-to-peer networks. In several important applications the
agents of a network have to solve an optimization problem with two key
features: (i) the dimension of the decision variable depends on the network
size, and (ii) cost function and constraints have a sparsity structure related
to the communication graph. For this class of problems a straightforward
application of existing consensus methods would show two inefficiencies: poor
scalability and redundancy of shared information. We propose an asynchronous
distributed algorithm, based on dual decomposition and coordinate methods, to
solve partitioned optimization problems. We show that, by exploiting the
problem structure, the solution can be partitioned among the nodes, so that
each node just stores a local copy of a portion of the decision variable
(rather than a copy of the entire decision vector) and solves a small-scale
local problem
Minimum-cost multicast over coded packet networks
We consider the problem of establishing minimum-cost multicast connections over coded packet networks, i.e., packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. We consider both wireline and wireless packet networks as well as both static multicast (where membership of the multicast group remains constant for the duration of the connection) and dynamic multicast (where membership of the multicast group changes in time, with nodes joining and leaving the group). For static multicast, we reduce the problem to a polynomial-time solvable optimization problem, and we present decentralized algorithms for solving it. These algorithms, when coupled with existing decentralized schemes for constructing network codes, yield a fully decentralized approach for achieving minimum-cost multicast. By contrast, establishing minimum-cost static multicast connections over routed packet networks is a very difficult problem even using centralized computation, except in the special cases of unicast and broadcast connections. For dynamic multicast, we reduce the problem to a dynamic programming problem and apply the theory of dynamic programming to suggest how it may be solved
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