适用于大规模可再生能源整合的高效单级鲁棒输电扩展规划方法

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-07-27 DOI:10.1016/j.segan.2024.101486
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引用次数: 0

摘要

稳健的输电扩展规划(RTEP)方法对于解决与可再生能源(RES)相关的不确定性至关重要。然而,现有的方法往往会产生过于保守的解决方案,而且计算效率较低,尤其是在处理大量可再生能源设备时。为了克服这些局限性,我们提出了一种简化的单级 RTEP 框架,该框架基于通过搜索凸壳顶点提前从历史数据中捕捉到的情景。这些情景被称为鲁棒情景,可保证产生与传统两阶段自适应鲁棒 RTEP(ARTEP)方法在鲁棒性和最优性方面一致的鲁棒解决方案。最后,通过采用基于概率的方法来确定稳健方案成为最坏情况的概率,从而提高了单层模型的求解速度。针对 Garver 6 总线系统、IEEE 118 总线系统和波兰 2383 总线系统得出的数值结果表明,与 ARTEP 方法相比,建议的方法分别节省了 91.71%、93.39% 和 98.84% 的所需计算时间。
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Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration

Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.

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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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