Real Power Dispatch on Large Scale Power Systems by Augmented Lagrange Hopfield Network

Real Power Dispatch on Large Scale Power Systems by Augmented Lagrange Hopfield Network

Vo Ngoc Dieu (Ho Chi Minh City University of Technology, Vietnam) and Peter Schegner (Technische Universität Dresden, Germany)
Copyright: © 2012 |Pages: 20
DOI: 10.4018/ijeoe.2012010102
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Abstract

This paper proposes an augmented Lagrange Hopfield network (ALHN) for real power dispatch on large-scale power systems. The proposed ALHN is a continuous Hopfield network with its energy function based on augmented Lagrange function. For this combination, the ALHN method can easily deal with large-scale problems with nonlinear constraints. The proposed ALHN has been tested on systems from 40 units to 240 units, IEEE 118-bus and IEEE 300-bus systems, and the obtained results have been compared to those from other methods. The test results have shown that the ALHN method can obtain better solutions than the compared methods in a very fast manner. Therefore, the proposed ALHN could be favorable for implementation on the real power dispatch problems for large-scale systems.
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Nomenclature

Box 1.
ai, bi, ciFuel cost coefficients for unit i
URi, DRiRamp up and down rate limits of unit i, respectively
NgNumber of online units
NbNumber of buses in the system
NlNumber of transmission lines
PDTotal load demand of the system
PgiReal power output of unit i
QgiReactive power generated at bus i
Pdi, QdiReal and reactive load demand at bus i, respectively
Pgi0Initial output power of unit i
Pgi,min, Pgi,maxLower and upper generation limits of unit i, respectively
Pgi,high, Pgi,lowMaximal and minimal possible power outputs of unit i, respectively
PLTotal network loss of the system
gij, bijTransfer conductance and susceptance between bus i and bus j, respectively
Vi, VjVoltage magnitudes at bus i and bus j, respectively
δi, δjVoltage angles at bus i and bus j, respectively
UλInput of multiplier neuron corresponding to the output Vλ
UgiInput of continuous neuron corresponding to the output Vi
VλOutput of multiplier neuron representing Lagrangian multiplier
VgiOutput of continuous neuron i representing for output power Pgi
σSlope of sigmoid function of continuous neurons
λLagrangian multiplier associated with power balance
βPenalty factor associated with power balance
αλStep updating size for multiplier neurons
αiStep updating size for continuous neurons

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