An improved particle swarm optimization for optimal configuration of standalone photovoltaic scheme components

Líu, Hao; Wu, Baojia; Maleki, Akbar; Pourfayaz, Fathollah

doi:10.1002/ese3.1052

Cited by 10 publications

(3 citation statements)

References 53 publications

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“…The output power of the photovoltaic system is mainly related to the solar irradiance and ambient temperature at the location of the photovoltaic panels in the panels power system, and its actual output power is proportional to the solar irradiance and inversely proportional to the ambient temperature, and its output power expression is shown in Equation () 13 . And because of the limited output voltage and power of photovoltaic modules, multiple photovoltaic panels need to be connected in series and parallel to form a photovoltaic array.…”

Section: Grid‐connected Wind–solar–storage Microgrid System and Mathe...mentioning

confidence: 99%

“…They analyze the LPSP, COE, and RF related to system cost and reliability using a multiobjective optimization method, verifying the reliability of this approach. In Liu et al, 13 an improved method based on an energy management strategy is proposed to optimize the size and configuration of independent photovoltaic modules. Combining solar radiation, ambient temperature, and load requirements, the improved particle swarm optimization algorithm is used to optimize the configuration of photovoltaic panels and battery systems.…”

Section: Introductionmentioning

confidence: 99%

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Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Wang,

Niu

et al. 2024

Energy Science & Engineering

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How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh.

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Section: Grid‐connected Wind–solar–storage Microgrid System and Mathe...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Wang,

Niu

et al. 2024

Energy Science & Engineering

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“…The division of teaching tasks leads to the selection of major and minor courses, as well as the priority order of the courses to be taught [5]. The division of teaching tasks is of course a must.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Implementation of Particle Swarm Optimization (Pso) Method on Lecturers Assignments to Students

Chaerullah,

Chalid,

Solichin

2023

J. Tek. Inform. (JUTIF)

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When preparing a program for a conference, it is very important to divide teaching and learning tasks according to the areas in which you are involved for teaching and learning to be effective. At the University, the assignment process is still done manually which is very time consuming. Therefore, an appropriate optimization method is needed to handle this. This problem can be solved using a population-based heuristic approach, Particle Swarm Optimization (PSO) has been applied to various fields such as scheduling and assignment. The data used in this research is lecturer assignment data in the form of prioritizing lecturer interest in teaching certain subjects. Based on the calculation results, a test was carried out to determine the effect of the test parameters on the fitness value obtained. From the results of the PSO parameter test, the best number of particles is 100, the best number of repetitions is 100, and the speed combination parameters c1 and c2 are 1.5 and 1.5 with the appropriate value of 94878. The system results, the solution obtained gives good results, i.e. always within tolerance limits, the error scores obtained by placing teachers on subjects that suit their preferences are lower

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Renewable Energy Optimization Solutions Using Meta-heuristics Methods

Raghuwanshi,

Masih

2023

Algorithms for Intelligent Systems

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An improved particle swarm optimization for optimal configuration of standalone photovoltaic scheme components

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 10 publications

References 53 publications

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Analysis of Implementation of Particle Swarm Optimization (Pso) Method on Lecturers Assignments to Students

Renewable Energy Optimization Solutions Using Meta-heuristics Methods

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