Abstract
Wind-solar-hydrogen production offers an effective solution to both power curtailment and green hydrogen production challenges. The capacity configuration of a wind-solar-hydrogen storage system plays a crucial role in optimizing its economic operation. To address this, a wind-solar-hydrogen storage energy system was developed. Initially, a density-based clustering algorithm with noise handling and an ordered clustering algorithm were employed to classify a year's worth of wind, solar output, and electrical load data into three representative days. The system's capacity optimization was then performed with the objective of maximizing the net system revenue for each device. A comparison was made with the traditional approach of minimizing the net present value of costs. The results revealed that maximizing net project revenue as the objective function leads to superior economic outcomes, outperforming the cost-minimization approach and resulting in a more optimized system configuration. © 2024 IEEE.
| Original language | English |
|---|---|
| Title of host publication | 2024 3rd International Conference on Neuromorphic Computing (ICNC) |
| Publisher | IEEE |
| Number of pages | 4 |
| ISBN (Electronic) | 979-8-3315-0866-1 |
| DOIs | |
| Publication status | Published - Dec 2024 |
| Externally published | Yes |
| Event | 3rd International Conference on Neuromorphic Computing, ICNC 2024 - Chongqing, China Duration: 13 Dec 2024 → 15 Dec 2024 |
Publication series
| Name | International Conference on Neuromorphic Computing, ICNC |
|---|
Conference
| Conference | 3rd International Conference on Neuromorphic Computing, ICNC 2024 |
|---|---|
| Place | China |
| City | Chongqing |
| Period | 13/12/24 → 15/12/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Research Keywords
- capacity optimization configuration
- hydrogen storage
- renewable energy
- wind-solar power generation
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