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面向X的电力储能电池技术经济性比较:基于P-图方法的多维评估
Techno-economic comparison of power-to-X batteries: A multidimensional assessment using the P-graph method
| 作者 | Haosheng Lin · Wei Wuab |
| 期刊 | Energy Conversion and Management |
| 出版日期 | 2025年1月 |
| 卷/期 | 第 343 卷 |
| 技术分类 | 储能系统技术 |
| 技术标签 | 储能系统 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | P-Graph superstructure enumeration identified 24 feasible P2X configurations. |
语言:
中文摘要
摘要 可再生能源需求的不断增长凸显了开发可扩展、成本效益高且可持续的储能系统的必要性,而这些系统正面临盈利能力和效率方面的挑战。新兴的面向X的电力(P2X)电池能够分布式且低成本地储存过剩的可再生能源,以满足特定需求。然而,随时间变化的需求以及众多的技术选择(例如储能介质、设备等)极大地阻碍了最优P2X电池的识别。因此,本文提出了一种低复杂度的基于图论的框架,用于识别最高效且最具成本效益的配置方案。案例研究强调,基于热能储存、采用耐火砖与吸收式热泵的电力转热能电池,其平准化储能成本低于0.01美元/千瓦时,并且相较于电化学储能方案,其储能套利价值提高了114%。本文还针对不同场景下的储能配置选择提供了战略见解,并揭示了效率与盈利能力之间的关系。
English Abstract
Abstract The growing demand for renewable energy underscores the necessity of developing scalable, cost-effective, and sustainable energy storage systems, which are encountering challenges related to profitability and efficiency. The emerging power-to-X (P2X) batteries can distributively and cost-effectively store surplus renewable energy to satisfy certain demands. However, the time-dependent demands and numerous technological selections (e.g., storage medium, equipment, etc.) greatly hinder the identification of optimal P2X batteries. Therefore, we present a low-complex graph-theory-based framework for identifying the most efficient and cost-effective configuration. The case study highlights the thermal-energy-storage-based power-to-heat batteries using fire bricks with absorption heat pumps deliver a levelized cost of storage below $0.01/kWh and a 114 % increase in storage arbitrage value compared to their electrochemical storage counterpart. We also offer strategic insights into selecting energy storage configurations in different scenarios and reveal the relationship between efficiency and profitability.
S
SunView 深度解读
该P2X储能技术经济性分析框架对阳光电源ST系列储能变流器及PowerTitan系统具有重要参考价值。研究揭示的热储能方案成本优势(LCOS低于0.01美元/kWh)启示我们在ESS解决方案中需拓展多元化储能介质集成能力。P-graph优化方法可应用于iSolarCloud平台,实现不同场景下电化学储能与热储能的智能配置决策,提升储能套利价值。建议结合GFM控制技术,开发混合储能系统拓扑,平衡效率与经济性,增强可再生能源消纳能力。