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人間–コボット製造への太陽光発電と蓄電池のリスク考慮型統合：持続可能な産業転換のためのデジタルツイン対応協調最適化モデル

Risk-Aware Integration of Photovoltaics and Battery Storage into Human–Cobot Manufacturing: A Digital-Twin-Ready Co-Optimization Model for Sustainable Industrial Transition (原題)

Łukasz Kański, Jakub Pizoń

Sustainability📚 査読済 / ジャーナル2026-10-02#エネルギー転換経営インパクト: コスト削減対象セクター: manufacturing
DOI: 10.3390/su181910065
原典: https://doi.org/10.3390/su181910065

🤖 gxceed AI 要約

日本語

人間とコボットの協働生産スケジューリング、オンサイト太陽光発電、蓄電池を同時に最適化するリスク考慮型モデルを開発。CVaRと1000シナリオのモンテカルロで5戦略を比較した。リスク中立型は平均日次コストを59.2%、排出を46.1%削減し生産遅延も回避。リスク考慮型はCVaR95をさらに2.6%改善するが平均コストは4.2%増加した。

English

This study builds a risk-aware co-optimization model linking human–cobot production scheduling, on-site PV generation, and battery storage in a digital-twin-ready (offline prescriptive) setting. Using MILP with CVaR and 1000 Monte Carlo daily scenarios, five strategies were compared. Risk-neutral co-optimization cut mean daily cost 59.2%, emissions 46.1%, and grid use 48.3% without delaying production; risk-aware optimization further reduced CVaR95 cost 2.6% at a 4.2% mean-cost increase. Viability remains site-specific.

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

日本の製造業が工場の脱炭素と電力コスト管理を両立させる際、生産計画と再エネ・蓄電池を統合する実務的枠組みを提供する。デジタルツイン化やSSBJ対応のScope1/2削減策の設計に示唆を与える。

In the global GX context

Adds to global industrial decarbonization scholarship by quantifying how scheduling flexibility and explicit risk aversion interact in on-site PV-plus-storage manufacturing, relevant to Scope 2 accounting and corporate transition planning under ISSB/CSRD.

👥 読者別の含意

🔬研究者:生産スケジューリングと再エネ・蓄電池の協調最適化にCVaRを組み込む定式化の実例として参考になる。

🏢実務担当者:工場の再エネ・蓄電池導入と生産計画の統合により、コストと排出の同時削減余地を評価する枠組みとして使える。

🏛政策担当者:産業の電力柔軟性がエネルギー転換の資源になり得る点を、需要側政策の設計に反映する材料となる。

📄 Abstract（原文）

The increasing penetration of variable renewable energy requires industrial facilities to manage energy supply, production flexibility, and cost exposure simultaneously. This study develops a risk-aware model for the co-optimization of human–cobot production scheduling, on-site photovoltaic generation, and battery energy storage within a digital-twin-ready decision environment. The artifact is an offline prescriptive model; it is not connected to a physical production system and does not include automatic or real-time data exchange. A two-stage computational experiment was conducted using mixed-integer linear programming with conditional value-at-risk and an out-of-sample Monte Carlo simulation comprising 1000 independent daily scenarios of electricity prices, photovoltaic generation, and grid carbon intensity. Five strategies were compared: grid-only supply, photovoltaic generation, photovoltaics with battery storage, risk-neutral co-optimization, and risk-aware co-optimization. Relative to the modeled baseline, the risk-neutral variant reduced mean daily operating cost by 59.2%, operational emissions by 46.1%, and grid-energy use by 48.3%, without delaying production. The risk-aware variant reduced the CVaR95 of daily cost by a further 2.6% relative to the risk-neutral solution, at a 4.2% increase in mean cost. These percentages are scenario-specific operational effects rather than plant-level, annual, or life-cycle estimates. A screening-level investment assessment indicates that economic viability remains contingent on site-specific capital costs, operating days, tariffs, and implementation expenditure. The findings show that scheduling flexibility is a resource of energy transition, while risk aversion should be calibrated explicitly as a managerial decision.

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