62,985 research outputs found
Frequency Regulation with Heterogeneous Energy Resources: A Realization using Distributed Control
This paper presents one of the first real-life demonstrations of coordinated
and distributed resource control for secondary frequency response in a power
distribution grid. We conduct a series of tests with up to 69 heterogeneous
active devices consisting of air handling units, unidirectional and
bidirectional electric vehicle charging stations, a battery energy storage
system, and 107 passive devices consisting of building loads and photovoltaic
generators. Actuation commands for the test devices are obtained by solving an
economic dispatch problem at every regulation instant using distributed
ratio-consensus, primal-dual, and Newton-like algorithms. The distributed
control setup consists of a set of Raspberry Pi end-points exchanging messages
via an ethernet switch. The problem formulation minimizes the sum of device
costs while tracking the setpoints provided by the system operator. We
demonstrate accurate and fast real-time distributed computation of the
optimization solution and effective tracking of the regulation signal by
measuring physical device outputs over 40-minute time horizons. We also perform
an economic benefit analysis which confirms eligibility to participate in an
ancillary services market and demonstrates up to $53K of potential annual
revenue for the selected population of devices
GraphH: High Performance Big Graph Analytics in Small Clusters
It is common for real-world applications to analyze big graphs using
distributed graph processing systems. Popular in-memory systems require an
enormous amount of resources to handle big graphs. While several out-of-core
approaches have been proposed for processing big graphs on disk, the high disk
I/O overhead could significantly reduce performance. In this paper, we propose
GraphH to enable high-performance big graph analytics in small clusters.
Specifically, we design a two-stage graph partition scheme to evenly divide the
input graph into partitions, and propose a GAB (Gather-Apply-Broadcast)
computation model to make each worker process a partition in memory at a time.
We use an edge cache mechanism to reduce the disk I/O overhead, and design a
hybrid strategy to improve the communication performance. GraphH can
efficiently process big graphs in small clusters or even a single commodity
server. Extensive evaluations have shown that GraphH could be up to 7.8x faster
compared to popular in-memory systems, such as Pregel+ and PowerGraph when
processing generic graphs, and more than 100x faster than recently proposed
out-of-core systems, such as GraphD and Chaos when processing big graphs
DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework
In a Spiking Neural Networks (SNN), spike emissions are sparsely and
irregularly distributed both in time and in the network architecture. Since a
current feature of SNNs is a low average activity, efficient implementations of
SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand,
simulations of large scale neural networks can take advantage of distributing
the neurons on a set of processors (either workstation cluster or parallel
computer). This article presents DAMNED, a large scale SNN simulation framework
able to gather the benefits of EDS and parallel computing. Two levels of
parallelism are combined: Distributed mapping of the neural topology, at the
network level, and local multithreaded allocation of resources for simultaneous
processing of events, at the neuron level. Based on the causality of events, a
distributed solution is proposed for solving the complex problem of scheduling
without synchronization barrier.Comment: 6 page
Scalable and Secure Aggregation in Distributed Networks
We consider the problem of computing an aggregation function in a
\emph{secure} and \emph{scalable} way. Whereas previous distributed solutions
with similar security guarantees have a communication cost of , we
present a distributed protocol that requires only a communication complexity of
, which we prove is near-optimal. Our protocol ensures perfect
security against a computationally-bounded adversary, tolerates
malicious nodes for any constant (not
depending on ), and outputs the exact value of the aggregated function with
high probability
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