Improved parameters for economic dispatch problems by teaching learning optimization

S, Durai.; Subramanian, S.; Ganesan, S.

doi:10.1016/j.ijepes.2014.11.010

Cited by 20 publications

(18 citation statements)

References 30 publications

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“…A power system of five thermal generating units (Sys5T) was tested. Data concerning the generated power ( P j ) and actual values of emissions ( E actual,j ) were taken from Durai et al For all these units, the linear model, the quadratic model (model 6), and the quadratic model with an exponential component (model 7) were tested. The linear model has two parameters, model 6 and model 7 have three, respectively five parameters.…”

Section: Case Studies and Resultsmentioning

confidence: 99%

“…We assume that I‐O curve of thermal power generating units refer either to fuel cost curve (which shows the dependence between generated power [ P ] of the thermal unit and the its corresponding cost F ), or to the emission curve (which shows the dependence between generated power [ P ] of the thermal unit and the corresponding emissions E released into the atmosphere). The fuel cost curve for a unit i belonging to a thermal power plant can be modelled by several smooth or nonsmooth functions, shown as follows: Model 1.The fuel cost curve, F i ( P i ), modelled by a linear function as follows:

F_{i} ((), P_{i}) = a_{0 i} + a_{1 i} P_{i} .

…”

Section: Modelling the I‐o Curves For Thermal And Hydro Power Unitsmentioning

confidence: 99%

“…"> Model 4.The fuel cost curve, F i ( P i ), modelled by a quadratic function having an additional sinusoidal component that characterize the valve‐point effect (nonsmooth function modelling) as follows:

F_{i} ((), P_{i}) = a_{0 i} + a_{1 i} P_{i} + a_{2 i} P_{i}^{2} + (||, a_{3 i} \sin ((), a_{4 i} ((), P_{i, \min} - P_{i}))) .

Model 5.The fuel cost curve, F i ( P i ), modelled by a cubic function having an additional sinusoidal component that characterize the valve‐point effect (nonsmooth function modelling) as follows:

F_{i} ((), P_{i}) = a_{0 i} + a_{1 i} P_{i} + a_{2 i} P_{i}^{2} + a_{3 i} P_{i}^{3} + (||, a_{4 i} \sin ((), a_{5 i} ((), P_{i, \min} - P_{i}))),

where a 0i , a 1i , a 2i , a 3i , a 4i , and a 5i are the cost parameters (coefficients) of a thermal generating unit i , P i is active power output of a thermal generating unit i , P min,i is minimum limit of active power P i of a thermal generating unit i , and F i ( P i ) is the fuel cost function of a thermal generating unit i . Curves that model thermal power plant emissions are, generally, nonlinear functions: The emission curve, E i ( P i ), modelled by a quadratic function as follows:

E_{i} ((), P_{i}) = e_{0 i} + e_{1 i} P_{i} + e_{2 i} P_{i}^{2} .

The emission curve, E i ( P i ), modelled by a quadratic function having an additional exponential component as follows:

E_{i} ((), P_{<...>})

…”

Section: Modelling the I‐o Curves For Thermal And Hydro Power Unitsmentioning

confidence: 99%

“…To avoid derivative calculations and obtain good quality estimations for smooth and nonsmooth models, methods based on artificial intelligence can be used. Some of them are listed here: genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), cuckoo search (CS) algorithm, teaching‐learning–based optimization (TLBO), radial movement optimization (RMO) technique, modified RMO (MRMO) algorithm, original differential evolution algorithm (DE), and improved differential evolution (IDE) algorithm …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Parameter estimation using a modified whale optimization algorithm for input‐output curves of thermal and hydro power plants

Secui

Hora

Bendea

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a modified whale optimization (MWO) algorithm for solving the problem of parameters estimation for input‐output curves of thermal and hydro generating units that operates in power plants. In order to improve the MWO algorithm, a chaotic component based on logistic map is added. This new algorithm is called chaotic MWO (CMWO). Both MWO and CMWO algorithms have kept the structure of original whale optimization (WO) algorithm, but some characteristics regarding how the solutions positions are updated inside CMWO and MWO phases were modified. Estimation of curve parameters is an optimization problem. Its objective is to minimize the sum of absolute errors between actual values and estimated values. The efficiency of CMWO, MWO, and WO algorithms is tested either on polynomial models or on models that use nonsmooth functions. The results of CMWO algorithm are then compared with the ones obtained by other recently published algorithms.

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Section: Case Studies and Resultsmentioning

confidence: 99%

F_{i} ((), P_{i}) = a_{0 i} + a_{1 i} P_{i} .

…”

Section: Modelling the I‐o Curves For Thermal And Hydro Power Unitsmentioning

confidence: 99%

F_{i} ((), P_{i}) = a_{0 i} + a_{1 i} P_{i} + a_{2 i} P_{i}^{2} + (||, a_{3 i} \sin ((), a_{4 i} ((), P_{i, \min} - P_{i}))) .

Model 5.The fuel cost curve, F i ( P i ), modelled by a cubic function having an additional sinusoidal component that characterize the valve‐point effect (nonsmooth function modelling) as follows:

F_{i} ((), P_{i}) = a_{0 i} + a_{1 i} P_{i} + a_{2 i} P_{i}^{2} + a_{3 i} P_{i}^{3} + (||, a_{4 i} \sin ((), a_{5 i} ((), P_{i, \min} - P_{i}))),

E_{i} ((), P_{i}) = e_{0 i} + e_{1 i} P_{i} + e_{2 i} P_{i}^{2} .

The emission curve, E i ( P i ), modelled by a quadratic function having an additional exponential component as follows:

E_{i} ((), P_{<...>})

…”

Section: Modelling the I‐o Curves For Thermal And Hydro Power Unitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter estimation using a modified whale optimization algorithm for input‐output curves of thermal and hydro power plants

Secui

Hora

Bendea

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…155. Durai et al (2015) Provided a framework using TLBO algorithm for the computation of coefficients for quadratic and cubic cost functions, valve point loading, piece-wise quadratic cost and emission functions. 156.…”

mentioning

confidence: 99%

Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

Rao¹

2016

10.5267/j.dsl

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The Enhanced Crow Search Algorithm for Fuel-Cost Function Parameters Assessment of the Cogeneration Units from Thermal Power Plants

Secui

Dziţac

BUNDA

2020

Soft Computing Applications

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Improved parameters for economic dispatch problems by teaching learning optimization

Cited by 20 publications

References 30 publications

Parameter estimation using a modified whale optimization algorithm for input‐output curves of thermal and hydro power plants

Parameter estimation using a modified whale optimization algorithm for input‐output curves of thermal and hydro power plants

Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

The Enhanced Crow Search Algorithm for Fuel-Cost Function Parameters Assessment of the Cogeneration Units from Thermal Power Plants

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