Energy Management System Optimization for Industrial Buildings
産業用ビルのエネルギー管理システム最適化 (AI 翻訳)
M. Quercio, L. Sabino, Davide Milillo, Nistor Alexandru Sorin, G. Lăzăroiu, F. R. Fulginei
🤖 gxceed AI 要約
日本語
この研究は産業施設のエネルギー管理最適化を調査。太陽光予測、EV充電状態推定、貯蔵ソリューションを対象に、6MWのPVと駐車場用キャノピー施設でシミュレーション。週間余剰電力123MWhを確認し、蓄電池・水素製造・系統連系の最適化法を提案、経済的実現可能性を示した。
English
This study investigates energy management optimization for an industrial facility cluster with 6 MW PV, EV charging, and storage. Simulations show a weekly surplus of 123 MWh. Optimized methods include battery storage, hydrogen production, and grid exchange protocols, demonstrating economic feasibility and synergies between renewables and EV infrastructure.
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
本論文は産業施設における太陽光とEVインフラの統合最適化を実証。FIT終了後の自家消費型太陽光普及やEV充電管理の高度化に示唆を与え、余剰電力を水素や蓄電池で活用する手法は日本の水素社会戦略とも整合する。
In the global GX context
This paper provides a practical case study on optimizing renewable energy, EV charging, and storage in an industrial setting. The integrated approach and economic feasibility analysis offer insights for global industrial decarbonization, especially for facilities with high renewable penetration.
👥 読者別の含意
🔬研究者:Offers a simulation-based framework for integrating PV, EV, and storage optimization in industrial premises.
🏢実務担当者:Useful for facility managers seeking to optimize onsite renewables, EV charging, and storage economics.
🏛政策担当者:Highlights potential of industrial demand-side management and could inform incentive design for renewable self-consumption combined with EV infrastructure.
📄 Abstract（原文）
This research investigates energy management and optimization techniques for an industrial facility cluster, focusing on solar power forecasting, State of Charge (SOC) estimation for electric vehicles (EVs), and the creation of a unified energy storage solution. The site features a 6 MW photovoltaic (PV) array, with additional plans to install PV canopies over a parking lot for 100 vehicles, including 25 EVs. Rooftop PV output powers the buildings during business hours, while parking-area PV systems feed Level 2 (L2) chargers rated at 11 kW for EV recharging. Simulations in MATLAB evaluated PV generation patterns, EV load curves, and excess energy use. Outcomes show the setup produces sample power for building and EV needs, yielding a 123 MWh weekly surplus. Additional studies identified efficient ways to leverage this excess, resulting in refined optimization methods such as battery storage, hydrogen production, and grid exchange protocols, bolstered by advanced prediction models. A probabilistic framework modeled EV arrival distributions, pinpointing peak charging intervals. Results propose reduced charging speeds to alleviate system overloads. Overall, the work demonstrates viable synergies between renewables and EV infrastructure and energy storage solutions, providing valuable insights into sustainable practices and their economic feasibility. Future work will focus on enhancing real-time energy management systems using SIMULINK-based modeling to further refine operational efficiency and sustainability.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/ssd69655.2026.11558944first seen 2026-06-21 05:55:32 · last seen 2026-06-30 05:41:42
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