Our system is currently under heavy load due to increased usage. We're actively working on upgrades to improve performance. Thank you for your patience.
2013
DOI: 10.1109/tpwrs.2013.2254503
|View full text |Cite
|
Sign up to set email alerts
|

Dynamic Economic Dispatch Considering Transmission Losses Using Quadratically Constrained Quadratic Program Method

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
37
0
1

Year Published

2014
2014
2022
2022

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 70 publications
(38 citation statements)
references
References 15 publications
0
37
0
1
Order By: Relevance
“…Unit commitment model. The mathematical formulation of the economic dispatch is discussed in [16]. Wind curtailment in aspects of frequency adjustment and peak-load regulation can be both estimated based on the unit commitment model.…”
Section: 22mentioning
confidence: 99%
“…Unit commitment model. The mathematical formulation of the economic dispatch is discussed in [16]. Wind curtailment in aspects of frequency adjustment and peak-load regulation can be both estimated based on the unit commitment model.…”
Section: 22mentioning
confidence: 99%
“…and the load demand is considered over 24 h time interval. The obtained optimal results are compared with results optimization techniques cited in the literature [28][29][30] such as hybrid EP and SQP, Hybrid PSO-SQP, modified hybrid EP-SQP (MHEP-SQP), improved PSO (IPSO), modified differential evolution (MDE), artificial immune system (AIS). The detailed comparison for quality solutions (minimum, average and maximum costs) are presented in Table 8.…”
Section: Test System 4: Dynamic Economic Dispatch Considering Valve Pmentioning
confidence: 99%
“…An efficient multi objective strategy based differential evolution considering multi FACTS technology is proposed in [11], a biogeography optimization [12] applied for solving multi-constraint optimal power flow with emission and non-smooth cost function, in [13] a multi-objective harmony search adapted and applied for solving the OPF problem, a multi-objective evolutionary algorithms [14] applied to enhance the solution of the combined economic and environment dispatch, authors in [15] proposed a multi objective optimal location of a combined shunt-series FACTS controllers for power system operation planning, in [16], A novel self-adaptive learning charged system search algorithm proposed for solving unit commitment problem, in [17] authors proposed a modified shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices, a modified differential evolution approach based on cultural algorithm and diversity measure proposed in [18] to solving the economic dispatch considering valve point effect, in [19] a combined method using chaotic self adaptive differential harmony search algorithm is proposed to solving the OPF problems, in [20] a semidefinite programming is adapted and applied for solving multi-objective economic dispatch, A novel adaptive modified harmony search algorithm proposed in [21] to solve multi-objective environmental/economic dispatch, in [22] the OPF in Micro-grids is solved considering the energy storage. The dynamic economic dispatch considering practical generator constraints is widely studied by researchers and many standard and new hybrid methods proposed and applied with success such as: Improved chaotic particle swarm optimization algorithm [23], adaptive hybrid differential evolution algorithm [24], A hybrid multi-agent based particle swarm optimization algorithm [25], an enhanced cross-entropy method [26], Quantum genetic algorithm [27], artificial immune system [28], an improved PSO [29], Quadratically constrained quadratic program method [30], High-speed real-time [31], A New fast self-adaptive modified firefly algorithm [32], A fuzzyoptimization approach [33], Enhanced adaptive particle swarm optimisation algorithm [34], adaptive modified particle swarm optimization [35], a n...…”
Section: Introductionmentioning
confidence: 99%
“…These methods can be classified into two categories of classical optimization methods and artificial intelligence based methods. The classical optimization methods such as Lagrangian relaxation (LR) [7] and dynamic programming (DP) [8], suffer from curse of the dimensionality in the case of large scale power systems and fail to lead to optimal solutions because of nonlinear and non-convex characteristics of the DED problem.…”
Section: Introductionmentioning
confidence: 99%