Scholarlink

<p>The integration of high penetration of solar photovoltaic (PV) and wind turbines into distribution networks offers various benefits that align with the increasing use of renewable energy sources (RESs). However, this also introduces power system problems such as a high probability of reverse power flow, increased power loss, voltage sag, low voltage stability, and intermittent nature problems. This study introduces a new approach, the Fast Iterative Process and Slime Mould Algorithm (FIP-SMA) method, which combines the classical method and meta-heuristic algorithm through multi-objective function and a new index factor. This method assesses the optimal placement of large-scale solar photovoltaic systems into bus feeders, considering real intermittency LSSPV and uncertainty load demand. A multi-objective function was formulated to simultaneously achieve precise confirmation of allocation, leading to cost reduction, minimised loss, improve voltage deviation and voltage stability and enhanced net savings. The results show the FIP-SMA algorithm improves the required multi-objective function compared to PSO and GA and provides more confidence in verifying the effectiveness. Analysis of the speed, convergence, and initial seed also verified that FIP-SMA performed better. Advanced simulations and numerical analyses have effectively minimised energy losses, voltage deviations, and boosted voltage stability, thereby improving the performance of distribution networks operating under intermittent situations characterised by minute fluctuations.</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

Multi-objective optimal integration of intermittency large-scale solar photovoltaic conditions connected to uncertainty load demand using FIPSMA algorithm

  • 期刊论文

  • 发表日期：

    2026年5月19日

查看全文
  • Mashitah Mohd Hussain,
  • Zuhaina Zakaria,
  • Mohd Najib Mohd Hussain,
  • Ihsan Mohd Yassin,
  • Mohd Ridzuan Mohd Jamir

摘要

The integration of high penetration of solar photovoltaic (PV) and wind turbines into distribution networks offers various benefits that align with the increasing use of renewable energy sources (RESs). However, this also introduces power system problems such as a high probability of reverse power flow, increased power loss, voltage sag, low voltage stability, and intermittent nature problems. This study introduces a new approach, the Fast Iterative Process and Slime Mould Algorithm (FIP-SMA) method, which combines the classical method and meta-heuristic algorithm through multi-objective function and a new index factor. This method assesses the optimal placement of large-scale solar photovoltaic systems into bus feeders, considering real intermittency LSSPV and uncertainty load demand. A multi-objective function was formulated to simultaneously achieve precise confirmation of allocation, leading to cost reduction, minimised loss, improve voltage deviation and voltage stability and enhanced net savings. The results show the FIP-SMA algorithm improves the required multi-objective function compared to PSO and GA and provides more confidence in verifying the effectiveness. Analysis of the speed, convergence, and initial seed also verified that FIP-SMA performed better. Advanced simulations and numerical analyses have effectively minimised energy losses, voltage deviations, and boosted voltage stability, thereby improving the performance of distribution networks operating under intermittent situations characterised by minute fluctuations.