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首页> 外文期刊>Computational intelligence and neuroscience >Multiobjective Sizing of an Autonomous Hybrid Microgrid Using a Multimodal Delayed PSO Algorithm: A Case Study of a Fishing Village
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Multiobjective Sizing of an Autonomous Hybrid Microgrid Using a Multimodal Delayed PSO Algorithm: A Case Study of a Fishing Village

机译：使用多峰延迟PSO算法的自主混合微电网的多目标尺寸：一个渔村案例研究

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摘要

Renewable energy (RE) systems play a key role in producing electricity worldwide. The integration of RE systems is carried out in a distributed aspect via an autonomous hybrid microgrid (A-HMG) system. The A-HMG concept provides a series of technological solutions that must be managed optimally. As a solution, this paper focuses on the application of a recent nature-inspired metaheuristic optimization algorithm named a multimodal delayed particle swarm optimization (MDPSO). The proposed algorithm is applied to an A-HMG to find the minimum levelized cost of energy (LCOE), the lowest loss of power supply probability (LPSP), and the maximum renewable factor (REF). Firstly, a smart energy management scheme (SEMS) is proposed to coordinate the power flow among the various system components that formed the A-HMG. Then, the MDPSO is integrated with the SEMS to perform the optimal sizing for the A-HMG of a fishing village that is located in the coastal city of Essaouira, Morocco. The proposed A-HMG comprises photovoltaic panels (PV), wind turbines (WTs), battery storage systems, and diesel generators (DGs). The results of the optimization in this location show that A-HMG system can be applied for this location with a high renewable factor that is equal to 90%. Moreover, the solution is very promising in terms of the LCOE and the LPSP indexes that are equal to 0.17$/kWh and 0.12%, respectively. Therefore, using renewable energy can be considered as a good alternative to enhance energy access in remote areas as the fishing village in the city of Essaouira, Morocco. Furthermore, a sensitivity analysis is applied to highlight the impact of varying each energy source in terms of the LCOE index.
机译：可再生能源（RE）系统在全球生产电力方面发挥着关键作用。 RE系统的集成通过自主混合微电网（A-HMG）系统在分布式方面进行。 A-HMG概念提供了一系列必须最佳管理的技术解决方案。作为解决方案，本文重点介绍了最近的自然启发的成群质优化算法，命名为多峰延迟粒子群优化（MDPSO）。所提出的算法应用于A-HMG，以找到最小的能量（LCOE）的能量（LPSE），电源概率（LPSP）的最低损耗以及最大可再生因子（REF）。首先，提出了一种智能能量管理方案（SEM）来协调形成A-HMG的各种系统组件之间的功率流。然后，MDPSO与SEM集成，以对位于摩洛哥北部市中心的渔村A-HMG的最佳尺寸。所提出的A-HMG包括光伏板（PV），风力涡轮机（WTS），电池存储系统和柴油发电机（DGS）。该位置优化的结果表明，A-HMG系统可以应用于该位置，其高可再生因子等于90％。此外，该解决方案在LCoE和LPSP指标方面非常有前途，即分别等于0.17美元/千瓦时和0.12％。因此，使用可再生能源可以被视为加强偏远地区的能源进入作为摩洛哥市渔村的能源进入的良好替代方案。此外，应用灵敏度分析以强调在LCoE指数方面改变每个能源的影响。

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