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<p>With the growing global emphasis on climate change mitigation and sustainable development, renewable energy sources such as wind, solar, and hydro power have become essential for transforming electricity systems. To facilitate the effective integration of renewables into electricity markets, researchers have increasingly focused on developing optimized and transparent market mechanisms. However, existing evaluation frameworks for green power projects remain limited, often emphasizing economic outcomes while neglecting critical social and environmental dimensions, as well as system-level constraints. To address these limitations, this study develops a comprehensive benefit evaluation framework for green electricity trading in new energy stations, encompassing economic, environmental, and social indicators. The model integrates fuzzy comprehensive evaluation and an improved AHP-entropy weighting method, with added robustness through Monte Carlo simulation and sensitivity analysis of expert weights. To account for real-world constraints, the framework considers potential transmission congestion, locational grid access limitations, and external climate risks—such as prolonged low-wind periods or dust storms—via scenario-based simulations.The results from multiple case studies demonstrate that the proposed model performs more reliably and consistently than baseline approaches across most evaluation criteria. The inclusion of uncertainty bounds and statistical validation (e.g., RMSE, significance tests) further supports the credibility of the outcomes. This study concludes that green power projects offer strong multidimensional benefits and proposes practical policy recommendations to support the rational development of green electricity markets. The framework contributes meaningfully to advancing the large-scale, resilient deployment of renewable energy within complex power systems.</p>

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The Evaluation of Benefits from Green Electricity Trading in New Energy Power Stations

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Bin Wang,
  • Shuailiang Yao,
  • Haixu Liu,
  • Dongxia Cheng,
  • Hongyong Liu,
  • Jian Kou,
  • Chengdong Yang,
  • Changjiang Su

摘要

With the growing global emphasis on climate change mitigation and sustainable development, renewable energy sources such as wind, solar, and hydro power have become essential for transforming electricity systems. To facilitate the effective integration of renewables into electricity markets, researchers have increasingly focused on developing optimized and transparent market mechanisms. However, existing evaluation frameworks for green power projects remain limited, often emphasizing economic outcomes while neglecting critical social and environmental dimensions, as well as system-level constraints. To address these limitations, this study develops a comprehensive benefit evaluation framework for green electricity trading in new energy stations, encompassing economic, environmental, and social indicators. The model integrates fuzzy comprehensive evaluation and an improved AHP-entropy weighting method, with added robustness through Monte Carlo simulation and sensitivity analysis of expert weights. To account for real-world constraints, the framework considers potential transmission congestion, locational grid access limitations, and external climate risks—such as prolonged low-wind periods or dust storms—via scenario-based simulations.The results from multiple case studies demonstrate that the proposed model performs more reliably and consistently than baseline approaches across most evaluation criteria. The inclusion of uncertainty bounds and statistical validation (e.g., RMSE, significance tests) further supports the credibility of the outcomes. This study concludes that green power projects offer strong multidimensional benefits and proposes practical policy recommendations to support the rational development of green electricity markets. The framework contributes meaningfully to advancing the large-scale, resilient deployment of renewable energy within complex power systems.