A modified teaching–learning based optimization for multi-objective optimal power flow problem

Shabanpour-Haghighi, Amin; Seifi, Ali Reza; Niknam, Taher

doi:10.1016/j.enconman.2013.09.028

Cited by 177 publications

(106 citation statements)

References 30 publications

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“…There has been plenty of research on the determination of optimal power flow (OPF) for various power system objectives, such as the generation cost, emissions, and transmission losses (see, for example, [1][2][3][4][5][6][7][8][9][10][11][12][13][14] for individual objectives, and [15][16][17][18][19][20][21][22][23][24][25] for single-area multi-objective systems). A number of approaches to the multi-area economic dispatch problem under various power system operating constraints are discussed in [26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Thus, the proposed method is more powerful and stronger than CCSA for searching optimal solutions. For comparisons with other methods, there is a standout lower cost from ITLBO [29] of $41,638.3822. However, the validation of the reported solution of ITLBO cannot be carried out because ITLBO has reported only active power outputs of generators for decision variables while other remaining decision variables such as capacitor banks' reactive power output, transformers' tap setting and generators' voltage have been omitted.…”

Section: Case 4: Ieee-57 Bus Power Systemmentioning

confidence: 99%

“…Here, we classify the three different equations into two groups, small zone search group with rand/1 and large zone search group with rand/2 and rand/3. The condition of using either rand/1 or rand/2 and rand/3 is dependent on the comparison of ∆ s and ∆ mean , which are shown in Equations (28) and (29).…”

Section: The Proposed Approachmentioning

confidence: 99%

“…The final comparison results have revealed that EADPSO has become more efficient than conventional PSO but less effective than PG-CF-PSO in terms of solution quality and solution searching speed. The authors in [27,29] have made a big effort in improving the performance of improved versions of BBOA and TLBO. However, the obtained results compared to BBOA and TLBO could not show any superiority of ARCBBOA and ITLBO over BBOA and TLBO.…”

Section: Introductionmentioning

confidence: 99%

“…Most methods are the family of meta-heuristic algorithms in which conventional methods, modified methods, combination of two different methods, and hybrid methods have been developed widely. In fact, there have been a huge number of applied methods such as the integration of improved genetic algorithm and effective decoupled quadratic load flow (IGA-EDQLF) [18], hybrid IGA with incremental power flow model (HIGA) [19], HIGA with boundary method (HIGA-BM) [20], differential evolution [21,22], conventional PSO [23], Evolving ant direction particle swarm optimization (EADPSO) [24], PSO with Pseudo-Gradient and constriction factor (PG-CF-PSO) [25], Biogeography-based optimization algorithm (BBOAA) [26] and adaptive real-coded biogeography-based optimization algorithm (ARCBBOA) [27], teaching-learning-based optimization algorithm (TLBO) [28], improved TLBO (ITLBO) [29], gravitational search algorithm (GSA) [30], Artificial bee colony algorithm (ABCA) [31], Grey wolf optimizer (GWO) [32], modified electromagnetism-like mechanism algorithm (MELMA) [33], modified Colliding Bodies Optimization algorithm (MCBOA) [34], moth swarm algorithm (MSA) [35], improved imperialist competitive algorithm (IICA) [36], cuckoo optimization algorithm (COA) [37], Gaussian bare-bones imperialist competitive algorithm (GBBICA) [38], and mathematical programming algorithm (MPA) [39]. In [18][19][20], different variants of GA have been developed in which GA has been improved first and then combined with another method for handling constraints of OPF problem.…”

Section: Introductionmentioning

confidence: 99%

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Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Nguyen

Quynh

et al. 2018

Energies

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Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

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Optimal power flow with stochastic solar power using barnacles mating optimizer

Sulaiman

Mustaffa

Mohamad

et al. 2021

Int Trans Electr Energ Syst

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Summary This work proposes the implementation of recent evolutionary metaheuristic algorithm namely, barnacles mating optimizer (BMO) to solve the Optimal Power Flow (OPF) issue. BMO is inspired by the mating behaviour of barnacles which happened in two ways: by normal copulation and sperm‐cast. The effectiveness of the proposed BMO in solving the OPF is tested on a modified IEEE‐30 bus system that is integrated with solar PV farms for five cases viz (1) cost minimization of the power generation that consists of thermal and stochastic solar power generations, (2) power loss minimization, (3) voltage deviation minimization, (4) emission minimization and (5) combined cost and emission minimization of power generations. To demonstrate the effectiveness and the veracity of the solution obtained by BMO, several recent algorithms that have been reported in the literature will be utilized and compared intensively. In the end, the simulation results demonstrate that the BMO can be effectively becoming an alternative solution for the OPF issue in general.

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A modified teaching–learning based optimization for multi-objective optimal power flow problem

Cited by 177 publications

References 30 publications

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

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

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Optimal power flow with stochastic solar power using barnacles mating optimizer

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