Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm

IF 8 Q1 ENERGY & FUELS Energy nexus Pub Date : 2024-10-24 DOI:10.1016/j.nexus.2024.100333
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Abstract

The study addresses the integration of hybrid hydrogen (H2) and battery (BT) energy storage systems into a renewable energy microgrid comprising solar photovoltaic (PV) and wind turbine (WT) systems. The research problem focuses on improving the effectiveness and computational efficiency of energy management systems (EMS) while ensuring high system reliability. Despite the existing optimization methods for hybrid microgrids, challenges remain in optimizing energy storage and capacity planning in grid-connected microgrids. To solve this, we propose the use of the Levy Flight Algorithm (LFA) to optimize the capacities of PV, WT, H2 tanks, electrolyzers (EL), fuel cells (FC), and BT, which presents a complex nonlinear optimization challenge. The novelty of this study lies in integrating the LFA with a rule-based EMS, enhancing system reliability and efficiency. The proposed approach significantly reduces the annualized system cost (ASC) and the levelized cost of energy (LCOE). The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. The LFA achieves the lowest LCOE at $0.275/kWh, compared to $0.278/kWh with SSA, $0.289/kWh with GA, $0.280/kWh with PSO and $0.283/kWh with GWO. This research contributes to the broader scientific community by providing a more efficient approach to optimizing renewable energy microgrids with hybrid storage systems, thus promoting eco-friendly and cost-effective energy solutions. The proposed system design offers a pathway to future energy systems with high renewable integration, especially as technology advances and costs continue to decrease.
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利用列维飞行算法优化基于规则的能源管理和带混合储能的并网可再生能源微电网规模
本研究探讨了将氢气(H2)和电池(BT)混合储能系统集成到由太阳能光伏(PV)和风力涡轮机(WT)系统组成的可再生能源微电网中的问题。研究问题的重点是提高能源管理系统(EMS)的有效性和计算效率,同时确保系统的高可靠性。尽管已有针对混合微电网的优化方法,但在优化并网微电网的储能和容量规划方面仍存在挑战。为了解决这个问题,我们建议使用列维飞行算法(LFA)来优化光伏、风电、H2 储能罐、电解槽(EL)、燃料电池(FC)和 BT 的容量,这是一个复杂的非线性优化挑战。本研究的新颖之处在于将 LFA 与基于规则的 EMS 相集成,从而提高了系统的可靠性和效率。所提出的方法大大降低了年化系统成本(ASC)和平准化能源成本(LCOE)。结果表明,LFA 优于 Salp Swarm 算法 (SSA)、粒子群优化 (PSO)、灰狼优化 (GWO) 和遗传算法 (GA),分别节约成本 3,309 美元、5,297 美元、4,484 美元和 5,129 美元。LFA 的 LCOE 最低,为 0.275 美元/千瓦时,而 SSA 为 0.278 美元/千瓦时,GA 为 0.289 美元/千瓦时,PSO 为 0.280 美元/千瓦时,GWO 为 0.283 美元/千瓦时。这项研究为优化带混合存储系统的可再生能源微电网提供了一种更有效的方法,从而促进了生态友好型和高成本效益型能源解决方案的发展,为更广泛的科学界做出了贡献。拟议的系统设计为未来可再生能源高度集成的能源系统提供了一条途径,特别是随着技术的进步和成本的不断降低。
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来源期刊
Energy nexus
Energy nexus Energy (General), Ecological Modelling, Renewable Energy, Sustainability and the Environment, Water Science and Technology, Agricultural and Biological Sciences (General)
CiteScore
7.70
自引率
0.00%
发文量
0
审稿时长
109 days
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