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Clustering-enhanced adaptive Benders decomposition for energy systems planning optimization

エネルギーシステム計画最適化のためのクラスタリング強化適応ベンダース分解 (AI 翻訳)

Jun Wen Law, Dharik S. Mallapragada

arXivプレプリント2026-05-29#エネルギー転換
原典: https://arxiv.org/abs/2606.00388
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🤖 gxceed AI 要約

日本語

本論文は、エネルギーシステムの容量拡張モデルにおける計算ボトルネックを解決するため、クラスタリングを活用したベンダース分解法を提案する。適応的なグループ化カットと代表サブ問題の選択により、大規模MILP問題の効率的な解決を実現し、特に弱い期間間結合の場合に有効であることを示した。

English

This paper proposes clustering-enhanced Benders decomposition methods to address computational bottlenecks in energy system capacity expansion models. Adaptive grouped cuts and representative subproblem selection improve efficiency for large-scale MILP formulations, especially under weak inter-temporal coupling and limited parallelization.

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

日本のエネルギー転換計画では高解像度のシステムモデルが不可欠だが、計算負荷が課題となる。本手法は再生可能エネルギー比率の高い将来シナリオにおける計画最適化の効率化に貢献する可能性がある。

In the global GX context

This work advances computational scalability of capacity expansion models, critical for global energy transition planning. The methods enable more detailed and stochastic analyses, supporting robust policy design and renewable integration studies.

👥 読者別の含意

🔬研究者:Provides an adaptive decomposition technique that improves efficiency for large-scale energy system optimization, useful for those working on computational methods or energy planning.

🏢実務担当者:Energy planners and system operators can apply these methods to reduce computational time for capacity expansion studies, enabling faster scenario analyses.

🏛政策担当者:Faster, more detailed models allow policymakers to evaluate the impacts of decarbonization policies and renewable targets more efficiently.

📄 Abstract（原文）

High-resolution energy system capacity expansion models (CEMs) for energy transition planning often result in large-scale mixed-integer linear programming (MILP) formulations. Benders decomposition (BD) offers a scalable solution approach by iteratively solving a master problem (MP) for investment decisions and multiple subproblems (SPs) for operational decisions. However, accumulated Benders cuts generated by the SPs can make MP solution a major computational bottleneck. Incomplete SP parallelization can also introduce further bottlenecks when SPs exceed available CPUs. We develop clustering-enhanced BD methods to address these challenges, by using clustering to group similar SPs for: a) aggregated Benders cut construction and b) identification of representative SPs to be solved most frequently. For grouped-cuts, we examine two adaptive formulations based on dual variables and a fixed-grouping formulation based on exogenous time-series inputs. We evaluate these methods in an electricity-sector CEM across varying system sizes, temporal SP lengths, inter-SP coupling strengths represented by CO2 policy, computational resources, and stochastic settings. Relative to a benchmark regularized multi-cut formulation, adaptive grouped cuts outperform fixed grouping and provide substantial benefits under weak inter-temporal coupling. The largest gains occur in larger systems with shorter SP horizons, where the MP accounts for a greater share of runtime. Their effectiveness declines under strong inter-temporal coupling, such as annual CO2 emissions limits, where the benchmark multi-cut performs best. The representative-SP method outperforms the benchmark under limited parallelization when SP solution dominates runtime. Overall, the preferred BD strategy depends on inter-SP coupling strength and whether computational burden lies in the MP or the SPs.

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