Mathematical optimization of renewable energy systems for sustainable development
Singh, Garima
🤖 gxceed AI 要約
日本語
本レビューは、再生可能エネルギーシステム（RES）の計画・統合・管理における数学的最適化手法（決定論的、確率論的、ヒューリスティック、AI）を体系的に分類・評価する。ハイブリッドRES、多目的最適化、エネルギー貯蔵、スマートグリッド最適化を詳述し、実装課題と今後の研究方向（デジタルツイン、説明可能AI、連合学習、ブロックチェーン）を示す。
English
This review systematically classifies and evaluates mathematical optimization methodologies (deterministic, stochastic, heuristic, AI) applied to Renewable Energy Systems (RES) planning, integration, and management. It covers hybrid RES, multi-objective optimization, energy storage, and smart grid optimization, discusses real-world deployment barriers (regulatory, economic, infrastructure), and outlines future research directions including Digital Twins, Explainable AI, Federated Learning, and Blockchain.
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の導入拡大と系統安定化がGX政策の柱である。本レビューは、出力変動対応の最適化手法を俯瞰するため、日本の電力系統運用者や再生可能エネルギー事業者にとって実践的な知見を提供する。
In the global GX context
Globally, the review addresses the critical challenge of integrating variable renewable energy into grids through optimization. It bridges theoretical modeling and practical implementation, relevant to energy transition planners and policymakers worldwide.
👥 読者別の含意
🔬研究者:Comprehensive taxonomy of optimization methods for RES, useful for identifying gaps and future research directions.
🏢実務担当者:Provides an overview of optimization techniques and real-world barriers for RES project planners and grid operators.
🏛政策担当者:Offers insights into regulatory and infrastructure barriers that need addressing for renewable energy deployment.
📄 Abstract（原文）
Due to the growing worldwide need for renewable energy sources, Renewable Energy Systems (RES) have been rapidly developed and deployed in response to global needs for cleaner energy and environmental damage from the use of fossil fuels. However, the inherent variability and uncertainty associated with renewable energy resources create significant challenges to RES planning, integration of RES into the overall energy system, and management of the RES. This overview examines the various types of mathematical optimization methodologies that have been applied to RES; it examines deterministic classical methods, stochastic approaches to optimizing RES, heuristics and metaheuristic algorithms, and artificial intelligence (AI) solutions through a systematic examination according to a structured taxonomy. The overall critical review describes the strength and weakness of optimization methodologies and provides an assessment of the computing resources available for each type of optimization method, describes the techniques for quantifying uncertainty, explains the principles and techniques used in probabilistic forecasting, and addresses the real-world deployment challenges associated with RES including regulatory, economic, financial, and infrastructure barriers. The overview also describes in detail hybrid renewable energy systems (HRES), multi-objective optimization methods, integration of energy storage systems, and smart grid optimization processes utilizing supporting comparison tables and illustrative examples. The review outlines areas for future research by suggesting using innovations such as Digital Twins, Explainable AI, Federated Learning, and Blockchain technologies for enhancing energy systems management. This review distinguishes itself from previous literature in that it synthesizes findings from multiple optimization paradigms and bridges the gap between theoretical modeling and practical implementation challenges.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20551819first seen 2026-06-23 04:15:59 · last seen 2026-06-25 04:23:29
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