Optimization of the optimal coefficients of non-monotonically increasing incremental cost curves

Soliman, S. A.; Emam, Sacha; Christensen, G. S.

doi:10.1016/0378-7796(91)90023-g

Cited by 16 publications

(7 citation statements)

References 3 publications

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“…El-Hawary and Mansour conducted performance analysis of LES, Bard algorithm, Marquardt algorithm and Powell regression algorithm for estimating coefficients [1]. The methods based on the least absolute value approximations and curve fitting techniques have been reported for fuel cost coefficients estimation [2]. Two polynomial curve fitting methods, Gram-Schmidt orthonormalization and least square are also applied to evaluate the fuel cost coefficients [3].…”

Section: Literature Reviewmentioning

confidence: 99%

Preferred Economic Dispatch of Thermal Power Units

S¹,

Subramanian²,

Ganesan³

2015

JPEE

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Economic Dispatch (ED) problem is one of the main concerns of the power generation operations which are basically solved to generate optimal amount of power from the generating units in the system by minimizing the fuel cost and by satisfying the system constraints. The accuracy of ED solutions is highly influenced by the fuel cost parameters of the generating units. Generally, the parameters are subjected to transform due to aging process and other external issues. Further the parameters associated with the transmission line modelling also change due to aforementioned issues. The loss coefficients which are the functions of transmission line parameters get altered from the original value over a time. Hence, the periodical estimation of these coefficients is highly essential in power system problems for obtaining ideal solutions for ED problem. Estimating the ideal parameters of the ED problem may be the best solution for this issue. This paper presents the Teaching Learning Based Optimization (TLBO) algorithm to estimate the parameters associated with ED problem. The estimation problem is formulated as an error minimization problem. This work provides a frame work for the computation of coefficients for quadratic function, piecewise quadratic cost function, emission function, transmission line parameters and loss coefficients. The effectiveness of TLBO is tested with 2 standard test systems and an Indian utility system.

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Section: Literature Reviewmentioning

confidence: 99%

Preferred Economic Dispatch of Thermal Power Units

S¹,

Subramanian²,

Ganesan³

2015

JPEE

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“…Some of these techniques are based on static estimation and dynamic estimation technique. Several static estimation techniques, such as least square [2], linear regression [3] and linear sequential regression technique [4], least absolute value [5], and Gram-Schmidt orthonormalization [6] have been proposed and implemented in estimating the fuel cost curve parameters. Most of these estimation techniques can improve computational efficiency and numerical stability, but the resulting errors are still large and reduce the accuracy of the estimation process.…”

Section: Introductionmentioning

confidence: 99%

GWO-based estimation of input-output parameters of thermal power plants

2020

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The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the difference between the actual value and the estimated value of the fuel cost function. The estimated values of parameter that produce the smallest total absolute error were the values of final solution. The simulation results showed that parameter estimation using gray wolf optimizer method further minimized the value of objective function. By using three models of fuel cost curve and given test data, parameter estimation using grey wolf optimizer method produced the better estimation results than those estimation results obtained using least square error, particle swarm optimization, genetic algorithm, artificial bee colony and cuckoo search methods.

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“…Over the time, several methods (static method, dynamic method, or artificial intelligence‐based methods) have been used for solving the problem of parameter estimation for thermal and hydro unit I‐O curves. Some of the static estimation methods are adaptive approach using error‐feedback and update policy, the sequential regression technique, a weighted least‐squares multiple linear regression method for estimating the parameters of the cubic model for I‐O curves of thermal units, the least absolute‐value approximation, a series of classical algorithms (weighted least‐squares, Gauss‐Newton method, Marquardt algorithm, Powell regression algorithm) for estimating the parameters of quadratic fuel cost curve, and the Gram‐Schmidt ortho‐normalization method combined with least‐squares method . A dynamic method (Kalman filtering algorithm), also used for identifying the I‐O curves of thermal units, is presented in Soliman and Al‐Kandari .…”

Section: Introductionmentioning

confidence: 99%

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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Optimization of the optimal coefficients of non-monotonically increasing incremental cost curves

Cited by 16 publications

References 3 publications

Preferred Economic Dispatch of Thermal Power Units

Preferred Economic Dispatch of Thermal Power Units

GWO-based estimation of input-output parameters of thermal power plants

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

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