Economic and various emission dispatch using differential evolution algorithm

Augusteen, W A; Kumari, Reena; Rengaraj, R.

doi:10.1109/icees.2016.7510619

Cited by 11 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input data for three generating unit in term of the fuel cost and emission function was given in Table 1 and Table 2 respectively. The data is acquired from [7]. In order to determine the relationship of power losses (P L ) and ELD, the data for loss coefficient or B-coefficients given in Table 3 was used.…”

Section: Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm

Yasin¹,

Aziz²,

Salim³

et al. 2018

IJEECS

View full text Add to dashboard Cite

In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques.

show abstract

Section: Results and Analysismentioning

confidence: 99%

“…In solving ELD problems, there are constraints that need to be considered such as transmission loss, power balanced, and generator limit. The equation for transmission loss is expressed as (7).…”

Section: Methodsmentioning

confidence: 99%

Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm

Yasin¹,

Aziz²,

Salim³

et al. 2018

IJEECS

View full text Add to dashboard Cite

show abstract

“…The differential evolution algorithm is a kind of optimization algorithm with real vector coding in continuous space and can complete parallel and random search, which has the characteristics of simple and less controlled parameter [10][11][12][13][14]. The basic idea of DE is similar to that of genetic algorithm, that is, using mutation operation to generate new individuals, and then implementing cross and selection operations, through constant iterative evolution to search the global optimal solution.…”

Section: The Basic Principle Of Differential Evolution Algorithmmentioning

confidence: 99%

Differential Evolution Cloud Computing Scheduling Strategy Based on Dynamic Adjustment

Li¹,

Chun-na²

2017

IJMUE

View full text Add to dashboard Cite

This paper presents an improved differential evolution algorithm(CDEI) to solve the problem of resource scheduling and load balancing in clofud computing environment. Firstly, based on the basic differential evolution algorithm, the chaotic strategy is introduced and the individual replacement is implemented at the right time to enhance the diversity of the population; furthermore, the scaling factor and crossover probability are dynamically adjusted in the iteration of the algorithm, and the strategy of early high to low is adopted to overcome the prematurity of the algorithm; finally, the array representation is used in the coding of cloud computing scheduling. The experimental results show that the improved algorithm has the advantages of fast convergence, high precision and low consumption.

show abstract

“…Therefore, optimization is important to minimize gaseous emissions, minimize the total cost and managing the system stability and security constraint. Several techniques have been carried out by researchers and electrical engineers to solve economic dispatch for the past ten years such as Simulated Annealing (SA) [1], Tabu Search (TS) [2], Differential Evolution (DE) [3], Genetic Algorithm (GA) [4], Artificial Immune System (AIS) [5], Evolutionary Programming (EP) [6] and Particle Swarm Optimization (PSO) [7]. Nevertheless, most of the techniques applied are single optimization techniques which have drawbacks such as premature optimization results, long computational time and also getting stuck in local optima.…”

Section: Introductionmentioning

confidence: 99%

Embedded Immune-Evolutionary Programming for Economic Dispatch of Generators with Prohibited Operating Zones

Razali

Mansor²,

Musirin

et al. 2018

IJET

View full text Add to dashboard Cite

Economic Dispatch (ED) is one of the popular power system problems. It is solved by the grid system operators (GSOs) to find the cheapest production cost for dispatching power from generations to the demands (loads). It is crucial to have cheap production cost and at the same time satisfying all system constraints such as generating units’ operating limits, transmission lines capacity and spinning reserve. ED of generators with prohibited operating zones is considered as a nonconvex ED as it has inequality constraints that need to be considered. This inequality constraint is the operating limits of generating units that considering the allowable and prohibited operating zones. The generating units must be ensured, that they are only operated within the allowable limits to avoid problem with the equipments that related to them. Some generating units will experience high vibration at certain level of operating limits and will burn more fuel to maintain at certain level of load. This paper presents an embedded technique to solve ED of generators problem with prohibited operating zones. The cloning process of Artificial Immune System (AIS) is inserted into Evolutionary Programming (EP) algorithm to form a new technique termed as Embedded Immune-Evolutionary Programming (EIEP). The proposed embedded technique has been tested on the IEEE 26-Bus Reliability Test System (RTS) with three different conditions of load. Besides that the results produced by the embedded technique has been compared with two single techniques which are AIS and EP. It is found that, the results of ED of generators with prohibited operating zones produced by EIEP is better than the two single techniques in terms of low total production cost.

show abstract

Economic and various emission dispatch using differential evolution algorithm

Cited by 11 publications

References 5 publications

Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm

Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm

Differential Evolution Cloud Computing Scheduling Strategy Based on Dynamic Adjustment

Embedded Immune-Evolutionary Programming for Economic Dispatch of Generators with Prohibited Operating Zones

Contact Info

Product

Resources

About