arXiv 2409.190792024-09-27eess.SYcs.SYmath.OC

使用代表性周期改进容量扩展模型中长期储能的公式

Improved formulation for long-duration storage in capacity expansion models using representative periods

Federico Parolin, Paolo Colbertaldo, Ruaridh Macdonald

AI总结：

本文研究容量扩展模型中时间聚合下长期储能的建模问题，提出一种紧凑公式以减少约束并跟踪荷电状态，在Dolphyn模型与美国大陆案例中验证，较现有方法运行时间快30%-70%、内存低1%-9%。

摘要：

随着容量扩展模型的复杂度和规模不断增加，时间聚合已成为提高计算可处理性的常用方法。然而，这种方法本质上使长期储能（LDS）系统的纳入变得复杂，因为其运行涉及连接所有时间步的整个时间跨度。本文对带有时间聚合的LDS建模进行了详细研究。提出了一种新颖的紧凑公式，以减少约束数量，同时有效跟踪储能容量并在整个时间跨度内强制执行荷电状态的限制。将所开发的方法与两种领先的现有公式进行了比较。所有三种方法均在Dolphyn容量扩展模型中实现，并在美国大陆的案例研究中进行了测试，考虑了空间分辨率和代表性周期的不同配置。使用商业求解器Gurobi和开源求解器HiGHS评估了性能。结果表明，所开发的紧凑公式在运行时间（比其他方法快30%-70%）和内存使用量（比其他方法低1%-9%）方面均始终优于其他方法。

英文摘要：

With the increasing complexity and size of capacity expansion models, temporal aggregation has emerged as a common method to improve computational tractability. However, this approach inherently complicates the inclusion of long-duration storage (LDS) systems, whose operation involves the entire time horizon connecting all time steps. This work presents a detailed investigation of LDS modelling with temporal aggregation. A novel compact formulation is proposed to reduce the number of constraints while effectively tracking the storage content and enforcing limits on the state of charge throughout the entire time horizon. The developed method is compared with two leading state-of-the-art formulations. All three methods are implemented in the Dolphyn capacity expansion model and tested on a case study for the continental United States, considering different configurations in terms of spatial resolutions and representative periods. The performance is assessed with both the commercial solver Gurobi and the open-source solver HiGHS. Results show that the developed compact formulation consistently outperforms the other methods in terms of both runtime (30%-70% faster than other methods) and memory usage (1%-9% lower than other methods).

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