Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm

Radosavljević, Jordan; Klimenta, Dardan; Jevtić, Miroljub; Arsić, Nebojša

doi:10.1080/15325008.2015.1061620

Cited by 104 publications

(57 citation statements)

References 27 publications

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“…Notably, the majority of obtained solutions using heuristic optimization algorithms (Table 2) are infeasible, which is principally due to voltage magnitude violations at one or more system load buses, as well as reactive power generation bound violations at one or more generation units. Notably, [23] also reported solution infeasibility for many previous methods, as compared in Table 2. Implementing Jaya method for fuel cost reduction when accommodating the DG at node 30 produces an even more significant reduction in fuel cost, reaching 768.0398 $/h, an attractive L max value (0.0969), and a great expansion of shunt compensators reactive power saving of up to 29.8391 MVAR.…”

Section: Case 1: Fuel Cost Minimizationmentioning

confidence: 96%

“…The network has six generator units at buses 1, 2, 5, 8, 11, and 13. Load buses 10,12,15,17,20,21,23,24, and 29 are equipped with switchable shunt capacitors. Four tap changing transformers are installed at lines 6-9, 6-10, 4-12, and 27-28.…”

Section: Ieee 30-bus Networkmentioning

confidence: 99%

“…First, the application of the Jaya algorithm to solve the optimum power flow problem has not been yet studied. Second, many population-based algorithms produce infeasible solutions for many kinds of OPF problems in terms of violation of operational variables constraints, as reported in [4,20,23]. Lastly, the DG effect when solving the OPF problem using the above-mentioned algorithms has not been examined.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

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Section: Case 1: Fuel Cost Minimizationmentioning

confidence: 96%

Section: Ieee 30-bus Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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“…Additionally, the results are compared with other methods, and the comparison is shown in Table 9. According to the value of the weighted sum, IKHA is better than KHA, particle swarm optimization and gravitational search algorithm (PSOGSA) [29], The proposed KHA [39], adaptive biogeography based predator-prey optimization (ABPPO) [40], MSA [22], LTLBO [23], Gbest guided artificial bee colony algorithm (GABC) [24] and ICBO [12] Looking at the two goals separately, the results of IKHA are lower than those of KHA and the proposed KHA For the results of the other methods in Table 8, such as the PSOGSA [29], only one of the two goals is better than IKHA. As the individual evaluation criteria are different, the optimal solution is different.…”

Section: Case 5: Minimization Of Quadratic Cost and Voltage Magnitudementioning

confidence: 99%

“…Case 1-b considers the valve point effect which is described as an absolute sinusoidal function [29]. In this case, the valve point effect is added to the basic quadratic cost functions of generators at buses 1 and 2, and the non-differential objective function is calculated as follows:…”

Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

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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Application of stud krill herd algorithm for solution of optimal power flow problems

Pulluri

Naresh

Sharma

2017

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents a creature‐inspired stud krill herd (SKH) algorithm consisting of stud selection and crossover operators, which are augmented in the krill herd algorithm to solve the optimal power flow (OPF) problems in a power system. Due to the nonlinear and nonconvex nature of the OPF problem, it is quite difficult to find optimal solution using conventional optimization algorithms. Here in SKH, a stud‐selection operator is introduced in the krill herd algorithm to further enhance the quality of the solution, and crossover operator is embedded to avoid premature convergence. To investigate the performance, the proposed SKH algorithm has been demonstrated on the OPF problems of IEEE 30‐bus, Algerian 59‐bus, and IEEE 118‐bus systems considering various objective functions such as total production cost, L‐index, power loss, and emission pollution to be minimized. The OPF results have been compared with those obtained with other methods, and it has been found that SKH algorithm is successful in providing better quality solutions in reasonable execution times.

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Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm

Cited by 104 publications

References 27 publications

Optimal Power Flow Using the Jaya Algorithm

Optimal Power Flow Using the Jaya Algorithm

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

Application of stud krill herd algorithm for solution of optimal power flow problems

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