Scholarlink

<p>The increasing integration of renewable-based distributed generation (DG) has significantly contributed to the development of sustainable and environmentally friendly power systems. However, the intermittent nature of renewable energy sources introduces operational challenges, including increased power losses, voltage instability, and higher operational costs. This study proposes a novel multi-objective optimization framework for the simultaneous optimal placement and sizing of multiple DG units and a Unified Power Flow Controller (UPFC) to enhance the techno-economic performance of power systems. The framework minimizes the total annualized economic cost by jointly considering the fuel cost of thermal generators, annualized DG cost, UPFC investment cost, and power-loss-related cost while satisfying all equality and inequality constraints of the optimal power flow problem. To solve the formulated optimization problem, an Improved Multi-Objective Grey Wolf Optimizer (IMOGWO) incorporating opposition-based learning is developed and evaluated on the IEEE 57-bus test system. The proposed method is validated against the Multi-Objective Differential Evolution (MODE) algorithm under three operating scenarios: normal operation, line contingency, and 110% loading with Electric Vehicle Charging Station (EVCS) integration. The results demonstrate that IMOGWO consistently outperforms MODE by reducing power losses from 27.80&#xa0;MW to 8.59&#xa0;MW, 61.11&#xa0;MW to 11.78&#xa0;MW, and 44.21&#xa0;MW to 10.70&#xa0;MW under normal, contingency, and stressed loading conditions, respectively. In addition, the proposed framework significantly improves the system voltage profile, maintaining bus voltages close to 1.08 p.u. while satisfying operational constraints. Comparative analyses with existing optimization techniques, including LAPO, PSO, and ALO, further demonstrate the effectiveness, robustness, and superior techno-economic performance of the proposed IMOGWO-based framework for modern power systems with renewable DG and FACTS integration.</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

Economic optimization and stability enhancement of a network integrating renewable DG sources with UPFC controller

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

查看全文
  • Rupika Gandotra,
  • Kirti Pal,
  • Mahmood Aldobali

摘要

The increasing integration of renewable-based distributed generation (DG) has significantly contributed to the development of sustainable and environmentally friendly power systems. However, the intermittent nature of renewable energy sources introduces operational challenges, including increased power losses, voltage instability, and higher operational costs. This study proposes a novel multi-objective optimization framework for the simultaneous optimal placement and sizing of multiple DG units and a Unified Power Flow Controller (UPFC) to enhance the techno-economic performance of power systems. The framework minimizes the total annualized economic cost by jointly considering the fuel cost of thermal generators, annualized DG cost, UPFC investment cost, and power-loss-related cost while satisfying all equality and inequality constraints of the optimal power flow problem. To solve the formulated optimization problem, an Improved Multi-Objective Grey Wolf Optimizer (IMOGWO) incorporating opposition-based learning is developed and evaluated on the IEEE 57-bus test system. The proposed method is validated against the Multi-Objective Differential Evolution (MODE) algorithm under three operating scenarios: normal operation, line contingency, and 110% loading with Electric Vehicle Charging Station (EVCS) integration. The results demonstrate that IMOGWO consistently outperforms MODE by reducing power losses from 27.80 MW to 8.59 MW, 61.11 MW to 11.78 MW, and 44.21 MW to 10.70 MW under normal, contingency, and stressed loading conditions, respectively. In addition, the proposed framework significantly improves the system voltage profile, maintaining bus voltages close to 1.08 p.u. while satisfying operational constraints. Comparative analyses with existing optimization techniques, including LAPO, PSO, and ALO, further demonstrate the effectiveness, robustness, and superior techno-economic performance of the proposed IMOGWO-based framework for modern power systems with renewable DG and FACTS integration.