風力発電と揚水発電を統合した深層学習ベースの水力熱運用計画：低炭素経済的負荷配分に向けて
Deep Learning-Based Hydrothermal Scheduling Integrating Wind Power and Pumped-Storage Hydropower for Low-Carbon Economic Dispatch (原題)
Clóvis Melo, V. Leonardo Paucar, Raimundo Nonato Diniz Costa Filho
🤖 gxceed AI 要約
日本語
本研究は、風力発電と揚水発電を統合したハイブリッド電力システムにおける水力熱運用計画問題に対して、KAN、LSTM、GRU、DFFの4つの深層学習アーキテクチャを適用し、経済性と二酸化炭素排出量のバランスを最適化する。10発電機テストシステムでの評価により、全手法が制約を満たしつつ問題を解決できることを示し、特にKANの初適用とその解釈可能性を強調している。
English
This study applies four deep learning architectures (KAN, LSTM, GRU, DFF) to solve hydrothermal scheduling in hybrid power systems with wind and pumped-storage hydropower, optimizing economic and emission objectives. Evaluated on a 10-generator test system, all methods satisfy constraints, with KAN showing promise for interpretability.
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
日本の電力システムでは再生可能エネルギーの導入拡大に伴い、需給調整や経済性と環境性の両立が課題となっている。本研究成果は、深層学習を用いた運用最適化の可能性を示し、日本の電力会社や系統運用者にとって、低炭素化とコスト削減を両立する運用計画の高度化に示唆を与える。
In the global GX context
Globally, integrating variable renewables into power systems is a key challenge for the energy transition. This study demonstrates deep learning's potential for low-carbon economic dispatch, offering insights for grid operators and policymakers seeking to balance cost and emissions in hybrid systems.
👥 読者別の含意
🔬研究者:Deep learning methods for hydrothermal scheduling with renewables; KAN's first application offers a new research direction.
🏢実務担当者:Power utilities can explore these AI-based scheduling tools to optimize operations with renewables and storage.
🏛政策担当者:Highlights the value of AI in enabling cost-effective low-carbon power system operations.
📄 Abstract（原文）
The increasing penetration of variable renewable energy sources into electric power systems requires advanced optimization tools to address the complexity of hybrid hydrothermal scheduling while minimizing generation costs and carbon emissions. This study investigates the application of four deep learning architectures—Kolmogorov–Arnold networks (KANs), long short-term memory (LSTM), gated recurrent unit (GRU), and deep feedforward (DFF)—to solve the hydrothermal scheduling problem in hybrid power systems that incorporate wind power generation and pumped-storage hydropower (PSH) plants. The methods were evaluated on a 10-generator test system over a 24-h planning horizon in three objective-weighting scenarios, considering economic dispatch only, pure emission minimization only, and balanced objectives. All architectures successfully solved the integrated problem and satisfied the system constraints. This study reports the first application of the KAN to the hydrothermal scheduling problem, demonstrating its viability and interpretability potential for future applications in electric power systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/electricity7030095first seen 2026-09-03 05:09:55
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