Minimum emissions power flow

Talaq, Jawad; El-Hawary, F.; El-Hawary, M.E.

doi:10.1109/59.317581

Cited by 44 publications

(13 citation statements)

References 7 publications

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“…The inequality constrains imposed on the th generator output are restricted to be within the lower and upper operating limits and (5) 3) Security Assessment: To ensure no overloading occurs in any transmission line, the security assessment index (SAI) of power transmission is defined as (6), shown at the bottom of the page, where represents the power-flow capacity of line is the active power flow of line ; the term indicates whether the th transmission line is overload, and is the number of transmission lines in the system.…”

Section: ) Inequality Constraintsmentioning

confidence: 99%

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Biobjective Power Dispatch Using Goal-Attainment Method and Adaptive Polynomial Networks

Chen

Huang

2004

IEEE Trans. On Energy Conversion

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This paper presents an integrated approach that combines goal-attainment (GA) and adaptive polynomial networks (APN) to real-time biobjective power dispatch. The goals considered are fuel cost and the environmental impact of multiple emissions. The complicated relationships between the input (power demand and operator's economic and emission preferences) and the output (power generated by each generator) can be efficiently modeled by the APN. Moreover, the APN can rapidly provide an accurate estimate of the real-time dispatch results for the power demand and the operator's preferences. The effectiveness of the proposed approach has been demonstrated by the IEEE 30-bus six-generator and the practical Taipower 388-bus 27-generator systems. Test results reveal that the proposed approach achieves significant savings in computation time and reduces the complexity of the real-time power dispatch. Furthermore, the proposed APN outperforms the artificial neural networks (ANNs) method, in both developing the model and estimating the power generated by each generator.Index Terms-Adaptive polynomial networks, biobjective power dispatch, goal-attainment (GA) method.

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Section: ) Inequality Constraintsmentioning

confidence: 99%

“…An excellent overview on the commonly used EEPD algorithms can be found in [2]. In the past, emission-constrained economic dispatch [3], [4] and minimum emission dispatch [5], [6] were proposed. An interactive search technique [7] was proposed to solve multiple emissions problems.…”

Section: Introductionmentioning

confidence: 99%

Biobjective Power Dispatch Using Goal-Attainment Method and Adaptive Polynomial Networks

Chen

Huang

2004

IEEE Trans. On Energy Conversion

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“…In the past, emission-constrained economic dispatch [2], [3] and minimum emission dispatch [4], [5] were proposed. Four emission functions (of NO , SO , particulates, and thermal pollutants) with suitManuscript received July 19, 2002 able weightings are aggregated into a single emission function [6] to solve problems of multiple emissions.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to real-time economic emission power dispatch

Huang

Yan²

2003

IEEE Trans. Power Syst.

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This paper describes a novel approach that combines abductive reasoning network (ARN) and a technique for order preference by similarity to ideal solution (TOPSIS) decision approach to achieve real-time economic emission power dispatch and the best compromise solution. The objectives of fuel cost and the environmental impact of emission are simultaneously considered in this paper. The proposed ARN handles complicated relationships between the load demands (input) and the generation power of each unit (output) using a hierarchical network with several layers of function nodes of simple low-order polynomials to make the computed outputs fit the historical data. Once the ARN is constructed, the desired outputs can be produced as soon as the inputs are given. According to the set of noninferior solutions for a specific load level, the TOPSIS approach is used to provide operators with the best compromise solution. The effectiveness of the proposed approach has been demonstrated by the IEEE 30-bus 6-generator and the practical Taipower 388-bus 27-generator test systems. The test results reveal that the proposed ARN outperforms the artificial neural networks (ANNs) method, in both developing the model and estimating the outputs of the generating units according to the input load demands.Index Terms-Abductive reasoning network, bi-objective optimization, real-time power dispatch.

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“…Finally, note that hydroelectric and wind generation units can be approximated as zero emission units [12] and do therefore not have any contribution to the EII. The cost is generally a strictly growing evolution of the generated power [13]. In that way, the cost associated to classical thermal units C t ($/h) is generally characterized by a fuel cost square function of the generated power [13] and a starting investment cost quite limited in comparison with large hydroelectric units [14]:…”

Section: Optimization Model Analyzed During Each Generated System Statementioning

confidence: 99%

Impact of Wind Power on the Gaseous Pollutants Emissions in Electrical Systems Operating under Constraints

Vallée

Versele

Moiny

et al. 2012

ISRN Renewable Energy

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Minimum emissions power flow

Cited by 44 publications

References 7 publications

Biobjective Power Dispatch Using Goal-Attainment Method and Adaptive Polynomial Networks

Biobjective Power Dispatch Using Goal-Attainment Method and Adaptive Polynomial Networks

A novel approach to real-time economic emission power dispatch

Impact of Wind Power on the Gaseous Pollutants Emissions in Electrical Systems Operating under Constraints

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