AI-Based Predictive Control of Hybrid Offshore Floating Wind–Wave–Solar Systems for Resilient Microgrids
AIベースの予測制御による浮体式洋上風力・波力・太陽光ハイブリッドシステムの強靭なマイクログリッドへの応用 (AI 翻訳)
Simon Utsu-Ingwu, Lloyd Endurance Ogbondamati
🤖 gxceed AI 要約
日本語
本研究は、洋上風力、波力、浮体式太陽光を統合したハイブリッドシステムを提案し、AIベースの予測制御（モデル予測制御と強化学習）によりエネルギー供給の平滑化とコスト削減を実現。シミュレーションでは、出力変動を抑制し、平均1567kWを維持、LCOEは13.84〜39.15 $/MWh、GHG削減は626.93トンと試算された。再生可能エネルギーの統合とAI制御の有効性を示す実証研究である。
English
This study proposes a hybrid offshore energy system integrating wind, wave, and floating solar, controlled by AI-based predictive control (MPC and reinforcement learning). Simulations show smoothed output averaging 1567 kW, LCOE of 13.84-39.15 $/MWh, and GHG reduction of 626.93 tons, demonstrating improved reliability and cost-effectiveness over single-source systems.
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
日本は洋上風力の導入拡大を進めており、浮体式技術は水深の深い海域でのポテンシャルが高い。本研究成果は、再生可能エネルギーの統合とAI制御による安定供給の実証として、日本のエネルギー政策や洋上風力産業に示唆を与える。
In the global GX context
Globally, the integration of multiple renewables with AI-based control is critical for grid stability and energy transition. This study provides quantitative evidence of hybrid system benefits, relevant to ISSB/TCFD-aligned climate risk assessment and transition finance for offshore energy projects.
👥 読者別の含意
🔬研究者:Provides a quantitative framework for AI-based control of hybrid offshore renewables, useful for further optimization studies.
🏢実務担当者:Offers insights into system design and control strategies for offshore hybrid microgrids, potentially informing project development.
🏛政策担当者:Demonstrates the technical and economic viability of hybrid offshore systems, supporting policy incentives for renewable integration.
📄 Abstract（原文）
The increasing demand for sustainable and reliable offshore energy necessitates the integration of multiple renewable sources into resilient microgrids. This study addresses the limitations of standalone wind, wave, and floating solar systems, which exhibit high intermittency and volatility, compromising energy reliability and grid stability. To overcome these challenges, a hybrid offshore energy system is proposed, combining wind turbines, wave energy converters, and floating solar PV arrays. An AI-based predictive control framework, incorporating model predictive control and reinforcement learning, optimizes real-time energy dispatch, storage coordination, and demand tracking under variable environmental conditions. Simulation results demonstrate that standalone wind, wave, and solar generation fluctuated between 532.22–1332.80 kW, 210.83–701.00 kW, and 228.27–281.16 kW, respectively. In contrast, the integrated hybrid system achieved a smoothed total output ranging from 971.39 kW to 2314.95 kW, maintaining a mean operational capacity of 1567.33 kW, consistently exceeding peak load demands. The model predictive control minimized transient cost from 101.09 to 6.41, while reinforcement learning optimized cumulative policy rewards to 84.90. Economic evaluation revealed a competitive levelized cost of energy between 13.84–39.15 $/MWh, and environmental analysis confirmed greenhouse gas mitigation of 626.93 metric tons, surpassing single-source systems. The findings provide actionable insights for policymakers and stakeholders in designing resilient offshore hybrid microgrids that enhance energy security, reduce operational risks, and promote sustainable energy deployment.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21927946first seen 2026-08-15 04:24:39 · last seen 2026-08-16 04:27:17
🔔 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。