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<p>Network congestion management has become increasingly challenging with the integration of renewable energy sources into power systems. This research systematically analyzes the five main types of congestion management techniques: cost-free methods, FACTS-based solutions, AI and optimization-based approaches, market-based strategies, and emerging hybrid techniques. Cost-free methods such as load shedding, transformer tap changes, and generator rescheduling are often modeled using Optimal Power Flow (OPF) due to its simplicity and low implementation cost. FACTS devices, including TCSC, STATCOM, and UPFC, facilitate real-time congestion relief by dynamically managing power flow. AI and optimization-based approaches leverage machine learning and metaheuristic algorithms to enable predictive and adaptive control. Market-based strategies, such as demand response, locational marginal pricing (LMP), and financial transmission rights (FTRs), use economic signals to manage congestion in deregulated environments. Lastly, emerging hybrid approaches integrate technologies like cloud computing, blockchain, FACTS, and AI to deliver decentralized, multi-layered control with improved accuracy and flexibility. While each strategy has its merits, challenges such as scalability, infrastructure costs, and coordination complexity persist. This review analysis highlights the relative effectiveness of each method and provides insights into the future of congestion control in power grids integrated with renewable energy sources.</p>

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A systematic review of congestion management strategies in renewable-integrated power systems

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • F. Ruby Vincy Roy,
  • Arun Joseph,
  • Madhan Mohankumar,
  • S. K. Vedhashree

摘要

Network congestion management has become increasingly challenging with the integration of renewable energy sources into power systems. This research systematically analyzes the five main types of congestion management techniques: cost-free methods, FACTS-based solutions, AI and optimization-based approaches, market-based strategies, and emerging hybrid techniques. Cost-free methods such as load shedding, transformer tap changes, and generator rescheduling are often modeled using Optimal Power Flow (OPF) due to its simplicity and low implementation cost. FACTS devices, including TCSC, STATCOM, and UPFC, facilitate real-time congestion relief by dynamically managing power flow. AI and optimization-based approaches leverage machine learning and metaheuristic algorithms to enable predictive and adaptive control. Market-based strategies, such as demand response, locational marginal pricing (LMP), and financial transmission rights (FTRs), use economic signals to manage congestion in deregulated environments. Lastly, emerging hybrid approaches integrate technologies like cloud computing, blockchain, FACTS, and AI to deliver decentralized, multi-layered control with improved accuracy and flexibility. While each strategy has its merits, challenges such as scalability, infrastructure costs, and coordination complexity persist. This review analysis highlights the relative effectiveness of each method and provides insights into the future of congestion control in power grids integrated with renewable energy sources.