Techno-Economic Optimization of Green Hydrogen Microgrids via NILM-Based Demand Side Management
NILMベースのデマンドサイドマネジメントによるグリーン水素マイクログリッドの技術経済的最適化 (AI 翻訳)
M. Raslan, M. Mostafa, Ahmed S. Abdellah, Diaa-Eldin A. Mansour, T. F. Megahed
🤖 gxceed AI 要約
日本語
本論文は、NILM（非侵入型負荷モニタリング）を用いたデマンドサイドマネジメント戦略を提案し、グリーン水素マイクログリッドのコスト最適化を図る。REDDデータセットから遅延可能家電（食洗機、洗濯機）を識別し、太陽光発電時間帯にシフトすることで、ピーク需要を約6%削減、燃料電池容量を16.7%低減、LCOEを11.6%改善した。エジプト・アレクサンドリアの地中海性気候を想定した年間シミュレーションで有効性を確認。
English
This paper proposes a Demand Side Management strategy using Non-Intrusive Load Monitoring (NILM) to optimize green hydrogen microgrid costs. By identifying and shifting deferrable appliances (dishwasher, washing machine) from evening peak to solar generation hours using the REDD dataset, peak demand is reduced by ~6%, fuel cell capacity by 16.7%, and LCOE by 11.6% (from 0.605 to 0.535 USD/kWh). A year-long simulation for Alexandria, Egypt confirms the techno-economic benefits.
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
日本では、再生可能エネルギー導入拡大に伴い、水素マイクログリッドの経済性向上が課題となっている。本論文のNILMによるDSM手法は、家庭用需要シフトによる水素システムのコスト削減に貢献する可能性があり、日本のスマートコミュニティや水素サプライチェーン構築に参考となる。
In the global GX context
Globally, green hydrogen microgrids face high costs due to component oversizing. This paper demonstrates how AI-driven load management (NILM) can reduce system costs by aligning demand with solar generation, a scalable approach for remote or islanded grids. The techno-economic optimization framework is relevant for off-grid renewable hydrogen projects in many regions.
👥 読者別の含意
🔬研究者:Demonstrates a practical integration of NILM with microgrid optimization using Fourier series scaling, useful for researchers in demand side management and hydrogen systems.
🏢実務担当者:Shows how simple load shifting can reduce hydrogen system costs; corporate energy managers can consider applying NILM to identify deferrable loads in facilities.
🏛政策担当者:Provides evidence that AI-enabled demand management can lower LCOE of green hydrogen, supporting policies that incentivize smart grid integration.
📄 Abstract（原文）
The main advantage of green hydrogen is its ability to provide long-term energy storage in the form of hydrogen, in a renewable manner in autonomous microgrids. The costs associated with hydrogen production via electrolysis and/or fuel cell conversion can be very expensive, particularly due to the inefficiencies of the round-trip process and component oversizing, which together result in high LCOEs. One reason for this over sizing is the disparity in the amount of electrical energy produced during the peak hours of solar generation and the time when most residential electricity demands occur in the evenings. This research proposes a specific DSM strategy based on NILM to manage the load, specifically through the use of appliance signature identification. Using REDD, the deferrable loads (dishwasher and washing machine) are identified and moved from their normal time of operation, which occurs during the evening peak (18:00-23:00) to coincide with the solar generation window (10:00-14:00). In order to perform a complete annual techno-economic analysis in HOMER Pro, the short-term empirical data is scaled up into an 8760-hour synthetic data set, utilizing a first-order Fourier series seasonal scaling model, designed to reflect the Mediterranean climate of Alexandria, Egypt. The results show that aligning deferrable appliances with PV generation resulted in a peak demand reduction of about 6 percent. As a consequence, the optimal fuel cell capacity was reduced by 16.7 percent (from 600 kW to 500 kW), resulting in an 11.6 reduction in LCOE (0.605 to 0.535 USD/kWh) and a savings of 297,768 USD in NPC.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/ipcs69631.2026.11604387first seen 2026-07-25 05:26:27
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