Development and modification of large-scale hydrogen liquefaction process empowered by LNG cold energy: A feasibility study

Gu, Jiwon; Choe, Changgwon; Haider, Junaid; Al-Abri, Rashid; Qyyum, Muhammad Abdul; Al-Muhtaseb, Ala'a H.; Lim, Hankwon

doi:10.1016/j.apenergy.2023.121893

Cited by 11 publications

(2 citation statements)

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“…These optimization problems are commonly solved using PSO. In their multi-objective model focusing on operating costs, environmental impacts, and power fluctuations, Cruz et al and Gu et al [21,22] used the traditional PSO algorithm. A number of advantages are associated with the PSO algorithm, including its simplicity, ease of implementation, rapid convergence, low complexity, and robust capabilities for optimizing the solution [23].…”

Section: Current Status Of Microgrid Optimal Scheduling Researchmentioning

confidence: 99%

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Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Guan,

Wang,

et al. 2024

Energies

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Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

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Section: Current Status Of Microgrid Optimal Scheduling Researchmentioning

confidence: 99%

“…(2) Particle Swarm Algorithm with Constriction Factor: The particle swarm algorithm considering the constriction factor for updating particle velocity is expressed as shown in Equation (21).…”

Section: Traditional Pso Algorithmmentioning

confidence: 99%