Stochastic EMS for Optimal 24/7 Carbon-Free Energy Operations
24時間365日カーボンフリーエネルギー運用最適化のための確率的EMS (AI 翻訳)
Natanon Tongamrak, Kannapha Amaruchkul, W. Wangdee, J. Songsiri
🤖 gxceed AI 要約
日本語
本論文では、24時間365日カーボンフリーエネルギー（CFE）コンプライアンスを最小コストで達成するための二段階確率最適化モデルを提案する。太陽光発電、バッテリー貯蔵、および多様な調達源を考慮し、深層学習による予測を用いて負荷と太陽光発電の不確実性に対処する。15分分解能での運用最適化により、タイの事例でCFE移行の実現可能性を示す。
English
This paper proposes a two-stage stochastic optimization model for 24/7 carbon-free energy (CFE) operations, minimizing cost while meeting CFE compliance. Using deep learning forecasts for load and solar generation, the model optimizes battery and procurement decisions at 15-minute resolution. Applied to a case in Thailand, it provides a pathway for transitioning to carbon-free operations in emerging energy markets.
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
本論文で提案された24時間365日カーボンフリーエネルギー運用の最適化フレームワークは、日本企業のRE100達成やカーボンニュートラル目標に向けた実運用計画に応用可能である。特に、15分単位の高分解能運用や不確実性対応は、日本の電力システム改革や再エネ調達に示唆を与える。
In the global GX context
This paper addresses the operational challenge of 24/7 carbon-free energy matching, a key requirement for leading corporations under RE100 and the Climate Pledge. The stochastic optimization with deep learning forecasting offers a practical tool for emerging economies, contributing to the literature on energy management systems for deep decarbonization.
👥 読者別の含意
🔬研究者:This paper provides a novel two-stage stochastic optimization formulation for 24/7 CFE operations with explicit compliance parameters, relevant for energy systems optimization research.
🏢実務担当者:The model offers a practical approach for corporate energy managers to optimize battery and procurement decisions for carbon-free energy targets.
🏛政策担当者:The framework can inform policies on 24/7 carbon-free energy tracking and procurement strategies in emerging markets.
📄 Abstract（原文）
This paper proposes a two-stage stochastic optimization formulation to determine optimal operation and procurement plans for achieving a 24/7 carbon-free energy (CFE) compliance at minimized cost. The system in consideration follows primary energy technologies in Thailand including solar power, battery storage, and a diverse portfolio of renewable and carbon-based energy procurement sources. Unlike existing literature focused on long-term planning, this study addresses near real-time operations using a 15-minute resolution. A novel feature of the formulation is the explicit treatment of CFE compliance as a model parameter, enabling flexible targets such as a minimum percentage of hourly matching or a required number of carbon-free days within a multi-day horizon. The mixed-integer linear programming formulation accounts for uncertainties in load and solar generation by integrating deep learning-based forecasting within a receding horizon framework. By optimizing battery profiles and multi-source procurement simultaneously, the proposed system provides a feasible pathway for transitioning to carbon-free operations in emerging energy markets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.48550/arxiv.2601.15135first seen 2026-07-24 06:42:18
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。