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<p>The growing demand for sustainable energy has driven the adoption of energy management (EM) strategies to enhance energy efficiency, reduce costs, and improve voltage stability in modern distribution systems. This study presents a conservation voltage reduction (CVR)-based EM scheme that optimally coordinates Volt-Var control (VVC) devices and distributed generation (DG) using the crayfish optimization algorithm (COA) under variable load events, including different loading conditions and future load growth<b>.</b> The presented approach aims to reduce substation demand, reduce costs, and optimized node voltage. The presented approach is verified on the IEEE-69 node system, achieving up to 14.14% substation demand reduction, 45.67% cost savings, and 60% node voltage optimization for normal loading conditions. Additionally, the proposed scheme demonstrates better performance across other loading conditions and future load growth<b>.</b> The efficacy of the COA is compared against other widely used meta-heuristic algorithms, demonstrating its faster convergence and adaptability to variable load events.</p>

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Crayfish Optimization Algorithm Based Conservation Voltage Reduction Considering Variable Load Events

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Neha Smitha Lakra,
  • Baidyanath Bag

摘要

The growing demand for sustainable energy has driven the adoption of energy management (EM) strategies to enhance energy efficiency, reduce costs, and improve voltage stability in modern distribution systems. This study presents a conservation voltage reduction (CVR)-based EM scheme that optimally coordinates Volt-Var control (VVC) devices and distributed generation (DG) using the crayfish optimization algorithm (COA) under variable load events, including different loading conditions and future load growth. The presented approach aims to reduce substation demand, reduce costs, and optimized node voltage. The presented approach is verified on the IEEE-69 node system, achieving up to 14.14% substation demand reduction, 45.67% cost savings, and 60% node voltage optimization for normal loading conditions. Additionally, the proposed scheme demonstrates better performance across other loading conditions and future load growth. The efficacy of the COA is compared against other widely used meta-heuristic algorithms, demonstrating its faster convergence and adaptability to variable load events.