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<p>Is there a necessity to go with the fact check to see if the theories imposed or created for vertically regulated markets are still valid in the deregulated electricity market era? Electricity has now become a commodity; more market participants and increased load demand therefore result in congestion. In response to the growing complexities of deregulated electricity markets, optimizing market clearing prices has become critical for improving efficiency and reducing the total consumer tariff (TCT). This paper introduces a novel hybrid optimization approach that integrates Lagrangian techniques with an adaptive Golden Jackal optimization (GJO) algorithm to address these challenges. The primary objective is to enhance market clearing efficiency while minimizing operational costs and consumer tariffs. The proposed method demonstrates a 3.31% decrease in the TCT compared to standalone GJO optimization technique, 2.04% deduction when compared with grey wolf optimization. Testing on an IEEE-33 bus system with seven generators and a 24-h load profile reveals that the hybrid approach improves convergence speed and shows resilience in various operational scenarios, including generator outages and fluctuating demand response conditions. By offering a systematic framework for optimizing market clearing prices and consumer tariffs, this study provides valuable insights for independent system operators and policymakers aiming to foster a more efficient and sustainable electricity market. </p>

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Hybrid Lagrangian and Improved Golden Jackal Optimization for Minimizing Market Clearing Price and Consumer Tariff

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Krishna Mohan Reddy Pothireddy,
  • Amrutha Raju Battula,
  • Sandeep Vuddanti

摘要

Is there a necessity to go with the fact check to see if the theories imposed or created for vertically regulated markets are still valid in the deregulated electricity market era? Electricity has now become a commodity; more market participants and increased load demand therefore result in congestion. In response to the growing complexities of deregulated electricity markets, optimizing market clearing prices has become critical for improving efficiency and reducing the total consumer tariff (TCT). This paper introduces a novel hybrid optimization approach that integrates Lagrangian techniques with an adaptive Golden Jackal optimization (GJO) algorithm to address these challenges. The primary objective is to enhance market clearing efficiency while minimizing operational costs and consumer tariffs. The proposed method demonstrates a 3.31% decrease in the TCT compared to standalone GJO optimization technique, 2.04% deduction when compared with grey wolf optimization. Testing on an IEEE-33 bus system with seven generators and a 24-h load profile reveals that the hybrid approach improves convergence speed and shows resilience in various operational scenarios, including generator outages and fluctuating demand response conditions. By offering a systematic framework for optimizing market clearing prices and consumer tariffs, this study provides valuable insights for independent system operators and policymakers aiming to foster a more efficient and sustainable electricity market.