Particle swarm optimization algorithm for optical-geometric optimization of linear fresnel solar concentrators

Ajdad, Hamid; Baba, Yousra Filali; Mers, Ahmed Al; Merroun, Ossama; Bouatem, Abdelfattah; Boutammachte, Noureddine

doi:10.1016/j.renene.2018.07.001

Cited by 51 publications

(15 citation statements)

References 18 publications

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“…Among the conducted literature, various optimization techniques have been investigated to achieve optimal solutions of hybrid energy systems . Among new optimization methods, particle swarm optimization (PSO) provides an efficient technique to address optimization of hybrid systems and has been shown to efficiently solve multidimensional functions with less computational effort and higher efficiency in various fields . Also, PSO is attractive because of its simplicity of concept, accuracy, quick convergence, and easy implementation for single‐objective optimization .…”

Section: Optimization Methodologymentioning

confidence: 99%

Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment

Liu

Wei

Huang³

et al. 2019

Energy Science & Engineering

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Over the past years, wind energy has been considered as a promising solution for clean and sustainable energy development, but wind curtailment remains a challenge to wind power development. On this basis, utilization of non-grid-connected wind power becomes crucial and necessary as it can mitigate wind curtailment and improve energy efficiency. This paper proposes the collaboration strategy and optimization model of wind farm-hybrid energy storage system (WF-HESS) for nongrid-connected wind power based on battery and superconducting magnetic energy storage (SMES) whose combination can effectively cope with fluctuation and intermittence of wind input. The optimization problem is simultaneously investigated by the minimization of total cost, wind curtailment magnitude, and loss of power supply probability (LPSP). The multi-objective particle swarm optimization (MOPSO) is introduced to find available solutions, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to determine the optimal one.Results are obtained for a case study, and the effectiveness and the feasibility of proposed model are verified by a comparative analysis and a sensitivity analysis.Results analysis and discussion show that the WF-HESS model and the application of HESS have important influence on promoting utilization of non-grid-connected wind power and mitigating wind curtailment. K E Y W O R D Shybrid energy storage system, multi-objective optimization, multi-objective particle swarm optimization, non-grid-connected wind power, wind curtailment

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Section: Optimization Methodologymentioning

confidence: 99%

Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment

Liu

Wei

Huang³

et al. 2019

Energy Science & Engineering

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“…PSO has capability to solve complex optimization problems. The algorithm has received a wide coverage of usage in varying fields of specialties such as applied sciences, energy, information technology, manufacturing and machine design [20][21][22][23][24].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…In solar energy systems, PSO has been used in optimizing the geometry of a linear-Fresnel solar concentrator and it gave good results [20]. The algorithm was also used with success in hybrid electric vehicles for gear shifting and optimal energy management.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Cleaning cycle optimisation in non-tracking ground mounted solar PV systems using Particle Swarm Optimisation

Chiteka

Arora

Sridhara

et al. 2020

Int. J. Simul. Multidisci. Des. Optim.

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The effect of installation azimuth angle in the optimization of the cleaning cycle of a solar photovoltaic plant was experimentally investigated in this study. The optimum cleaning cycle was determined using Particle Swarm Optimization algorithm cognizance of the fact that different orientations have different soiling rates. Soiling rates on three different azimuth configurations were experimentally investigated and an exponential soiling loss model was developed for each configuration for use in the optimization problem. Azimuth angle differences of ±12.5% were found to have a significant influence on soiling of as much as 28.29% for the selected location. The North of North West configuration was found to be optimal as opposed to the generally accepted North configuration for maximum energy generation at a minimum cost of energy. This configuration generated 0.87% more energy at unit energy cost of $0.093 compared to the North configuration which had a minimum cost of $0.113. The optimized cleaning cycles were 35 days for the optimal configuration while the North configuration had an optimized cleaning cycle of 28 days. A 17.7% difference in the cost of energy was recorded due the influence of soiling. The study revealed that for minimizing the unit energy cost, it is necessary to take into effect the influence of soiling.

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“…PSO has also been used for geometric optimization problems in order to find the best system configurations that best fit the design constraints. In this context, we can mention studies involving optical-geometric optimization of solar concentrators [21] and geometric optimization of radiative enclosures that satisfy temperature distribution and heat flow [22].…”

Section: Recent Applications and Challengesmentioning

confidence: 99%

Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

Almeida¹,

Leite²

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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This chapter will introduce the particle swarm optimization (PSO) algorithm giving an overview of it. In order to formally present the mathematical formulation of PSO algorithm, the classical version will be used, that is, the inertial version; meanwhile, PSO variants will be summarized. Besides that, hybrid methods representing a combination of heuristic and deterministic optimization methods are going to be presented as well. Before the presentation of these algorithms, the reader will be introduced to the main challenges when approaching PSO algorithm. Two study cases of diverse nature, one regarding the PSO in its classical version and another one regarding the hybrid version, are provided in this chapter showing how handful and versatile it is to work with PSO. The former case is the optimization of a mechanical structure in the nuclear fuel bundle and the last case is the optimization of the cost function of a cogeneration system using PSO in a hybrid optimization. Finally, a conclusion is presented.

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Particle swarm optimization algorithm for optical-geometric optimization of linear fresnel solar concentrators

Cited by 51 publications

References 18 publications

Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment

Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment

Cleaning cycle optimisation in non-tracking ground mounted solar PV systems using Particle Swarm Optimisation

Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

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