Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization

Kumar, A. Ramesh; Premalatha, L.

doi:10.1016/j.ijepes.2015.05.011

Cited by 131 publications

(45 citation statements)

References 33 publications

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“…NA means that the datum is not reported in the referred literature. From the tables, it is clear that the minimization of quadratic fuel cost obtained by IKHA is better than KHA, modified shuffle frog leaping algorithm (MSLFA) [30], ABC [31], moth swarm algorithm (MSA) [22], modified Gaussian bare-bones imperialist competitive algorithm (MGBICA) [32], Jaya [33] and adaptive real coded biogeography-based optimization (ARCBBO) [34]. The single iteration computation times of ABC [31] and Jaya [33] are longer than IKHA, which shows the search efficiency of IKHA.…”

Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

“…According to the experimental data of the three situations, the proposed method can successfully solve the non-differential and non-continuous OPF problem which contains the discrete and continuous variables. [30] 802.2870 NA/100 ABC [31] 800.6600 130.16/200 MSA [22] 800.5099 NA/100 MGBICA [32] 801.1409 NA/NA ARCBBO [34] 800.5159 NA/200 Jaya [33] 800.479 72.4/100 GSA [35] 798.6751 a 10.7582/200 BBO [36] 799.1116 a NA/200 a Infeasible solution. …”

Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

“…The setting of optimal control variables for case 7 of IKHA are presented in Table 11, which shows that the minimum of quadratic fuel cost obtained by IKHA is 41,663.391 $/h. The simulation result is compared with other methods in Table 12, and less than the results of KHA, MSA [22], LTLBO [23], ICBO [12], DSA [9], ARCBBO [34] and GABC [42]. Figure 13 shows the optimal convergence curves over the iterations and Figure 14 shows the results in 30 independent simulations of IKHA and KH for case 7.…”

Section: Case 7: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

“…Moreover, the distribution range of results of IKHA is smaller than KHA according to Figure 14, which demonstrates that IKHA also has a stronger robustness in larger systems. [12] 41,697.3324 NA/1500 DSA [9] 41,686.82 NA/500 ARCBBO [34] 41,686 NA/500 GABC [42] 41,684.2011 NA/100 Figure 13. The optimal convergence curves for Case 7 of IEEE 57 bus system.…”

Section: Case 7: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

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Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Chen

Zhang

2018

Energies

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Abstract:As one of the most important tools used in operation and planning of power systems, the optimal power flow (OPF) problem considering the economy and security is large-scale, complex and hard to solve. In this paper, an improved krill herd algorithm (IKHA) has been proposed. In IKHA, the onlooker search mechanism is introduced to reduce the probability of falling into local optimum; and the parameter values of the proposed algorithm including inertia weight and step-length scale factor are varied according to the iteration of evolutionary process, which improves the exploration and exploitation capabilities. Moreover, a novel constraint handling method is proposed to guide the individual to the feasible space and ensure that the optimal solution satisfies the security constraints. Then, IKHA is combined with the novel constraint handling method to solve the multi-constrained OPF problem, and its performance is tested on the IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems for 10 different simulation cases containing linear and non-linear objective functions. The simulation results demonstrate that the proposed method can solve the OPF problem successfully and obtain better solutions compared with other methods reported in the recent literatures, which prove the feasibility and effectiveness of the improvements in this work.

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Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

Section: Case 7: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

Section: Case 7: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

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Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Chen

Zhang

2018

Energies

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“…Most metaheuristics have the following features: they are inspired from nature, they do not use the objective function's Hessian or gradient matrix, they make use of stochastic components, and they have many parameters that need to be adapted to the problem [18]. The following artificial intelligence based optimization methods have been successfully used to solve OPF problems: moth swarm algorithm, MSA [19]; modified particle swarm optimization, MPSO [20]; modified differential evolution, MDE [21]; moth-flame optimization, MFO [22]; flower pollination algorithm, FPA [23]; adaptive real coded biogeography-based optimization, ARCBO and real coded biogeography-based optimization, RCBBO [24]; grey wolf algorithm, GWO and differential evolution, DE [25]; modified Gaussian bare bones imperialist competitive algorithm, MGBICA and Gaussian bare bones imperialist competitive algorithm, GBICA [26]; artificial bee colony, ABC [27]; simulated annealing and hybrid shuffle frog leaping algorithm [28]; Lévy mutation teaching-learning-based optimization, LTLBO [29]; teaching learning-based optimization, TLBO [30]; hybrid MPSO and shuffle frog leaping algorithms, HMPSOSFLA, and particle swarm optimization, PSO [31]; Gbest-guided artificial bee colony, GABC [32]; differential search algorithm, DSA [33]; efficient evolutionary algorithm, EEA and eclectic genetic algorithm, EGA [34]; particle swarm optimization with aging leader and challengers, ALCPSO [35]. The above optimization approaches have been developed to solve simple and multiobjective OPF problems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

et al. 2018

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The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.

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Optimal power flow solution incorporating a simplified UPFC model using lightning attachment procedure optimization

Taher¹,

Kamel

Ebeed

2019

Int Trans Electr Energ Syst

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Summary Unified power flow controller (UPFC) is utilized to regulate the bus voltage as well as the power flow through a power system. A solution of optimal power flow (OPF) problems with UPFC is a crucial and complex task due to the required modifications for considering the parameters of UPFC. This work proposes a simplified UPFC modeling into an OPF code in order to avoid the programming complexity. Moreover, the solution of an OPF problem with a UPFC model is obtained by a recent physical‐based optimization technique called lightning attachment procedure optimization (LAPO). Different objective functions including minimizing fuel cost and fuel cost with valve point effect (VPE), reduction of emission, improvement of voltage profile, and improving the voltage stability index are considered. The proposed algorithm is tested using the standard IEEE 30‐bus system. The results obtained by the proposed algorithm are compared with those obtained by other optimization techniques. However, the obtained results verify the applicability of the simplified UPFC model in OPF.

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Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization

Cited by 131 publications

References 33 publications

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

Optimal power flow solution incorporating a simplified UPFC model using lightning attachment procedure optimization

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