High-resolution modeling for the integrated planning of generation, storage, and transmission in energy networks
発電、貯蔵、送電の統合計画のための高解像度モデリング (AI 翻訳)
Cristian Cafarella, Michele Ronchi, Marco Bortolini, Mauro Gamberi, Erik Delarue
🤖 gxceed AI 要約
日本語
本論文は、高時間解像度、多ノード空間構成、クラスタ化ユニットコミットメント(CUC)を組み合わせた発電・貯蔵・送電統合拡張計画モデルを提案。イタリア電力システムを対象に2021年ベースラインと2030年シナリオを分析し、再生可能エネルギー比率が40%から51-56.5%に増加、排出原単位が0.315から0.196-0.177 kg CO2-eq/kWhに低減することを示した。統合高解像度計画の価値を実証。
English
This paper proposes a high-resolution optimization model for integrated expansion planning of generation, storage, and transmission, combining high temporal resolution, multi-node spatial configuration, and Clustered Unit Commitment (CUC). Applied to the Italian power system for 2021 baseline and 2030 scenarios, it shows renewable share increasing from 40% to 51-56.5% and emission intensity dropping from 0.315 to 0.196-0.177 kg CO2-eq/kWh, demonstrating the value of integrated high-resolution planning.
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
本論文の高解像度モデルは、日本の再生可能エネルギー大量導入時の系統計画に示唆を与える。特に地域間連系線増強や蓄電池配置の検討に有用であり、SSBJやTCFDのシナリオ分析にも応用可能。
In the global GX context
This paper provides a high-resolution optimization framework for integrated generation, storage, and transmission planning, applicable to power systems worldwide. Its scenario analysis with carbon tax and IRES support levels offers insights for policy design and corporate transition planning, relevant to TCFD/ISSB disclosure.
👥 読者別の含意
🔬研究者:A high-resolution model capturing generation-storage-transmission interactions, useful for energy system modelers and those studying renewable integration.
🏢実務担当者:The framework can inform utility-scale renewable and storage investment decisions and grid expansion planning.
🏛政策担当者:The scenario analysis shows the impact of carbon tax and IRES support levels, aiding design of energy and climate policies.
📄 Abstract(原文)
Abstract The increasing integration of Intermittent Renewable Energy Sources (IRES) and the need for decarbonization are driving significant transformations in the expansion planning of electrical power systems. In this context, the literature highlights the need to integrate long-term strategic decisions with short-term operational constraints in planning models, while preserving high resolution across time, space, and techno-economic detail. This paper addresses this challenge by proposing a high-resolution optimization model for the integrated expansion planning of generation, storage, and transmission in electrical power systems. The proposed model combines high temporal resolution, a multi-node spatial configuration, and a Clustered Unit Commitment (CUC) formulation to account for key operational constraints while limiting computational cost. The practical applicability of the model in real-world contexts is demonstrated through a multi-scenario analysis focused on the Italian electricity system, including a 2021 baseline scenario and 2030 future scenarios differing in terms of carbon tax and IRES support level. The validation of the model outputs in the baseline scenario against the 2021 historical electricity generation mix results in a Mean Absolute Percentage Error (MAPE) of 0.8%. In the 2030 scenarios, the renewable share in electricity generation increases from 40.0% in the 2021 baseline to 51.0–56.5%, while average emission intensity decreases from 0.315 to 0.196–0.177 kg CO₂-eq/kWh. These outcomes are associated with targeted IRES investments, especially in southern Italy and the islands, together with storage deployment and additional transmission capacity along the south–north connections, highlighting the value of integrated high-resolution planning for capturing generation-storage-transmission interactions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s12667-026-00812-4first seen 2026-07-26 04:55:33
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