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<p>Cross-border electricity trade is a critical enabler for integrating high penetrations of variable renewable energy (VRE), yet its optimization in unique geopolitical contexts remains a key challenge. This study addresses the specific case of the electrically isolated Baja California (Mexico) - California (USA) interconnection, where strategically managed trade is vital for both economic efficiency and grid reliability. We develop and apply a Multi-Objective Cross-Border Trade Optimization (MO-CBTO) model, based on a validated Particle Swarm Optimization (MOPSO) algorithm, to quantify the Pareto-optimal trade-offs between maximizing economic surplus, ensuring operational reliability, and minimizing VRE curtailment. The analysis is conducted across a comprehensive matrix of future scenarios, benchmarked against a non-cooperative baseline. The results demonstrate that a cooperative optimization strategy creates over $4 million in economic value compared to the baseline while reducing VRE curtailment by over 85%. Furthermore, scenario analysis reveals that a diversified solar and wind portfolio is critical for future planning, cutting energy waste by nearly half compared to solar-dominant strategies in high-penetration futures. The study delivers a robust decision-support framework and provides quantitative evidence that prioritizing investments in both transmission capacity and VRE portfolio diversity are key to unlocking an economical, reliable, and sustainable energy future.</p>

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Optimizing Mexico-US cross-border electricity trade with high renewable penetration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月28日

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  • Aboud Barsekh-Onji,
  • Fernando Lámbarry-Vilchis

摘要

Cross-border electricity trade is a critical enabler for integrating high penetrations of variable renewable energy (VRE), yet its optimization in unique geopolitical contexts remains a key challenge. This study addresses the specific case of the electrically isolated Baja California (Mexico) - California (USA) interconnection, where strategically managed trade is vital for both economic efficiency and grid reliability. We develop and apply a Multi-Objective Cross-Border Trade Optimization (MO-CBTO) model, based on a validated Particle Swarm Optimization (MOPSO) algorithm, to quantify the Pareto-optimal trade-offs between maximizing economic surplus, ensuring operational reliability, and minimizing VRE curtailment. The analysis is conducted across a comprehensive matrix of future scenarios, benchmarked against a non-cooperative baseline. The results demonstrate that a cooperative optimization strategy creates over $4 million in economic value compared to the baseline while reducing VRE curtailment by over 85%. Furthermore, scenario analysis reveals that a diversified solar and wind portfolio is critical for future planning, cutting energy waste by nearly half compared to solar-dominant strategies in high-penetration futures. The study delivers a robust decision-support framework and provides quantitative evidence that prioritizing investments in both transmission capacity and VRE portfolio diversity are key to unlocking an economical, reliable, and sustainable energy future.