サブサハラ・アフリカにおける不確実性下の最適電源拡張計画：確率的双対動的計画法アプローチ
Optimal power expansion planning under uncertainty in sub-Saharan Africa: A stochastic dual dynamic programming approach (原題)
Arega Getaneh Abate, Xiao-Bing Zhang, Xiufeng Liu, Ruyu Liu, Per Sieverts Nielsen
🤖 gxceed AI 要約
日本語
需要と水文の不確実性をマルコフ過程で表現し、建設リードタイムと信頼性を内生化した多段階確率電源拡張モデルをSDDPで解いた研究。エチオピアでは水力中心、ジンバブエでは太陽光・水力・ガスと残余石炭が最適経路となる。決定論的計画に比べ未供給電力量を約78TWh削減。炭素税は資源コストを3〜5%押し上げるが、優遇ソーラー融資との組合せが低排出と低コストを両立する。
English
A multistage stochastic generation expansion model solved with SDDP, incorporating Markov demand and country-specific hydrological uncertainty, construction lead times, and endogenous reliability, applied to Ethiopia and Zimbabwe for 2025-2050. Ethiopia's least-cost path is hydropower with growing wind and solar; Zimbabwe expands solar, hydro, and gas while residual coal retires. The stochastic plan cuts expected unserved energy by ~78 TWh versus a deterministic plan. Carbon pricing raises resource costs 3-5%, and pairing a carbon tax with concessional solar finance yields the lowest emissions while keeping costs below baseline.
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
日本企業が関与する途上国IPP・脱炭素ファイナンス案件の投資リスク評価に、確率的計画手法と炭素価格設計の視点を提供する。
In the global GX context
Adds to global transition-finance and climate-policy scholarship by quantifying how carbon pricing design and concessional clean finance shape decarbonization costs in developing power systems, relevant to ISSB-aligned transition planning and blended finance debates.
👥 読者別の含意
🔬研究者:不確実性下の電源拡張計画にSDDPとマルコフ水文を組み込む手法の実証例として有用。
🏢実務担当者:途上国電力・再エネ投資の案件評価で、炭素価格と優遇融資がコストに与える影響を見積もる際の参考になる。
🏛政策担当者:炭素税と排出上限の費用比較、および優遇クリーン融資の組合せ設計の政策的含意を提供する。
📄 Abstract（原文）
Universal electricity access and decarbonization are interlinked challenges for sub-Saharan Africa. Generation expansion planning methods used in the region rarely represent sequential and irreversible investment under demand and hydrological uncertainty. This paper develops a multistage stochastic generation expansion model solved with stochastic dual dynamic programming (SDDP) to address the problem. The model represents demand and country-specific Markov hydrological uncertainty, accounts for technology-specific construction lead times, and endogenous reliability. The model is applied to the Ethiopia and Zimbabwe power systems over 2025--2050. The results show that for Ethiopia, the least-cost pathway is hydropower, with wind and solar sources providing increasing diversification. The model reduces cumulative expected unserved energy by about 78 TWh relative to a lower-cost deterministic expected value investment plan. For Zimbabwe, the least-cost pathway expands solar, hydropower, and gas while retaining residual coal as inherited coal capacity retires. Additionally, carbon pricing reduces emissions and increases resource costs by about 3\% to 5\%, creating a gradual cost-emissions trade-off. Combining a carbon tax policy with concessional solar financing renders the lowest average annual emissions while keeping resource costs below the baseline, which solves the model without emission constraint. At approximately matched expected emissions, emission caps have higher net resource costs than the corresponding carbon tax scenarios. The overall results imply that adaptive investment model can reduce power shortage exposure, while carbon policy design and lower clean investment costs shape the cost of decarbonization in developing power systems.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2611.11490first seen 2026-11-10 04:10:42 · last seen 2026-11-12 04:10:31
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