Collaborative planning and optimization for electric-thermal-hydrogen-coupled energy systems with portfolio selection of the complete hydrogen energy chain
作者: Xinning Yi, Tianguang Lu, Yixiao Li, Qian Ai, Ran Hao
分类: eess.SY
发布日期: 2023-11-14
备注: 32 pages, 17 figures
💡 一句话要点
提出高分辨率协同规划模型以解决氢能链不完整问题
🎯 匹配领域: 支柱八：物理动画 (Physics-based Animation)
关键词: 氢能链 低碳能源 协同规划 可再生能源 聚类优化 多能量系统 环境效益
📋 核心要点
- 现有规划模型普遍忽视氢能链的完整性，导致对氢在能源系统中作用的分析不够全面。
- 本文提出高分辨率的协同规划模型，结合可再生能源的时空特性与氢能链的投资策略，优化能源系统配置。
- 实验结果显示，该模型可将CO2排放减少60%，并将可再生能源削减率降低至97%，具有显著的经济效益。
📝 摘要（中文）
在全球低碳目标下，可再生能源资源的时空分布不均加剧了不确定性和季节性电力失衡。此外，氢能链的不完整性在规划模型中被广泛忽视，阻碍了对氢在能源系统中作用的全面分析。为此，本文提出了一种高分辨率的电-热-氢耦合能源系统协同规划模型，考虑了可再生能源资源的时空分布特征及完整氢能链的多尺度投资策略。通过氢链快速聚类优化方法，优化了2050年东北中国能源系统的地理分布和容量配置，涵盖了单能量设备、多能量耦合转换设备及电氢传输网络。研究表明，该模型在减少CO2排放方面具有显著效果，提供了零碳路径。
🔬 方法详解
问题定义：本文旨在解决氢能链不完整对能源系统规划的影响，现有方法未能充分考虑可再生能源的时空分布及氢能的多样性。
核心思路：提出高分辨率协同规划模型，综合考虑电、热、氢耦合，利用氢链快速聚类优化方法处理高维数据和多时间尺度特性。
技术框架：模型包括数据收集、时空特征分析、氢链聚类优化、地理分布与容量配置优化等主要模块，确保系统的灵活性与高效性。
关键创新：创新性地整合了完整氢能链的投资策略与多能量系统规划，显著提升了系统的经济性与环境效益。
关键设计：模型中采用了高分辨率的时序数据分析，聚类算法优化了氢能设备的配置，确保在不同时间尺度下的灵活运行。具体参数设置和损失函数设计未详细披露。
📊 实验亮点
实验结果表明，所提模型在减少CO2排放方面具有显著优势，能够实现60%的减排效果，同时将可再生能源的削减率降低至97.0%。与社会碳成本相比，成本降低了4%，显示出良好的经济性。
🎯 应用场景
该研究在电力、热能和氢能的综合利用方面具有广泛的应用潜力，尤其适用于低碳城市和可再生能源丰富的地区。未来可为政策制定者和能源规划者提供科学依据，推动氢能的全面应用与发展。
📄 摘要（原文）
Under the global low-carbon target, the uneven spatiotemporal distribution of renewable energy resources exacerbates the uncertainty and seasonal power imbalance. Additionally, the issue of an incomplete hydrogen energy chain is widely overlooked in planning models, which hinders the complete analysis of the role of hydrogen in energy systems. Therefore, this paper proposes a high-resolution collaborative planning model for electricity-thermal-hydrogen-coupled energy systems considering both the spatiotemporal distribution characteristics of renewable energy resources and the multi-scale bottom-to-top investment strategy for the complete hydrogen energy chain. Considering the high-resolution system operation flexibility, this paper proposes a hydrogen chain-based fast clustering optimization method that can handle high-dimensional data and multi-time scale operation characteristics. The model optimizes the geographical distribution and capacity configuration of the Northeast China energy system in 2050, with hourly operational characteristics. The planning optimization covered single-energy devices, multi-energy-coupled conversion devices, and electric-hydrogen transmission networks. Last but not least, this paper thoroughly examines the optimal portfolio selection of different hydrogen technologies based on the differences in cost, flexibility, and efficiency. In the Pareto analysis, the proposed model reduces CO2 emissions by 60% with a competitive cost. This paper provides a zero-carbon pathway for multi-energy systems with a cost 4% less than the social cost of carbon $44.6/ton, and the integration of the complete hydrogen energy chain reduces the renewable energy curtailment by 97.0%. Besides, the portfolio selection results indicate that the system favors the SOEC with the highest energy efficiency and the PEMFC with the fastest dynamic response when achieving zero-carbon emissions