Solution of Non-Convex Dynamic Economic Dispatch (DED) Problem Using Dragonfly Algorithm

Iqbal, Qasim; Ahmad, Aftab; Sattar, Muhammad Kashif; Fayyaz, Saqib; Hussain, Hafiz Ashiq; Saddique, Muhammad Shahzar

doi:10.1109/icecce49384.2020.9179177

Cited by 8 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of electrical engineering, DA and its hybrids have been used for various problems, most of which are related to power systems. In [37], DA has been applied to the economic load dispatch problem in power systems, which consists of minimizing the generation cost while satisfying constraints such as ramp rate, demand and generator operating limit; in [38], it has been applied to the static economic dispatch problem incorporating solar energy; in [39], it has been applied to the combined economic emission dispatch problem, and in [40], it has been applied to the dynamic economic dispatch problem. In [41,42], DA has been applied to the automatic generation control problem to optimize the gains of the controller.…”

Section: Electrical Engineeringmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

View full text Add to dashboard Cite

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

show abstract

Section: Electrical Engineeringmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Jethmalani et.al employed real coded genetic algorithm to evaluate losses in a dynamic economic dispatch problem [14]. Reference [15] discussed about the employment of Dragonfly algorithm to solve DED problem for just IEEE 5 unit test case. Aamir Nawaz et.al discussed about the constrained globalized Nelder-Mead algorithm to solve DED test cases for both convex and non-convex problems [16].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Economic Dispatch of Power System Network Having Thermal Units and Electric Vehicles using IGWO

Jain

Mani

Siddiqui

2021

mjee

View full text Add to dashboard Cite

Grey Wolf optimizer is a well-known metaheuristic technique but it faces the problem of premature convergence, trapping in local optima and poor balance between exploration and exploitation. Hence, various modifications have been proposed over past few years. One such modification is modelling the prey position as dynamic in nature as shown in GWOLF. However, still it has been observed that to get better solution, GWOLF needs to be modified too, hence an improved version of GWO (IGWO) is proposed. IGWO has the advantage of both GWO and GWOLF. In this paper, IGWO is used to solve Dynamic Economic Dispatch (DED) problem. IGWO is having a better balance between exploitation and exploration for the complex problem such as Dynamic Economic Dispatch (DED) taking into account of valve-point effect, transmission losses and ramp-rate limits with and without Electric Vehicles (EVs). The efficiency of the algorithm is demonstrated on solving different DED problems for 5 generator and 15 generator test systems with and without losses along with different charging profile distribution of electric vehicles. The results showcased by IGWO is compared with the other algorithm. The results obtained by IGWO algorithm adopted in solving dynamic economic dispatch problem is giving competitive results as compared to the results given by other algorithm present in literature.

show abstract

“…It requires the optimization of a power system to provide a smooth economical operation to fulfill the load demand at minimum cost by exploiting the available energy resources in the best manner while satisfying several constraints. 1 In operation and planning of the power system, the unit commitment (UC) is considered as one of the complex optimization problems because it is highly dimensional, nonlinear, and hard-constrained. The different constraints associated with the UC problem are load demand, reserve balance constraints, power generation limits, minimum up and down time constraints, and the ramp-rate constraints, which lead to a more complex optimization.…”

Section: Introductionmentioning

confidence: 99%

“…The population-based meta-heuristic techniques interchange the information of both exploration (global search) and exploitation (local search) phases during the searching process. 1 The techniques depend on the choice of initial population to have a slower or faster convergence for the solution of the analytical problem. Therefore, we need to combine the efficient techniques to achieve the best performance at each stage of the optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

A novel binary variant model of swarm inspired polar bear optimization algorithm employed for scalable unit commitment

Haq

Ahmad

Ikram

et al. 2020

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Summary For an effective and reliable power system, determining the optimal state of the accessible producing units is the key step, which comprises of two segments; making decision in committing the generators (unit commitment) followed by the decision‐making of their active power allocation (economic dispatch). Unit commitment (UC) problem is a mixed‐integer bit transition‐state optimization problem to which classical optimizing tools strive to evaluate, whereas metaheuristic methods prove to be a healthy attempt. In this study, a binary model of the polar bear optimization (BPBO) algorithm is developed and is utilized to solve this combinatorial problem. Polar bear optimization (PBO) is a newly developed swarm‐based approach, simulating three mechanisms in one technique in the form of effectual exploration and exploitation phase along with dynamic control of population size. BPBO uses a modified sigmoid conversion function to produce the state bits (ON/OFF) of the units. These three potent procedures help in determining the optimal transition state of units by varying the size of the search agents. For solving the economic dispatch problem (EDP), the conventional lambda iteration method is used. Repairing of the infeasible bit is also carried out by applying heuristic adjustments. To illustrate the vitality of the BPBO, it is modeled in “MATLAB 2016a” and tested on IEEE 4, 5, 10, 20, 40, 60, 80, and 100‐unit test systems. Comparative evaluation in terms of optimum combinations, active power allocation, and simulation time with other available methodologies is also done, resulting in a progressive approach with superiority to other recent methods.

show abstract

Solution of Non-Convex Dynamic Economic Dispatch (DED) Problem Using Dragonfly Algorithm

Cited by 8 publications

References 19 publications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Dynamic Economic Dispatch of Power System Network Having Thermal Units and Electric Vehicles using IGWO

A novel binary variant model of swarm inspired polar bear optimization algorithm employed for scalable unit commitment

Contact Info

Product

Resources

About