Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm

Vaisakh, K.; Praveena, P.; Rao, Shama; Meah, Kala

doi:10.1016/j.ijepes.2012.01.005

Cited by 94 publications

(39 citation statements)

References 33 publications

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“…The dynamic economic dispatch (DED) problem has been extensively studied by SI algorithms such as BFO [242], PSO [243,244,245,246], FA [247], AFSO [248] and ABC algorithms [249,250,251,252,253]. Moreover, a hybrid SFLA algorithm [254] outperformed PSO-and ABC-based algorithms.…”

Section: Continuous Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Continuous Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

View full text Add to dashboard Cite

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“…The several approaches to solve the SCED from 1952 to 2014are presented in [1], [66], [67]- [71]. Single objective SCDED (SOSCDED) problem is formulated as…”

Section: Moded Formulationmentioning

confidence: 99%

Multi Objective Dynamic Economic Dispatch with Cubic Cost Functions

Musau¹

2015

IJEPE

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Abstract:The formulation and solution of the Dynamic Economic Dispatch (DED) problem is one of the key disciplines in modern power system operation, planning, operation and control. Past researches have considered DED on Quadratic Cost Functions (QCF), with only few works considering higher order cost functions which are more accurate. The Static Economic Dispatch (SED) has been widely tackled in past researches, however, it is the DED problem that represents a real life power system. There is need to review this problem and establish a more practical formulation of the same taking into consideration all the objectives and constraints possible. The methods used in the solution of DED problem have evolved from the traditional deterministic ones, to the pure heuristic, and finally to the state of the heart hybrids. The hybrids methods have been developed to exalt the strengths and improve the weaknesses of the base method. Such optimisation methods need to be reviewed and classified. This paper will do an in depth review of the DED problem on both quadratic and cubic cost functions. Further it will provide a detailed classification of the methods used to solve the problem as its complexity increases. Consequently, three method Hybrids is the way go as far as the solution of the cubic Multi Objective DED (MODED) with five objectives is concerned.

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“…These solutions are stored in a repository that is used to estimate the density of solutions around a particular solution. The distance (crowding distance) between each pair of solutions is calculated, and then, the solutions are arranged based on their crowding distances, as explained in [62,63].…”

Section: Multi-objective Solution Strategymentioning

confidence: 99%

Multi-area Multi-fuel Economic–Emission Dispatch Using a Generalized Unified Power Flow Controller Under Practical Constraints

Suresh

Sivanagaraju

Rao³

2014

Arab J Sci Eng

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This paper presents a novel optimization algorithm for the multi-area multi-fuel economic-emission dispatch problem and total power loss objective. The proposed method uses the uniform distribution to determine the control parameters and employs a two-stage initialization process. This enables various objectives to be optimized under practical constraints and device limits. We formulate a realistic generation cost that includes the cost of reactive power generation, shunt power injections, and total power losses, along with the conventional active power generation cost. A novel objective based on the concept of multi-fuel emissions makes the problem more realistic, and a generalized unified power flow controller (GUPFC) is considered. A detailed power injection model is developed for a GUPFC with two series converters, including switching losses of converters. A nondominated sorting methodology and particle swarm optimization algorithm are then used to solve the multi-objective problem on the standard IEEE-30 bus and real-time Indian-124 bus test systems.Keywords GUPFC power injection model · Multi-fuel active power cost · Multi-fuel reactive power cost · Multi-fuel emissions · GUPFC installation cost · NSUDTPSO

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Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm

Cited by 94 publications

References 33 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Multi Objective Dynamic Economic Dispatch with Cubic Cost Functions

Multi-area Multi-fuel Economic–Emission Dispatch Using a Generalized Unified Power Flow Controller Under Practical Constraints

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