Scholarlink

<p>This paper proposes an enhanced nonlinear control strategy combined with efficient energy flow management for a low-voltage AC microgrid integrating a wind turbine, a photovoltaic system, and a battery energy storage unit. The microgrid operates in a grid-connected configuration, aiming to optimize energy generation, storage, and consumption. To achieve this, a comprehensive mathematical model of the system is developed, and backstepping controllers are designed to fulfill the control objectives. The stability of the closed-loop system is rigorously verified using Lyapunov theory. Furthermore, a novel algorithm is introduced to maximize renewable energy extraction while effectively managing battery storage to enhance system performance and reliability. The proposed approach also ensures grid stability through power factor correction and ensures the load demand is met. Simulation results validate the effectiveness of the control strategy, demonstrating significant improvements in energy efficiency, system stability, and overall dynamic performance under varying load and environmental conditions.</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

Efficient energy management of a low-voltage AC microgrid with renewable and energy storage integration using nonlinear control

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月4日

查看全文
  • Karim El Mezdi,
  • Abdelmounime El Magri,
  • Aziz Watil,
  • Ilyass El Myasse,
  • Nabil El Aadouli,
  • Pankaj Kumar

摘要

This paper proposes an enhanced nonlinear control strategy combined with efficient energy flow management for a low-voltage AC microgrid integrating a wind turbine, a photovoltaic system, and a battery energy storage unit. The microgrid operates in a grid-connected configuration, aiming to optimize energy generation, storage, and consumption. To achieve this, a comprehensive mathematical model of the system is developed, and backstepping controllers are designed to fulfill the control objectives. The stability of the closed-loop system is rigorously verified using Lyapunov theory. Furthermore, a novel algorithm is introduced to maximize renewable energy extraction while effectively managing battery storage to enhance system performance and reliability. The proposed approach also ensures grid stability through power factor correction and ensures the load demand is met. Simulation results validate the effectiveness of the control strategy, demonstrating significant improvements in energy efficiency, system stability, and overall dynamic performance under varying load and environmental conditions.