Quantum algorithm-enhanced evaluation method for hybrid wind-PV system projects considering carbon trading
カーボン取引を考慮した風力・太陽光ハイブリッドシステムの量子アルゴリズム強化評価手法 (AI 翻訳)
Zhanpeng Xu, Zhehan Li, Longze Wang, Shizhao Wang, Yuteng Mao, Zhen Weng, Yan Zhang, Zihan Xie, Meicheng Li
🤖 gxceed AI 要約
日本語
本論文は、カーボン排出権取引（CET）を考慮したハイブリッド風力・太陽光発電システム（HWPSP）の評価手法を提案。量子粒子群最適化（QPSO）とTOPSISを統合し、CETの影響を指標化し、重みを最適化する。中国北京市の火力発電所の事例で、QPSOが従来手法よりも収束速度を35.8%向上させ、評価結果のロバスト性が向上したことを示した。CETを考慮することでプロジェクト評価が4.17%改善した。
English
This paper proposes a quantum-enhanced evaluation framework for hybrid wind-PV systems under carbon emission trading (CET). It integrates quantum particle swarm optimization (QPSO) with TOPSIS to handle uncertainty from evolving CET policies and price volatility. Using a case study of a thermal power plant in Beijing, China, the method improves convergence speed by 35.8% over classical PSO and increases robustness, with ranking fluctuations within ±2% under perturbations. Incorporating CET reduces the Euclidean distance to the ideal scheme by 4.17%, raising the project rating to "Excellent".
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の再生可能エネルギー導入拡大やカーボンプライシング政策検討において、本手法はプロジェクト評価の客観性向上に貢献する可能性がある。特に、日本のFIT/FIP制度下での設備評価や、非化石価値取引との組み合わせにも応用が期待される。
In the global GX context
This study advances global GX by providing a robust multi-criteria decision-making method for renewable energy projects under carbon pricing mechanisms. The quantum-enhanced approach offers improved accuracy and stability over conventional methods, which is valuable for project financiers and policymakers in carbon market jurisdictions like the EU or emerging systems in Asia.
👥 読者別の含意
🔬研究者:Quantum algorithm applications in renewable energy evaluation offer a new methodological angle.
🏢実務担当者:Companies can use the framework to assess hybrid project viability under carbon market uncertainties.
🏛政策担当者:Insights on how carbon trading influences project evaluation can inform policy design for renewable energy incentives.
📄 Abstract（原文）
Hybrid wind–PV system projects (HWPSPs) help increase renewable energy penetration and gain additional advantages under carbon emission trading (CET) mechanisms. However, evolving CET policies and carbon price volatility introduce uncertainty into the objective evaluation of such projects. This study proposes a quantum-enhanced evaluation framework that integrates quantum particle swarm optimization (QPSO) with the technique for order preference by similarity to ideal solution (TOPSIS) to assess HWPSP performance in a carbon-trading context. A CET-integrated evaluation indicator system is first constructed based on an in-depth analysis of CET's influence on HWPSPs. The coupled relationships among indicators are then modeled via quantum superposition and entanglement, while indicator weights are optimized using QPSO. Subsequently, TOPSIS is applied to obtain the final assessment results. A newly constructed HWPSP at a thermal power plant in Beijing, China, is used as a case study to validate the method. The results show that QPSO improves convergence speed by 35.8% compared with classical PSO and enhances robustness, with ranking fluctuations remaining within ±2% under 5%–10% indicator perturbations —vs 8%–12% for traditional linear multi-criteria decision-making (MCDM) methods. Finally, the proposed method is validated using a real HWPSP project at a thermal power plant in Beijing. Incorporating CET reduces the Euclidean distance between the project's comprehensive evaluation score and the ideal scheme by 4.17%, elevating its final ranking to the “Excellent” level.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1063/5.0306208first seen 2026-05-05 22:58:27
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