Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm

Bakır, Hüseyin; Güvenç, Uğur; Kahraman, Hamdi Tolga

doi:10.1007/s00521-022-07670-y

Cited by 10 publications

(3 citation statements)

References 76 publications

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“…In [51], many-objective marine predators algorithm was introduced to solve MaOOPF problems by considering cost, emission, transmission loss, and voltage stability index as part of the objective functions in the IEEE 30-and 118-bus systems. Multi-objective grasshopper optimization algorithm (MOGOA) was proposed to solve MaOOPF problems including voltage source converter-based multi-terminal high-voltage direct current systems and renewable energy sources by considering cost, cost with the valve-point effect and cost with emission and carbon tax, voltage deviation, and power loss as part of the objective functions [52]. S. Duman, M. Akbel, and H. T. Kahraman presented multiobjective adaptive guided diffential evolution algorithm for real-world problem: multiobjectivealternating current OPF problem involving wind/PV/tidal energy sources by considering cost, power loss, voltage stability index, and voltage deviation as the objective functions in the IEEE 30-bus system [53].…”

Section: Related Studiesmentioning

confidence: 99%

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

Khunkitti,

Premrudeepreechacharn,

Siritaratiwat

2023

IEEE Access

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To improve power system operation and management and accomplish modern power system requirements, a new algorithm named two-archive harris hawk optimization (TwoArchHHO) is proposed to solve many-objective optimal power flow (MaOOPF) problems in this work. For modern power systems, only single-objective and multiobjective (2-3 objectives) optimal power flow problems (MOOPF) are inadequate. So, the problems become many-objective (more than 3 objectives) optimal power flow problem which is more complicated to be solved. Although several metaheuristic algorithms have been proposed to solve MOOPF problems, very few algorithms have been introduced to solve MaOOPF problems and highperformance algorithms are still required to solve MaOOPF problems which are more complicated. To solve the complicated MaOOPF problems, TwoArchHHO is proposed by adding the two-archive concepts of the improved two-archive algorithm into the harris hawk optimization (HHO) in order to enhance the searchability and eventually provide superior solutions. The objective functions considered to be minimized include fuel cost, emission, transmission line loss, and voltage deviation to improve power systems in the economic, environmental, and secure aspects. Several sizes of IEEE standard systems, which are IEEE 30-, 57-, and 118-bus systems, are tested to evaluate the performance of the proposed TwoArchHHO. The simulation results comprise Pareto fronts, best-compromised solutions, and hypervolume analysis are generated and compared with results from several algorithms in the literature. The data provided by the experimental trials and the hypervolume performance metric were examined using statistical testing methods. The results indicate that the TwoArchHHO obtained better optimal solutions than those of the compared algorithms including its traditional algorithms, especially in large systems. Based on the best-compromised solutions, the TwoArchHHO provided one best objective aspect among the compared algorithm for most cases. Based on the hypervolume, the TwoArchHHO generated better hypervolume values than those of the compared algorithms around 33.96% to 99.59% in the tested systems.INDEX TERMS Harris Hawk Optimization, metaheuristic algorithms, many-objective optimal power flow, two-archive algorithm.

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Section: Related Studiesmentioning

confidence: 99%

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

Khunkitti,

Premrudeepreechacharn,

Siritaratiwat

2023

IEEE Access

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“…The strategy for handling the overestimation and underestimation of solar power should fundamentally align with that of wind power. For ease of calculation, the models for penalty and reserve costs are constructed according to the concept outlined in reference [48]. Section 3.3 further elaborates on the detailed approach used to compute the probability of power output at different solar irradiance.…”

Section: Modelling the Uncertainty Costs Associated With Solar Powermentioning

confidence: 99%

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Hashish,

Hasanien,

Ullah

et al. 2023

Sustainability

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In this study, the Giant Trevally Optimizer (GTO) is employed to solve the probabilistic optimum power flow (P-OPF) issue, considering Renewable Energy Source (RES) uncertainties, achieving notable cost reduction. The objective function is established to minimize the overall generation cost, including the RES cost, which significantly surpassing existing solutions. The uncertain nature of the RES is represented through the employment of a Monte Carlo Simulation (MCS), strengthened by the K-means Clustering approach and the Elbow technique. Various cases are investigated, including various combinations of PV systems, WE systems, and both fixed and fluctuating loads. The study demonstrates that while considering the costs of solar, wind, or both might slightly increase the total generation cost, the cumulative generation cost remains significantly less than the scenario that does not consider the cost of RESs. The superior outcomes presented in this research underline the importance of considering RES costs, providing a more accurate representation of real-world system dynamics and enabling more effective decision making.

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“…OPF is a non-convex optimization problem with high computational complexity [4,5]. Solving the OPF problem is a challenging task for power system researchers [6]. To cope with this, it has been observed that various metaheuristic optimization algorithms have been successfully applied to the solution of the OPF problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal power flow analysis with circulatory system-based optimization algorithm

BAKIR

2024

Turkish Journal of Engineering

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Optimal power flow (OPF) is one of the most challenging optimization problems of power engineering. Owing to the high computational complexity of the OPF problem, a powerful and robust optimization algorithm is required to solve it. This paper has been centered on the optimization of OPF problem using circulatory system-based optimization (CSBO) algorithm. The solution quality of CSBO is compared with the recently introduced state-of-the-art metaheuristic algorithms i.e., artificial rabbits optimization (ARO), african vultures optimization algorithm (AVOA), and chaos game optimization (CGO). The practicability of the algorithms was evaluated on the IEEE-57 and 118-bus power networks for the optimization of various objectives, i.e., fuel cost, power loss, voltage deviation, and enhancement of voltage stability. Based on OPF results of the IEEE 57-bus power system, it is seen that the best fuel cost and voltage deviation results are calculated to be 41666.2344 $/h and 0.5871 p.u with the CSBO method. Given the OPF results of the IEEE 118-bus power network, it is observed that the CSBO algorithm presented the best fuel cost and active power loss values of 134934.3140 $/h, and 16.4688 MW. Moreover, OPF solutions obtained from 30 algorithm runs were analyzed using the Wilcoxon statistical test method. Consequently, the present paper reports that the CSBO algorithm produces better-quality OPF solutions compared to its competitors and other literature studies.

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Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm

Cited by 10 publications

References 76 publications

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Optimal power flow analysis with circulatory system-based optimization algorithm

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