119,366 research outputs found
Reverse and Forward Engineering of Local Voltage Control in Distribution Networks
The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. In this paper we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with non-incremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the non-incremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms respectively for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This work presents another step towards developing a new foundation - network dynamics as optimization algorithms - for distributed real-time control and optimization of future power networks
Reverse and Forward Engineering of Local Voltage Control in Distribution Networks
The increasing penetration of renewable and distributed energy resources in
distribution networks calls for real-time and distributed voltage control. In
this paper we investigate local Volt/VAR control with a general class of
control functions, and show that the power system dynamics with non-incremental
local voltage control can be seen as distributed algorithm for solving a
well-defined optimization problem (reverse engineering). The reverse
engineering further reveals a fundamental limitation of the non-incremental
voltage control: the convergence condition is restrictive and prevents better
voltage regulation at equilibrium. This motivates us to design two incremental
local voltage control schemes based on the subgradient and pseudo-gradient
algorithms respectively for solving the same optimization problem (forward
engineering). The new control schemes decouple the dynamical property from the
equilibrium property, and have much less restrictive convergence conditions.
This work presents another step towards developing a new foundation -- network
dynamics as optimization algorithms -- for distributed realtime control and
optimization of future power networks
A Game-Theoretic Framework for Medium Access Control
In this paper, we generalize the random access game model, and show that it provides a general game-theoretic framework for designing contention based medium access control. We extend the random access game model to the network with multiple contention measure signals, study the design of random access games, and analyze different distributed algorithms achieving their equilibria. As examples, a series of utility functions is proposed for games achieving the maximum throughput in a network of homogeneous nodes. In a network with n traffic classes, an N-signal game model is proposed which achieves the maximum throughput under the fairness constraint among different traffic classes. In addition, the convergence of different dynamic algorithms such as best response, gradient play and Jacobi play under propagation delay and estimation error is established. Simulation results show that game model based protocols can achieve superior performance over the standard IEEE 802.11 DCF, and comparable performance as existing protocols with the best performance in literature
Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms
Motivation :Reconstructing the topology of a gene regulatory network is one
of the key tasks in systems biology. Despite of the wide variety of proposed
methods, very little work has been dedicated to the assessment of their
stability properties. Here we present a methodical comparison of the
performance of a novel method (RegnANN) for gene network inference based on
multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER),
focussing our analysis on the prediction variability induced by both the
network intrinsic structure and the available data.
Results: The extensive evaluation on both synthetic data and a selection of
gene modules of "Escherichia coli" indicates that all the algorithms suffer of
instability and variability issues with regards to the reconstruction of the
topology of the network. This instability makes objectively very hard the task
of establishing which method performs best. Nevertheless, RegnANN shows MCC
scores that compare very favorably with all the other inference methods tested.
Availability: The software for the RegnANN inference algorithm is distributed
under GPL3 and it is available at the corresponding author home page
(http://mpba.fbk.eu/grimaldi/regnann-supmat
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