A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem

Elattar, Ehab E.

doi:10.1016/j.ijepes.2014.12.091

Cited by 83 publications

(19 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These methods include various algorithms: genetic algorithm (GA) [4], Evolutionary Programming (EP) [5], Tabu Search [6], and Particle Swarm Optimization (PSO) [3,7] etc. For calculation simplicity, current ways are used the second order fuel value functions that involve approximation and constraints area unit which is handled singly, though generally valve-point effects area unit thought-about.…”

Section: Eld Problem Formulationmentioning

confidence: 99%

A Swarm-based Algorithm for Solving Economic Load Dispatch Problem

Al-Manaseer¹,

Al-hamadeen²,

Hammouri³

2019

IJCA

View full text Add to dashboard Cite

Economic Load Dispatch problem (ELD) is considered a NPhard combinatorial optimization problem. The function of (ELD) determines low price process regarding a power system through dispatching the power generation sources in order according to supply the load demand. In this paper, one of the most known electrical problems has been displayed by the (ELD). Various methods have been used to make the ELD solutions better, most well-known employing meta-heuristic algorithms. The aim of paper is to find the optimal or nearoptimal ELD fuel cost (fuel cost with the minimum cost) by involving a newly created meta-heuristic algorithm, mainly Salp Swarm Algorithm (SSA). Using four test systems generating datasets, the swarm intelligence (SI) has contributed in creating the notion of SSA to get the required value of the present approach. Moreover, it will be measured and contrasted with other similar types or with those of the same significant style that are available in the literature. Accordingly, the results make it clear that the SSA is able to represent the ELD problem and it able to obtain acceptable solutions.

show abstract

Section: Eld Problem Formulationmentioning

confidence: 99%

A Swarm-based Algorithm for Solving Economic Load Dispatch Problem

Al-Manaseer¹,

Al-hamadeen²,

Hammouri³

2019

IJCA

View full text Add to dashboard Cite

show abstract

“…The best solution obtained for this system is shown in Table 3. The performance of the proposed method is compared with adaptive particle swarm optimization (APSO) algorithm [20], simulated annealing (SA) algorithm [19], artificial immune system (AIS) [21], Maclaurin series-based Lagrangian (MSL) method [22], GA [23], PSO [23], artificial bee colony (ABC) algorithm [23], time varying acceleration coefficients improved particle swarm optimization (TVAC-IPSO) [24], hybrid immune-genetic algorithm (HIGA) [2] and hybrid genetic algorithm and bacterial foraging (HGABF) [25]. This comparison is shown in Table 4.…”

Section: Test System 1: 5-unit Systemmentioning

confidence: 99%

“…The performance of the proposed method is compared with evolutionary programming (EP) [26], hybrid evolutionary programming and sequential quadratic programming (EP-SQP) [26], modified EP-SQP (MHEP-SQP) [26], GA [23], PSO [23], ABC [23], improved PSO (IPSO) [27], Enhanced crossentropy (ECE) [28], AIS [21], enhanced bee swarm optimization (EBSO) [8], HIGA [2], enhanced adaptive particle swarm optimization (EAPSO) [29] and HGABF [25]. This comparison is shown in Table 5.…”

Section: Test System 2: 10-unit Systemmentioning

confidence: 99%

Dynamic Economic Dispatch Using Modified Bacterial Foraging Algorithm Oriented With Weighted Particle Swarm Optimization

Elattar

2016

ERJ. Engineering Research Journal

View full text Add to dashboard Cite

The goal of dynamic economic dispatch (DED) is to minimize the total operating cost of all committed generating units considering all physical and operational constraints. The DED problem is very complicated nonlinear optimization problem due to transmission losses, ramp-rate limits and valve-point effects constraints. Both bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) methods have poor convergence characteristics. So, weighted PSO (WPSO) is employed to avoid the drawbacks of original PSO by employing the adaptive inertia weight factor. In this paper, modified BFA (MBFA) oriented with WPSO method is proposed by integrating MBFA and WPSO. By combining these two algorithms, the advantages of both of them can be extracted. This leads to get better solution of DED problem. Also, the diversity strategy is used in the proposed algorithm to solve the problem of early convergence. To prove the efficiency of the proposed method in solving the DED problem, different systems are employed. The obtained results prove the efficiency of the proposed method when comparing with other state of the art methods.

show abstract

“…24 hours) at minimum generation cost taking into consideration the ramp rate limits of the thermal generating units [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Several researchers have devoted their efforts to propose optimization methods and techniques for solving the DED problem with different objectives and constraints such as linear programming [15]; Lagrangian relaxation [16]; quadratic programming [17]; dynamic programming [18]; evolutionary programming [19]; particle swarm optimization [20]; artificial bee colony algorithm [21]; genetic algorithm [22]; simulated annealing [23]; artificial immune system [24]; differential evolution [25]; enhanced cross-entropy [26]; imperialist competitive algorithm [27]; harmony search [6,28]; Quasi-oppositional group search optimization [9]; Crisscross optimization algorithm [1], hybrid evolutionary programming and SQP [19]; hybrid particle swarm optimization and SQP [29]; hybrid differential evolution and SQP [29]; hybrid barebones particle swarm optimization [30]; hybrid genetic algorithm and bacterial foraging approach [31]; hybrid weighted probabilistic neural network and biogeography based optimization [32].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

Inf. Sci. Lett.

View full text Add to dashboard Cite

Since the fuel cost of electric power generation from thermal plants is very high, several researchers have devoted their efforts to offer new strategies to minimize such cost. But reducing the fuel cost will increase the emission of gaseous pollutants such as CO2, CO, NOx and SO2. Therefore, the dynamic economic emission dispatch problem (DEED) is formulated with the objective of simultaneously minimizing the fuel cost and emission so as to meet the predicted demand over a certain period under ramp rate limits and other operational and system constraints. Spinning reserve plays a crucial role in maintaining the power system reliability and security against sudden load changes and generation outages. To consider the spinning reserve into the DEED, we formulate dynamic economic emission and spinning reserve dispatch (DEESRD) problem which integrates the spinning reserve into the DEED problem. DEESRD determines the optimal power and spinning reserve schedule by simultaneously minimizing the power and spinning reserve costs, and the amount of emission under some constraints. The optimal solutions of the DEESRD problem are open loop. The open-loop nature cannot deals with inaccuracies, modeling uncertainties and unexpected external disturbances where the power system components suffer from. To overcome this problem we designed closed-loop solutions using a suitable version of MPC approach. The performance of the MPC has been investigated by applying the MPC strategy to the DEESRD problem with test system consisting of five generating units and five customers.

show abstract

A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem

Cited by 83 publications

References 41 publications

A Swarm-based Algorithm for Solving Economic Load Dispatch Problem

A Swarm-based Algorithm for Solving Economic Load Dispatch Problem

Dynamic Economic Dispatch Using Modified Bacterial Foraging Algorithm Oriented With Weighted Particle Swarm Optimization

Untitled

Contact Info

Product

Resources

About