Planning optimization for islanded microgrid with electric-hydrogen hybrid energy storage system based on electricity cost and power supply reliability

Qiu, Yibin; Li, Qi; Zhao, Shudan; Chen, Weirong

doi:10.1016/b978-0-12-821726-9.00003-5

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…The overall cost optimization is achieved by using the muddy soil fish optimization algorithm (MSFOA). In Reference 18, the energy model integrates with SPV, WES, MHP, and BES energy systems for electrification of the community where grid development is complex: merging locally accessible energy resources and examining the system's techno‐economic and feasibility. In Reference 19, an integrated energy model was constructed with the combination of DG/PV/WT and battery.…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of renewable integrated rural microgrid for sustainable energy supply

Kama¹,

Ashraf

Fernandez

2022

Energy Storage

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Renewable resources can help reduce carbon emissions, but their inconsistency causes inconsistent energy generation, mandating the introduction of a battery bank to overcome the demand‐supply gap. The integration of renewables for electrical supply systems in rural regions has received much attention, as has the hybrid energy system with storage for microgrids. This work presents an optimization technique for the design and operation of a standalone microgrid with load demand. The research framework is demonstrated by a case study in a remote rural location in Uttarakhand (India). This research proposes an integrated techno‐economic optimization and power management of a standalone integrated renewable energy system to meet the electrical energy demand. The levelized cost of energy (LCOE), the net present cost (NPC), and pollution emission are minimized using grey wolf optimization. Four alternative models are compared based on environmental considerations and economic research. According to the simulation findings, the optimal configuration has a 0.203 $/kWh LCOE and a 92% renewable portion. In addition, the optimization results have been presented and compared. Finally, a sensitivity analysis is performed to determine the impact of various factors on the system in question.

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Section: Introductionmentioning

confidence: 99%