A Solution for Combined Economic and Emission Dispatch Problem using Hybrid Optimization Techniques

Rex, C. R. Edwin Selva; Beno, M. Marsaline; Annrose, J.

doi:10.1007/s42835-019-00192-z

Cited by 23 publications

(10 citation statements)

References 19 publications

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“…The results of presented hybrid algorithms (nPSO, nDE and ihPSODE) are compared with CDE-SQP (Coelho and Mariani 2006), PPSO (Chen and Yeh 2006), APPSO (Chen and Yeh 2006), SFL (Serapião 2009), BFO (Serapião 2009), CCPSO (Park et al 2010), SOMA (Coelho and Mariani 2010), CSOMA (Coelho and Mariani 2010), DE/BBO (Bhattacharya and Chattopadhyay 2010), EDA/DE (Wang et al 2010), ARCGA (Amjady and Rad 2010), TSAGA (Subbaraj et al 2011), MODE (Basu 2011), NSGAII (Basu 2011), PDE (Basu 2011), SPEA-2 (Basu 2011), GA (Kumar and Alwarsamy 2012), PSO (Kumar and Alwarsamy 2012), GSA (Güvenç et al 2012), ABC_PSO (Manteaw and Odero 2012), DE (Kumar and Alwarsamy 2012), QP (Hardiansyah et al 2013), EMOCA (Zhang et al 2013), SA (Hardiansyah et al 2013), CS (Serapiao 2013), DHS (Wang and Li 2013), ABC (Serapiao 2013), FA (Serapiao 2013), MPSO (Hardiansyah 2013), MABC/D/Cat (Secui 2015) and MABC/D/Log (Secui 2015), WOA (Mirjalili and Lewis 2016), FPA (Abdelaziz et al 2016), GA-WOA (Edwin Selva Rex et al 2019), β-GWO (Betar et al 2020), on ELD and CEED test systems.…”

Section: Resultsmentioning

confidence: 99%

An Innovative Hybrid Algorithm for Solving Combined Economic and Emission Dispatch Problems

Verma

Parouha

2022

Preprint

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As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces a new mutation approach and crossover rate (to prevent premature convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) are inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6- and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10- and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances.

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Section: Resultsmentioning

confidence: 99%

An Innovative Hybrid Algorithm for Solving Combined Economic and Emission Dispatch Problems

Verma

Parouha

2022

Preprint

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“…(23) and 24respectively Determine the best concessive solution using Eqs. (29), (30) and (31) Update the position of squirrels to a random location using Eq. 25Is…”

Section: Resultsmentioning

confidence: 99%

“…Hybrid heuristic algorithms have been introduced to solve the ELD, and economic and emission dispatch problems in order to accomplish the preeminent features and performances of different algorithms. 29,30 Nevertheless, these algorithms suffer from high computational complexities.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Multi‐objective squirrel search algorithm to solve economic environmental power dispatch problems

Sakthivel¹,

Murugesan

Sathya

2020

Int Trans Electr Energ Syst

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Major challenge in the modern power system assessment is to address the issue of Combined Economic and Environmental Power Dispatch (CEEPD). The prime intention of CEEPD is to mitigate the total expense in the power generation process considering the environmental collision caused owing to the discharge of gaseous pollutants of fossil energy. This paper bestows a new swarm intelligence technique, Squirrel Search Algorithm (SSA) to deal with multiobjective CEEPD problem in power systems. The SSA is devised from the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. Multi-Objective SSA (MOSSA) approach employs the Pareto dominance and crowding distance concepts for finding the Pareto front solutions set. An external elitist depository mechanism is employed to preserve Pareto front solutions acquired during the optimization process. Then, an optimality based fuzzy decision maker is used to decide the best compromise solution. Furthermore, a renovate strategy and selection rules are utilized in the MOSSA approach to appropriately handle the CEEPD constraints. To access the practicability and effectiveness of the proposed MOSSA algorithm, it has been applied for 6, 10, and 40 units' power systems and compared with those of List of symbols and abbreviations: a i , b i , c i , cost coefficients of generator i; a ij , b ij , c ij , e ij and f ij , cost coefficients of the unit i for fuel type j; B ij , line loss coefficients; C D , drag coefficient; C L , lift coefficient; d i , e i , cost coefficients of the VPL effect of generator i; D, drag force; ed i , euclidean distance between nondominated solution and the nearest Pareto front solution in objective space; d, mean value of ed i ; d g , gliding distance; E i , emission of the generator i; F i , total fuel cost of the generators; F max j and F min j , maximum and minimum values of jth objective function respectively; F(P i)F(P i) max , cost function of the worst feasible solution in the population; F bcs and E bcs , fuel cost and emission attained by CEEPD; F min and E max , fuel cost and emission attained by ELD minimization respectively; F max and E min , fuel cost and emission attained by emission minimization respectively; G c , gliding constant; h g , gliding height; i and j, indices of unit and fuel type, respectively; k, index of prohibited zone; L, lift force; M, number of nondominated solutions; n, number of solutions in the nondominated set; nf, number of fuel types for each unit; ng, total number of generating units; nz, total number of POZs; P D , power demand; P dp , predator presence probability; P i , P 0 i , current and previous power output of ith unit respectively; P ij, min and P ij, max , minimum and maximum power output of unit i with fuel option j, respectively; P L i,k , P U i,k , lower and upper power outputs of the kth prohibited zone of the ith generator, respectively; Pi, max i, min , minimum and maximum generation of unit i; P L , transmission losses; r 1 , r 2 and r 3 , random numbers in the range...

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“…Goudarzi et al (2019) proposed MGAIPSO, it utilizes three operators, an arithmetic crossover and a mutation operator from GA to produce elite off-springs and maintain diversity; a non-linear time-varying double-weighted technique from PSO to obtain a substantial balance between exploration and exploitation. Edwin Selva Rex et al (2019) proposed a novel hybrid algorithm (GA-WOA) using GA and Whale optimization for solving CEED problem. This method verified on 4 different test systems and it is superior to other heuristic methods with slight increase in the CPU execution time.…”

Section: Masmentioning

confidence: 99%

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

Verma

Parouha

2022

Soft Comput

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As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces a new mutation approach and crossover rate (to prevent premature convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) are inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6-and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10-and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances.

show abstract

A Solution for Combined Economic and Emission Dispatch Problem using Hybrid Optimization Techniques

Cited by 23 publications

References 19 publications

An Innovative Hybrid Algorithm for Solving Combined Economic and Emission Dispatch Problems

An Innovative Hybrid Algorithm for Solving Combined Economic and Emission Dispatch Problems

Multi‐objective squirrel search algorithm to solve economic environmental power dispatch problems

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

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