洋上エネルギー最適地をAIモデルで特定(Location, Location, Location: Model IDs Best Spots for Offshore Energy Projects)

2026-01-05 ノースカロライナ州立大学(NC State)

ノースカロライナ州立大学の研究チームは、洋上再生可能エネルギー施設の最適立地を科学的に評価する新たな分析手法を提示した。洋上風力や波力、潮流エネルギーの導入では、発電効率だけでなく、海洋生態系への影響、送電インフラ、建設・維持コスト、極端気象リスクなど複数要因を同時に考慮する必要がある。研究では、気象・海象データ、地形条件、環境制約、社会経済要因を統合したモデルを構築し、地域ごとに最も適したエネルギー源と設置海域を特定できることを示した。この手法により、無計画な開発を避けつつ、再生可能エネルギーの導入効果を最大化できる。沿岸地域のエネルギー戦略立案や持続可能な洋上エネルギー開発に資する意思決定ツールとして期待されている。

<関連情報>

海洋再生可能エネルギー資源の活用のための融合ポートフォリオ最適化 Fused portfolio optimization for harnessing marine renewable energy resources

Mary Maceda, Rob Miller, Victor A.D. de Faria, Matthew Bryant, Chris Vermillion, Anderson R. de Queiroz
Energy  Available online: 12 December 2025
DOI:https://doi.org/10.1016/j.energy.2025.139660

洋上エネルギー最適地をAIモデルで特定(Location, Location, Location: Model IDs Best Spots for Offshore Energy Projects)

Highlights

  • Reliable, transparent marine hydrokinetic energy-harvesting kite design model developed.
  • Novel fusion of energy-harvesting device optimization and portfolio optimization.
  • Fusion can result in lower costs per unit energy across the entire deployment.

Abstract

Offshore wind and marine hydrokinetic energy are underutilized energy resources. Efficiently exploiting these energy resources requires the identification of optimal deployment locations and optimal designs for offshore energy harvesting devices. These devices have the potential to be deployed in tandem such that the suite of devices consistently saturates a given power transmission system. To better understand the economic viability of harvesting marine renewable energy, a portfolio optimization is presented here. Portfolio optimization frameworks help to identify optimal deployment maps for energy-harvesting devices in a given domain and unify solutions of resource, technical performance, transmission, and cost model sub-problems into a unique and comprehensive tool. These frameworks select the energy-harvesting device designs in advance. This work proposes a portfolio optimization framework combined with optimal device design, sizing, and selection to enable a more realistic energy depiction that is beneficial to stakeholders. By maximizing power sent back to shore subject to a constraint on the levelized cost of energy, the algorithm creates an optimal mapping of devices that produces the maximum transmittable power and stabilizes portfolio variability in a cost-effective manner. Any reliably modeled offshore energy-harvesting device can be used within this framework. In this work, wind turbines and marine hydrokinetic kites are selected as a case study considering they are leading technologies for harvesting their respective energies. Results from this case study demonstrate optimal portfolios of devices for a location off the coast of North Carolina and show the utility of fusing device design optimization with the portfolio optimization.

0202海洋空間利用
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