88,245 research outputs found
A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system.
This study focuses on the potential role of plug-in electric vehicles (PEVs) as a distributed energy storage unit to provide peak demand minimization in power distribution systems. Vehicle-to-grid (V2G) power and currently available information transfer technology enables utility companies to use this stored energy. The V2G process is first formulated as an optimal control problem. Then, a two-stage V2G discharging control scheme is proposed. In the first stage, a desired level for peak shaving and duration for V2G service are determined off-line based on forecasted loading profile and PEV mobility model. In the second stage, the discharging rates of PEVs are dynamically adjusted in real time by considering the actual grid load and the characteristics of PEVs connected to the grid. The optimal and proposed V2G algorithms are tested using a real residential distribution transformer and PEV mobility data collected from field with different battery and charger ratings for heuristic user case scenarios. The peak shaving performance is assessed in terms of peak shaving index and peak load reduction. Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads. The impact of the V2G management strategy on the system loading at night is also analyzed by implementing an off-line charging scheduling algorithm
Derandomization of Online Assignment Algorithms for Dynamic Graphs
This paper analyzes different online algorithms for the problem of assigning
weights to edges in a fully-connected bipartite graph that minimizes the
overall cost while satisfying constraints. Edges in this graph may disappear
and reappear over time. Performance of these algorithms is measured using
simulations. This paper also attempts to derandomize the randomized online
algorithm for this problem
Online Assignment Algorithms for Dynamic Bipartite Graphs
This paper analyzes the problem of assigning weights to edges incrementally
in a dynamic complete bipartite graph consisting of producer and consumer
nodes. The objective is to minimize the overall cost while satisfying certain
constraints. The cost and constraints are functions of attributes of the edges,
nodes and online service requests. Novelty of this work is that it models
real-time distributed resource allocation using an approach to solve this
theoretical problem. This paper studies variants of this assignment problem
where the edges, producers and consumers can disappear and reappear or their
attributes can change over time. Primal-Dual algorithms are used for solving
these problems and their competitive ratios are evaluated
Compositional competitiveness for distributed algorithms
We define a measure of competitive performance for distributed algorithms
based on throughput, the number of tasks that an algorithm can carry out in a
fixed amount of work. This new measure complements the latency measure of Ajtai
et al., which measures how quickly an algorithm can finish tasks that start at
specified times. The novel feature of the throughput measure, which
distinguishes it from the latency measure, is that it is compositional: it
supports a notion of algorithms that are competitive relative to a class of
subroutines, with the property that an algorithm that is k-competitive relative
to a class of subroutines, combined with an l-competitive member of that class,
gives a combined algorithm that is kl-competitive.
In particular, we prove the throughput-competitiveness of a class of
algorithms for collect operations, in which each of a group of n processes
obtains all values stored in an array of n registers. Collects are a
fundamental building block of a wide variety of shared-memory distributed
algorithms, and we show that several such algorithms are competitive relative
to collects. Inserting a competitive collect in these algorithms gives the
first examples of competitive distributed algorithms obtained by composition
using a general construction.Comment: 33 pages, 2 figures; full version of STOC 96 paper titled "Modular
competitiveness for distributed algorithms.
The Transactional Conflict Problem
The transactional conflict problem arises in transactional systems whenever
two or more concurrent transactions clash on a data item.
While the standard solution to such conflicts is to immediately abort one of
the transactions, some practical systems consider the alternative of delaying
conflict resolution for a short interval, which may allow one of the
transactions to commit. The challenge in the transactional conflict problem is
to choose the optimal length of this delay interval so as to minimize the
overall running time penalty for the conflicting transactions. In this paper,
we propose a family of optimal online algorithms for the transactional conflict
problem.
Specifically, we consider variants of this problem which arise in different
implementations of transactional systems, namely "requestor wins" and
"requestor aborts" implementations: in the former, the recipient of a coherence
request is aborted, whereas in the latter, it is the requestor which has to
abort. Both strategies are implemented by real systems.
We show that the requestor aborts case can be reduced to a classic instance
of the ski rental problem, while the requestor wins case leads to a new version
of this classical problem, for which we derive optimal deterministic and
randomized algorithms.
Moreover, we prove that, under a simplified adversarial model, our algorithms
are constant-competitive with the offline optimum in terms of throughput.
We validate our algorithmic results empirically through a hardware simulation
of hardware transactional memory (HTM), showing that our algorithms can lead to
non-trivial performance improvements for classic concurrent data structures
- …