An integrated geospatial modelling framework of hybrid microgrid sizing for rural electrification planning

被引:0
作者
Silinto, Berino Francisco [1 ,2 ]
Edeme, Darlain [3 ]
Corigliano, Silvia [3 ]
Dimovski, Aleksandar [3 ]
Merlo, Marco [3 ]
Zuidema, Christian [1 ]
Faaij, Andre [4 ,5 ]
机构
[1] Univ Groningen, Fac Spatial Sci, Dept Planning, Landleven 1, NL-9747 AD Groningen, Netherlands
[2] Univ Eduardo Mondlane, Fac Engn, Dept Mech Engn, Ave Mocamb Km 1-5, Maputo, Mozambique
[3] Polytech Univ Milan, Piazza Leonardo da Vinci 32, Milan, Italy
[4] Univ Groningen, Integrated Res Energy Environm & Soc, Nijenborgh 6, NL-9700 AE Groningen, Netherlands
[5] TNO Energy & Mat Transit, Utrecht, Netherlands
关键词
Optimization; Least-cost electrification; Grid routing; Hybrid renewable energy; GISEle; Geospatial planning; GENERATION; PATTERNS; CLIMATE; BASIN; RIVER;
D O I
10.1016/j.mex.2025.103153
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Pursuing rural electrification in developing countries through hybrid generation systems is con- strained by a lack of suitable energy modelling tools. Few tools include geographical parameters relevant to capturing specific spatial and socio-economic circumstances. Even less are openly available and find applications for rural areas of developing countries. This work presents an integrated geospatial energy modelling framework based on an extended tool, the GISELE (GIS for rural electrification) model, which aims for a least-cost energy solution. GISEle is an open-source tool supporting rural electrification planning strategies and challenges through optimal hybrid microgrid integration. The developed framework is universally applicable and explains how the extended GISEle tool can be used to become suitable for analysing decentralised hybrid generation systems within the context of rural areas of developing countries. This presented framework includes: . center dot Advancing the approach to proper data collection to better capture local specificities and (future) demand and reporting results in rural areas of developing countries; center dot Adding the Remote-Areas Multi-energy systems load Profiles (RAMP) to improve load demand assessments, while considering the impact of electrification on growing demand scenarios; center dot Linking the Soil and Water Assessment Tool (SWAT) model to allow for hydropower sizing in GISEle.
引用
收藏
页数:19
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