社会経済的・気候的不確実性が西アフリカ全域の太陽光発電コストと貯蔵ニーズを左右する
Socioeconomic and climate uncertainty shape solar electricity costs and storage needs across West Africa (原題)
Xiaojing Lin, Xin Sun, Sarah Féron, Raúl R. Cordero, BO BAI, Wided Medjroubi, Yadong Yu, Qianzhi Zhang, Shi Chen, Shuangqi Li, Ming Xu, Klaus Hubacek
🤖 gxceed AI 要約
日本語
西アフリカの184地区を対象に、気候シナリオ13種のもとで太陽光発電とバッテリー・水素貯蔵のコスト分解型評価を実施。太陽光発電コストは0.100〜0.409 USD/kWhと幅広く、リベリアやシエラレオネは気候不確実性に脆弱だが、セネガルやガンビアは投資耐性が高い。最適貯蔵構成は地域ごとに異なり、バッテリー重視と水素重視の地区が混在。太陽光のみと比べ、全地区で電力不足確率を低減し、74%の地区で余剰電力廃棄確率を低減した。
English
This study evaluates utility-scale solar and off-grid systems with battery and hydrogen storage across 184 districts in West Africa under 13 climate scenarios. Solar electricity costs range from $0.100 to $0.409/kWh, with Liberia and Sierra Leone most vulnerable to climate uncertainty, while Senegal and The Gambia show resilience. Optimized storage configurations vary geographically, balancing cost, supply adequacy, and renewable utilization. Compared to solar-only systems, they reduce loss of power supply probability in all districts and potential energy waste in 74% of districts, supporting climate-aware electrification 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
日本では再生可能エネルギー導入拡大と蓄電・水素の役割が議論されており、本論文のコスト分解と最適貯蔵構成の方法論は、日本の離島や地域エネルギー計画にも応用可能。また、気候変動適応とエネルギー計画の統合は、日本の国際協力(JICA等)の西アフリカ支援にも示唆を与える。
In the global GX context
This paper contributes to global GX scholarship by integrating climate uncertainty into techno-economic planning for solar and storage, a critical issue for energy transition in data-scarce regions. Its cost-disaggregated framework and multi-objective optimization offer a replicable methodology for other developing regions. The findings highlight the importance of climate-aware planning for renewable deployment, relevant to international climate finance and SDG7 goals.
👥 読者別の含意
🔬研究者:Provides a replicable cost-disaggregated framework for solar+storage planning under climate uncertainty, useful for energy system modeling in data-scarce regions.
🏢実務担当者:Offers insights for project developers and investors on regional cost variability and optimal storage configurations in West Africa, aiding investment decisions.
🏛政策担当者:Highlights the need for climate-aware electrification planning and the role of storage in improving reliability, informing energy policy in West Africa and similar regions.
📄 Abstract(原文)
West Africa faces electricity deficits, yet solar deployment remains hindered by insufficient spatial economic planning and uncertainty in climate conditions. We develop a cost-disaggregated framework to evaluate utility-scale solar power and off-grid solar systems with battery and hydrogen storage across 184 districts in the West African Power Pool under 13 climate scenarios. Here we show that baseline solar electricity costs range from 0.100 to 0.409 US dollars per kilowatt-hour, with Liberia and Sierra Leone most vulnerable to uncertainty in solar resources, whereas Senegal and The Gambia show greater investment resilience. Multi-objective optimized storage configurations vary geographically, reflecting trade-offs among system cost, supply adequacy and renewable-energy utilization. Some districts favor battery-intensive systems, whereas others favor larger hydrogen capacities. Relative to solar-only systems, the selected configurations reduce loss of power supply probability in all districts and potential energy waste probability in 74% of districts. These results support climate-aware electrification planning in data-scarce regions. Solar costs vary widely across West Africa, with climate uncertainty shaping regional viability and optimized storage improving reliability; according to techno-economic modeling and optimization across 184 districts and 13 climate scenarios.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1038/s43247-026-03996-wfirst seen 2026-09-01 05:16:23
🔔 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。