二層最適化に基づく複合戦略を用いた溶融塩蓄熱システム連携火力発電所の深部ピークシェービング性能
Deep peak shaving performance of thermal power plant coupled with molten-salt heat storage system with combined strategy based on bi-level optimization (原題)
Yujie Xu, Chengfeng Zhang, Xianrong Zhang, Yang Liu, Yilin Zhu, Guoqing Shen, Haisheng Chen, Meng Liu
🤖 gxceed AI 要約
日本語
再生可能エネルギーの大量導入に伴う深部ピークシェービング圧力に対応するため、溶融塩蓄熱システムを連携した火力発電所（TPP-MSHSS）の3つの連携スキームを設計し、熱力学的・経済的性能を分析した。二層最適化による複合戦略を提案し、R3スキームが最高のピークシェービング能力（追加深度4.87～6.08%）を、R2スキームが最高の熱力学的性能と経済収益（典型日で290.71万元、炭素排出削減42.51 kg/MWh）を示した。
English
Three coupling schemes for a thermal power plant with molten-salt heat storage (TPP-MSHSS) are designed and analyzed for deep peak shaving under high renewable penetration. A bi-level optimization combined strategy is proposed: scheme R3 achieves the best peak-shaving depth (4.87–6.08%), while R2 shows the best thermodynamic performance and economic revenue (2.9071×10^6 yuan/day, 42.51 kg/MWh carbon reduction).
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
中国の石炭火力を活用した再エネ大量導入下での調整力確保策は、日本の電力システム改革や石炭火力のフェードアウト検討と共通する課題を提示する。炭素税を考慮した経済性評価は、日本企業の脱炭素投資判断や政策設計に示唆を与える。
In the global GX context
This study addresses the global challenge of integrating variable renewables by retrofitting thermal plants with storage, a key flexibility solution under decarbonization. Its inclusion of carbon tax and peak-shaving compensation in economic analysis offers insights for transition finance and policy design in carbon-constrained markets.
👥 読者別の含意
🔬研究者:溶融塩蓄熱と火力発電の連携最適化に関する熱力学的・経済的評価手法を提供する。
🏢実務担当者:蓄熱システム導入によるピークシェービング収益と炭素コスト削減の定量的な評価例として参考になる。
🏛政策担当者:炭素税やピークシェービング補償制度の設計において、蓄熱連携火力の経済性評価が政策立案に資する。
📄 Abstract（原文）
The large-scale deployment of renewable energy has intensified deep peak shaving pressure on power gird, forcing thermal power plants (TPP) into prolonged deep peak shaving frequently. TPP's thermodynamic characteristics and economic performance under deep peak shaving is pretty critical. However, TPP's current coupling schemes primarily focus on thermal energy storage technology or localized retrofits, lacking a systematic comparison and optimization of multiple coupling pathways. Besides, thermodynamic and economic analysis fails to consider power generation revenue, deep peak shaving compensation and carbon tax costs comprehensively. To end this, three coupled deep peak shaving schemes are designed for a thermal power plant coupled with molten-salt heat storage system (TPP-MSHSS), and thermodynamic and deep peak shaving performance is further analyzed. Furthermore, a combined deep peak shaving strategy for TPP-MSHSS is proposed, and the optimal capability of thermal energy storage is achieved with bi-level optimization. Specifically, deep peak shaving feasible scheme is achieved when achieving peak regulation demand with the combined strategy (upper-level), while the optimal thermal power plant coupled with molten-salt heat storage system is obtained with maximizing its comprehensive economic revenue (lower-level). Results indicate that TPP-MSHSS's thermal efficiency and deep peak shaving performance is enhanced significantly. The scheme R3 (#2 steam extraction-HP heater drain pipe) exhibits the best deep peak shaving capability, for which the upper and lower limits of newly added peak shaving depth are 4.87% (29.25 MW) and 6.08% (36.5 MW) respectively. The scheme R2 (reheating steam‑oxygen remover) for TPP-MSHSS with combined strategy demonstrates the best thermodynamic performance, along with a comprehensive revenue reaching of 2.9071 × 10 6 yuan for typical operating day and excess carbon emissions per unit of electricity decreasing by 42.51 kg/MWh. There is optimized heat storage load of 6.23 MW, heat release load of 124.98 MW and thermal storage capacity of 130 MWh for scheme R2 for TPP-MSHSS, thanks to bi-level optimization.
🔗 Provenance — このレコードを発見したソース
- primary_source https://doi.org/10.1016/j.est.2026.124589first seen 2026-09-27 23:54:19
🔔 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。