Scholarlink

<p>The integration of renewable energy sources into power grids poses significant challenges, particularly regarding system strength and inertia, which are crucial for grid stability. Key stability indices such as the rate of change of frequency, critical clearing times, and short-circuit level require complex modeling and extensive computational resources. This complexity often leads to an overemphasis on certain indices at the expense of others, potentially underestimating their interdependencies. Neglecting these relationships can result in improvements to one index inadvertently compromising another. This paper addresses these challenges by exploring stability indices through correlation–regression analysis. We introduce bivariate quadratic regression models to capture the interdependencies between steady-state and dynamic stability indices, with short-circuit current as a primary independent variable. Additionally, we propose stability screening tests within a practical decision-making framework to support renewable energy sources integration. Using a symmetrical four-area system and the IEEE-39 bus system modeled in DIgSILENT PowerFactory, the results provide valuable insights into the intricate relationships among stability indices, enabling more efficient and informed decisions to ensure grid stability in the midst of growing renewable energy sources penetration.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Stability indices in power networks: a correlation–regression analysis for renewable energy integration

  • 期刊论文

  • 发表日期：

    2025年7月4日

查看全文
  • Souha El Wejhani,
  • Slim Tnani,
  • Mohamed Elleuch,
  • Khadija Ben Kilani,
  • Ghaleb Ennine

摘要

The integration of renewable energy sources into power grids poses significant challenges, particularly regarding system strength and inertia, which are crucial for grid stability. Key stability indices such as the rate of change of frequency, critical clearing times, and short-circuit level require complex modeling and extensive computational resources. This complexity often leads to an overemphasis on certain indices at the expense of others, potentially underestimating their interdependencies. Neglecting these relationships can result in improvements to one index inadvertently compromising another. This paper addresses these challenges by exploring stability indices through correlation–regression analysis. We introduce bivariate quadratic regression models to capture the interdependencies between steady-state and dynamic stability indices, with short-circuit current as a primary independent variable. Additionally, we propose stability screening tests within a practical decision-making framework to support renewable energy sources integration. Using a symmetrical four-area system and the IEEE-39 bus system modeled in DIgSILENT PowerFactory, the results provide valuable insights into the intricate relationships among stability indices, enabling more efficient and informed decisions to ensure grid stability in the midst of growing renewable energy sources penetration.