Optimal Power Flow Using an Improved Electromagnetism-like Mechanism Method

Bouchekara, Houssem R. E. H.; Abido, M. A.; Chaib, A.E.

doi:10.1080/15325008.2015.1115919

Cited by 65 publications

(27 citation statements)

References 37 publications

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“…In case of M3, the L‐index value reduces by 1.88 % in comparison to the M1, which indicates that voltage stability is improved in M3. In M4, the SKH algorithm is capable to reduce the APL to a minimum value 2.8546 (MW) compared to another algorithms viz 2.8670 (MW), 2.8699 (MW), 3.09 (MW), and 3.5035 (MW), respectively. From these results, the SKH algorithm provides highest APL savings compared to another existing algorithms.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…To this, 20 trials were conducted on the minimum TPC for different possible combinations of tuning parameters are mentioned in Table , and the optimal combination determined for these parameters with single point crossover is N max = 0.04, V f = 0.03, and D max = 0.005.The IEEE 30‐bus system has been solved with both KH and proposed SKH algorithms. The minimum, the average, the worst values, the standard deviation (SD), the execution time (ET), and the normalized ET (NET) for complete 200 iterations over 20 trials obtained for the all objective functions models have been compared with adaptive biogeography‐based predator‐prey optimization (ABPPO), DSA, improved EM (IEM), LCA, Levy TLBO (LTLBO), chaotic IWO (CIWO), BHBO, chebyshev map method (CMM), sine map method (SMM), tent map method (TMM), genetic evolving ant direction PSO (GEADPSO), modified DE (MDE), PSO, ABC, g‐best guided ABC (GABC), new PSO (NPSO), fuzzy genetic algorithm (FGA), and parallel GA (PGA), and details are furnished in Table . Execution time of the SKH algorithm mentioned in Table may not be directly compared with another optimization methods available in the literature.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The inertia weights are set to 0.9 initially and later decreased linearly up to 0.1, and these values have been used throughout this paper. 2.8670 ------IEM 24 2.8699 ------ABC 18 3.09 ------BHBO 21 3.5035 17 The penalty coefficients for all the models are chosen as w P = w V = w Q = w S = 2 , 00 , 000. To find the optimal performance of the SKH algorithm, the maximum induced speed N max , diffusion speed D max , foraging speed V f , and type of crossover are necessary to be determined.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…(2) The proposed algorithm has been applied on 3 test systems, namely, IEEE 30-bus, Algerian 59-bus, and IEEE 118-bus systems with different objective functions such as minimization of TPC, L-index, APL, and emission pollution. (3) The obtained simulation results of the SKH algorithm are compared with those obtained from ABPPO, 17 DSA, 22 IEM, 24 LCA, 28 LTLBO, 25 CIWO, 27 BHBO, 21 CMM, 27 SMM, 27 TMM, 27 MDE, 22 GM, 4 GEADPSO, 9 ABC, 19 GABC, 2 DE, 14 PSO, 11 NPSO, 25 FGA, 28 FSLP, 5 ACO, 13 GA, 13 PGA, 28 GSA, 15 and PSOGSA 15 and proved that the SKH algorithm attains better quality solutions in reasonable ET making this algorithm suitable for practical applications. The basic concern related to the proposed algorithm is that the present form of SKH algorithm is not directly suitable to solve the multiobjective OPF (MOOPF) problems.…”

Section: Discussionmentioning

confidence: 99%

“…In case of M3, the Minimum and maximum load bus voltages with different models (Algerian 59-bus system) FIGURE 11 Convergence characteristics with krill herd (KH) and stud krill herd (SKH) methods of M2 (Algerian 59-bus system) FIGURE 10 Convergence characteristics with krill herd (KH) and stud krill herd (SKH) methods of M1 (Algerian 59-bus system) L-index value reduces by 1.88 % in comparison to the M1, which indicates that voltage stability is improved in M3. In M4, the SKH algorithm is capable to reduce the APL to a minimum value 2.8546 (MW) compared to another algorithms viz 2.8670 (MW), 17 2.8699 (MW), 24 3.09 (MW), 18 and 3.5035 (MW), 21 respectively. From these results, the SKH algorithm provides highest APL savings compared to another existing algorithms.…”

Section: Input Parameters Input Parameters Input Parameters Input Parmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Input Parameters Input Parameters Input Parameters Input Parmentioning

confidence: 99%

See 3 more Smart Citations

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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Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources

Hassan

Elsayed

Kamel

et al. 2022

Intl J of Energy Research

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Summary During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs.

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