TensorConvolutionPlus: A python package for distribution system flexibility area estimation

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-06-26 DOI:10.1016/j.softx.2025.102241

Demetris Chrysostomou, José Luis Rueda Torres, Jochen Lorenz Cremer

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引用次数: 0

Abstract

Power system operators need new, efficient operational tools to use the flexibility of distributed resources and deal with the challenges of highly uncertain and variable power systems. Transmission system operators can consider the available flexibility in distribution systems (DSs) without breaching the DS constraints through flexibility areas. However, there is an absence of open-source packages for flexibility area estimation. This paper introduces TensorConvolutionPlus, a user-friendly Python-based package for flexibility area estimation. The main features of TensorConvolutionPlus include estimating flexibility areas using the TensorConvolution+ algorithm, the power flow-based algorithm, an exhaustive PF-based algorithm, and an optimal power flow-based algorithm. Additional features include adapting flexibility area estimations from different operating conditions and including flexibility service providers offering discrete setpoints of flexibility. The TensorConvolutionPlus package facilitates a broader adaptation of flexibility estimation algorithms by system operators and power system researchers.

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TensorConvolutionPlus：一个用于配电系统灵活性面积估计的python包

电力系统运营商需要新的、高效的操作工具来利用分布式资源的灵活性，并应对高度不确定和可变的电力系统的挑战。输电系统运营商可以在不违反柔性区域限制的情况下，考虑配电系统的可用灵活性。然而，缺少用于灵活性区域估计的开源包。本文介绍了TensorConvolutionPlus，一个用户友好的基于python的灵活性面积估计包。TensorConvolutionPlus的主要特点包括使用TensorConvolution+算法、基于潮流的算法、基于穷举pf的算法和基于最优潮流的算法来估计灵活性区域。其他功能包括适应不同操作条件下的灵活性面积估计，以及包括灵活性服务提供商提供的离散灵活性设定值。TensorConvolutionPlus包有助于系统操作员和电力系统研究人员更广泛地适应灵活性估计算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.