低炭素グリッドシナリオ下での太陽光発電システム規模決定のための多目的最適化モデルの分析
Analysis of multi-objective optimization models for sizing solar photovoltaic systems under a low-carbon grid scenario (原題)
Santiago Valencia Gonzalez, Joakim Munkhammar, Andreas Theocharis, Xingxing Zhang
🤖 gxceed AI 要約
日本語
本研究は、建物の太陽光発電（PV）システムの容量最適化において、技術的・経済的目的と環境目的の間のトレードオフを分析した。スウェーデンの低炭素電力グリッド条件下で、NSGA-IIを用いた3つの多目的最適化モデルを比較し、技術的・経済的目的の最適化がライフサイクルGWPを最大10.3%増加させる可能性があることを示した。PVシステムの体化影響に依存するが、最適化目的の選択が環境影響に大きく影響することを明らかにした。
English
This study analyzes trade-offs between technical/economic and environmental objectives in sizing building solar PV systems. Using NSGA-II, three multi-objective models are compared under Swedish low-carbon grid conditions. Results show that optimizing technical or economic objectives can increase life-cycle GWP by up to 10.3% or reduce it by up to 5.3%, depending on embodied impacts. The study highlights that optimization objectives significantly affect environmental outcomes.
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、FIT卒業後の自家消費型太陽光発電の導入拡大が進む中、PV容量設計の最適化は経済性だけでなく、ライフサイクルでの排出削減効果を考慮する必要がある。本研究成果は、日本の建物部門でのPV導入戦略やカーボンニュートラル目標達成に示唆を与える。
In the global GX context
Globally, this study contributes to the discourse on optimizing renewable energy systems with life-cycle thinking. It challenges the assumption that technical/economic optimization automatically yields environmental benefits, relevant for grid decarbonization and building energy policies worldwide.
👥 読者別の含意
🔬研究者:Highlights the importance of considering life-cycle GWP in PV optimization models, offering a methodological framework for multi-objective analysis.
🏢実務担当者:Informs building owners and PV system designers that economic optimization alone may not reduce carbon footprint, suggesting inclusion of environmental objectives.
🏛政策担当者:Suggests that policies promoting PV adoption should consider life-cycle impacts and encourage multi-objective optimization to align with climate goals.
📄 Abstract（原文）
Abstract The optimization of solar photovoltaic (PV) systems has been proposed to reduce greenhouse gas emissions in building energy use. However, despite the environmental concerns shown, most of the studies optimize technical and/or economic objectives. Recent studies show that optimizing costs rather than carbon can worsen life cycle emissions, driven by battery operation. Whether this conflict remains in storage-free PV systems in buildings remains unexplored. Therefore, this study aims to quantify the life cycle global warming potential (GWP) effects of selecting different optimization objectives in a PV capacity optimization model. To achieve this, three multi-objective optimization models of a PV system are compared, using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II): (1) maximizing self-sufficiency and self-consumption, (2) maximizing the internal rate of return and net present value (NPV), and (3) minimizing global warming potential and maximizing NPV. The results show that the solutions provided by models with only technical or economic objectives do not always lead to a reduction in GWP. Furthermore, optimizing technical or economic objectives can increase GWP by up to 10.3% or reduce it by up to 5.3%, depending on the embodied impact of the PV system. These results are obtained under Swedish conditions, where the electricity supplied by the grid has a low carbon intensity. Nevertheless, this study shows that optimizing PV generation capacity for technical or economic objectives does not necessarily reduce life-cycle GWP.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s43621-026-04502-0first seen 2026-09-02 04:52:42
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