国家レベルのエネルギー強靱性評価に向けた重要発電ユニットの分散指標の開発

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🔬研究者:空間統計指標（ジニ係数・モラン統計量）をエネルギー強靱性評価に応用する方法論的枠組みを提供する。

🏢実務担当者:自社電源・サプライチェーンの地理的集中リスクを評価し、BCPや調達分散戦略の根拠として活用できる。

🏛政策担当者:国家エネルギー計画や強靱化政策において、電源分散度を定量的指標として組み込む際の比較可能な手法を提示する。

Energy Security as a concept has gained increasing importance these past years, as sociopolitical developments, such as energy crises, climate change and energy transition are dynamically reshaping the way European Union’s countries secure access to reliable and affordable energy. The concept is no longer limited to ensuring energy availability but also includes affordability, diversification and mix of energy sources and sustainability. Within this concept, energy resilience, defined as the ability of energy systems to withstand shocks and crises and recover rapidly and effectively, compliments and strengthens the broader approach to energy security. A key element of energy resilience is the spatial distribution of power generation units, which is rarely considered explicitly in national energy-security or resilience assessments, despite the fact that dispersion of infrastructures could reduce the overall vulnerability of the energy system to localized disruptions, as previous studies have highlighted. Based on this background, the present study aims to investigate how the spatial distribution and characteristics of power generation units are associated with a country's energy resilience. This thesis develops a geospatial framework for assessing the dispersion and concentration of power-generation infrastructure at national level and examines the suitability of different spatial indicators for cross-country comparison. The methodology combines Ripley’s K analysis, capacity-weighted point-pattern analysis, Voronoi/Thiessen Tessellation, a Capacity- to-Area indicator, Gini coefficients, Global Moran’s I and Local Indicators of Spatial Association (LISA). The methodology was first applied in detail to Switzerland and subsequently extended to a European sample of 27 countries. Some key results were revealed. First, spatial analysis weighted by capacity shifted the results toward greater dispersion, compared with plant locations alone, in 25 of 27 countries in the application of Ripley's K algorithm. Second, the ordering Gini_ A < Gini MW < Gini CA held in 25 of 27 countries, a finding that shows that inequality grew stronger as each of these three quantities (Area, Capacity and Capacity to Area) was considered in turn. Following, combining global and local autocorrelation results divided the sample into four structural types: twelve countries show both national-scale clustering and local high-density hubs, one shows national clustering without local hubs, eight show local hubs without a national pattern, and five, including Switzerland which was analysed in detail in Chapter 4, show neither. The comparison of indicators showed that not all of them are equally reliable across countries of different size, as the Gini coefficients depended very little on plant count or geographic extent, while Net Clustering Index NCI* magnitude, by contrast, stayed closely tied to a country's maximum internal distance even after normalisation, and raw LISA cluster counts are correlated with sample size. Gini coefficients are the most stable basis for comparing countries, complemented by Global Moran's I and the percentage of statistically significant LISA zones. Overall, the thesis’ findings indicate that spatial dispersion can not be assessed by a single indicator, but a set of related dimensions. As a conclusion, the proposed methodology is intended to cover the spatial dimension, complementing a fuller assessment of energy resilience.

• openalex https://doi.org/10.26233/heallink.tuc.105782first seen 2026-10-02 04:38:08