A Bilevel Optimisation Model for Distributed Energy Storage Considering Wind‐Solar Uncertainty and Carbon Trading
風力・太陽光の不確実性と炭素取引を考慮した分散型エネルギー貯蔵のための二段階最適化モデル (AI 翻訳)
Xiaonan Li, Rongxin Sun, Wenxu Zhang, Zhanhong Wei, Xiping Ma
🤖 gxceed AI 要約
日本語
本論文は、風力・太陽光の出力不確実性と段階的炭素取引制度を考慮した分散型エネルギー貯蔵の二段階最適化モデルを提案。GANとクラスタリングでシナリオ生成の質を向上させ、改良型Osprey最適化アルゴリズムで解法を開発。シミュレーションにより、炭素排出量22.6%、電力損失22.3%、電力購入コスト9.2%の削減を実証。
English
This paper proposes a bilevel optimization model for distributed energy storage configuration that integrates wind-solar uncertainty and a tiered carbon trading scheme. It uses SNGAN and K-means for scenario generation, and an improved Osprey Optimization Algorithm. Simulation results show 22.6% reduction in carbon emissions, 22.3% reduction in daily active power loss, and 9.2% reduction in daily electricity purchase cost.
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
日本でも再エネ大量導入時の系統安定化とカーボンプライシングの設計が課題。本手法は、電力貯蔵の最適配置と炭素取引の連携を示しており、日本のGX政策における系統運用や炭素市場設計の参考となり得る。
In the global GX context
This paper addresses the global challenge of integrating high shares of renewables with carbon pricing. The bilevel optimization framework and scenario generation method are applicable to any region implementing carbon trading, such as the EU ETS or emerging systems in Asia. It contributes to the literature on energy storage planning under uncertainty and market mechanisms.
👥 読者別の含意
🔬研究者:Researchers working on energy system optimization, machine learning for scenario generation, or carbon trading mechanisms will find the bilevel model and SNGAN approach novel.
🏢実務担当者:Utility and energy storage planners can use the optimization framework to configure distributed storage while accounting for carbon costs and renewable uncertainty.
🏛政策担当者:Policymakers designing carbon trading schemes can see how coupling carbon prices with storage incentives can reduce emissions and system costs.
📄 Abstract（原文）
To achieve the ‘Dual Carbon’ strategic goals, the integration of a high proportion of renewable energy poses significant challenges to the stability and economic efficiency of power systems. This paper proposes a bilevel optimisation method for distributed energy storage configuration that integrates wind‐solar power uncertainty and a carbon trading mechanism. First, a spectral normalisation generative adversarial network (SNGAN) is employed to generate a large number of wind‐solar power output scenarios. These scenarios are then reduced using the K‐means clustering algorithm to obtain typical representative scenarios, significantly improving the quality of scenario generation. Second, a bilevel optimisation model is constructed for distributed energy storage configuration considering a tiered carbon trading scheme. The upper‐level model aims to minimise the total cost, including energy storage investment, grid electricity purchase and carbon trading costs, whereas the lower‐level model focuses on minimising power grid vulnerability indices and active power losses. Finally, an improved Osprey Optimisation Algorithm is developed to solve the bilevel model. Simulation results demonstrate that the proposed method reduces annual carbon emissions by 22.6%, daily active power loss by 22.3% and daily electricity purchase cost by 9.2% compared to the case without ESS and carbon trading while significantly enhancing overall power system performance.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1049/stg2.70059first seen 2026-05-05 22:52:46
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