Robust optimal scheduling method for multi-energy microgrid clusters considering wind and solar uncertainties
風力と太陽光の不確実性を考慮したマルチエネルギーマイクログリッドクラスターのロバスト最適スケジューリング手法 (AI 翻訳)
Tao Zhang, Yifan Zhang, Tengshuo Li, Ruijin Zhu
🤖 gxceed AI 要約
日本語
本論文は、中国の「双炭」目標の下、風力・太陽光発電の不確実性に直面する弱配電網に対し、マルチエネルギーマイクログリッドクラスターのロバスト最適スケジューリング手法を提案。P2G・CCS技術と段階的炭素取引を導入し、Wassersteinファジー集合モデルで不確実性を記述、ADMMによる分散実装で運用コスト削減と再生可能エネルギー利用率向上を実現する。
English
This paper proposes a robust optimal scheduling method for multi-energy microgrid clusters in weak distribution networks under wind and solar uncertainties. It integrates power-to-gas and CCS technologies with a stepped carbon trading scheme. A Wasserstein fuzzy set models uncertainty, and ADMM enables distributed implementation, reducing operating costs while improving renewable utilization and meeting carbon reduction targets.
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
中国の「双炭」政策を背景とした実践的なスケジューリング手法であり、日本でもP2G・CCSと炭素取引を組み合わせた分散型エネルギー管理の参考となる。
In the global GX context
This engineering-oriented framework combines carbon capture, power-to-gas, and stepped carbon trading in microgrid scheduling, offering a practical solution for integrating high renewables into weak distribution grids globally.
👥 読者別の含意
🔬研究者:Provides a robust optimization approach for multi-energy microgrids with carbon trading, advancing the state of the art in stochastic scheduling.
🏢実務担当者:Offers an implementable distributed scheduling algorithm (ADMM) that can be applied to real microgrid clusters with renewable uncertainty.
🏛政策担当者:Demonstrates how stepped carbon pricing can be embedded into operational scheduling to incentivize low-carbon operation at the distribution level.
📄 Abstract（原文）
In the context of China’s “dual-carbon” objectives and the accelerating growth of renewable energy, weak distribution networks encounter considerable uncertainty arising from fluctuations in wind and photovoltaic generation as well as electricity price volatility. These factors pose significant difficulties for the secure and economical operation of multi-energy microgrid clusters. To address these challenges, this study develops an engineering-oriented optimal scheduling strategy for such clusters. The framework incorporates power-to-gas (P2G) conversion and carbon capture and storage (CCS) technologies, thereby enabling electricity-heat-gas coupling. Additionally, a stepped carbon trading scheme is introduced to support low-carbon operation. To handle renewable energy variability, uncertainties are described using a Wasserstein fuzzy set model, and robust optimization is applied to enhance the reliability of scheduling outcomes. The scheduling process is coordinated by a central operator and implemented in a distributed fashion through the alternating direction method of multipliers (ADMM). Simulation results indicate that the proposed approach can lower overall operating expenses, enhance wind and solar utilization, and meet carbon reduction requirements, offering a practical and applicable scheduling solution for weak distribution networks.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1088/1742-6596/3171/1/012008first seen 2026-05-15 17:25:39
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