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動的系統条件下における住宅用24時間365日カーボンフリー電力のための蓄電池と蓄熱のカーボンアウェア最適化

Carbon-Aware Optimization of Battery and Thermal Storage for Residential 24/7 Carbon-Free Electricity Under Dynamic Grid Conditions (原題)

Chen Zhou, Yingjun Ruan, Hua Meng, Yuting Yao, Yueqiu Xia

Buildings📚 査読済 / ジャーナル2026-09-04#エネルギー転換Origin: CN経営インパクト: コスト削減対象セクター: residential
DOI: 10.3390/buildings16173529
原典: https://doi.org/10.3390/buildings16173529

🤖 gxceed AI 要約

日本語

本研究は、不確実な負荷・PV発電・系統炭素強度の下で、住宅用24/7カーボンフリー電力を実現する確率的カーボンアウェア最適化フレームワークを開発。KDE-Copulaシナリオ生成とARMA残差シナリオを用い、二段階混合整数線形計画法で蓄電池と蓄熱の容量・運用を同時最適化し、ライフサイクルCO2排出を最小化する。結果、蓄電池容量100kWhで年間排出量が約30%削減され、蓄熱は補完的役割を担うことを示した。

English

This study develops a probabilistic carbon-aware optimization framework for residential 24/7 carbon-free electricity under uncertain load, PV, and grid carbon intensity. Using KDE-Copula scenarios and ARMA residuals, a two-stage MILP jointly sizes and dispatches battery and thermal storage to minimize lifecycle CO2. Results show that increasing battery capacity to 100 kWh cuts annual emissions by ~30%, while thermal storage plays a complementary role.

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

日本の住宅部門の脱炭素化やZEH推進、カーボンフリー電力調達の動きに示唆を与える。蓄電池と蓄熱の協調運用による系統負荷平準化は、再エネ導入拡大と電力需給逼迫対策に貢献し、今後の住宅用エネルギー管理システム設計に有用。

In the global GX context

This paper contributes to global efforts on 24/7 carbon-free energy matching, a key trend in corporate PPAs and clean energy procurement. The optimization framework for residential storage can inform demand-side flexibility and grid decarbonization strategies, relevant to ISSB-aligned disclosure of emissions reduction initiatives.

👥 読者別の含意

🔬研究者:Provides a novel probabilistic optimization method for residential storage sizing and dispatch under carbon constraints.

🏢実務担当者:Offers a practical framework for designing residential electrification systems with storage to reduce carbon footprint.

🏛政策担当者:Highlights the role of behind-the-meter storage in achieving 24/7 clean energy targets, informing incentive programs.

📄 Abstract（原文）

This study develops a probabilistic carbon-aware optimization framework for residential 24/7 carbon-free electricity (CFE) under uncertain load, PV generation and dynamic grid carbon intensity. Historical half-hourly monitoring data are represented through KDE-Copula scenario generation, while grid carbon factors are described by time-series decomposition and ARMA-based residual scenarios. A two-stage mixed-integer linear program jointly sizes and dispatches battery energy storage (BESS) and thermal energy storage (TES) by minimizing annualized lifecycle CO2 emissions. The results show that coordinated BESS–TES operation improves PV utilization and avoids carbon-intensive grid imports, especially in winter. In the examined capacity range, increasing BESS capacity from 0 to 100 kWh reduces annual lifecycle emissions from approximately 8600 to 6000 kg CO2 yr−1, corresponding to about a 30% reduction, whereas the marginal benefit of additional TES is constrained by DHW demand and its embodied emissions. Electrical storage is therefore the principal carbon-shifting resource, while a moderately sized TES complements it by moving heat-pump operation toward low-carbon and PV-rich periods. The framework provides a practical basis for carbon-aware design of residential electrification systems.

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